From 39b762c8284ce537ed16b7185c468926540e0d12 Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Mon, 19 Dec 2011 13:14:23 +0100 Subject: [PATCH 01/88] added more citations --- paper_draft.tex | 6 +-- papers.bib | 99 +++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 102 insertions(+), 3 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 4b656a7..3d62534 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -98,11 +98,11 @@ \section*{Author Summary} \section*{Introduction} -Genome Wide Association Studies (GWAS) are a comparatively easy and cheap way to find Single Nucleotide Polymorphisms (SNPs) which can be interesting because of their medical relevance. SNPs found through GWAS can be used to find candidate genes for a closer inspection or to predict disease risks. Genome Wide Association Studies make use of statistics to compare the alleles of patients to the alleles of healthy controls. By this the method does not allow to find causal differences but mere correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group (doi:10.1126/science.1109557). Since the beginning the number of participants in those studies is rising and over 1200 GWAS have been performed (doi:10.1186/1471-2350-10-6.) and over 5000 SNPs have been linked to different diseases and traits in those studies %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table). +Genome Wide Association Studies (GWAS) are a comparatively easy and cheap way to find Single Nucleotide Polymorphisms (SNPs) which can be interesting because of their medical relevance. SNPs found through GWAS can be used to find candidate genes for a closer inspection or to predict disease risks. Genome Wide Association Studies make use of statistics to compare the alleles of patients to the alleles of healthy controls. By this the method does not allow to find causal differences but mere correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning the number of participants in those studies is rising and over 1200 GWAS have been performed \cite{Johnson2009} and over 5000 SNPs have been linked to different diseases and traits in those studies %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table). Since 2006 companies like 23andMe, deCODEme or FamilyTreeDNA offer Direct-To-Consumer (DTC) genetic testing. Those companies use DNA micro arrays to screen for around 1 million SNPs spread over the human genome. In return customers get an analysis of the results, as well as a raw file that includes the SNP-IDs and their respective allele for the customer. In 2011 23andMe alone had over 100.000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} - the company recognizes the potential to perform GWAS with that amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer those data is used for association studies. 23andMe published several articles in which they replicate known findings but also find new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. Over 30,000 23andme-customers participated in those association studies. -Although companies like 23andMe are willing to contribute to science it is not easy for individual scientists to access the data. This is mainly due to privacy concerns of the customers. Nevertheless there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like GitHub. While this is makes it possible for scientists to access the data, it requires a lot of work to keep track of all new genotyping data that is available to the public. While projects like the SNPedia try to keep track of all the files, this still does not allow to perform GWAS, as the phenotypic information is not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the Personal Genome Project, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data. +Although companies like 23andMe are willing to contribute to science it is not easy for individual scientists to access the data. This is mainly due to privacy concerns of the customers. Nevertheless there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like GitHub. While this is makes it possible for scientists to access the data, it requires a lot of work to keep track of all new genotyping data that is available to the public. While projects like the SNPedia try to keep track of all the files \cite{Cariaso2011}, this still does not allow to perform GWAS, as the phenotypic information is not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the Personal Genome Project, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data. A possible solution to this can be a community-driven platform that aggregates genetical and phenotypical information of people who are willing to share their data with the general public and have given their informed consent. We designed a survey to assess interest in such a crowd sourcing platform, in which we asked how many people would be willing to share their genetic and phenotypic information with the public. Additionally we built a platform which allows customers of DTC genetic testing to publicate of genetic and phenotypic information and gives researchers multiple ways to reuse the data. @@ -159,7 +159,7 @@ \subsection*{Technical realization of the platform} SNPs are cataloged by their unique identifier, which consists of a prefix (mostly \textit{rs}) and a unique number. This is a common format which is used by the NCBI dbSNP database and is also widely used and easily parsed from different literature-sources. Publications out of the different databases and the genotypes of the users are associated with single SNPs by the Rs-ID. Allele and genotype frequencies are are updated regularly, based on the data present in openSNP. -Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are be mailed to users and queries to external resources are handled using the ruby-gem Resque and a Redis-server. Search-features on the platform itself are implemented using SOLR and the ruby-gem sunspot. Additionally data can be requested from openSNP using the Distributed Annotation System. The data for this is stored in a mySQL-database, the delivery of the data is done by using ProServer, which was modified by Gel et al. for use in easyDAS. +Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are be mailed to users and queries to external resources are handled using the ruby-gem Resque and a Redis-server. Search-features on the platform itself are implemented using SOLR and the ruby-gem sunspot. Additionally data can be requested from openSNP using the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}. The data for this is stored in a mySQL-database, the delivery of the data is done by using ProServer \cite{Finn2007}, which was modified by Gel et al. for use in easyDAS \cite{GelMoreno2011}. A flowchart of all services incorporated in openSNP and how users can upload or access the data is given in figure n+3. The source code of openSNP is published under Creative Commons BY-SA 3.0 and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. % Do NOT remove this, even if you are not including acknowledgments diff --git a/papers.bib b/papers.bib index 462f5e0..b89037b 100644 --- a/papers.bib +++ b/papers.bib @@ -98,3 +98,102 @@ @ARTICLE{Eriksson2010 url = {http://dx.doi.org/10.1371/journal.pgen.1000993} } +@Article{Johnson2009, +AUTHOR = {Johnson, Andrew and O'Donnell, Christopher}, +TITLE = {An Open Access Database of Genome-wide Association Results}, +JOURNAL = {BMC Medical Genetics}, +VOLUME = {10}, +YEAR = {2009}, +NUMBER = {1}, +PAGES = {6}, +URL = {http://www.biomedcentral.com/1471-2350/10/6}, +DOI = {10.1186/1471-2350-10-6}, +PubMedID = {19161620}, +ISSN = {1471-2350}, +ABSTRACT = {BACKGROUND:The number of genome-wide association studies (GWAS) is growing rapidly leading to the discovery and replication of many new disease loci. Combining results from multiple GWAS datasets may potentially strengthen previous conclusions and suggest new disease loci, pathways or pleiotropic genes. However, no database or centralized resource currently exists that contains anywhere near the full scope of GWAS results.METHODS:We collected available results from 118 GWAS articles into a database of 56,411 significant SNP-phenotype associations and accompanying information, making this database freely available here. In doing so, we met and describe here a number of challenges to creating an open access database of GWAS results. Through preliminary analyses and characterization of available GWAS, we demonstrate the potential to gain new insights by querying a database across GWAS.RESULTS:Using a genomic bin-based density analysis to search for highly associated regions of the genome, positive control loci (e.g., MHC loci) were detected with high sensitivity. Likewise, an analysis of highly repeated SNPs across GWAS identified replicated loci (e.g., APOE, LPL). At the same time we identified novel, highly suggestive loci for a variety of traits that did not meet genome-wide significant thresholds in prior analyses, in some cases with strong support from the primary medical genetics literature (SLC16A7, CSMD1, OAS1), suggesting these genes merit further study. Additional adjustment for linkage disequilibrium within most regions with a high density of GWAS associations did not materially alter our findings. Having a centralized database with standardized gene annotation also allowed us to examine the representation of functional gene categories (gene ontologies) containing one or more associations among top GWAS results. Genes relating to cell adhesion functions were highly over-represented among significant associations (p < 4.6 x 10-14), a finding which was not perturbed by a sensitivity analysis.CONCLUSION:We provide access to a full gene-annotated GWAS database which could be used for further querying, analyses or integration with other genomic information. We make a number of general observations. Of reported associated SNPs, 40% lie within the boundaries of a RefSeq gene and 68% are within 60 kb of one, indicating a bias toward gene-centricity in the findings. We found considerable heterogeneity in information available from GWAS suggesting the wider community could benefit from standardization and centralization of results reporting.}, +} + +@article{Klein2005, +author = {Klein, Robert J. and Zeiss, Caroline and Chew, Emily Y. and Tsai, Jen-Yue and Sackler, Richard S. and Haynes, Chad and Henning, Alice K. and SanGiovanni, John Paul and Mane, Shrikant M. and Mayne, Susan T. and Bracken, Michael B. and Ferris, Frederick L. and Ott, Jurg and Barnstable, Colin and Hoh, Josephine}, +title = {Complement Factor H Polymorphism in Age-Related Macular Degeneration}, +volume = {308}, +number = {5720}, +pages = {385-389}, +year = {2005}, +doi = {10.1126/science.1109557}, +abstract ={Age-related macular degeneration (AMD) is a major cause of blindness in the elderly. We report a genome-wide screen of 96 cases and 50 controls for polymorphisms associated with AMD. Among 116,204 single-nucleotide polymorphisms genotyped, an intronic and common variant in the complement factor H gene (CFH) is strongly associated with AMD (nominal P value <10-7). In individuals homozygous for the risk allele, the likelihood of AMD is increased by a factor of 7.4 (95% confidence interval 2.9 to 19). Resequencing revealed a polymorphism in linkage disequilibrium with the risk allele representing a tyrosine-histidine change at amino acid 402. This polymorphism is in a region of CFH that binds heparin and C-reactive protein. The CFH gene is located on chromosome 1 in a region repeatedly linked to AMD in family-based studies.}, +URL = {http://www.sciencemag.org/content/308/5720/385.abstract}, +eprint = {http://www.sciencemag.org/content/308/5720/385.full.pdf}, +journal = {Science} +} + +@article{Cariaso2011, +author = {Cariaso, Michael and Lennon, Greg}, +title = {SNPedia: a wiki supporting personal genome annotation, interpretation and analysis}, +year = {2011}, +doi = {10.1093/nar/gkr798}, +abstract ={SNPedia (http://www.SNPedia.com) is a wiki resource of the functional consequences of human genetic variation as published in peer-reviewed studies. Online since 2006 and freely available for personal use, SNPedia has focused on the medical, phenotypic and genealogical associations of single nucleotide polymorphisms. Entries are formatted to allow associations to be assigned to single genotypes as well as sets of genotypes (genosets). In this article, we discuss the growth of this resource and its use by affiliated software to create personal genome reports.}, +URL = {http://nar.oxfordjournals.org/content/early/2011/12/02/nar.gkr798.abstract}, +eprint = {http://nar.oxfordjournals.org/content/early/2011/12/02/nar.gkr798.full.pdf+html}, +journal = {Nucleic Acids Research} +} + +@Article{Dowell2001, +AUTHOR = {Dowell, Robin and Jokerst, Rodney and Day, Allen and Eddy, Sean and Stein, Lincoln}, +TITLE = {The Distributed Annotation System}, +JOURNAL = {BMC Bioinformatics}, +VOLUME = {2}, +YEAR = {2001}, +NUMBER = {1}, +PAGES = {7}, +URL = {http://www.biomedcentral.com/1471-2105/2/7}, +DOI = {10.1186/1471-2105-2-7}, +PubMedID = {11667947}, +ISSN = {1471-2105}, +ABSTRACT = {BACKGROUND:Currently, most genome annotation is curated by centralized groups with limited resources. Efforts to share annotations transparently among multiple groups have not yet been satisfactory.RESULTS:Here we introduce a concept called the Distributed Annotation System (DAS). DAS allows sequence annotations to be decentralized among multiple third-party annotators and integrated on an as-needed basis by client-side software. The communication between client and servers in DAS is defined by the DAS XML specification. Annotations are displayed in layers, one per server. Any client or server adhering to the DAS XML specification can participate in the system; we describe a simple prototype client and server example.CONCLUSIONS:The DAS specification is being used experimentally by Ensembl, WormBase, and the Berkeley Drosophila Genome Project. Continued success will depend on the readiness of the research community to adopt DAS and provide annotations. All components are freely available from the project website http://www.biodas.org/ webcite.}, +} + +@Article{Jenkinson2008, +AUTHOR = {Jenkinson, Andrew and Albrecht, Mario and Birney, Ewan and Blankenburg, Hagen and Down, Thomas and Finn, Robert and Hermjakob, Henning and Hubbard, Tim and Jimenez, Rafael and Jones, Philip and Kahari, Andreas and Kulesha, Eugene and Macias, Jose and Reeves, Gabrielle and Prlic, Andreas}, +TITLE = {Integrating biological data - the Distributed Annotation System}, +JOURNAL = {BMC Bioinformatics}, +VOLUME = {9}, +YEAR = {2008}, +NUMBER = {Suppl 8}, +PAGES = {S3}, +URL = {http://www.biomedcentral.com/1471-2105/9/S8/S3}, +DOI = {10.1186/1471-2105-9-S8-S3}, +PubMedID = {18673527}, +ISSN = {1471-2105}, +ABSTRACT = {BACKGROUND:The Distributed Annotation System (DAS) is a widely adopted protocol for dynamically integrating a wide range of biological data from geographically diverse sources. DAS continues to expand its applicability and evolve in response to new challenges facing integrative bioinformatics.RESULTS:Here we describe the various infrastructure components of DAS and present a new extended version of the DAS specification. Version 1.53E incorporates several recent developments, including its extension to serve new data types and an ontology for protein features.CONCLUSION:Our extensions to the DAS protocol have facilitated the integration of new data types, and our improvements to the existing DAS infrastructure have addressed recent challenges. The steadily increasing numbers of available data sources demonstrates further adoption of the DAS protocol.}, +} + +@article{Finn2007, +author = {Finn, Robert D. and Stalker, James W. and Jackson, David K. and Kulesha, Eugene and Clements, Jody and Pettett, Roger}, +title = {ProServer: a simple, extensible Perl DAS server}, +volume = {23}, +number = {12}, +pages = {1568-1570}, +year = {2007}, +doi = {10.1093/bioinformatics/btl650}, +abstract ={Summary: The increasing size and complexity of biological databases has led to a growing trend to federate rather than duplicate them. In order to share data between federated databases, protocols for the exchange mechanism must be developed. One such data exchange protocol that is widely used is the Distributed Annotation System (DAS). For example, DAS has enabled small experimental groups to integrate their data into the Ensembl genome browser. We have developed ProServer, a simple, lightweight, Perl-based DAS server that does not depend on a separate HTTP server. The ProServer package is easily extensible, allowing data to be served from almost any underlying data model. Recent additions to the DAS protocol have enabled both structure and alignment (sequence and structural) data to be exchanged. ProServer allows both of these data types to be served.Availability: ProServer can be downloaded from http://www.sanger.ac.uk/proserver/ or CPAN http://search.cpan.org/~rpettett/. Details on the system requirements and installation of ProServer can be found at http://www.sanger.ac.uk/proserver/.Contact: rmp@sanger.ac.ukSupplementary Materials: DasClientExamples.pdf}, +URL = {http://bioinformatics.oxfordjournals.org/content/23/12/1568.abstract}, +eprint = {http://bioinformatics.oxfordjournals.org/content/23/12/1568.full.pdf+html}, +journal = {Bioinformatics} +} + +@Article{GelMoreno2011, +AUTHOR = {Gel Moreno, Bernat and Jenkinson, Andrew and Jimenez, Rafael and Messeguer Peypoch, Xavier and Hermjakob, Henning}, +TITLE = {easyDAS: Automatic creation of DAS servers}, +JOURNAL = {BMC Bioinformatics}, +VOLUME = {12}, +YEAR = {2011}, +NUMBER = {1}, +PAGES = {23}, +URL = {http://www.biomedcentral.com/1471-2105/12/23}, +DOI = {10.1186/1471-2105-12-23}, +PubMedID = {21244646}, +ISSN = {1471-2105}, +ABSTRACT = {BACKGROUND:The Distributed Annotation System (DAS) has proven to be a successful way to publish and share biological data. Although there are more than 750 active registered servers from around 50 organizations, setting up a DAS server comprises a fair amount of work, making it difficult for many research groups to share their biological annotations. Given the clear advantage that the generalized sharing of relevant biological data is for the research community it would be desirable to facilitate the sharing process.RESULTS:Here we present easyDAS, a web-based system enabling anyone to publish biological annotations with just some clicks. The system, available at http://www.ebi.ac.uk/panda-srv/easydas webcite is capable of reading different standard data file formats, process the data and create a new publicly available DAS source in a completely automated way. The created sources are hosted on the EBI systems and can take advantage of its high storage capacity and network connection, freeing the data provider from any network management work. easyDAS is an open source project under the GNU LGPL license.CONCLUSIONS:easyDAS is an automated DAS source creation system which can help many researchers in sharing their biological data, potentially increasing the amount of relevant biological data available to the scientific community.}, +} + From 9bc79135bee162fca8947ed3627468525aaa77f9 Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Mon, 19 Dec 2011 13:21:17 +0100 Subject: [PATCH 02/88] some more references --- paper_draft.tex | 2 +- papers.bib | 17 +++++++++++++++-- 2 files changed, 16 insertions(+), 3 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 3d62534..3d9c008 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -98,7 +98,7 @@ \section*{Author Summary} \section*{Introduction} -Genome Wide Association Studies (GWAS) are a comparatively easy and cheap way to find Single Nucleotide Polymorphisms (SNPs) which can be interesting because of their medical relevance. SNPs found through GWAS can be used to find candidate genes for a closer inspection or to predict disease risks. Genome Wide Association Studies make use of statistics to compare the alleles of patients to the alleles of healthy controls. By this the method does not allow to find causal differences but mere correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning the number of participants in those studies is rising and over 1200 GWAS have been performed \cite{Johnson2009} and over 5000 SNPs have been linked to different diseases and traits in those studies %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table). +Genome Wide Association Studies (GWAS) are a comparatively easy and cheap way to find Single Nucleotide Polymorphisms (SNPs) which can be interesting because of their medical relevance. SNPs found through GWAS can be used to find candidate genes for a closer inspection or to predict disease risks. Genome Wide Association Studies make use of statistics to compare the alleles of patients to the alleles of healthy controls. By this the method does not allow to find causal differences but mere correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning the number of participants in those studies is rising and over 1200 GWAS have been performed \cite{Johnson2009} and over 5000 SNPs have been linked to different diseases and traits in those studies \cite{Hindorff2009}. %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) Since 2006 companies like 23andMe, deCODEme or FamilyTreeDNA offer Direct-To-Consumer (DTC) genetic testing. Those companies use DNA micro arrays to screen for around 1 million SNPs spread over the human genome. In return customers get an analysis of the results, as well as a raw file that includes the SNP-IDs and their respective allele for the customer. In 2011 23andMe alone had over 100.000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} - the company recognizes the potential to perform GWAS with that amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer those data is used for association studies. 23andMe published several articles in which they replicate known findings but also find new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. Over 30,000 23andme-customers participated in those association studies. diff --git a/papers.bib b/papers.bib index b89037b..2b6f1c4 100644 --- a/papers.bib +++ b/papers.bib @@ -22,8 +22,8 @@ @ARTICLE{Do2011 genome-wide association study (GWAS) of PD based on a single collection of individuals to date (3,426 cases and 29,624 controls). We discovered two novel, genome-wide significant associations with PD-rs6812193 - near SCARB2 (p = 7.6 × 10(-10), OR = 0.84) and rs11868035 near SREBF1/RAI1 - (p = 5.6 × 10(-8), OR = 0.85)-both replicated in an independent cohort. + near SCARB2 (p = 7.6 × 10(-10), OR = 0.84) and rs11868035 near SREBF1/RAI1 + (p = 5.6 × 10(-8), OR = 0.85)-both replicated in an independent cohort. We also replicated 20 previously discovered genetic associations (including LRRK2, GBA, SNCA, MAPT, GAK, and the HLA region), providing support for our novel study design. Relying on a recently proposed @@ -197,3 +197,16 @@ @Article{GelMoreno2011 ABSTRACT = {BACKGROUND:The Distributed Annotation System (DAS) has proven to be a successful way to publish and share biological data. Although there are more than 750 active registered servers from around 50 organizations, setting up a DAS server comprises a fair amount of work, making it difficult for many research groups to share their biological annotations. Given the clear advantage that the generalized sharing of relevant biological data is for the research community it would be desirable to facilitate the sharing process.RESULTS:Here we present easyDAS, a web-based system enabling anyone to publish biological annotations with just some clicks. The system, available at http://www.ebi.ac.uk/panda-srv/easydas webcite is capable of reading different standard data file formats, process the data and create a new publicly available DAS source in a completely automated way. The created sources are hosted on the EBI systems and can take advantage of its high storage capacity and network connection, freeing the data provider from any network management work. easyDAS is an open source project under the GNU LGPL license.CONCLUSIONS:easyDAS is an automated DAS source creation system which can help many researchers in sharing their biological data, potentially increasing the amount of relevant biological data available to the scientific community.}, } +@article{Hindorff2009, +author = {Hindorff, Lucia A. and Sethupathy, Praveen and Junkins, Heather A. and Ramos, Erin M. and Mehta, Jayashri P. and Collins, Francis S. and Manolio, Teri A.}, +title = {Potential etiologic and functional implications of genome-wide association loci for human diseases and traits}, +volume = {106}, +number = {23}, +pages = {9362-9367}, +year = {2009}, +doi = {10.1073/pnas.0903103106}, +abstract ={We have developed an online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs). Reported TASs were common [median risk allele frequency 36%, interquartile range (IQR) 21%−53%] and were associated with modest effect sizes [median odds ratio (OR) 1.33, IQR 1.20–1.61]. Among 20 genomic annotation sets, reported TASs were significantly overrepresented only in nonsynonymous sites [OR = 3.9 (2.2−7.0), p = 3.5 × 10−7] and 5kb-promoter regions [OR = 2.3 (1.5−3.6), p = 3 × 10−4] compared to SNPs randomly selected from genotyping arrays. Although 88% of TASs were intronic (45%) or intergenic (43%), TASs were not overrepresented in introns and were significantly depleted in intergenic regions [OR = 0.44 (0.34−0.58), p = 2.0 × 10−9]. Only slightly more TASs than expected by chance were predicted to be in regions under positive selection [OR = 1.3 (0.8−2.1), p = 0.2]. This new online resource, together with bioinformatic predictions of the underlying functionality at trait/disease-associated loci, is well-suited to guide future investigations of the role of common variants in complex disease etiology.}, +URL = {http://www.pnas.org/content/106/23/9362.abstract}, +eprint = {http://www.pnas.org/content/106/23/9362.full.pdf+html}, +journal = {Proceedings of the National Academy of Sciences} +} From b75aa414db08af042af0dcea07c0ec5d419c2ad2 Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Mon, 19 Dec 2011 14:53:53 +0100 Subject: [PATCH 03/88] started working on discussion --- outline.txt | 6 +++--- paper_draft.tex | 13 ++++++++++++- 2 files changed, 15 insertions(+), 4 deletions(-) diff --git a/outline.txt b/outline.txt index 9498423..16bb615 100644 --- a/outline.txt +++ b/outline.txt @@ -65,9 +65,9 @@ to-do list together with outlook of personalised medicine First focus on biological issues: -Polymorphisms to phenotypes, correlation only. -Bias in the samples collected by openSNP? -Sample size to small? +Polymorphisms to phenotypes, correlation only. done +Bias in the samples collected by openSNP? done +Sample size to small? done Quality check for phenotypes? Mapping to haplotype blocks? Combination with the hapmap diff --git a/paper_draft.tex b/paper_draft.tex index 3d9c008..7e0d7fd 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -102,7 +102,7 @@ \section*{Introduction} Since 2006 companies like 23andMe, deCODEme or FamilyTreeDNA offer Direct-To-Consumer (DTC) genetic testing. Those companies use DNA micro arrays to screen for around 1 million SNPs spread over the human genome. In return customers get an analysis of the results, as well as a raw file that includes the SNP-IDs and their respective allele for the customer. In 2011 23andMe alone had over 100.000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} - the company recognizes the potential to perform GWAS with that amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer those data is used for association studies. 23andMe published several articles in which they replicate known findings but also find new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. Over 30,000 23andme-customers participated in those association studies. -Although companies like 23andMe are willing to contribute to science it is not easy for individual scientists to access the data. This is mainly due to privacy concerns of the customers. Nevertheless there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like GitHub. While this is makes it possible for scientists to access the data, it requires a lot of work to keep track of all new genotyping data that is available to the public. While projects like the SNPedia try to keep track of all the files \cite{Cariaso2011}, this still does not allow to perform GWAS, as the phenotypic information is not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the Personal Genome Project, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data. +Although companies like 23andMe are willing to contribute to science it is not easy for individual scientists to access the data. This is mainly due to privacy concerns of the customers. Nevertheless there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. While this is makes it possible for scientists to access the data, it requires a lot of work to keep track of all new genotyping data that is available to the public. While projects like the SNPedia try to keep track of all the files \cite{Cariaso2011}, this still does not allow to perform GWAS, as the phenotypic information is not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the Personal Genome Project, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data. A possible solution to this can be a community-driven platform that aggregates genetical and phenotypical information of people who are willing to share their data with the general public and have given their informed consent. We designed a survey to assess interest in such a crowd sourcing platform, in which we asked how many people would be willing to share their genetic and phenotypic information with the public. Additionally we built a platform which allows customers of DTC genetic testing to publicate of genetic and phenotypic information and gives researchers multiple ways to reuse the data. @@ -138,6 +138,17 @@ \subsection*{openSNP} The external services themselves are ranked by how easy users can access information out of these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley-results, as those aren't necessary freely available for every user. SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley-entry. \section*{Discussion} +Although prices for exome or even full genome sequencing are dropping rapidly, in comparison to this GWAS tend to stay cheaper and offer a possibility to get insights on genetic variations on a population level and allow an analysis of SNPs which are linked to different traits. Despite this benefit in terms of costs it must be pointed out that GWAS can only detect correlations of SNPs with those traits and don't allow the detection of the causes for this and needs a large enough sample which has to be statistically analysed by using sound methods. + +One way to bring costs for GWAS further down is to make use of already available genotyping results and datasets. Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high numbers of customers which are willing to pay for those genotypings by themselves. As we have found in the survey on the sharing such results many of these customers are also willing to share their results with scientists and the public to help scientific progress, although people who have taken DTC genetic testing are aware of the privacy implications that come with openly sharing those results. + +With openSNP we've build a platform that can be used by customers of DTC genetic testing to share their data to easily share their genetic and phenotypic data with a wide audience as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. Customers of DTC genetic testing also benefit of an easy access to primary literature on SNPs and genetic variation they carry. While we don't have collected enough data to perform full scale GWAS yet this might be possible in the future, as user numbers are rising. + +By crowdsourcing the acquisition of genetic and phenotypic data openSNP faces the same problems as any other open platform on the Internet, namely we have to trust users about the data they upload and enter on openSNP. Additionally the quality of the data varies, especially in terms of accuracy on the phenotypic variation. While we try to suggest similar entries to users there are some cases where users wont follow those suggestions, so duplicates or similar phenotypes or varations in traits may arise. + +There are two possible solutions to this problem: We could only allow some (trusted) users to enter new phenotypes or we could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users can't add variations that have not been available from the start on. Both methods have their own disadvantage: In either case it makes it harder for users to enter their data and by this highers the bar for participation, which ultimately could lead to less data entered. Facing this trade-off we decided to keep entering data easy, at the cost that users who want to perform GWAS with the data need to perform more quality control. + +Another thing that should kept in mind is a possible bias, as we can't rule out the possibility that only a biased subset of people buy DTC genetic testing and an even smaller subset is willing to publish the results, along with information of medical relevance. From cb5b4485c5cd520148ccd4543a3064b4ce5d508d Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Mon, 19 Dec 2011 19:40:08 +0100 Subject: [PATCH 04/88] finished first draft of discussion, added some dtc-privacy-pubs --- outline.txt | 8 +++--- paper_draft.tex | 24 ++++++++++-------- privacy_publications/0.pdf | Bin 0 -> 158636 bytes privacy_publications/15265160902893965.pdf | Bin 0 -> 65987 bytes .../annurev%2Egenom%2E9%2E081307%2E164319.pdf | Bin 0 -> 246220 bytes .../annurev-med-062110-123753.pdf | Bin 0 -> 154060 bytes privacy_publications/gm71.pdf | Bin 0 -> 47036 bytes privacy_publications/nrg3113.pdf | Bin 0 -> 221260 bytes 8 files changed, 18 insertions(+), 14 deletions(-) create mode 100644 privacy_publications/0.pdf create mode 100644 privacy_publications/15265160902893965.pdf create mode 100644 privacy_publications/annurev%2Egenom%2E9%2E081307%2E164319.pdf create mode 100644 privacy_publications/annurev-med-062110-123753.pdf create mode 100644 privacy_publications/gm71.pdf create mode 100644 privacy_publications/nrg3113.pdf diff --git a/outline.txt b/outline.txt index 16bb615..3834fc8 100644 --- a/outline.txt +++ b/outline.txt @@ -68,7 +68,7 @@ First focus on biological issues: Polymorphisms to phenotypes, correlation only. done Bias in the samples collected by openSNP? done Sample size to small? done -Quality check for phenotypes? +Quality check for phenotypes? done Mapping to haplotype blocks? Combination with the hapmap Manage increasing amount of data? @@ -77,7 +77,7 @@ Manage increasing amount of data? from great power follows great responsibility! Imagine a world where all data is free... -What privacy issues do arise? +What privacy issues do arise? done Is there an effective mechanism to avoid abuse or -genetic discrimination? -How should governments treat the additional data? \ No newline at end of file +genetic discrimination? done +How should governments treat the additional data? done \ No newline at end of file diff --git a/paper_draft.tex b/paper_draft.tex index 7e0d7fd..c50d3b5 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -98,13 +98,13 @@ \section*{Author Summary} \section*{Introduction} -Genome Wide Association Studies (GWAS) are a comparatively easy and cheap way to find Single Nucleotide Polymorphisms (SNPs) which can be interesting because of their medical relevance. SNPs found through GWAS can be used to find candidate genes for a closer inspection or to predict disease risks. Genome Wide Association Studies make use of statistics to compare the alleles of patients to the alleles of healthy controls. By this the method does not allow to find causal differences but mere correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning the number of participants in those studies is rising and over 1200 GWAS have been performed \cite{Johnson2009} and over 5000 SNPs have been linked to different diseases and traits in those studies \cite{Hindorff2009}. %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) +Genome Wide Association Studies (GWAS) are a comparatively easy and cheap way to find Single Nucleotide Polymorphisms (SNPs) which are of interest because of their medical relevance. Such SNPs of interest can be used to find candidate genes for a closer inspection or to predict disease risks or other traits. Genome Wide Association Studies make use of statistics to compare the alleles of patients to the alleles of healthy controls. By this the method does not allow to find causal differences but mere correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning the number of participants in those studies is rising and over 1200 GWAS have been performed \cite{Johnson2009} and over 5000 SNPs have been linked to different diseases and traits in those studies \cite{Hindorff2009}. %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) -Since 2006 companies like 23andMe, deCODEme or FamilyTreeDNA offer Direct-To-Consumer (DTC) genetic testing. Those companies use DNA micro arrays to screen for around 1 million SNPs spread over the human genome. In return customers get an analysis of the results, as well as a raw file that includes the SNP-IDs and their respective allele for the customer. In 2011 23andMe alone had over 100.000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} - the company recognizes the potential to perform GWAS with that amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer those data is used for association studies. 23andMe published several articles in which they replicate known findings but also find new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. Over 30,000 23andme-customers participated in those association studies. +Since 2006 companies like 23andMe, deCODEme or FamilyTreeDNA offer Direct-To-Consumer (DTC) genetic testing. Those companies use DNA micro arrays to screen for around 1 million SNPs spread over the human genome. In return customers get an analysis of the results, as well as a raw file that includes the individual genotypes of the customer. In 2011 23andMe alone had over 100.000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} - the company recognizes the potential to perform GWAS with that amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer those data is used for association studies. 23andMe published several articles in which they replicate known findings but also find new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. Over 30,000 23andme-customers participated in those association studies. -Although companies like 23andMe are willing to contribute to science it is not easy for individual scientists to access the data. This is mainly due to privacy concerns of the customers. Nevertheless there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. While this is makes it possible for scientists to access the data, it requires a lot of work to keep track of all new genotyping data that is available to the public. While projects like the SNPedia try to keep track of all the files \cite{Cariaso2011}, this still does not allow to perform GWAS, as the phenotypic information is not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the Personal Genome Project, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data. +Although companies like 23andMe are willing to contribute to science it is not easy for individual scientists to access the data. This is mainly due to concerns about privacy, liability and consent. Nevertheless there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. While this is makes it possible for scientists to access the data, it requires a lot of work to keep track of all new genotyping data that is available to the public. While projects like the SNPedia try to keep track of all the files \cite{Cariaso2011}, this still does not allow to perform GWAS, as the phenotypic information is not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the \textit{Personal Genome Project}, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data. -A possible solution to this can be a community-driven platform that aggregates genetical and phenotypical information of people who are willing to share their data with the general public and have given their informed consent. We designed a survey to assess interest in such a crowd sourcing platform, in which we asked how many people would be willing to share their genetic and phenotypic information with the public. Additionally we built a platform which allows customers of DTC genetic testing to publicate of genetic and phenotypic information and gives researchers multiple ways to reuse the data. +A possible solution to this can be a community-driven platform that aggregates genetic and phenotypic information of people who are willing to share their data with the general public and have given their informed consent. We designed a survey to assess interest in such a crowd sourcing platform, in which we asked how many people would be willing to share their genetic and phenotypic information with the public. Additionally we built a platform which allows customers of DTC genetic testing to publicate of genetic and phenotypic information and gives researchers multiple ways to reuse the data. % Results and Discussion can be combined. \section*{Results} @@ -127,7 +127,7 @@ \subsection*{openSNP} The possible answers in terms of variations for a single phenotype are not limited and every user can add completely new phenotypes if the corresponding questions about this are lacking. To reduce the amount of manual data curation openSNP tries to avoid the entry of the same phenotype or variation, but with a slightly different spelling by helping users at entering data by an autocompletion-feature which lists similar entries which are already in the openSNP-database. -On the side of getting access to the data users can download single genotyping files for specific users, get archives of multiple genotyping files grouped by phenotypic variation or can access a single download that includes all genotyping-files and all phenotypes in a comma separated table. Additionally users can access the genetic data through the Distributed Annotation System, which allows to get all data for specific chromosomes and specific positions on single chromosomes. +On the side of getting access to the data users can download single genotyping files for specific users, get archives of multiple genotyping files grouped by phenotypic variation or can access a single download that includes all genotyping-files and all phenotypes in a comma separated table. Additionally users can access the genetic data through the Distributed Annotation System, which allows to get all data for specific chromosomes and specific positions on single chromosomes. Users can discuss SNPs and phenotypes on the platform using a simple commenting-system or private messages. Between the start of openSNP on 09/27/2011 and 12/18/2011 214 people have signed up with openSNP, 79 of those have uploaded their genotyping files. Through this the openSNP database lists 69486471 SNPs which are distributed over 1938604 unique Rs-IDs. In the same timeframe all users combined have entered 675 variations which are distributed over 47 different phenotypes. See figure n for a distribution of data acquisition over time. @@ -135,23 +135,27 @@ \subsection*{openSNP} A total number of 15229 documents relevant to the SNP-IDs which are listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. Of the primary literature 25 \% are released in Open Access-journals and can be freely free of charge by every user (Figure n+2). For usability reasons SNPs are ranked by the amount of information gathered by the external services. -The external services themselves are ranked by how easy users can access information out of these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley-results, as those aren't necessary freely available for every user. SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley-entry. +The external services themselves are ranked by how easy users can access information out of these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley-results, as those aren't necessary freely available for every user. A entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley-entry. \section*{Discussion} -Although prices for exome or even full genome sequencing are dropping rapidly, in comparison to this GWAS tend to stay cheaper and offer a possibility to get insights on genetic variations on a population level and allow an analysis of SNPs which are linked to different traits. Despite this benefit in terms of costs it must be pointed out that GWAS can only detect correlations of SNPs with those traits and don't allow the detection of the causes for this and needs a large enough sample which has to be statistically analysed by using sound methods. +Although prices for exome or even full genome sequencing are dropping rapidly, in comparison to this GWAS tend to stay cheaper and offer a possibility to get insights on genetic variations on a population level and allow an analysis of SNPs which are linked to different traits. Despite this benefit in terms of costs it must be pointed out that GWAS can only detect correlations of SNPs with those traits and don't allow the detection of the causes for this and needs a large enough sample which has to be statistically analysed by using sound methods. Nevertheless, GWAS are still frequently used and new associations still can be found (we should list some recent results here -> oxytocin maybe? we could look at the twitter-timeline of @opensnporg, i publish papers i find there). -One way to bring costs for GWAS further down is to make use of already available genotyping results and datasets. Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high numbers of customers which are willing to pay for those genotypings by themselves. As we have found in the survey on the sharing such results many of these customers are also willing to share their results with scientists and the public to help scientific progress, although people who have taken DTC genetic testing are aware of the privacy implications that come with openly sharing those results. +One way to bring costs for GWAS even further down is to make use of already available genotyping results and datasets. Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high numbers of customers which are willing to pay for those genotypings by themselves. As we have found in the survey on the sharing such results many of these customers are also willing to share their results with scientists and the public to help scientific progress, although people who have taken DTC genetic testing are aware of the privacy implications that come with openly sharing those results. With openSNP we've build a platform that can be used by customers of DTC genetic testing to share their data to easily share their genetic and phenotypic data with a wide audience as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. Customers of DTC genetic testing also benefit of an easy access to primary literature on SNPs and genetic variation they carry. While we don't have collected enough data to perform full scale GWAS yet this might be possible in the future, as user numbers are rising. By crowdsourcing the acquisition of genetic and phenotypic data openSNP faces the same problems as any other open platform on the Internet, namely we have to trust users about the data they upload and enter on openSNP. Additionally the quality of the data varies, especially in terms of accuracy on the phenotypic variation. While we try to suggest similar entries to users there are some cases where users wont follow those suggestions, so duplicates or similar phenotypes or varations in traits may arise. -There are two possible solutions to this problem: We could only allow some (trusted) users to enter new phenotypes or we could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users can't add variations that have not been available from the start on. Both methods have their own disadvantage: In either case it makes it harder for users to enter their data and by this highers the bar for participation, which ultimately could lead to less data entered. Facing this trade-off we decided to keep entering data easy, at the cost that users who want to perform GWAS with the data need to perform more quality control. +There are two possible solutions to this problem: One could only allow some (trusted) users to enter new phenotypes or one could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users can't add variations that have not been available from the start on. Both methods have a disadvantage: In either case it makes it harder for users to enter their data and by this highers the bar for participation, which ultimately could lead to less data entered. Facing this trade-off we decided to keep entering data easy, at the cost that users who want to perform GWAS with the data need to perform more quality control. -Another thing that should kept in mind is a possible bias, as we can't rule out the possibility that only a biased subset of people buy DTC genetic testing and an even smaller subset is willing to publish the results, along with information of medical relevance. +Another thing that should kept in mind is a possible bias in which data is available on openSNP, as we can't rule out the possibility that only a biased subset of people buy DTC genetic testing and an even smaller subset is willing to publish the results, along with information of medical relevance. +The advent of DTC genetic testing has lead to new ethical and social issues. Much of the critique on DTC genetic testing focusses on the practice of delivering medical information without consulting a physician or genetic counselor to help patients/customers make sense of the information and help them to put the new knowledge to good use. Less attention has been given to the privacy implications that come with this cheap way of obtaining genetical information. +By making the own genetic and medical information public it becomes clear that the issue of privacy needs to get a closer view. Our survey has shown that people are concerned about their privacy and are concerned that stakeholders like employers, insurance companies, governments or advertisers misuse this information. Policy makers start to react to those changes by introducing laws like the \textit{Genetic Information Non-Discrimination Act} in the United States or the \emph{Gendiagnostikgesetz} in Germany to minimize the impact widely available genetic information. DTC genetic testing companies on their side also try to educate their customers about the risks of releasing genetic data. +We transparently address the problem of privacy implications that come with releasing the data twice, during registering for openSNP and during the upload of the DTC results. Users have to check that they have read and understood the disclaimer about possible side-effects that can arise by making their data public. To further improve this process and get informed consent we are working to implement the procedures which are currently developed by \textit{Consent for Research} (http://www.weconsent.us). + % You may title this section "Methods" or "Models". % "Models" is not a valid title for PLoS ONE authors. 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Direct-To-Consumer Companies like 23andme offer their customers to sequence their SNPs alongside with an evaluation of the customer's genetic risks. However, the data 23andme and other companies generate is not accessible for other scientists, and withholds some information from their customers for various reasons. In this paper, we present an open approach to GWAS by introducing openSNP, a web platform which allows GWAS-customers to openly share their SNPs with scientists for free. % misses survey +Genome-wide association studies are a cheap and quick way of assessing health risks by comparing Single Nucleotide Polymorphisms between groups of participants. Direct-To-Consumer Companies like 23andme offer SNP sequencing to their customers alongside with an evaluation of the customer's genetic risks. However, the data 23andme and other companies generate is not accessible to other scientists, and withholds some information from customers for various reasons. In this paper, we present an open approach to Genome-wide association studies by introducing openSNP, a web platform which allows customers to openly share their polymorphisms with scientists for free. % misses survey % Please keep the Author Summary between 150 and 200 words -% Use first person. PLoS ONE authors please skip this step. +% Use first person. PLoS ONE authors please skip this step. % Author Summary not valid for PLoS ONE submissions. \section*{Author Summary} \section*{Introduction} -Genome Wide Association Studies (GWAS) are a comparatively easy and cheap way to find Single Nucleotide Polymorphisms (SNPs) which are of interest because of their medical relevance. Such SNPs of interest can be used to find candidate genes for a closer inspection or to predict disease risks or other traits. Genome Wide Association Studies make use of statistics to compare the alleles of patients to the alleles of healthy controls. By this the method does not allow to find causal differences but mere correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning the number of participants in those studies is rising and over 1200 GWAS have been performed \cite{Johnson2009} and over 5000 SNPs have been linked to different diseases and traits in those studies \cite{Hindorff2009}. %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) +Genome-Wide Association Studies (GWAS) are a comparatively easy and cheap way of finding Single Nucleotide Polymorphisms (SNPs) of medical relevance. Such SNPs of interest can be used to find candidate genes for a closer inspection or to predict disease risks or other traits. Genome-Wide Association Studies make use of statistics for comparing the alleles of patients to the alleles of healthy controls. This method does not allow inference of causal differences but merely identifies correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning, the number of participants in such studies has been rising: Over 1200 GWAS have been performed \cite{Johnson2009} and over 5000 SNPs have been linked to different diseases and traits in those studies \cite{Hindorff2009}. %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) -Since 2006 companies like 23andMe, deCODEme or FamilyTreeDNA offer Direct-To-Consumer (DTC) genetic testing. Those companies use DNA micro arrays to screen for around 1 million SNPs spread over the human genome. In return customers get an analysis of the results, as well as a raw file that includes the individual genotypes of the customer. In 2011 23andMe alone had over 100.000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} - the company recognizes the potential to perform GWAS with that amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer those data is used for association studies. 23andMe published several articles in which they replicate known findings but also find new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. Over 30,000 23andme-customers participated in those association studies. +Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. Those companies use DNA microarrays to screen for around 1 million SNPs spread over the human genome. In return customers get an analysis of the results, as well as a raw file that includes the customer's individual genotypes. In 2011, 23andMe alone had over 100,000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} - the company recognizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer the data is used for association studies. 23andMe has published several articles in which it replicates known findings, but also finds new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. Over 30,000 23andme-customers have participated in those association studies. -Although companies like 23andMe are willing to contribute to science it is not easy for individual scientists to access the data. This is mainly due to concerns about privacy, liability and consent. Nevertheless there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. While this is makes it possible for scientists to access the data, it requires a lot of work to keep track of all new genotyping data that is available to the public. While projects like the SNPedia try to keep track of all the files \cite{Cariaso2011}, this still does not allow to perform GWAS, as the phenotypic information is not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the \textit{Personal Genome Project}, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data. +Although companies like 23andMe are willing to contribute to science, it is not easy for individual scientists to access the data. This is mainly due to concerns about privacy, liability and consent. Nevertheless, there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. While this makes it possible for scientists to access the data, it requires a lot of work to keep track of all new genotyping data that is available to the public. While projects like the SNPedia try to keep track of all the files \cite{Cariaso2011}, they do not provide the information necessary to perform GWAS, as the phenotypic information is not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the \textit{Personal Genome Project}, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data. -A possible solution to this can be a community-driven platform that aggregates genetic and phenotypic information of people who are willing to share their data with the general public and have given their informed consent. We designed a survey to assess interest in such a crowd sourcing platform, in which we asked how many people would be willing to share their genetic and phenotypic information with the public. Additionally we built a platform which allows customers of DTC genetic testing to publicate of genetic and phenotypic information and gives researchers multiple ways to reuse the data. +A possible solution to this is a community-driven platform that aggregates genetic and phenotypic information of people who are willing to share their data with the general public and have given their informed consent. We designed an online survey to assess interest in such a crowdsourcing platform, in which we asked an audience interested in biology topics about their willingness to share their genetic and phenotypic information with the public. The aim of the survey was not to determine the percentage of the population interested in sharing genotypic information, but to get an idea of whether a crowdsourcing platform could get enough support to go forward with the project. Additionally, we built a platform which allows customers of DTC genetic testing to publicize genetic and phenotypic information and gives researchers multiple ways of reusing the data. % Results and Discussion can be combined. \section*{Results} \subsection*{Survey on Sharing Genetic Information} -229 people, 180 with a self-reported chromosomal sex of XY, 56 with a self-reported chromosomal sex of XX, participated in the survey. The mean age of the participants is 33 (SD = 11,29) and over 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already customer of at least one DTC genetic testing company and further 30.1 \% of them plan to become one in the future. 29.7 \% don't plan to become a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Somers-d). +229 people, 180 with a self-reported chromosomal sex of XY, 56 with a self-reported chromosomal sex of XX, participated in the survey. The mean age of the participants is 33 (SD = 11,29) and 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% don't plan on becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Somers-d). -67.7 \% of all participants would share their data with their DTC-company without any constraints, 25.8 \% would do so, if the company does not share the data with third parties. 6.6 \% of the participants would not share their data. There is no significant difference between sharing-habits between both chromosomal sexes (Somers-d). Those who are a customer of a DTC company or are planing to become one in the future are more likely to share their results, compared to those who don't plan to get themselves genotyped (Somers-d). +67.7 \% of all participants would share their data with their DTC-company without any constraints, 25.8 \% would do so given the company didn't share the data with third parties. 6.6 \% of the participants would not share their data. There is no significant difference in readiness to share between chromosomal sexes (Somers-d). Those who are a customers of a DTC company or are planning on becoming one in the future are more likely to share their results, compared to those who don't plan on getting themselves genotyped (Somers-d). -There are significant differences, tested by Tukey's HSD test, between those people who are already genotyped and those who don't plan to get genotyped: The first group is more likely to agree to share their information because they want to help scientists (mean difference = 0.465, SE = 0.128, p = 0.001), because they think of personal benefits (mean difference = 0.448, SE = 0.183, p = 0.04) and because they are curious (mean difference = 1.159, SE = 0.193, p < 0.001). +There are significant differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with the following motivations for sharing genotypic information: because they want to help scientists (mean difference = 0.465, SE = 0.128, p = 0.001), because they think of personal benefits (mean difference = 0.448, SE = 0.183, p = 0.04) and because they are curious (mean difference = 1.159, SE = 0.193, p < 0.001). -On the other hand those people who are not planning to get genotyped are more likely to not share their data, because they agree to fear discrimination (mean difference = 1.060, SE = 0.195, p < 0.001), because they agree that they feel it is a breach of their privacy (mean difference = 0.821, SE = 0.225, p = 0.001), because agree that they fear negative consequences for their family (mean difference = 0.733, SE = 0.21, p = 0.002) or because they agree that they fear personalized advertising (mean difference = 0.848, SE = 0.208, p < 0.001). +On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations for not sharing their data: because they fear discrimination (mean difference = 1.060, SE = 0.195, p < 0.001), because they feel it is a breach of their privacy (mean difference = 0.821, SE = 0.225, p = 0.001), because they fear negative consequences for their family members (mean difference = 0.733, SE = 0.21, p = 0.002) or because they fear personalized advertising (mean difference = 0.848, SE = 0.208, p < 0.001). -Similarly those people who would share data with their DTC provider are more likely to agree on sharing the data, because they want to help scientists (mean difference = 1.57, SE = 0.199, p < 0.001), because they think of personal benefits (mean difference = 0.951, SE = 0.308, p = 0.006), and because they are curious (mean difference = 1.99, SE = 0.321, p < 0.001). +Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with the following motivations for sharing than those who would not share their data with their DTC company: because they want to help scientists (mean difference = 1.57, SE = 0.199, p < 0.001), because they think of personal benefits (mean difference = 0.951, SE = 0.308, p = 0.006), and because they are curious (mean difference = 1.99, SE = 0.321, p < 0.001). -Those participants who are not planning to get genotyped are more likely to agree to not share their data, because they fear discrimination (mean difference = 1.52, SE = 0.322, p < 0.001), because they feel it is a breach of their privacy (mean difference = 1.871, SE = 0.324, p < 0.001), because they fear consequences for their family (mean difference = 1.146, SE = 0.32, p = 0.001) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357, p = 0.006). +Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the following motivations for not sharing their data when compared to those who would share their data with their DTC company under any circumstances: because they fear discrimination (mean difference = 1.52, SE = 0.322, p < 0.001), because they feel it is a breach of their privacy (mean difference = 1.871, SE = 0.324, p < 0.001), because they fear consequences for their familiy members (mean difference = 1.146, SE = 0.32, p = 0.001) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357, p = 0.006). \subsection*{openSNP} We created openSNP, a website which allows users to upload their genotypings from the companies 23andme, deCODEme and Family Tree under the Creative Commons Zero-license, which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - allows a complete reuse of the data without any constraints. Users are encouraged to list as many phenotypes as possible through to a simple achievement-system which rewards users that upload their data and enter phenotypic information with small badges that are shown on their profile page. @@ -161,13 +161,13 @@ \section*{Discussion} % authors may use "Analysis" \section*{Materials and Methods} \subsection*{Survey on Sharing Genetic Information} -The survey was done with Google Docs and was distributed to possible participants through the 23andMe-community, the DIYBiology mailinglist, blogs which focus on genetics and DTC genetic testing and social media like Twitter, Google Plus and Facebook. Because of this the results are biased in a way that customers of DTC genetic testing are overrepresented. As we wanted to find out how people who have purchased DTC genetic testing care about sharing their data with third parties this does not lead to further problems. +The survey was done with Google Docs and was distributed to possible participants through the 23andMe community, the DIYBiology mailing list, blogs which focus on genetics and DTC genetic testing and social media like Twitter, Google Plus and Facebook. Because of this approach, the results do not reflect the general population, but overrepresent those people most likely to be interested in a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. As we wanted to find out about interest in crowdsourcing GWAS and about how people who had purchased DTC genetic testing felt about sharing their data with third parties, this approach was warranted. -The survey included questions on the age, chromosomal sex and ethnicity of the participants. Additionally it included questions on if they are already customer of a DTC company, are planning to become one or don't plan to become one. If they are already a customer they also got asked if they already share their genetic and phenotypic data. All participants got asked if they would share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, but only if they did not share my medical information with anybody else" and No". +The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their (planned) customership of a DTC company. If they already were customers, they were also asked if they were already sharing their genetic and phenotypic data. All participants were asked if they would share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, but only if they did not share my medical information with anybody else" and No". -The survey also asked some scale-questions which measured how strong participants agree/disagree on different reasons to share or not to share their information with third parties. The scale went from 1 = strongly disagree over 3 = neutral to 5 = strongly agree. Motivations given to share data were "because you want to help scientists with their research", "because of possible personal benefits (e.g. getting treatments for a disease you have, possibility of new medication, etc.)", "because it may deliver advertising that is relevant to me" and "out of curiosity". Motivations given not to share data were "because advertisers could use the information for targeted campaigns", "because of possible negative consequences for closely related persons", "because of the breach of your privacy" and " because of the fear of discrimination (e.g. by the employer, the state, some insurance company)". Additionally participants had the possibility to give own reasons to share or not to share their data. +The survey also asked some scaled questions which measured how strongly participants agreed/disagreed with different reasons for sharing or not sharing their information with third parties. The scale went from 1 = strongly disagree over 3 = neutral to 5 = strongly agree. Motivations queried for sharing data were "because I want to help scientists with their research", "because of possible personal benefits (e.g. getting treatments for a disease I have, possibility of new medication, etc.)", "because it may deliver advertising that is relevant to me" and "out of curiosity". Motivations queried for not sharing data were "because advertisers could use the information for targeted campaigns", "because of possible negative consequences for closely related persons", "because of the breach of my privacy" and " because of the fear of discrimination (e.g. by the employer, the state, some insurance company)". Additionally, participants had the possibility of giving their own reasons for sharing or not sharing their data. -The data of the survey was analyzed with SPSS 19. +The survey data was analyzed with SPSS 19. \subsection*{Technical realization of the platform} The main platform is realized using the web-framework Ruby on Rails. Postgres is used as the main database backend for Rails. The database stores genotyping results, phenotypic information of the users, literature results of Mendeley, the Public Library of Science and summaries on SNPs which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, through querying the content through the MediaWiki-API. All databases are queried using the unique identifier of each SNP as search term. From 66bfca393b37089ab55ed9546ed1bd1c6723bb51 Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Tue, 20 Dec 2011 22:00:12 +0100 Subject: [PATCH 06/88] reworking of the draft, more references --- paper_draft.tex | 43 +++++++++++++++--------------- papers.bib | 69 +++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 90 insertions(+), 22 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 10efa27..b432a88 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -123,38 +123,38 @@ \subsection*{Survey on Sharing Genetic Information} Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the following motivations for not sharing their data when compared to those who would share their data with their DTC company under any circumstances: because they fear discrimination (mean difference = 1.52, SE = 0.322, p < 0.001), because they feel it is a breach of their privacy (mean difference = 1.871, SE = 0.324, p < 0.001), because they fear consequences for their familiy members (mean difference = 1.146, SE = 0.32, p = 0.001) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357, p = 0.006). \subsection*{openSNP} -We created openSNP, a website which allows users to upload their genotypings from the companies 23andme, deCODEme and Family Tree under the Creative Commons Zero-license, which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - allows a complete reuse of the data without any constraints. Users are encouraged to list as many phenotypes as possible through to a simple achievement-system which rewards users that upload their data and enter phenotypic information with small badges that are shown on their profile page. +We created openSNP, a website which allows users to upload their genotypings from the companies 23andme, deCODEme and Family Tree under the Creative Commons Zero-license, which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - allows a complete reuse of the data without any constraints. Users are encouraged to list as many phenotypes as possible through a simple achievement system which rewards users that upload their data and enter phenotypic information with small badges that are shown on their profile pages. -The possible answers in terms of variations for a single phenotype are not limited and every user can add completely new phenotypes if the corresponding questions about this are lacking. To reduce the amount of manual data curation openSNP tries to avoid the entry of the same phenotype or variation, but with a slightly different spelling by helping users at entering data by an autocompletion-feature which lists similar entries which are already in the openSNP-database. +The specification of variations on a single phenotype and the phenotypes themselves is open and not limited to pre-defined categories. Every user can add completely new phenotypes that are not yet queried by openSNP. To reduce the amount of manual data curation, openSNP tries to harmonize the expression and spelling of the same phenotype or variation by implementing an autocompletion-feature which lists similar entries to what is being entered that are already in the openSNP database. -On the side of getting access to the data users can download single genotyping files for specific users, get archives of multiple genotyping files grouped by phenotypic variation or can access a single download that includes all genotyping-files and all phenotypes in a comma separated table. Additionally users can access the genetic data through the Distributed Annotation System, which allows to get all data for specific chromosomes and specific positions on single chromosomes. Users can discuss SNPs and phenotypes on the platform using a simple commenting-system or private messages. +OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypes in a comma-separated table. Additionally, the genetic data is accessible through the Distributed Annotation System, which offers all data for specific chromosomes and specific positions on single chromosomes. Users can discuss SNPs and phenotypes on the platform using a simple commenting system or private messages. -Between the start of openSNP on 09/27/2011 and 12/18/2011 214 people have signed up with openSNP, 79 of those have uploaded their genotyping files. Through this the openSNP database lists 69486471 SNPs which are distributed over 1938604 unique Rs-IDs. In the same timeframe all users combined have entered 675 variations which are distributed over 47 different phenotypes. See figure n for a distribution of data acquisition over time. +Between the start of openSNP on 09/27/2011 and 12/18/2011, 214 people have signed up with openSNP, 79 of which have uploaded their genotyping files. The openSNP database thereby lists 69,486,471 SNPs, which are distributed over 1,938,604 unique Rs-IDs. In the same timeframe all users combined have entered a total of 675 variations on 47 different phenotypes. See figure n for a distribution of data acquisition over time. -The mean number of users that have entered their variation for a single phenotype is 14.36 (SD 12.65), the median is 10. The distribution of how many users have entered their data per phenotype can be seen in figure n+1. The phenotype which has been provided by the most users is the eye color which has been provided by 54 different users. There are two phenotypes which have only been provided by a single user: The score of the Writing-SAT and triglyceride-levels. +The mean number of users that have entered their variations for a single phenotype is 14.36 (SD 12.65), the median is 10. The distribution of how many users have entered their data per phenotype can be seen in figure n+1. The phenotype provided by the most users is the eye color, which has been entered by 54 users. There are two phenotypes which have so far only been provided by a single user: The SAT Writing score and triglyceride-levels. -A total number of 15229 documents relevant to the SNP-IDs which are listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. Of the primary literature 25 \% are released in Open Access-journals and can be freely free of charge by every user (Figure n+2). For usability reasons SNPs are ranked by the amount of information gathered by the external services. +In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. A total number of 15,229 documents relevant to the SNP-IDs listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure n+2). For usability reasons, SNPs are ranked by the amount of information gathered by the external services. -The external services themselves are ranked by how easy users can access information out of these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley-results, as those aren't necessary freely available for every user. A entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley-entry. +The external services themselves are ranked by how easily users can access information from these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley results, as those aren't necessary freely available to every user. An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. \section*{Discussion} -Although prices for exome or even full genome sequencing are dropping rapidly, in comparison to this GWAS tend to stay cheaper and offer a possibility to get insights on genetic variations on a population level and allow an analysis of SNPs which are linked to different traits. Despite this benefit in terms of costs it must be pointed out that GWAS can only detect correlations of SNPs with those traits and don't allow the detection of the causes for this and needs a large enough sample which has to be statistically analysed by using sound methods. Nevertheless, GWAS are still frequently used and new associations still can be found (we should list some recent results here -> oxytocin maybe? we could look at the twitter-timeline of @opensnporg, i publish papers i find there). +Although prices of exome or even full genome sequencing are dropping rapidly, GWAS tend to stay considerably cheaper and offer a possibility of getting insight into genetic variations on a population level and allow an analysis of SNPs which are linked to different traits. Despite this benefit in terms of cost, it must be pointed out that GWAS can only detect correlations of SNPs with those traits and don't allow inference on the cause for any correlation. Furthermore, GWAS need a large enough sample which has to be statistically analysed by using sound methods. Nevertheless, GWAS are still frequently used and new associations still can be found (we should list some recent results here -> oxytocin maybe? we could look at the twitter-timeline of @opensnporg, i publish papers i find there). -One way to bring costs for GWAS even further down is to make use of already available genotyping results and datasets. Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high numbers of customers which are willing to pay for those genotypings by themselves. As we have found in the survey on the sharing such results many of these customers are also willing to share their results with scientists and the public to help scientific progress, although people who have taken DTC genetic testing are aware of the privacy implications that come with openly sharing those results. +One way of bringing down costs for GWAS even further is to make use of already available genotyping results and datasets. Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high numbers of customers which are willing to pay for the genotyping by themselves. As we have found in the survey on sharing such results, many of these customers are also willing to share their results with scientists and the public to help scientific progress, although people who have taken DTC genetic testing are aware of the privacy implications that come with openly sharing those results. -With openSNP we've build a platform that can be used by customers of DTC genetic testing to share their data to easily share their genetic and phenotypic data with a wide audience as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. Customers of DTC genetic testing also benefit of an easy access to primary literature on SNPs and genetic variation they carry. While we don't have collected enough data to perform full scale GWAS yet this might be possible in the future, as user numbers are rising. +With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. While we don't have collected enough data to perform full scale GWAS yet, this might be possible in the future, as user numbers are rising. -By crowdsourcing the acquisition of genetic and phenotypic data openSNP faces the same problems as any other open platform on the Internet, namely we have to trust users about the data they upload and enter on openSNP. Additionally the quality of the data varies, especially in terms of accuracy on the phenotypic variation. While we try to suggest similar entries to users there are some cases where users wont follow those suggestions, so duplicates or similar phenotypes or varations in traits may arise. +By crowdsourcing the acquisition of genetic and phenotypic data, openSNP faces the same problems as any other open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation. While we try to suggest similar entries to the users, there are some cases where users wont follow those suggestions, so duplicates or similar phenotypes or varations in traits may arise. -There are two possible solutions to this problem: One could only allow some (trusted) users to enter new phenotypes or one could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users can't add variations that have not been available from the start on. Both methods have a disadvantage: In either case it makes it harder for users to enter their data and by this highers the bar for participation, which ultimately could lead to less data entered. Facing this trade-off we decided to keep entering data easy, at the cost that users who want to perform GWAS with the data need to perform more quality control. +There are two possible solutions to this problem: One could only allow some (trusted) users to enter new phenotypes or one could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users can't add variations that have not been available from the start. Both methods have a disadvantage: In either case it makes it harder for users to enter their data and thereby raises the bar for participation, which ultimately could lead to less data entered. Facing this trade-off, we decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS with the data to perform more quality control. -Another thing that should kept in mind is a possible bias in which data is available on openSNP, as we can't rule out the possibility that only a biased subset of people buy DTC genetic testing and an even smaller subset is willing to publish the results, along with information of medical relevance. +Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP, as we cannot rule out the possibility that only an unrepresentative subset of people buy DTC genetic testing and an even smaller subset is willing to publish the results, along with information of medical relevance. -The advent of DTC genetic testing has lead to new ethical and social issues. Much of the critique on DTC genetic testing focusses on the practice of delivering medical information without consulting a physician or genetic counselor to help patients/customers make sense of the information and help them to put the new knowledge to good use. Less attention has been given to the privacy implications that come with this cheap way of obtaining genetical information. +The advent of DTC genetic testing has led to new ethical and social issues. Much of the critique on DTC genetic testing focusses on the practice of delivering medical information without consulting a physician or genetic counselor to help patients/customers make sense of the information and to put the new knowledge to good use \cite{Hauskeller2011,Hogarth2008,Wasson2009}. Less attention has been paid to the privacy implications that come with this cheap way of obtaining genetical information \cite{Caulfield2011,Joh2011}. -By making the own genetic and medical information public it becomes clear that the issue of privacy needs to get a closer view. Our survey has shown that people are concerned about their privacy and are concerned that stakeholders like employers, insurance companies, governments or advertisers misuse this information. Policy makers start to react to those changes by introducing laws like the \textit{Genetic Information Non-Discrimination Act} in the United States or the \emph{Gendiagnostikgesetz} in Germany to minimize the impact widely available genetic information. DTC genetic testing companies on their side also try to educate their customers about the risks of releasing genetic data. +With a move towards making one's own genetic and medical information public, it becomes clear that the issue of privacy needs to get a closer look. Our survey has shown that people are concerned about their privacy and fear that stakeholders like employers, insurance companies, governments or advertisers might misuse the information. Policy makers start to react to those changes by introducing laws like the \textit{Genetic Information Non-Discrimination Act} in the United States or the \emph{Gendiagnostikgesetz} in Germany to minimize the impact of widely available genetic information. DTC genetic testing companies themselves also try to educate their customers about the risks of releasing genetic data. -We transparently address the problem of privacy implications that come with releasing the data twice, during registering for openSNP and during the upload of the DTC results. Users have to check that they have read and understood the disclaimer about possible side-effects that can arise by making their data public. To further improve this process and get informed consent we are working to implement the procedures which are currently developed by \textit{Consent for Research} (http://www.weconsent.us). +We openly address the problem of privacy implications that come with releasing genetic data twice, during registration for openSNP and during the upload of the DTC genetic testing results. Users have to confirm that they have read and understood the disclaimer about possible side-effects that can arise by publishing their data. To further improve this process and get informed consent, we are working on implementing the procedures which are currently developed by \textit{Consent for Research} (http://www.weconsent.us). % You may title this section "Methods" or "Models". % "Models" is not a valid title for PLoS ONE authors. However, PLoS ONE @@ -170,13 +170,13 @@ \subsection*{Survey on Sharing Genetic Information} The survey data was analyzed with SPSS 19. \subsection*{Technical realization of the platform} -The main platform is realized using the web-framework Ruby on Rails. Postgres is used as the main database backend for Rails. The database stores genotyping results, phenotypic information of the users, literature results of Mendeley, the Public Library of Science and summaries on SNPs which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, through querying the content through the MediaWiki-API. All databases are queried using the unique identifier of each SNP as search term. +The main platform is realized using the web framework Ruby on Rails. Postgres is used as the main database backend for Rails. The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, through querying the content via the MediaWiki API. All databases are queried using the unique identifier of each SNP as the search term. -SNPs are cataloged by their unique identifier, which consists of a prefix (mostly \textit{rs}) and a unique number. This is a common format which is used by the NCBI dbSNP database and is also widely used and easily parsed from different literature-sources. Publications out of the different databases and the genotypes of the users are associated with single SNPs by the Rs-ID. Allele and genotype frequencies are are updated regularly, based on the data present in openSNP. +SNPs are cataloged by their unique identifier, which consists of a prefix (mostly \textit{rs}) and a unique number. This is a common format, which is employed by the NCBI dbSNP database and is also widely used and easily parsed from different literature sources. Publications from the different databases as well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. -Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are be mailed to users and queries to external resources are handled using the ruby-gem Resque and a Redis-server. Search-features on the platform itself are implemented using SOLR and the ruby-gem sunspot. Additionally data can be requested from openSNP using the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}. The data for this is stored in a mySQL-database, the delivery of the data is done by using ProServer \cite{Finn2007}, which was modified by Gel et al. for use in easyDAS \cite{GelMoreno2011}. +Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources are handled using the ruby gem Resque and a Redis server. Search features on the platform itself are implemented using SOLR and the ruby gem Sunspot. Additionally, data can be requested from openSNP using the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}. The required data is stored in a mySQL database, the delivery of the data is done by using ProServer \cite{Finn2007}, which was modified by Gel et al. for use in easyDAS \cite{GelMoreno2011}. -A flowchart of all services incorporated in openSNP and how users can upload or access the data is given in figure n+3. The source code of openSNP is published under Creative Commons BY-SA 3.0 and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. +A flowchart of all services incorporated in openSNP and of all the ways users can upload or access the data is given in figure n+3. The source code of openSNP is published under Creative Commons BY-SA 3.0 and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. % Do NOT remove this, even if you are not including acknowledgments \section*{Acknowledgments} @@ -211,5 +211,4 @@ \section*{Tables} %\label{tab:label} % \end{table} -\end{document} - +\end{document} \ No newline at end of file diff --git a/papers.bib b/papers.bib index 2b6f1c4..2a61e7e 100644 --- a/papers.bib +++ b/papers.bib @@ -210,3 +210,72 @@ @article{Hindorff2009 eprint = {http://www.pnas.org/content/106/23/9362.full.pdf+html}, journal = {Proceedings of the National Academy of Sciences} } + +@article{Caulfield2011, +author = {Caulfield, Timothy and McGuire, Amy L}, +title = {Direct-to-Consumer Genetic Testing: Perceptions, Problems and Policy Responses}, +volume = {63}, +pages = {1.1-1.11}, +year = {2011}, +doi = {10.1146/annurev-med-062110-123753}, +journal = {Annual Review of Medicine} +} + + +@article{Hauskeller2011, +author = {Hauskeller, C.}, +doi = {10.1136/bmj.d2317}, +file = {:Users/bastian/Documents/Paper/Hauskeller/Bmj/0.pdf:pdf}, +issn = {0959-8138}, +journal = {Bmj}, +month = apr, +number = {apr21 1}, +pages = {d2317--d2317}, +title = {{Direct to consumer genetic testing}}, +url = {http://www.bmj.com/cgi/doi/10.1136/bmj.d2317}, +volume = {342}, +year = {2011} +} +@article{Hogarth2008, +abstract = {This review surveys the developing market for direct-to-consumer (DTC) genetic tests and examines the range of companies and tests available, the regulatory landscape, the concerns raised about DTC testing, and the calls for enhanced oversight. We provide a comparative overview of the situation, particularly in the United States and Europe, by exploring the regulatory frameworks for medical devices and clinical laboratories. We also discuss a variety of other mechanisms such as general controls on advertising and consumer law mechanisms.}, +author = {Hogarth, Stuart and Javitt, Gail and Melzer, David}, +doi = {10.1146/annurev.genom.9.081307.164319}, +file = {:Users/bastian/Documents/Paper/Hogarth, Javitt, Melzer/Annual review of genomics and human genetics/annurev\%2Egenom\%2E9\%2E081307\%2E164319.pdf:pdf}, +issn = {1527-8204}, +journal = {Annual review of genomics and human genetics}, +keywords = {Advertising as Topic,Europe,Genetic Techniques,Genetic Techniques: economics,Genetic Techniques: ethics,Genetics, Medical,Genetics, Medical: economics,Genetics, Medical: ethics,Genetics, Medical: legislation \& jurisprudence,Humans,Marketing of Health Services,Public Policy,United States}, +month = jan, +pages = {161--82}, +pmid = {18767961}, +title = {{The current landscape for direct-to-consumer genetic testing: legal, ethical, and policy issues.}}, +url = {http://www.ncbi.nlm.nih.gov/pubmed/18767961}, +volume = {9}, +year = {2008} +} +@article{Joh2011, +author = {Joh, Elizabeth E}, +doi = {10.1038/nrg3113}, +file = {:Users/bastian/Documents/Paper/Joh/Nature reviews. Genetics/nrg3113.pdf:pdf}, +issn = {1471-0064}, +journal = {Nature reviews. Genetics}, +month = oct, +number = {October}, +pages = {3113}, +pmid = {22025003}, +title = {{Ethics watch: DNA theft: your genetic information at risk.}}, +url = {http://www.ncbi.nlm.nih.gov/pubmed/22025003}, +volume = {12}, +year = {2011} +} +@article{Wasson2009, +author = {Wasson, Katherine}, +file = {:Users/bastian/Documents/Paper/Wasson/The American Journal of Bioethics/15265160902893965.pdf:pdf}, +journal = {The American Journal of Bioethics}, +number = {6-7}, +pages = {56--58}, +publisher = {Taylor \& Francis}, +title = {{Direct-to-consumer genomics and research ethics: should a more robust informed consent process be included?}}, +url = {http://www.tandfonline.com/doi/abs/10.1080/15265160902893965}, +volume = {9}, +year = {2009} +} From 78e9092d25786dbe3f34f5472255e43dd2a3f509 Mon Sep 17 00:00:00 2001 From: Fabian Zimmer Date: Sun, 22 Jan 2012 13:41:03 +0100 Subject: [PATCH 07/88] Added a new introduction, structured the results and discussion, moved a lot of paragraphs --- paper_draft.tex | 62 ++++++++++++++++++++++++++++++++++++------------- 1 file changed, 46 insertions(+), 16 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index b432a88..45e4f73 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -99,12 +99,34 @@ \section*{Author Summary} \section*{Introduction} Genome-Wide Association Studies (GWAS) are a comparatively easy and cheap way of finding Single Nucleotide Polymorphisms (SNPs) of medical relevance. Such SNPs of interest can be used to find candidate genes for a closer inspection or to predict disease risks or other traits. Genome-Wide Association Studies make use of statistics for comparing the alleles of patients to the alleles of healthy controls. This method does not allow inference of causal differences but merely identifies correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning, the number of participants in such studies has been rising: Over 1200 GWAS have been performed \cite{Johnson2009} and over 5000 SNPs have been linked to different diseases and traits in those studies \cite{Hindorff2009}. %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) +\\ +\\ +FZ REWRITE +\\ +The comparison of DNA sequences from indivduals in a population reveals sites that are variable. +These variations are called Single Nucleotide Polymorphisms (SNP). Each different nucleotide +at a specific site is defined as an allele. FZ: Explain genotype!! +Different methods have been developed to measure/find/access/sequence SNPs in an individual, a +process called genotyping. Microarrays are a cheap and fast way to genotype individuals, in comparison +to whole genome sequencing. If the genotypes for more then one individual are known, the analysis of +allele frequencies can be used to associated SNPs with for example diseass. Genome-Wide Assocation Studies +(GWAS) make use of statistics for comparing the alleles of patients to the alleles of healthy controls. This +enables GWAS to find significant overrepresantations of alleles in patients and associate them wir a disease. +This method does not allow inference of causal differences but merely identifies correlations. +The first GWAS was published in 2005 and compared age-related macular degeneration in contrast +to a healthy control group \cite{Klein2005}. Since the beginning, the number of participants in +such studies has been rising: Over 1200 GWAS have been performed \cite{Johnson2009} and over +5000 SNPs have been linked to different diseases and traits in those studies \cite{Hindorff2009}. +%(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) + Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. Those companies use DNA microarrays to screen for around 1 million SNPs spread over the human genome. In return customers get an analysis of the results, as well as a raw file that includes the customer's individual genotypes. In 2011, 23andMe alone had over 100,000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} - the company recognizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer the data is used for association studies. 23andMe has published several articles in which it replicates known findings, but also finds new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. Over 30,000 23andme-customers have participated in those association studies. -Although companies like 23andMe are willing to contribute to science, it is not easy for individual scientists to access the data. This is mainly due to concerns about privacy, liability and consent. Nevertheless, there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. While this makes it possible for scientists to access the data, it requires a lot of work to keep track of all new genotyping data that is available to the public. While projects like the SNPedia try to keep track of all the files \cite{Cariaso2011}, they do not provide the information necessary to perform GWAS, as the phenotypic information is not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the \textit{Personal Genome Project}, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data. +Although companies like 23andMe are willing to contribute to science, it is not easy for individual scientists to access the data. This is mainly due to concerns about privacy, liability and consent. Nevertheless, there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. -A possible solution to this is a community-driven platform that aggregates genetic and phenotypic information of people who are willing to share their data with the general public and have given their informed consent. We designed an online survey to assess interest in such a crowdsourcing platform, in which we asked an audience interested in biology topics about their willingness to share their genetic and phenotypic information with the public. The aim of the survey was not to determine the percentage of the population interested in sharing genotypic information, but to get an idea of whether a crowdsourcing platform could get enough support to go forward with the project. Additionally, we built a platform which allows customers of DTC genetic testing to publicize genetic and phenotypic information and gives researchers multiple ways of reusing the data. +FZ: The data is scattered and unorganized, making it hard to use this infornomation. While projects like the SNPedia try to keep track of all the files \cite{Cariaso2011}, they do not provide the information necessary to perform GWAS, as the phenotypic information is not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the \textit{Personal Genome Project}, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data. + +We designed an online survey to evaluate the support in the community for a crowdsourced online plattform. Here we present the results of the survey in combination with the openSNP project. The openSNP project is an online platform, which makes it possible for DTC customers to share genotypic and phenoytypic information and connect it with external data sources. The data is made available to research via a Creative-Commones-Zero licence. % Results and Discussion can be combined. \section*{Results} @@ -123,32 +145,40 @@ \subsection*{Survey on Sharing Genetic Information} Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the following motivations for not sharing their data when compared to those who would share their data with their DTC company under any circumstances: because they fear discrimination (mean difference = 1.52, SE = 0.322, p < 0.001), because they feel it is a breach of their privacy (mean difference = 1.871, SE = 0.324, p < 0.001), because they fear consequences for their familiy members (mean difference = 1.146, SE = 0.32, p = 0.001) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357, p = 0.006). \subsection*{openSNP} -We created openSNP, a website which allows users to upload their genotypings from the companies 23andme, deCODEme and Family Tree under the Creative Commons Zero-license, which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - allows a complete reuse of the data without any constraints. Users are encouraged to list as many phenotypes as possible through a simple achievement system which rewards users that upload their data and enter phenotypic information with small badges that are shown on their profile pages. - -The specification of variations on a single phenotype and the phenotypes themselves is open and not limited to pre-defined categories. Every user can add completely new phenotypes that are not yet queried by openSNP. To reduce the amount of manual data curation, openSNP tries to harmonize the expression and spelling of the same phenotype or variation by implementing an autocompletion-feature which lists similar entries to what is being entered that are already in the openSNP database. - -OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypes in a comma-separated table. Additionally, the genetic data is accessible through the Distributed Annotation System, which offers all data for specific chromosomes and specific positions on single chromosomes. Users can discuss SNPs and phenotypes on the platform using a simple commenting system or private messages. -Between the start of openSNP on 09/27/2011 and 12/18/2011, 214 people have signed up with openSNP, 79 of which have uploaded their genotyping files. The openSNP database thereby lists 69,486,471 SNPs, which are distributed over 1,938,604 unique Rs-IDs. In the same timeframe all users combined have entered a total of 675 variations on 47 different phenotypes. See figure n for a distribution of data acquisition over time. +The openSNP project (http://opensnp.org) is an open, crowdsourced online platform for both DTC customers and researchers. DTC customers are encouraged to share their genotyping results and connect them with phenotypes to enable GWAS for researchers or any other reuse in a scientific context. +\subsubsection*{Sharing genotypic information} +Currently users can upload their genotypings from the companies 23andme, deCODEme and Family Tree under the Creative Commons Zero-license, which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - allows a complete reuse of the data without any constraints. +Between the start of openSNP on 09/27/2011 and 12/18/2011, 214 people have signed up with openSNP, 79 of which have uploaded their genotyping files. The openSNP database thereby lists 69,486,471 SNPs, which are distributed over 1,938,604 unique Rs-IDs. +\subsubsection*{Crowdsourcing phenotypes} +Users are encouraged to list as many phenotypes as possible through a simple achievement system which rewards users that upload their data and enter phenotypic information with small badges that are shown on their profile pages. The specification of variations on a single phenotype and the phenotypes themselves is open and not limited to pre-defined categories. Every user can add completely new phenotypes that are not yet queried by openSNP. To reduce the amount of manual data curation, openSNP tries to harmonize the expression and spelling of the same phenotype or variation by implementing an autocompletion-feature which lists similar entries to what is being entered that are already in the openSNP database. In the same timeframe all users combined have entered a total of 675 variations on 47 different phenotypes. See figure n for a distribution of data acquisition over time. The mean number of users that have entered their variations for a single phenotype is 14.36 (SD 12.65), the median is 10. The distribution of how many users have entered their data per phenotype can be seen in figure n+1. The phenotype provided by the most users is the eye color, which has been entered by 54 users. There are two phenotypes which have so far only been provided by a single user: The SAT Writing score and triglyceride-levels. +\subsubsection*{Connection to external services} +In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. A total number of 15,229 documents relevant to the SNP-IDs listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure n+2). For usability reasons, SNPs are ranked by the amount of information gathered by the external services.The external services themselves are ranked by how easily users can access information from these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley results, as those aren't necessary freely available to every user. An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. +\subsubsection*{Data access} +OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypes in a comma-separated table. Additionally, the genetic data is accessible through the Distributed Annotation System, which offers all data for specific chromosomes and specific positions on single chromosomes. Users can discuss SNPs and phenotypes on the platform using a simple commenting system or private messages. -In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. A total number of 15,229 documents relevant to the SNP-IDs listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure n+2). For usability reasons, SNPs are ranked by the amount of information gathered by the external services. - -The external services themselves are ranked by how easily users can access information from these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley results, as those aren't necessary freely available to every user. An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. \section*{Discussion} + +\subsubsection*{Survey issues} +Because of this approach, the results do not reflect the general population, but overrepresent those people most likely to be interested in a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. As we wanted to find out about interest in crowdsourcing GWAS and about how people who had purchased DTC genetic testing felt about sharing their data with third parties, this approach was warranted. +\subsubsection*{GWAS and open data} Although prices of exome or even full genome sequencing are dropping rapidly, GWAS tend to stay considerably cheaper and offer a possibility of getting insight into genetic variations on a population level and allow an analysis of SNPs which are linked to different traits. Despite this benefit in terms of cost, it must be pointed out that GWAS can only detect correlations of SNPs with those traits and don't allow inference on the cause for any correlation. Furthermore, GWAS need a large enough sample which has to be statistically analysed by using sound methods. Nevertheless, GWAS are still frequently used and new associations still can be found (we should list some recent results here -> oxytocin maybe? we could look at the twitter-timeline of @opensnporg, i publish papers i find there). -One way of bringing down costs for GWAS even further is to make use of already available genotyping results and datasets. Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high numbers of customers which are willing to pay for the genotyping by themselves. As we have found in the survey on sharing such results, many of these customers are also willing to share their results with scientists and the public to help scientific progress, although people who have taken DTC genetic testing are aware of the privacy implications that come with openly sharing those results. - -With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. While we don't have collected enough data to perform full scale GWAS yet, this might be possible in the future, as user numbers are rising. +One way of bringing down costs for GWAS even further is to make use of already available genotyping results and datasets. Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high numbers of customers which are willing to pay for the genotyping by themselves. By crowdsourcing the acquisition of genetic and phenotypic data, openSNP faces the same problems as any other open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation. While we try to suggest similar entries to the users, there are some cases where users wont follow those suggestions, so duplicates or similar phenotypes or varations in traits may arise. There are two possible solutions to this problem: One could only allow some (trusted) users to enter new phenotypes or one could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users can't add variations that have not been available from the start. Both methods have a disadvantage: In either case it makes it harder for users to enter their data and thereby raises the bar for participation, which ultimately could lead to less data entered. Facing this trade-off, we decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS with the data to perform more quality control. Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP, as we cannot rule out the possibility that only an unrepresentative subset of people buy DTC genetic testing and an even smaller subset is willing to publish the results, along with information of medical relevance. +\subsubsection*{Why should a user publish their genetic informationß Pro vs. Con} +With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. While we don't have collected enough data to perform full scale GWAS yet, this might be possible in the future, as user numbers are rising. +\subsubsection*{Privacy and health implications and ethical considerations} + +As we have found in the survey on sharing such results, many of these customers are also willing to share their results with scientists and the public to help scientific progress, although people who have taken DTC genetic testing are aware of the privacy implications that come with openly sharing those results. The advent of DTC genetic testing has led to new ethical and social issues. Much of the critique on DTC genetic testing focusses on the practice of delivering medical information without consulting a physician or genetic counselor to help patients/customers make sense of the information and to put the new knowledge to good use \cite{Hauskeller2011,Hogarth2008,Wasson2009}. Less attention has been paid to the privacy implications that come with this cheap way of obtaining genetical information \cite{Caulfield2011,Joh2011}. @@ -161,7 +191,7 @@ \section*{Discussion} % authors may use "Analysis" \section*{Materials and Methods} \subsection*{Survey on Sharing Genetic Information} -The survey was done with Google Docs and was distributed to possible participants through the 23andMe community, the DIYBiology mailing list, blogs which focus on genetics and DTC genetic testing and social media like Twitter, Google Plus and Facebook. Because of this approach, the results do not reflect the general population, but overrepresent those people most likely to be interested in a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. As we wanted to find out about interest in crowdsourcing GWAS and about how people who had purchased DTC genetic testing felt about sharing their data with third parties, this approach was warranted. +The survey was done with Google Docs and was distributed to possible participants through the 23andMe community, the DIYBiology mailing list, blogs which focus on genetics and DTC genetic testing and social media like Twitter, Google Plus and Facebook. The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their (planned) customership of a DTC company. If they already were customers, they were also asked if they were already sharing their genetic and phenotypic data. All participants were asked if they would share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, but only if they did not share my medical information with anybody else" and No". @@ -211,4 +241,4 @@ \section*{Tables} %\label{tab:label} % \end{table} -\end{document} \ No newline at end of file +\end{document} From ba37b3748af4e2b2c83931b376c057264dc554ef Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Sun, 22 Jan 2012 15:30:48 +0100 Subject: [PATCH 08/88] some changes in the introduction --- paper_draft.tex | 25 ++++++++++--------------- 1 file changed, 10 insertions(+), 15 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 45e4f73..b3c6889 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -98,20 +98,15 @@ \section*{Author Summary} \section*{Introduction} -Genome-Wide Association Studies (GWAS) are a comparatively easy and cheap way of finding Single Nucleotide Polymorphisms (SNPs) of medical relevance. Such SNPs of interest can be used to find candidate genes for a closer inspection or to predict disease risks or other traits. Genome-Wide Association Studies make use of statistics for comparing the alleles of patients to the alleles of healthy controls. This method does not allow inference of causal differences but merely identifies correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning, the number of participants in such studies has been rising: Over 1200 GWAS have been performed \cite{Johnson2009} and over 5000 SNPs have been linked to different diseases and traits in those studies \cite{Hindorff2009}. %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) -\\ -\\ -FZ REWRITE -\\ -The comparison of DNA sequences from indivduals in a population reveals sites that are variable. -These variations are called Single Nucleotide Polymorphisms (SNP). Each different nucleotide -at a specific site is defined as an allele. FZ: Explain genotype!! -Different methods have been developed to measure/find/access/sequence SNPs in an individual, a -process called genotyping. Microarrays are a cheap and fast way to genotype individuals, in comparison +The comparison of DNA sequences from individuals in a population reveals sites which are variable. +If differences between individuals span only over a single nucleotide then these variations are also called Single Nucleotide Polymorphisms (SNP) and different nucleotides +at a specific site are defined as an allele. For diploid organisms, as us humans, the genotype at a given site consists of two alleles, which are independently inherited from each parent. +Different methods have been developed to read SNPs in an individual, a +process called genotyping. Microarrays are a cheap, fast and widespread used way to genotype individuals, in comparison to whole genome sequencing. If the genotypes for more then one individual are known, the analysis of -allele frequencies can be used to associated SNPs with for example diseass. Genome-Wide Assocation Studies +allele frequencies can be used to associated SNPs with diseases and other inheritable traits. Genome-Wide Assocation Studies (GWAS) make use of statistics for comparing the alleles of patients to the alleles of healthy controls. This -enables GWAS to find significant overrepresantations of alleles in patients and associate them wir a disease. +enables GWAS to find alleles which are significantly overrepresented in patients and associate them with a disease. This method does not allow inference of causal differences but merely identifies correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning, the number of participants in @@ -124,7 +119,7 @@ \section*{Introduction} Although companies like 23andMe are willing to contribute to science, it is not easy for individual scientists to access the data. This is mainly due to concerns about privacy, liability and consent. Nevertheless, there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. -FZ: The data is scattered and unorganized, making it hard to use this infornomation. While projects like the SNPedia try to keep track of all the files \cite{Cariaso2011}, they do not provide the information necessary to perform GWAS, as the phenotypic information is not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the \textit{Personal Genome Project}, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data. +This data is scattered and unorganized, making it hard to use this information. While projects like SNPedia try to keep track of all the genotyping files \cite{Cariaso2011}, they do not provide the information necessary to perform GWAS, as the phenotypic information is most often not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the \textit{Personal Genome Project}, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data and not every customer of DTC genetic testing can participate in it. We designed an online survey to evaluate the support in the community for a crowdsourced online plattform. Here we present the results of the survey in combination with the openSNP project. The openSNP project is an online platform, which makes it possible for DTC customers to share genotypic and phenoytypic information and connect it with external data sources. The data is made available to research via a Creative-Commones-Zero licence. @@ -142,7 +137,7 @@ \subsection*{Survey on Sharing Genetic Information} Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with the following motivations for sharing than those who would not share their data with their DTC company: because they want to help scientists (mean difference = 1.57, SE = 0.199, p < 0.001), because they think of personal benefits (mean difference = 0.951, SE = 0.308, p = 0.006), and because they are curious (mean difference = 1.99, SE = 0.321, p < 0.001). -Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the following motivations for not sharing their data when compared to those who would share their data with their DTC company under any circumstances: because they fear discrimination (mean difference = 1.52, SE = 0.322, p < 0.001), because they feel it is a breach of their privacy (mean difference = 1.871, SE = 0.324, p < 0.001), because they fear consequences for their familiy members (mean difference = 1.146, SE = 0.32, p = 0.001) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357, p = 0.006). +Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the following motivations for not sharing their data when compared to those who would share their data with their DTC company under any circumstances: because they fear discrimination (mean difference = 1.52, SE = 0.322, p < 0.001), because they feel it is a breach of their privacy (mean difference = 1.871, SE = 0.324, p < 0.001), because they fear consequences for their family members (mean difference = 1.146, SE = 0.32, p = 0.001) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357, p = 0.006). \subsection*{openSNP} @@ -174,7 +169,7 @@ \subsubsection*{GWAS and open data} There are two possible solutions to this problem: One could only allow some (trusted) users to enter new phenotypes or one could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users can't add variations that have not been available from the start. Both methods have a disadvantage: In either case it makes it harder for users to enter their data and thereby raises the bar for participation, which ultimately could lead to less data entered. Facing this trade-off, we decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS with the data to perform more quality control. Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP, as we cannot rule out the possibility that only an unrepresentative subset of people buy DTC genetic testing and an even smaller subset is willing to publish the results, along with information of medical relevance. -\subsubsection*{Why should a user publish their genetic informationß Pro vs. Con} +\subsubsection*{Why should a user publish their genetic information? Pro vs. Con} With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. While we don't have collected enough data to perform full scale GWAS yet, this might be possible in the future, as user numbers are rising. \subsubsection*{Privacy and health implications and ethical considerations} From 27e40b563687e832c4833a085377b24f997930c5 Mon Sep 17 00:00:00 2001 From: Fabian Zimmer Date: Mon, 23 Jan 2012 22:04:46 +0100 Subject: [PATCH 09/88] New structure for the sharing genotypes and the crowdsourcing phenotypes section. The verbatim package is used to create multi-line comments --- paper_draft.tex | 55 +++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 49 insertions(+), 6 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index b3c6889..4dff66a 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -28,7 +28,8 @@ %\usepackage{setspace} %\doublespacing - +%FZ: Enable the comment command +\usepackage{verbatim} % Text layout \topmargin 0.0cm \oddsidemargin 0.5cm @@ -142,13 +143,55 @@ \subsection*{Survey on Sharing Genetic Information} \subsection*{openSNP} The openSNP project (http://opensnp.org) is an open, crowdsourced online platform for both DTC customers and researchers. DTC customers are encouraged to share their genotyping results and connect them with phenotypes to enable GWAS for researchers or any other reuse in a scientific context. - +\begin{comment} +FZ: That is a good summary, but as Bastian pointed out the reuse is not only +enabled in a scientific context? +\end{comment} + +\begin{comment} +FZ: +\end{comment} \subsubsection*{Sharing genotypic information} -Currently users can upload their genotypings from the companies 23andme, deCODEme and Family Tree under the Creative Commons Zero-license, which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - allows a complete reuse of the data without any constraints. -Between the start of openSNP on 09/27/2011 and 12/18/2011, 214 people have signed up with openSNP, 79 of which have uploaded their genotyping files. The openSNP database thereby lists 69,486,471 SNPs, which are distributed over 1,938,604 unique Rs-IDs. +Currently users can upload their genotypings from the companies 23andme, deCODEme and Family Tree to/via a webinterface to the openSNP +project. The uploaded data is published under the Creative Commons Zero-license, +which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - +allows a complete reuse of the data without any constraints. +\begin{comment} +FZ: If that is true, rephrase sentence above +FZ: Made two sentences out of one. +\end{comment} +Between the start of openSNP on 09/27/2011 and 12/18/2011, 214 people have signed +up with openSNP, 79 of which have uploaded their genotyping files. The openSNP +database thereby lists 69,486,471 SNPs, which are distributed over 1,938,604 unique Rs-IDs. + \subsubsection*{Crowdsourcing phenotypes} -Users are encouraged to list as many phenotypes as possible through a simple achievement system which rewards users that upload their data and enter phenotypic information with small badges that are shown on their profile pages. The specification of variations on a single phenotype and the phenotypes themselves is open and not limited to pre-defined categories. Every user can add completely new phenotypes that are not yet queried by openSNP. To reduce the amount of manual data curation, openSNP tries to harmonize the expression and spelling of the same phenotype or variation by implementing an autocompletion-feature which lists similar entries to what is being entered that are already in the openSNP database. In the same timeframe all users combined have entered a total of 675 variations on 47 different phenotypes. See figure n for a distribution of data acquisition over time. -The mean number of users that have entered their variations for a single phenotype is 14.36 (SD 12.65), the median is 10. The distribution of how many users have entered their data per phenotype can be seen in figure n+1. The phenotype provided by the most users is the eye color, which has been entered by 54 users. There are two phenotypes which have so far only been provided by a single user: The SAT Writing score and triglyceride-levels. +Beside uploading genotypic data, users may list new phenotypes that are not yet +queried by openSNP. The specification of these phenotypes is open and not limited +to pre-defined categories. To reduce the amount of manual data curation, openSNP tries to harmonize +the expression and spelling of the same phenotype or variation. We implemented an +autocompletion-feature which helps users to reuse already entered phenotypes. +Users are encouraged to list as many phenotypes as possible through a simple +achievement system which rewards users that upload their data and enter phenotypic +information with small badges that are shown on their profile pages. +\begin{comment} +FZ: I changed the structure of the paragraph to make it a little bit more +concise. +I think its best to connect the phenotypes to the above paragraph, explain +the procedure and then talk about the achievments +\end{comment} +In the same timeframe that was mentioned above, all users combined have +entered a total of 675 variations on 47 different phenotypes. See figure n for a +distribution of data acquisition over time. +The mean number of users that have entered their variations for a single phenotype +is 14.36 (SD 12.65), the median is 10. The distribution of how many users have +entered their data per phenotype can be seen in figure n+1. The phenotype provided +y the most users is the eye color, which has been entered by 54 users. There are +two phenotypes which have so far only been provided by a single user: +The SAT Writing score and triglyceride-levels. +\begin{comment} +FZ: I think we should clarify the difference between variaton and phenotype! +\end{comment} + \subsubsection*{Connection to external services} In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. A total number of 15,229 documents relevant to the SNP-IDs listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure n+2). For usability reasons, SNPs are ranked by the amount of information gathered by the external services.The external services themselves are ranked by how easily users can access information from these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley results, as those aren't necessary freely available to every user. An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. \subsubsection*{Data access} From c2fa2708eb3a4d04f2a8da62914e72697389c32f Mon Sep 17 00:00:00 2001 From: Fabian Zimmer Date: Mon, 23 Jan 2012 22:15:58 +0100 Subject: [PATCH 10/88] Added new commenting system taken from here: http://stackoverflow.com/questions/539791/tips-for-collaboratively-editing-a-latex-document/539848#539848; changed already present comments --- paper_draft.tex | 37 ++++++++++++++++--------------------- 1 file changed, 16 insertions(+), 21 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 4dff66a..5fea100 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -59,6 +59,13 @@ %% ** EDIT HERE ** %% PLEASE INCLUDE ALL MACROS BELOW +\long\def\authornote#1{% + \leavevmode\unskip\raisebox{-3.5pt}{\rlap{$\scriptstyle\diamond$}}% + \marginpar{\raggedright\hbadness=10000 + \def\baselinestretch{0.8}\tiny + \it #1\par}} +\newcommand{\bastian}[1]{\authornote{BG: #1}} +\newcommand{\fabian}[1]{\authornote{FZ: #1}} %% END MACROS SECTION @@ -143,23 +150,14 @@ \subsection*{Survey on Sharing Genetic Information} \subsection*{openSNP} The openSNP project (http://opensnp.org) is an open, crowdsourced online platform for both DTC customers and researchers. DTC customers are encouraged to share their genotyping results and connect them with phenotypes to enable GWAS for researchers or any other reuse in a scientific context. -\begin{comment} -FZ: That is a good summary, but as Bastian pointed out the reuse is not only -enabled in a scientific context? -\end{comment} - -\begin{comment} -FZ: -\end{comment} +\fabian{That is a good summary, but as Bastian pointed out the reuse is not only +enabled in a scientific context?} \subsubsection*{Sharing genotypic information} Currently users can upload their genotypings from the companies 23andme, deCODEme and Family Tree to/via a webinterface to the openSNP project. The uploaded data is published under the Creative Commons Zero-license, which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - allows a complete reuse of the data without any constraints. -\begin{comment} -FZ: If that is true, rephrase sentence above -FZ: Made two sentences out of one. -\end{comment} +\fabian{If that is true, rephrase sentence above. Made two sentences out of one.} Between the start of openSNP on 09/27/2011 and 12/18/2011, 214 people have signed up with openSNP, 79 of which have uploaded their genotyping files. The openSNP database thereby lists 69,486,471 SNPs, which are distributed over 1,938,604 unique Rs-IDs. @@ -173,12 +171,11 @@ \subsubsection*{Crowdsourcing phenotypes} Users are encouraged to list as many phenotypes as possible through a simple achievement system which rewards users that upload their data and enter phenotypic information with small badges that are shown on their profile pages. -\begin{comment} -FZ: I changed the structure of the paragraph to make it a little bit more -concise. -I think its best to connect the phenotypes to the above paragraph, explain -the procedure and then talk about the achievments -\end{comment} +\fabian{ +I changed the structure of the paragraph to make it a little bit more +concise.I think its best to connect the phenotypes to the above paragraph, explain +the procedure and then talk about the achievments.} + In the same timeframe that was mentioned above, all users combined have entered a total of 675 variations on 47 different phenotypes. See figure n for a distribution of data acquisition over time. @@ -188,9 +185,7 @@ \subsubsection*{Crowdsourcing phenotypes} y the most users is the eye color, which has been entered by 54 users. There are two phenotypes which have so far only been provided by a single user: The SAT Writing score and triglyceride-levels. -\begin{comment} -FZ: I think we should clarify the difference between variaton and phenotype! -\end{comment} +\fabian{I think we should clarify the difference between variaton and phenotype!} \subsubsection*{Connection to external services} In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. A total number of 15,229 documents relevant to the SNP-IDs listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure n+2). For usability reasons, SNPs are ranked by the amount of information gathered by the external services.The external services themselves are ranked by how easily users can access information from these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley results, as those aren't necessary freely available to every user. An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. From 5b5aef8b7206833c3d7d63cfd5af2913bb33f1b2 Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Sun, 29 Jan 2012 13:08:43 +0100 Subject: [PATCH 11/88] worked on fabians comments --- paper_draft.tex | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 5fea100..457915f 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -149,15 +149,13 @@ \subsection*{Survey on Sharing Genetic Information} \subsection*{openSNP} -The openSNP project (http://opensnp.org) is an open, crowdsourced online platform for both DTC customers and researchers. DTC customers are encouraged to share their genotyping results and connect them with phenotypes to enable GWAS for researchers or any other reuse in a scientific context. -\fabian{That is a good summary, but as Bastian pointed out the reuse is not only -enabled in a scientific context?} +The openSNP project (http://opensnp.org) is an open, crowdsourced online platform for both DTC customers and researchers. DTC customers are encouraged to share their genotyping results and connect them with phenotypes to enable GWAS for researchers but the data can also be used for other research and in any other context. +\bastian{Changed this to make it more true ;)} \subsubsection*{Sharing genotypic information} Currently users can upload their genotypings from the companies 23andme, deCODEme and Family Tree to/via a webinterface to the openSNP project. The uploaded data is published under the Creative Commons Zero-license, which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - allows a complete reuse of the data without any constraints. -\fabian{If that is true, rephrase sentence above. Made two sentences out of one.} Between the start of openSNP on 09/27/2011 and 12/18/2011, 214 people have signed up with openSNP, 79 of which have uploaded their genotyping files. The openSNP database thereby lists 69,486,471 SNPs, which are distributed over 1,938,604 unique Rs-IDs. @@ -171,13 +169,11 @@ \subsubsection*{Crowdsourcing phenotypes} Users are encouraged to list as many phenotypes as possible through a simple achievement system which rewards users that upload their data and enter phenotypic information with small badges that are shown on their profile pages. -\fabian{ -I changed the structure of the paragraph to make it a little bit more -concise.I think its best to connect the phenotypes to the above paragraph, explain -the procedure and then talk about the achievments.} In the same timeframe that was mentioned above, all users combined have -entered a total of 675 variations on 47 different phenotypes. See figure n for a +entered a total of 675 variations on 47 different phenotypes with those variations being the different values on a given trait or phenotype. +\bastian{Clarification of variation/phenotype?} +See figure n for a distribution of data acquisition over time. The mean number of users that have entered their variations for a single phenotype is 14.36 (SD 12.65), the median is 10. The distribution of how many users have @@ -185,7 +181,7 @@ \subsubsection*{Crowdsourcing phenotypes} y the most users is the eye color, which has been entered by 54 users. There are two phenotypes which have so far only been provided by a single user: The SAT Writing score and triglyceride-levels. -\fabian{I think we should clarify the difference between variaton and phenotype!} + \subsubsection*{Connection to external services} In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. A total number of 15,229 documents relevant to the SNP-IDs listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure n+2). For usability reasons, SNPs are ranked by the amount of information gathered by the external services.The external services themselves are ranked by how easily users can access information from these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley results, as those aren't necessary freely available to every user. An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. From 9066461bb03e4b8854fc44946dd6eeeb81bef0cb Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Fri, 3 Feb 2012 13:00:00 +1000 Subject: [PATCH 12/88] Fixed some typos, introduced JSON, changed some formulations --- paper_draft.tex | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 457915f..7f28de9 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -185,9 +185,9 @@ \subsubsection*{Crowdsourcing phenotypes} \subsubsection*{Connection to external services} In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. A total number of 15,229 documents relevant to the SNP-IDs listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure n+2). For usability reasons, SNPs are ranked by the amount of information gathered by the external services.The external services themselves are ranked by how easily users can access information from these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley results, as those aren't necessary freely available to every user. An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. -\subsubsection*{Data access} -OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypes in a comma-separated table. Additionally, the genetic data is accessible through the Distributed Annotation System, which offers all data for specific chromosomes and specific positions on single chromosomes. Users can discuss SNPs and phenotypes on the platform using a simple commenting system or private messages. +\subsubsection*{Data access} +OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypes in a comma-separated table. The genetic data is also accessible through the Distributed Annotation System, which offers all data for specific chromosomes and specific positions on single chromosomes. The data is additionally available over a JSON-API, which allows users to directly access data in the JSON-format. Users can discuss SNPs and phenotypes on the platform using a simple commenting system, or send each other private messages. \section*{Discussion} @@ -198,12 +198,13 @@ \subsubsection*{GWAS and open data} One way of bringing down costs for GWAS even further is to make use of already available genotyping results and datasets. Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high numbers of customers which are willing to pay for the genotyping by themselves. -By crowdsourcing the acquisition of genetic and phenotypic data, openSNP faces the same problems as any other open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation. While we try to suggest similar entries to the users, there are some cases where users wont follow those suggestions, so duplicates or similar phenotypes or varations in traits may arise. +By crowdsourcing the acquisition of genetic and phenotypic data, openSNP faces the same problems as any other open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation, with users entering data in different measurement systems. While we try to suggest similar entries to the users, there are some cases where users wont follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. There are two possible solutions to this problem: One could only allow some (trusted) users to enter new phenotypes or one could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users can't add variations that have not been available from the start. Both methods have a disadvantage: In either case it makes it harder for users to enter their data and thereby raises the bar for participation, which ultimately could lead to less data entered. Facing this trade-off, we decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS with the data to perform more quality control. -Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP, as we cannot rule out the possibility that only an unrepresentative subset of people buy DTC genetic testing and an even smaller subset is willing to publish the results, along with information of medical relevance. -\subsubsection*{Why should a user publish their genetic information? Pro vs. Con} +Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP, as we cannot rule out the possibility that only an unrepresentative subset of people buy DTC genetic testing, from which an even smaller subset is willing to publish the results, along with phenotypic information of medical relevance. + +\subsubsection*{Why should a user publish their genetic information? Pros vs. Cons} With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. While we don't have collected enough data to perform full scale GWAS yet, this might be possible in the future, as user numbers are rising. \subsubsection*{Privacy and health implications and ethical considerations} @@ -220,7 +221,7 @@ \subsubsection*{Privacy and health implications and ethical considerations} % authors may use "Analysis" \section*{Materials and Methods} \subsection*{Survey on Sharing Genetic Information} -The survey was done with Google Docs and was distributed to possible participants through the 23andMe community, the DIYBiology mailing list, blogs which focus on genetics and DTC genetic testing and social media like Twitter, Google Plus and Facebook. +The survey was performed using Google Docs and was distributed to possible participants through the 23andMe community, the DIYBiology mailing list, blogs which focus on genetics and DTC genetic testing and social media like Twitter, Google Plus and Facebook. The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their (planned) customership of a DTC company. If they already were customers, they were also asked if they were already sharing their genetic and phenotypic data. All participants were asked if they would share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, but only if they did not share my medical information with anybody else" and No". @@ -233,7 +234,7 @@ \subsection*{Technical realization of the platform} SNPs are cataloged by their unique identifier, which consists of a prefix (mostly \textit{rs}) and a unique number. This is a common format, which is employed by the NCBI dbSNP database and is also widely used and easily parsed from different literature sources. Publications from the different databases as well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. -Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources are handled using the ruby gem Resque and a Redis server. Search features on the platform itself are implemented using SOLR and the ruby gem Sunspot. Additionally, data can be requested from openSNP using the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}. The required data is stored in a mySQL database, the delivery of the data is done by using ProServer \cite{Finn2007}, which was modified by Gel et al. for use in easyDAS \cite{GelMoreno2011}. +Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources are handled using the ruby gem Resque and the standalone server Redis. Search features on the platform itself are implemented using SOLR and the ruby gem Sunspot. Additionally, data can be requested from openSNP using the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}. The required data is stored in a PostgreSQL database, the delivery of the data is done by using ProServer \cite{Finn2007}, which was modified by Gel et al. for use in easyDAS \cite{GelMoreno2011}. % probably no proserver, more own stuff A flowchart of all services incorporated in openSNP and of all the ways users can upload or access the data is given in figure n+3. The source code of openSNP is published under Creative Commons BY-SA 3.0 and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. % Do NOT remove this, even if you are not including acknowledgments From 3a5ca999db20fb0f94e94613e2c51f6763344bf7 Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Wed, 8 Feb 2012 18:27:50 +1000 Subject: [PATCH 13/88] Wrote paragraph about the current DAS-methods --- paper_draft.tex | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/paper_draft.tex b/paper_draft.tex index 7f28de9..266bed8 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -234,7 +234,9 @@ \subsection*{Technical realization of the platform} SNPs are cataloged by their unique identifier, which consists of a prefix (mostly \textit{rs}) and a unique number. This is a common format, which is employed by the NCBI dbSNP database and is also widely used and easily parsed from different literature sources. Publications from the different databases as well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. -Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources are handled using the ruby gem Resque and the standalone server Redis. Search features on the platform itself are implemented using SOLR and the ruby gem Sunspot. Additionally, data can be requested from openSNP using the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}. The required data is stored in a PostgreSQL database, the delivery of the data is done by using ProServer \cite{Finn2007}, which was modified by Gel et al. for use in easyDAS \cite{GelMoreno2011}. % probably no proserver, more own stuff +Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources are handled using the ruby gem Resque and the standalone server Redis. Search features on the platform itself are implemented using SOLR and the ruby gem Sunspot. Additionally, data can be requested from openSNP using the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}. The required data is stored in a PostgreSQL database, the delivery of the data is done by using ProServer \cite{Finn2007}, which was modified by Gel et al. for use in easyDAS \cite{GelMoreno2011}. Requested data is delivered in XML-format to facilitate parsing. % kick out proserver? what's the citation Finn2007 for? + +OpenSNP only serves as a platform for SNPs, so methods for the delivery of nucleotide sequences as described in the DAS-standard are not implemented. Currently, two methods are implemented: \textit{features}, which is used to deliver SNPs located on specific chromosomes or between specific nucleotide positions, based on the user's query. The second method is \textit{sources}, which advertises all DAS-sources for all genotypes present in openSNP. A flowchart of all services incorporated in openSNP and of all the ways users can upload or access the data is given in figure n+3. The source code of openSNP is published under Creative Commons BY-SA 3.0 and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. % Do NOT remove this, even if you are not including acknowledgments From 4835781d72168e3d17f3e1ee25d760f483041baf Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Mon, 27 Feb 2012 22:13:07 +1000 Subject: [PATCH 14/88] Added some details on DAS, changed some statistics (phenotypes missing, as user_snps is still counting), fixed some types, changed some sentences. --- paper_draft.tex | 24 +++++++++++++----------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 266bed8..610345f 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -96,7 +96,7 @@ % Please keep the abstract between 250 and 300 words \section*{Abstract} -Genome-wide association studies are a cheap and quick way of assessing health risks by comparing Single Nucleotide Polymorphisms between groups of participants. Direct-To-Consumer Companies like 23andme offer SNP sequencing to their customers alongside with an evaluation of the customer's genetic risks. However, the data 23andme and other companies generate is not accessible to other scientists, and withholds some information from customers for various reasons. In this paper, we present an open approach to Genome-wide association studies by introducing openSNP, a web platform which allows customers to openly share their polymorphisms with scientists for free. % misses survey +Genome-wide association studies are a cheap and quick way of assessing health risks by comparing Single Nucleotide Polymorphisms between groups of participants. Direct-To-Consumer (DTC) Companies like 23andme offer SNP sequencing to their customers alongside with an evaluation of the customer's genetic risks. However, the data 23andme and other companies generate is not accessible to other scientists, and withholds some information from customers for various reasons. In this paper, we present an open approach to Genome-wide association studies by introducing openSNP, a web platform which allows customers to openly share their polymorphisms with scientists for free. We also present results of a survey to assess the willingness of DTC-customers to openly share their genotyping and polymorphisms. % Please keep the Author Summary between 150 and 200 words @@ -113,7 +113,7 @@ \section*{Introduction} process called genotyping. Microarrays are a cheap, fast and widespread used way to genotype individuals, in comparison to whole genome sequencing. If the genotypes for more then one individual are known, the analysis of allele frequencies can be used to associated SNPs with diseases and other inheritable traits. Genome-Wide Assocation Studies -(GWAS) make use of statistics for comparing the alleles of patients to the alleles of healthy controls. This +(GWAS) make use of statistics to compare the alleles of patients to the alleles of healthy controls. This enables GWAS to find alleles which are significantly overrepresented in patients and associate them with a disease. This method does not allow inference of causal differences but merely identifies correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast @@ -122,8 +122,7 @@ \section*{Introduction} 5000 SNPs have been linked to different diseases and traits in those studies \cite{Hindorff2009}. %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) - -Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. Those companies use DNA microarrays to screen for around 1 million SNPs spread over the human genome. In return customers get an analysis of the results, as well as a raw file that includes the customer's individual genotypes. In 2011, 23andMe alone had over 100,000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} - the company recognizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer the data is used for association studies. 23andMe has published several articles in which it replicates known findings, but also finds new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. Over 30,000 23andme-customers have participated in those association studies. +Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. Those companies use DNA microarrays to screen for around 1 million SNPs spread over the human genome. In return, customers get an analysis of the results, as well as a file that includes the customer's raw individual genotypes. In 2011, 23andMe alone had over 100,000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} - the company recognizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer the data is used for association studies. 23andMe has published several articles in which it replicates known findings, but also finds new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. Over 30,000 23andme-customers have participated in those association studies. Although companies like 23andMe are willing to contribute to science, it is not easy for individual scientists to access the data. This is mainly due to concerns about privacy, liability and consent. Nevertheless, there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. @@ -135,7 +134,7 @@ \section*{Introduction} \section*{Results} \subsection*{Survey on Sharing Genetic Information} -229 people, 180 with a self-reported chromosomal sex of XY, 56 with a self-reported chromosomal sex of XX, participated in the survey. The mean age of the participants is 33 (SD = 11,29) and 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% don't plan on becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Somers-d). +229 people, 180 with a self-reported chromosomal sex of XY and 56 with a self-reported chromosomal sex of XX participated in the survey. The mean age of the participants is 33 (SD = 11,29). 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% don't plan on becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Somers-d). 67.7 \% of all participants would share their data with their DTC-company without any constraints, 25.8 \% would do so given the company didn't share the data with third parties. 6.6 \% of the participants would not share their data. There is no significant difference in readiness to share between chromosomal sexes (Somers-d). Those who are a customers of a DTC company or are planning on becoming one in the future are more likely to share their results, compared to those who don't plan on getting themselves genotyped (Somers-d). @@ -156,9 +155,9 @@ \subsubsection*{Sharing genotypic information} project. The uploaded data is published under the Creative Commons Zero-license, which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - allows a complete reuse of the data without any constraints. -Between the start of openSNP on 09/27/2011 and 12/18/2011, 214 people have signed -up with openSNP, 79 of which have uploaded their genotyping files. The openSNP -database thereby lists 69,486,471 SNPs, which are distributed over 1,938,604 unique Rs-IDs. +Between the start of openSNP on 09/27/2011 and 27/02/2012, 286 people have signed +up with openSNP, and 117 genotyping files were made available. The openSNP +database lists 69,486,471 SNPs, which are distributed over 1,938,603 unique SNP-IDs. \subsubsection*{Crowdsourcing phenotypes} Beside uploading genotypic data, users may list new phenotypes that are not yet @@ -193,6 +192,8 @@ \section*{Discussion} \subsubsection*{Survey issues} Because of this approach, the results do not reflect the general population, but overrepresent those people most likely to be interested in a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. As we wanted to find out about interest in crowdsourcing GWAS and about how people who had purchased DTC genetic testing felt about sharing their data with third parties, this approach was warranted. +\philipp{"Because of this approach", what approach?} + \subsubsection*{GWAS and open data} Although prices of exome or even full genome sequencing are dropping rapidly, GWAS tend to stay considerably cheaper and offer a possibility of getting insight into genetic variations on a population level and allow an analysis of SNPs which are linked to different traits. Despite this benefit in terms of cost, it must be pointed out that GWAS can only detect correlations of SNPs with those traits and don't allow inference on the cause for any correlation. Furthermore, GWAS need a large enough sample which has to be statistically analysed by using sound methods. Nevertheless, GWAS are still frequently used and new associations still can be found (we should list some recent results here -> oxytocin maybe? we could look at the twitter-timeline of @opensnporg, i publish papers i find there). @@ -205,6 +206,7 @@ \subsubsection*{GWAS and open data} Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP, as we cannot rule out the possibility that only an unrepresentative subset of people buy DTC genetic testing, from which an even smaller subset is willing to publish the results, along with phenotypic information of medical relevance. \subsubsection*{Why should a user publish their genetic information? Pros vs. Cons} +\philipp{Should this be a subsubsection, not rather a subsection?} With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. While we don't have collected enough data to perform full scale GWAS yet, this might be possible in the future, as user numbers are rising. \subsubsection*{Privacy and health implications and ethical considerations} @@ -230,11 +232,11 @@ \subsection*{Survey on Sharing Genetic Information} The survey data was analyzed with SPSS 19. \subsection*{Technical realization of the platform} -The main platform is realized using the web framework Ruby on Rails. Postgres is used as the main database backend for Rails. The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, through querying the content via the MediaWiki API. All databases are queried using the unique identifier of each SNP as the search term. +The main platform is realized using the web framework Ruby on Rails 3.0.10. Postgres 9.2 is used as the main database backend for Rails. The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, through querying the content via the MediaWiki API. All databases are queried using the unique identifier of each SNP as the search term. -SNPs are cataloged by their unique identifier, which consists of a prefix (mostly \textit{rs}) and a unique number. This is a common format, which is employed by the NCBI dbSNP database and is also widely used and easily parsed from different literature sources. Publications from the different databases as well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. +SNPs are catalogued by their unique identifier, which consists of a prefix (mostly \textit{rs}, rarely \textit{i}) and a unique number. This is a common format, which is employed by the NCBI dbSNP database and is also widely used and easily parsed from different literature sources. Publications from the different databases as well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. -Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources are handled using the ruby gem Resque and the standalone server Redis. Search features on the platform itself are implemented using SOLR and the ruby gem Sunspot. Additionally, data can be requested from openSNP using the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}. The required data is stored in a PostgreSQL database, the delivery of the data is done by using ProServer \cite{Finn2007}, which was modified by Gel et al. for use in easyDAS \cite{GelMoreno2011}. Requested data is delivered in XML-format to facilitate parsing. % kick out proserver? what's the citation Finn2007 for? +Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources are handled using the ruby gem Resque and the standalone server Redis. Search features on the platform itself are implemented using Solr and the ruby gem Sunspot. Additionally, data can be requested from openSNP using the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}. The required data is stored in a PostgreSQL database. Requested data is delivered in XML-format to facilitate parsing. Additionally, users can request data in the JSON-format, using a system not specified in any standard. OpenSNP only serves as a platform for SNPs, so methods for the delivery of nucleotide sequences as described in the DAS-standard are not implemented. Currently, two methods are implemented: \textit{features}, which is used to deliver SNPs located on specific chromosomes or between specific nucleotide positions, based on the user's query. The second method is \textit{sources}, which advertises all DAS-sources for all genotypes present in openSNP. From 6c34b189c1a0e8b4da12152066a9efdcf05984ec Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Mon, 27 Feb 2012 22:32:21 +1000 Subject: [PATCH 15/88] Reverted some stats to be congruent with pics --- paper_draft.tex | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 610345f..0384eda 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -155,8 +155,8 @@ \subsubsection*{Sharing genotypic information} project. The uploaded data is published under the Creative Commons Zero-license, which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - allows a complete reuse of the data without any constraints. -Between the start of openSNP on 09/27/2011 and 27/02/2012, 286 people have signed -up with openSNP, and 117 genotyping files were made available. The openSNP +Between the start of openSNP on 09/27/2011 and 12/18/2012, 214 people have signed +up with openSNP, and 79 genotyping files were made available. The openSNP database lists 69,486,471 SNPs, which are distributed over 1,938,603 unique SNP-IDs. \subsubsection*{Crowdsourcing phenotypes} From 1a2a6abd996f52125e1421f8286c83f50faf7971 Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Sat, 3 Mar 2012 23:53:01 +0100 Subject: [PATCH 16/88] rewored draft with fabian --- paper_draft.tex | 287 ++++++++++++++++++++++++++++++++++-------------- papers.bib | 12 +- 2 files changed, 217 insertions(+), 82 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 0384eda..ae1a298 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -66,9 +66,8 @@ \it #1\par}} \newcommand{\bastian}[1]{\authornote{BG: #1}} \newcommand{\fabian}[1]{\authornote{FZ: #1}} - +\newcommand{\philipp}[1]{\authornote{PB: #1}} %% END MACROS SECTION - \begin{document} % Title must be 150 characters or less @@ -96,7 +95,13 @@ % Please keep the abstract between 250 and 300 words \section*{Abstract} -Genome-wide association studies are a cheap and quick way of assessing health risks by comparing Single Nucleotide Polymorphisms between groups of participants. Direct-To-Consumer (DTC) Companies like 23andme offer SNP sequencing to their customers alongside with an evaluation of the customer's genetic risks. However, the data 23andme and other companies generate is not accessible to other scientists, and withholds some information from customers for various reasons. In this paper, we present an open approach to Genome-wide association studies by introducing openSNP, a web platform which allows customers to openly share their polymorphisms with scientists for free. We also present results of a survey to assess the willingness of DTC-customers to openly share their genotyping and polymorphisms. +Genome-wide association studies are a cheap and quick way of assessing health risks by comparing Single Nucleotide Polymorphisms +between groups of participants. Direct-To-Consumer (DTC) Companies like 23andme offer SNP sequencing to their customers alongside +with an evaluation of the customer's genetic risks. However, the data 23andme and other companies generate is not accessible +to other scientists, and withholds some information from customers for various reasons. In this paper, we present an open +approach to Genome-wide association studies by introducing openSNP, a web platform which allows customers to openly share +their polymorphisms with scientists for free. We also present results of a survey to assess the willingness of DTC-customers +to openly share their genotyping and polymorphisms. % Please keep the Author Summary between 150 and 200 words @@ -106,13 +111,22 @@ \section*{Author Summary} \section*{Introduction} -The comparison of DNA sequences from individuals in a population reveals sites which are variable. -If differences between individuals span only over a single nucleotide then these variations are also called Single Nucleotide Polymorphisms (SNP) and different nucleotides -at a specific site are defined as an allele. For diploid organisms, as us humans, the genotype at a given site consists of two alleles, which are independently inherited from each parent. +The availabilty of new DNA sequencing techniques has shifted the focus of biological data acuisiton towards new biomedical applications. +Many diseases – for example Alzheimers, Parkinsons or different types of cancers – are at least partly heritable and thus the genome +of patients can be used for diagnostic purposes. Using the genetic information of patients is made possible through the sharp decrease in costs for analysing genetic information. +Thus the importance genetic information in biomedicine will/may increase over the next years and may become a standard tool for diagnostics. + +The comparison of DNA sequences from individuals in a population can reveal variable sites, which are of major interest in investigating diseases. +A variation of only one nucleotide length at a given site is called a single Nucleotide Polymorphisms (SNP). +The different nucleotides at this site are defined as an allele. +For diploid organisms, as us humans, the genotype at a given site consists of two alleles, which are independently inherited from each parent. Different methods have been developed to read SNPs in an individual, a -process called genotyping. Microarrays are a cheap, fast and widespread used way to genotype individuals, in comparison -to whole genome sequencing. If the genotypes for more then one individual are known, the analysis of -allele frequencies can be used to associated SNPs with diseases and other inheritable traits. Genome-Wide Assocation Studies +process called genotyping. A widespread used method for genotyping individuals is the use of microarrays. +In comparison to whole genome sequencing only sites which are known to be variable are analysed, making microarrays cheaper and faster \cite{Brown1999}. + + +If the genotypes for more then one individual are known, the analysis of +allele frequencies can be used to associate SNPs with diseases and other inheritable traits. Genome-Wide Assocation Studies (GWAS) make use of statistics to compare the alleles of patients to the alleles of healthy controls. This enables GWAS to find alleles which are significantly overrepresented in patients and associate them with a disease. This method does not allow inference of causal differences but merely identifies correlations. @@ -122,125 +136,236 @@ \section*{Introduction} 5000 SNPs have been linked to different diseases and traits in those studies \cite{Hindorff2009}. %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) -Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. Those companies use DNA microarrays to screen for around 1 million SNPs spread over the human genome. In return, customers get an analysis of the results, as well as a file that includes the customer's raw individual genotypes. In 2011, 23andMe alone had over 100,000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} - the company recognizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer the data is used for association studies. 23andMe has published several articles in which it replicates known findings, but also finds new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. Over 30,000 23andme-customers have participated in those association studies. - -Although companies like 23andMe are willing to contribute to science, it is not easy for individual scientists to access the data. This is mainly due to concerns about privacy, liability and consent. Nevertheless, there are individual customers who are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. - -This data is scattered and unorganized, making it hard to use this information. While projects like SNPedia try to keep track of all the genotyping files \cite{Cariaso2011}, they do not provide the information necessary to perform GWAS, as the phenotypic information is most often not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the \textit{Personal Genome Project}, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data and not every customer of DTC genetic testing can participate in it. - -We designed an online survey to evaluate the support in the community for a crowdsourced online plattform. Here we present the results of the survey in combination with the openSNP project. The openSNP project is an online platform, which makes it possible for DTC customers to share genotypic and phenoytypic information and connect it with external data sources. The data is made available to research via a Creative-Commones-Zero licence. +Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. +Those companies use DNA microarrays to screen for around 1 million SNPs spread over the human genome. In return, customers +get an analysis of the results, as well as a file that includes the customer's raw individual genotypes. In 2011, 23andMe +alone had over 100,000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} +- the company recognizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about +traits and diseases. With the consent of the customer the data is used for association studies. 23andMe has published several +articles in which it replicates known findings, but also finds new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. +Over 30,000 23andme-customers have participated in those association studies. + +Although companies like 23andMe are willing to use the data for scientific research, +it is not easy for individual scientists to access the data. +The generation of biomedical data by private companies raises concerns about privacy \footnote{https://www.23andme.com/about/privacy/}, +liabilty and consent \footnote{addcitations}. +Nevertheless, individual customers are willingly sharing their data. Most do so by uploading their data to +their personal website or to open software repositories like \textit{GitHub}. +This data is scattered and unorganized, making it hard to use this information. While projects like SNPedia try to keep track of all +the genotyping files \cite{Cariaso2011}, they do not provide the information necessary to perform GWAS, as the phenotypic information is +most often not attached to the genetic information. Projects that attach the phenotype to the genetic information, +like the \textit{Personal Genome Project}, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) +or other methods by which researchers could download the data and not every customer of DTC genetic testing can participate in it. + +We designed an online survey to evaluate the support in the personal genetics community for a crowdsourced online plattform. Here we present the results of the survey in combination with the openSNP project. +The openSNP project is an online platform, which makes it possible for DTC customers to share genotypic and phenoytypic information and connect it with external data sources. +The data is made available to research via a Creative-Commones-Zero licence. % Results and Discussion can be combined. \section*{Results} \subsection*{Survey on Sharing Genetic Information} -229 people, 180 with a self-reported chromosomal sex of XY and 56 with a self-reported chromosomal sex of XX participated in the survey. The mean age of the participants is 33 (SD = 11,29). 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% don't plan on becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Somers-d). +In total 229 people, 180 with a self-reported chromosomal sex of XY and 56 with a self-reported chromosomal sex of XX participated in our survey on sharing genetic information with the public. +The mean age of the participants is 33 (SD = 11,29). 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already +customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% don't plan on +becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Somers-d). + +67.7 \% of all participants would share their data with their DTC-company without any constraints, 25.8 \% would do so given the company +didn't share the data with third parties. 6.6 \% of the participants would not share their data. There is no significant difference in +readiness to share between chromosomal sexes (Somers-d). Those who are a customers of a DTC company or are planning on becoming one in +the future are more likely to share their results, compared to those who don't plan on getting themselves genotyped (Somers-d). + +There are significant differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped +and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with the following +motivations for sharing genotypic information: because they want to help scientists (mean difference = 0.465, SE = 0.128, p = 0.001), +because they think of personal benefits (mean difference = 0.448, SE = 0.183, p = 0.04) and because they are curious (mean difference = 1.159, SE = 0.193, p < 0.001). + +On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations +for not sharing their data: because they fear discrimination (mean difference = 1.060, SE = 0.195, p < 0.001), because they feel +it is a breach of their privacy (mean difference = 0.821, SE = 0.225, p = 0.001), because they fear negative consequences for their +family members (mean difference = 0.733, SE = 0.21, p = 0.002) or because they fear personalized advertising (mean difference = 0.848, SE = 0.208, p < 0.001). + +Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with +the following motivations for sharing than those who would not share their data with their DTC company: because they want to help +scientists (mean difference = 1.57, SE = 0.199, p < 0.001), because they think of personal benefits (mean difference = 0.951, SE = 0.308, p = 0.006), +and because they are curious (mean difference = 1.99, SE = 0.321, p < 0.001). + +Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the following motivations +for not sharing their data when compared to those who would share their data with their DTC company under any circumstances: because they +fear discrimination (mean difference = 1.52, SE = 0.322, p < 0.001), because they feel it is a breach of their privacy (mean difference = 1.871, SE = 0.324, p < 0.001), +because they fear consequences for their family members (mean difference = 1.146, SE = 0.32, p = 0.001) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357, p = 0.006). -67.7 \% of all participants would share their data with their DTC-company without any constraints, 25.8 \% would do so given the company didn't share the data with third parties. 6.6 \% of the participants would not share their data. There is no significant difference in readiness to share between chromosomal sexes (Somers-d). Those who are a customers of a DTC company or are planning on becoming one in the future are more likely to share their results, compared to those who don't plan on getting themselves genotyped (Somers-d). - -There are significant differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with the following motivations for sharing genotypic information: because they want to help scientists (mean difference = 0.465, SE = 0.128, p = 0.001), because they think of personal benefits (mean difference = 0.448, SE = 0.183, p = 0.04) and because they are curious (mean difference = 1.159, SE = 0.193, p < 0.001). - -On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations for not sharing their data: because they fear discrimination (mean difference = 1.060, SE = 0.195, p < 0.001), because they feel it is a breach of their privacy (mean difference = 0.821, SE = 0.225, p = 0.001), because they fear negative consequences for their family members (mean difference = 0.733, SE = 0.21, p = 0.002) or because they fear personalized advertising (mean difference = 0.848, SE = 0.208, p < 0.001). - -Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with the following motivations for sharing than those who would not share their data with their DTC company: because they want to help scientists (mean difference = 1.57, SE = 0.199, p < 0.001), because they think of personal benefits (mean difference = 0.951, SE = 0.308, p = 0.006), and because they are curious (mean difference = 1.99, SE = 0.321, p < 0.001). +\subsection*{openSNP} -Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the following motivations for not sharing their data when compared to those who would share their data with their DTC company under any circumstances: because they fear discrimination (mean difference = 1.52, SE = 0.322, p < 0.001), because they feel it is a breach of their privacy (mean difference = 1.871, SE = 0.324, p < 0.001), because they fear consequences for their family members (mean difference = 1.146, SE = 0.32, p = 0.001) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357, p = 0.006). +The openSNP project (http://opensnp.org) is an open, crowdsourced online platform for both DTC customers and researchers. +DTC customers are encouraged to share their genotyping results and connect them with phenotypes to enable GWAS for researchers, +but the data can also be used for other research and in any other context. +Users can create a personal profile, discuss SNPs and phenotypes on the +platform using a simple commenting system, or send each other private messages. -\subsection*{openSNP} -The openSNP project (http://opensnp.org) is an open, crowdsourced online platform for both DTC customers and researchers. DTC customers are encouraged to share their genotyping results and connect them with phenotypes to enable GWAS for researchers but the data can also be used for other research and in any other context. -\bastian{Changed this to make it more true ;)} \subsubsection*{Sharing genotypic information} -Currently users can upload their genotypings from the companies 23andme, deCODEme and Family Tree to/via a webinterface to the openSNP +Currently users can upload their genotypings from the companies 23andme, deCODEme and FamilyTreeDNA via a webinterface to the openSNP project. The uploaded data is published under the Creative Commons Zero-license, which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - allows a complete reuse of the data without any constraints. Between the start of openSNP on 09/27/2011 and 12/18/2012, 214 people have signed up with openSNP, and 79 genotyping files were made available. The openSNP database lists 69,486,471 SNPs, which are distributed over 1,938,603 unique SNP-IDs. +Figure n depicts the increase of users and genotyping files over time. + \subsubsection*{Crowdsourcing phenotypes} -Beside uploading genotypic data, users may list new phenotypes that are not yet -queried by openSNP. The specification of these phenotypes is open and not limited +Beside uploading genotypic data, users may create new phenotypes that are not yet +listed by openSNP. +The specification of these phenotypes is open and not limited to pre-defined categories. To reduce the amount of manual data curation, openSNP tries to harmonize the expression and spelling of the same phenotype or variation. We implemented an -autocompletion-feature which helps users to reuse already entered phenotypes. +autocompletion-feature, which helps users to reuse already entered phenotypes. Users are encouraged to list as many phenotypes as possible through a simple -achievement system which rewards users that upload their data and enter phenotypic +achievement system, rewarding users that upload their data and enter phenotypic information with small badges that are shown on their profile pages. In the same timeframe that was mentioned above, all users combined have -entered a total of 675 variations on 47 different phenotypes with those variations being the different values on a given trait or phenotype. -\bastian{Clarification of variation/phenotype?} -See figure n for a -distribution of data acquisition over time. +entered a total of 675 variations on 47 different phenotypes with those variations being +the different values on a given trait or phenotype. See figure n for the increase of phenotypic information over time. + The mean number of users that have entered their variations for a single phenotype is 14.36 (SD 12.65), the median is 10. The distribution of how many users have entered their data per phenotype can be seen in figure n+1. The phenotype provided -y the most users is the eye color, which has been entered by 54 users. There are +by the most users is the eye color, which has been entered by 54 users. There are two phenotypes which have so far only been provided by a single user: The SAT Writing score and triglyceride-levels. \subsubsection*{Connection to external services} -In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. A total number of 15,229 documents relevant to the SNP-IDs listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure n+2). For usability reasons, SNPs are ranked by the amount of information gathered by the external services.The external services themselves are ranked by how easily users can access information from these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley results, as those aren't necessary freely available to every user. An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. +In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. +A total number of 15,229 documents relevant to the SNP-IDs listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. +Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure n+2). For usability reasons, +SNPs are ranked by the amount of information gathered by the external services. The external services themselves are ranked by how easily users can access information +from these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of +the Public Library of Science. Lowest values are given to the Mendeley results, as those aren't necessary freely available to every user. +An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. \subsubsection*{Data access} -OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypes in a comma-separated table. The genetic data is also accessible through the Distributed Annotation System, which offers all data for specific chromosomes and specific positions on single chromosomes. The data is additionally available over a JSON-API, which allows users to directly access data in the JSON-format. Users can discuss SNPs and phenotypes on the platform using a simple commenting system, or send each other private messages. - -\section*{Discussion} - -\subsubsection*{Survey issues} -Because of this approach, the results do not reflect the general population, but overrepresent those people most likely to be interested in a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. As we wanted to find out about interest in crowdsourcing GWAS and about how people who had purchased DTC genetic testing felt about sharing their data with third parties, this approach was warranted. -\philipp{"Because of this approach", what approach?} - -\subsubsection*{GWAS and open data} -Although prices of exome or even full genome sequencing are dropping rapidly, GWAS tend to stay considerably cheaper and offer a possibility of getting insight into genetic variations on a population level and allow an analysis of SNPs which are linked to different traits. Despite this benefit in terms of cost, it must be pointed out that GWAS can only detect correlations of SNPs with those traits and don't allow inference on the cause for any correlation. Furthermore, GWAS need a large enough sample which has to be statistically analysed by using sound methods. Nevertheless, GWAS are still frequently used and new associations still can be found (we should list some recent results here -> oxytocin maybe? we could look at the twitter-timeline of @opensnporg, i publish papers i find there). - -One way of bringing down costs for GWAS even further is to make use of already available genotyping results and datasets. Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high numbers of customers which are willing to pay for the genotyping by themselves. - -By crowdsourcing the acquisition of genetic and phenotypic data, openSNP faces the same problems as any other open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation, with users entering data in different measurement systems. While we try to suggest similar entries to the users, there are some cases where users wont follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. - -There are two possible solutions to this problem: One could only allow some (trusted) users to enter new phenotypes or one could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users can't add variations that have not been available from the start. Both methods have a disadvantage: In either case it makes it harder for users to enter their data and thereby raises the bar for participation, which ultimately could lead to less data entered. Facing this trade-off, we decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS with the data to perform more quality control. - -Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP, as we cannot rule out the possibility that only an unrepresentative subset of people buy DTC genetic testing, from which an even smaller subset is willing to publish the results, along with phenotypic information of medical relevance. - -\subsubsection*{Why should a user publish their genetic information? Pros vs. Cons} -\philipp{Should this be a subsubsection, not rather a subsection?} -With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. While we don't have collected enough data to perform full scale GWAS yet, this might be possible in the future, as user numbers are rising. -\subsubsection*{Privacy and health implications and ethical considerations} +OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files +grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypes in a comma-separated table. The genetic data is also +accessible through the Distributed Annotation System, which offers all data for specific chromosomes and specific positions on single chromosomes. +The data is additionally available over a JSON-API, which allows users to directly access data in the JSON-format. -As we have found in the survey on sharing such results, many of these customers are also willing to share their results with scientists and the public to help scientific progress, although people who have taken DTC genetic testing are aware of the privacy implications that come with openly sharing those results. -The advent of DTC genetic testing has led to new ethical and social issues. Much of the critique on DTC genetic testing focusses on the practice of delivering medical information without consulting a physician or genetic counselor to help patients/customers make sense of the information and to put the new knowledge to good use \cite{Hauskeller2011,Hogarth2008,Wasson2009}. Less attention has been paid to the privacy implications that come with this cheap way of obtaining genetical information \cite{Caulfield2011,Joh2011}. - -With a move towards making one's own genetic and medical information public, it becomes clear that the issue of privacy needs to get a closer look. Our survey has shown that people are concerned about their privacy and fear that stakeholders like employers, insurance companies, governments or advertisers might misuse the information. Policy makers start to react to those changes by introducing laws like the \textit{Genetic Information Non-Discrimination Act} in the United States or the \emph{Gendiagnostikgesetz} in Germany to minimize the impact of widely available genetic information. DTC genetic testing companies themselves also try to educate their customers about the risks of releasing genetic data. +\section*{Discussion} -We openly address the problem of privacy implications that come with releasing genetic data twice, during registration for openSNP and during the upload of the DTC genetic testing results. Users have to confirm that they have read and understood the disclaimer about possible side-effects that can arise by publishing their data. To further improve this process and get informed consent, we are working on implementing the procedures which are currently developed by \textit{Consent for Research} (http://www.weconsent.us). +\subsection*{Survey issues} +As the survey was taken online by voluntary participants and was mainly spread in the personal genetics community, +the results do not reflect the general population, but overrepresent those people most likely to be interested in +a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. +As we wanted to find out about interest in crowdsourcing GWAS and about how people who had purchased DTC genetic +testing felt about sharing their data with third parties, this approach was warranted. + +\subsection*{GWAS and open data} +Although prices of exome or even full genome sequencing are dropping rapidly, GWAS tend to stay considerably cheaper +and offer a possibility of getting insight into genetic variations, for example SNPs which are linked to different traits +on a population level. + +Despite this benefit in terms of cost GWAS can only detect correlations of SNPs with those traits and don't allow +inference on the cause for any correlation. Furthermore, GWAS need a large enough sample size, +which has to be statistically analysed by using sound methods. +Nevertheless, GWAS are still frequently used and new associations are found. +(we should list some recent results here -> oxytocin maybe? we could look at the twitter-timeline of @opensnporg, i publish papers i find there). + +One way of bringing down costs for GWAS even further is to make use of already available genotyping results and datasets. +Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high +numbers of customers which are willing to pay for the genotyping by themselves. + +By crowdsourcing the acquisition of genetic and phenotypic data, openSNP faces the same problems as any other +open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. +Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation, +with users entering data in different measurement systems. While we try to suggest similar entries to the users, +there are some cases where users wont follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. + +There are two possible solutions to this problem: One could only allow some (trusted) users to enter new phenotypes or one +could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users can't add +variations that have not been available from the start. Both methods have a disadvantage: In either case it makes it harder +for users to enter their data and thereby raises the bar for participation, which ultimately could lead to less data entered. +Facing this trade-off, we decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS +with the data to perform more quality control. + +Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP, as we cannot +rule out the possibility that only an unrepresentative subset of people buy DTC genetic testing, from which an even smaller subset +is willing to publish the results, along with phenotypic information of medical relevance. + +With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic +data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. +Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. +While we don't have collected enough data to perform full scale GWAS yet, this might be possible in the future, as user numbers are rising. + +\subsection*{Privacy and health implications and ethical considerations} +As we have found in the survey on sharing such results, many of these customers are also willing to share their results with scientists and +the public to help scientific progress, although people who have taken DTC genetic testing are considering the privacy implications that come +with openly sharing those results. + +The advent of DTC genetic testing has led to new ethical and social issues. Much of the critique on DTC genetic testing focusses on the practice +of delivering medical information without consulting a physician or genetic counselor to help patients/customers make sense of the information +and to put the new knowledge to good use \cite{Hauskeller2011,Hogarth2008,Wasson2009}. Less attention has been paid to the privacy implications +that come with this cheap way of obtaining genetical information \cite{Caulfield2011,Joh2011}. + +With a move towards making one's own genetic and medical information public, it becomes clear that the issue of privacy needs to get a closer look. +Our survey has shown that people are concerned about their privacy and fear that stakeholders like employers, insurance companies, governments +or advertisers might misuse the information. Policy makers start to react to those changes by introducing laws like the +\textit{Genetic Information Non-Discrimination Act} in the United States or the \emph{Gendiagnostikgesetz} in Germany to minimize the impact of +widely available genetic information. DTC genetic testing companies themselves also try to educate their customers about the risks of releasing genetic data. + +We openly address the problem of privacy implications that come with releasing genetic data twice, during registration for openSNP and during +the upload of the DTC genetic testing results. Users have to confirm that they have read and understood the disclaimer about possible side-effects +that can arise by publishing their data. To further improve this process we are working on implementing informed consent-processes, +like the one by \textit{Consent for Research} (http://www.weconsent.us). % You may title this section "Methods" or "Models". % "Models" is not a valid title for PLoS ONE authors. However, PLoS ONE % authors may use "Analysis" \section*{Materials and Methods} \subsection*{Survey on Sharing Genetic Information} -The survey was performed using Google Docs and was distributed to possible participants through the 23andMe community, the DIYBiology mailing list, blogs which focus on genetics and DTC genetic testing and social media like Twitter, Google Plus and Facebook. - -The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their (planned) customership of a DTC company. If they already were customers, they were also asked if they were already sharing their genetic and phenotypic data. All participants were asked if they would share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, but only if they did not share my medical information with anybody else" and No". - -The survey also asked some scaled questions which measured how strongly participants agreed/disagreed with different reasons for sharing or not sharing their information with third parties. The scale went from 1 = strongly disagree over 3 = neutral to 5 = strongly agree. Motivations queried for sharing data were "because I want to help scientists with their research", "because of possible personal benefits (e.g. getting treatments for a disease I have, possibility of new medication, etc.)", "because it may deliver advertising that is relevant to me" and "out of curiosity". Motivations queried for not sharing data were "because advertisers could use the information for targeted campaigns", "because of possible negative consequences for closely related persons", "because of the breach of my privacy" and " because of the fear of discrimination (e.g. by the employer, the state, some insurance company)". Additionally, participants had the possibility of giving their own reasons for sharing or not sharing their data. +The survey was performed using Google Docs and was distributed to possible participants through the 23andMe community, the DIYBiology mailing list, +blogs which focus on genetics and DTC genetic testing and social media like Twitter, Google Plus and Facebook. + +The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their +(planned) customership of a DTC company. If the participants already were customers, they were also asked if they were already sharing their genetic and phenotypic data. +All participants were asked if they would share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, +but only if they did not share my medical information with anybody else" and No". + +The survey also asked some scaled questions which measured how strongly participants agreed/disagreed with different reasons for sharing or not sharing their +information with third parties. The scale went from 1 = strongly disagree over 3 = neutral to 5 = strongly agree. Motivations queried for sharing data +were "because I want to help scientists with their research", "because of possible personal benefits (e.g. getting treatments for a disease I have, +possibility of new medication, etc.)", "because it may deliver advertising that is relevant to me" and "out of curiosity". Motivations queried for not sharing +data were "because advertisers could use the information for targeted campaigns", "because of possible negative consequences for closely related persons", +"because of the breach of my privacy" and " because of the fear of discrimination (e.g. by the employer, the state, some insurance company)". +Additionally, participants had the possibility of giving their own reasons for sharing or not sharing their data. The survey data was analyzed with SPSS 19. \subsection*{Technical realization of the platform} -The main platform is realized using the web framework Ruby on Rails 3.0.10. Postgres 9.2 is used as the main database backend for Rails. The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, through querying the content via the MediaWiki API. All databases are queried using the unique identifier of each SNP as the search term. - -SNPs are catalogued by their unique identifier, which consists of a prefix (mostly \textit{rs}, rarely \textit{i}) and a unique number. This is a common format, which is employed by the NCBI dbSNP database and is also widely used and easily parsed from different literature sources. Publications from the different databases as well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. - -Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources are handled using the ruby gem Resque and the standalone server Redis. Search features on the platform itself are implemented using Solr and the ruby gem Sunspot. Additionally, data can be requested from openSNP using the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}. The required data is stored in a PostgreSQL database. Requested data is delivered in XML-format to facilitate parsing. Additionally, users can request data in the JSON-format, using a system not specified in any standard. - -OpenSNP only serves as a platform for SNPs, so methods for the delivery of nucleotide sequences as described in the DAS-standard are not implemented. Currently, two methods are implemented: \textit{features}, which is used to deliver SNPs located on specific chromosomes or between specific nucleotide positions, based on the user's query. The second method is \textit{sources}, which advertises all DAS-sources for all genotypes present in openSNP. - -A flowchart of all services incorporated in openSNP and of all the ways users can upload or access the data is given in figure n+3. The source code of openSNP is published under Creative Commons BY-SA 3.0 and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. +The main platform is realized using the web framework Ruby on Rails 3.0.10. Postgres 9.2 is used as the main database backend for Rails. +The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs +which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of +the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, +through querying the content via the MediaWiki API. All databases are queried using the unique identifier of each SNP as the search term. + +SNPs are catalogued by their unique identifier, which consists of a prefix (mostly \textit{rs}, rarely \textit{i}) and a unique number. This is a common format, +which is employed by the NCBI dbSNP database and is also widely used and easily parsed from different literature sources. Publications from the different databases as +well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. + +Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources +are handled using the ruby gem Resque and the standalone server Redis. Search features on the platform itself are implemented using Solr and the ruby gem Sunspot. +Additionally, data can be requested from openSNP using the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}. The required data is stored in a PostgreSQL database. +Requested data is delivered in XML-format to facilitate parsing. Additionally, users can request data in the JSON-format, using a system not specified in any standard. + +OpenSNP only serves as a platform for SNPs, so methods for the delivery of nucleotide sequences as described in the DAS-standard are not implemented. Currently, +two methods are implemented: \textit{features}, which is used to deliver SNPs located on specific chromosomes or between specific nucleotide positions, +based on the user's query. The second method is \textit{sources}, which advertises all DAS-sources for all genotypes present in openSNP. + +A flowchart of all services incorporated in openSNP and of all the ways users can upload or access the data is given in figure n+3. The source code of openSNP is +published under Creative Commons BY-SA 3.0 and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. % Do NOT remove this, even if you are not including acknowledgments \section*{Acknowledgments} diff --git a/papers.bib b/papers.bib index 2a61e7e..9186e15 100644 --- a/papers.bib +++ b/papers.bib @@ -3,7 +3,7 @@ @ARTICLE{Do2011, author = {Chuong B Do and Joyce Y Tung and Elizabeth Dorfman and Amy K Kiefer - and Emily M Drabant and Uta Francke and Joanna L Mountain and Samuel + and Emily M Drabant and Uta Francke and Joanna L Mountain and Samuel M Goldman and Caroline M Tanner and J. William Langston and Anne Wojcicki and Nicholas Eriksson}, title = {Web-based genome-wide association study identifies two novel loci @@ -279,3 +279,13 @@ @article{Wasson2009 volume = {9}, year = {2009} } + +@article{Brown1999, +author = {Brown, Patrick O. and Botstein, David}, +journal = {nature genetics supplement}, +volume = {21}, +year = {1999}, +pages = {33--37}, +title = {{ +Exploring the new world of the genomewith DNA microarray}} +} \ No newline at end of file From bdf320d50967adf16140e0373301c96bdb284cdf Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Sun, 4 Mar 2012 17:19:49 +0100 Subject: [PATCH 17/88] added citations --- paper_draft.tex | 32 +++++++++++++-------------- papers.bib | 57 +++++++++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 70 insertions(+), 19 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index ae1a298..cbc8bf0 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -148,7 +148,7 @@ \section*{Introduction} Although companies like 23andMe are willing to use the data for scientific research, it is not easy for individual scientists to access the data. The generation of biomedical data by private companies raises concerns about privacy \footnote{https://www.23andme.com/about/privacy/}, -liabilty and consent \footnote{addcitations}. +liabilty and consent \cite{Caulfield2011}. Nevertheless, individual customers are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. This data is scattered and unorganized, making it hard to use this information. While projects like SNPedia try to keep track of all @@ -168,32 +168,31 @@ \subsection*{Survey on Sharing Genetic Information} In total 229 people, 180 with a self-reported chromosomal sex of XY and 56 with a self-reported chromosomal sex of XX participated in our survey on sharing genetic information with the public. The mean age of the participants is 33 (SD = 11,29). 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% don't plan on -becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Somers-d). +becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Cramer's V = 0.077). 67.7 \% of all participants would share their data with their DTC-company without any constraints, 25.8 \% would do so given the company -didn't share the data with third parties. 6.6 \% of the participants would not share their data. There is no significant difference in -readiness to share between chromosomal sexes (Somers-d). Those who are a customers of a DTC company or are planning on becoming one in -the future are more likely to share their results, compared to those who don't plan on getting themselves genotyped (Somers-d). +didn't share the data with third parties. 6.6 \% of the participants would not share their data. Participants self-identified as XX-chromosomal are slightly more likely to answer that DTC companies are allowed to use their results (Cramer's V = 0.221). Those who are a customers of a DTC company or are planning on becoming one in +the future are more likely to share their results, compared to those who don't plan on getting themselves genotyped (Somers-d = 0.331). -There are significant differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped +There are substantial differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with the following -motivations for sharing genotypic information: because they want to help scientists (mean difference = 0.465, SE = 0.128, p = 0.001), -because they think of personal benefits (mean difference = 0.448, SE = 0.183, p = 0.04) and because they are curious (mean difference = 1.159, SE = 0.193, p < 0.001). +motivations for sharing genotypic information: because they want to help scientists (mean difference = 0.465, SE = 0.128), +because they think of personal benefits (mean difference = 0.448, SE = 0.183) and because they are curious (mean difference = 1.159, SE = 0.193). On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations -for not sharing their data: because they fear discrimination (mean difference = 1.060, SE = 0.195, p < 0.001), because they feel -it is a breach of their privacy (mean difference = 0.821, SE = 0.225, p = 0.001), because they fear negative consequences for their -family members (mean difference = 0.733, SE = 0.21, p = 0.002) or because they fear personalized advertising (mean difference = 0.848, SE = 0.208, p < 0.001). +for not sharing their data: because they fear discrimination (mean difference = 1.060, SE = 0.195), because they feel +it is a breach of their privacy (mean difference = 0.821, SE = 0.225), because they fear negative consequences for their +family members (mean difference = 0.733, SE = 0.21) or because they fear personalized advertising (mean difference = 0.848, SE = 0.208). Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with the following motivations for sharing than those who would not share their data with their DTC company: because they want to help -scientists (mean difference = 1.57, SE = 0.199, p < 0.001), because they think of personal benefits (mean difference = 0.951, SE = 0.308, p = 0.006), -and because they are curious (mean difference = 1.99, SE = 0.321, p < 0.001). +scientists (mean difference = 1.57, SE = 0.199), because they think of personal benefits (mean difference = 0.951, SE = 0.308), +and because they are curious (mean difference = 1.99, SE = 0.321). In the case of curiosity as a motive, there is also a substantial difference between those who would share their data with their DTC company under the condition that it did not share the information and those who would not (mean difference = 1.116 SE = 0.344) as well as those who would share under any circumstances (mean difference = -0.874 SE = 0.182). Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the following motivations for not sharing their data when compared to those who would share their data with their DTC company under any circumstances: because they -fear discrimination (mean difference = 1.52, SE = 0.322, p < 0.001), because they feel it is a breach of their privacy (mean difference = 1.871, SE = 0.324, p < 0.001), -because they fear consequences for their family members (mean difference = 1.146, SE = 0.32, p = 0.001) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357, p = 0.006). +fear discrimination (mean difference = 1.52, SE = 0.322), because they feel it is a breach of their privacy (mean difference = 1.871, SE = 0.324), +because they fear consequences for their family members (mean difference = 1.146, SE = 0.32) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357). In the cases of fear of discrimination and fear of a breach of privacy, substantial differences between all three categories exist: Those who would share their data with their DTC company as long as it did not share the information agree less strongly than those who would not share the data with both fear of discrimination as a motive for not sharing (mean difference = -0.615, SE = 0.345) as well as fear of a breach of privacy (mean difference = -0.668, SE = 0.346). Those who would share their data under any circumstances are even less likely to agree with these motives than those who would share only if their DTC company did not share the information (fear of discrimination: mean value = -0.906, SE = 0.182; breach of privacy: mean difference = -1.203, SE = 0.183). \subsection*{openSNP} @@ -271,8 +270,7 @@ \subsection*{GWAS and open data} Despite this benefit in terms of cost GWAS can only detect correlations of SNPs with those traits and don't allow inference on the cause for any correlation. Furthermore, GWAS need a large enough sample size, which has to be statistically analysed by using sound methods. -Nevertheless, GWAS are still frequently used and new associations are found. -(we should list some recent results here -> oxytocin maybe? we could look at the twitter-timeline of @opensnporg, i publish papers i find there). +Nevertheless, GWAS are still frequently used and new associations are found \cite{10.1371.journal.pone.0031470,10.1371.journal.pone.0030309,10.1371.journal.pone.0029848}. One way of bringing down costs for GWAS even further is to make use of already available genotyping results and datasets. Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high diff --git a/papers.bib b/papers.bib index 9186e15..cd63bd9 100644 --- a/papers.bib +++ b/papers.bib @@ -279,7 +279,6 @@ @article{Wasson2009 volume = {9}, year = {2009} } - @article{Brown1999, author = {Brown, Patrick O. and Botstein, David}, journal = {nature genetics supplement}, @@ -288,4 +287,58 @@ @article{Brown1999 pages = {33--37}, title = {{ Exploring the new world of the genomewith DNA microarray}} -} \ No newline at end of file +} +@article{10.1371.journal.pone.0031470, + author = {Mei, , Hao AND Chen, , Wei AND Jiang, , Fan AND He, , Jiang AND Srinivasan, , Sathanur AND Smith, , Erin N. AND Schork, , Nicholas AND Murray, , Sarah AND Berenson, , Gerald S.}, + journal = {PLoS ONE}, + publisher = {Public Library of Science}, + title = {Longitudinal Replication Studies of GWAS Risk SNPs Influencing Body Mass Index over the Course of Childhood and Adulthood}, + year = {2012}, + month = {02}, + volume = {7}, + url = {http://dx.doi.org/10.1371%2Fjournal.pone.0031470}, + pages = {e31470}, + abstract = {

Genome-wide association studies (GWAS) have identified multiple common variants associated with body mass index (BMI). In this study, we tested 23 genotyped GWAS-significant SNPs (p-value<5*10-8) for longitudinal associations with BMI during childhood (3–17 years) and adulthood (18–45 years) for 658 subjects. We also proposed a heuristic forward search for the best joint effect model to explain the longitudinal BMI variation. After using false discovery rate (FDR) to adjust for multiple tests, childhood and adulthood BMI were found to be significantly associated with six SNPs each (q-value<0.05), with one SNP associated with both BMI measurements: KCTD15 rs29941 (q-value<7.6*10-4). These 12 SNPs are located at or near genes either expressed in the brain (BDNF, KCTD15, TMEM18, MTCH2, and FTO) or implicated in cell apoptosis and proliferation (FAIM2, MAP2K5, and TFAP2B). The longitudinal effects of FAIM2 rs7138803 on childhood BMI and MAP2K5 rs2241423 on adulthood BMI decreased as age increased (q-value<0.05). The FTO candidate SNPs, rs6499640 at the 5 ′-end and rs1121980 and rs8050136 downstream, were associated with childhood and adulthood BMI, respectively, and the risk effects of rs6499640 and rs1121980 increased as birth weight decreased. The best joint effect model for childhood and adulthood BMI contained 14 and 15 SNPs each, with 11 in common, and the percentage of explained variance increased from 0.17% and 9.0*10−6% to 2.22% and 2.71%, respectively. In summary, this study evidenced the presence of long-term major effects of genes on obesity development, implicated in pathways related to neural development and cell metabolism, and different sets of genes associated with childhood and adulthood BMI, respectively. The gene effects can vary with age and be modified by prenatal development. The best joint effect model indicated that multiple variants with effects that are weak or absent alone can nevertheless jointly exert a large longitudinal effect on BMI.

}, + number = {2}, + doi = {10.1371/journal.pone.0031470} +} +@article{10.1371.journal.pone.0030309, + author = {Xu, , Bin AND Tong, , Na AND Chen, , Shu-Qiu AND Yang, , Yu AND Zhang, , Xiao-Wen AND Liu, , Jin AND Hu, , Xiang-Nong AND Sha, , Guo-Zhu AND Chen, , Ming}, + journal = {PLoS ONE}, + publisher = {Public Library of Science}, + title = {Contribution of HOGG1 Ser326Cys Polymorphism to the Development of Prostate Cancer in Smokers: Meta-Analysis of 2779 Cases and 3484 Controls}, + year = {2012}, + month = {01}, + volume = {7}, + url = {http://dx.doi.org/10.1371%2Fjournal.pone.0030309}, + pages = {e30309}, + abstract = {

The HOGG1 gene catalyzes the excision of modified bases and removal of DNA damage adducts. It may play an important role in the prevention of carcinogenesis. Ser326Cys polymorphism localizes in exon 7 of the hOGG1 gene. It takes the form of an amino acid substitution, from serine to cysteine, in codon 326. Several epidemiological association studies have been conducted on this polymorphism and its relationship with the risk of prostate cancer. However, results have been conflicting. To resolve this conflict, we conducted a meta-analysis on the association between this polymorphism and prostate cancer, taking into account race, country, sources of controls, and smoking status. A total of nine studies covering 2779 cases and 3484 controls were included in the current meta-analysis. Although no significant association was found between hOGG1 Ser326Cys polymorphism and prostate cancer susceptibility in the pooled analysis, individuals with Ser/Cys+Cys/Cys genotypes were found to have greater risk of prostate cancer if they were also smokers (OR = 2.66, 95% CI = 1.58−4.47) rather than non-smokers (OR = 2.18, 95% CI = 1.13−4.19), compared with those with Ser/Ser genotype. In conclusion, our meta-analysis demonstrates that hOGG1 Ser326Cys polymorphism is a risk factor for prostate cancer in smokers. Further studies are needed to confirm this relationship.

}, + number = {1}, + doi = {10.1371/journal.pone.0030309} +} +@article{10.1371.journal.pone.0029848, + author = {Sebastiani, , Paola AND Solovieff, , Nadia AND DeWan, , Andrew T. AND Walsh, , Kyle M. AND Puca, , Annibale AND Hartley, , Stephen W. AND Melista, , Efthymia AND Andersen, , Stacy AND Dworkis, , Daniel A. AND Wilk, , Jemma B. AND Myers, , Richard H. AND Steinberg, , Martin H. AND Montano, , Monty AND Baldwin, , Clinton T. AND Hoh, , Josephine AND Perls, , Thomas T.}, + journal = {PLoS ONE}, + publisher = {Public Library of Science}, + title = {Genetic Signatures of Exceptional Longevity in Humans}, + year = {2012}, + month = {01}, + volume = {7}, + url = {http://dx.doi.org/10.1371%2Fjournal.pone.0029848}, + pages = {e29848}, + abstract = {

Like most complex phenotypes, exceptional longevity is thought to reflect a combined influence of environmental (e.g., lifestyle choices, where we live) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity in 801 centenarians (median age at death 104 years) and 914 genetically matched healthy controls. Using these data, we built a genetic model that includes 281 single nucleotide polymorphisms (SNPs) and discriminated between cases and controls of the discovery set with 89% sensitivity and specificity, and with 58% specificity and 60% sensitivity in an independent cohort of 341 controls and 253 genetically matched nonagenarians and centenarians (median age 100 years). Consistent with the hypothesis that the genetic contribution is largest with the oldest ages, the sensitivity of the model increased in the independent cohort with older and older ages (71% to classify subjects with an age at death>102 and 85% to classify subjects with an age at death>105). For further validation, we applied the model to an additional, unmatched 60 centenarians (median age 107 years) resulting in 78% sensitivity, and 2863 unmatched controls with 61% specificity. The 281 SNPs include the SNP rs2075650 in TOMM40/APOE that reached irrefutable genome wide significance (posterior probability of association = 1) and replicated in the independent cohort. Removal of this SNP from the model reduced the accuracy by only 1%. Further in-silico analysis suggests that 90% of centenarians can be grouped into clusters characterized by different “genetic signatures†of varying predictive values for exceptional longevity. The correlation between 3 signatures and 3 different life spans was replicated in the combined replication sets. The different signatures may help dissect this complex phenotype into sub-phenotypes of exceptional longevity.

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Direct-To-Consumer (DTC) Companies like 23andme offer SNP sequencing to their customers alongside -with an evaluation of the customer's genetic risks. However, the data 23andme and other companies generate is not accessible -to other scientists, and withholds some information from customers for various reasons. In this paper, we present an open -approach to Genome-wide association studies by introducing openSNP, a web platform which allows customers to openly share -their polymorphisms with scientists for free. We also present results of a survey to assess the willingness of DTC-customers -to openly share their genotyping and polymorphisms. +Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants like Single Nucleotide Polymorphisms. The advent of Direct-To-Consumer genetic testing offers the analysis of Single Nucleotide Polymorphisms for an individual at a low cost without the need to consult a physician or geneticist. Genome-wide association studies compare the genetic variation between two groups to find correlations between membership in a group and the genetic variation. To find significant correlations it is necessary to have a large enough sample size. Over 100.000 people already got themselves genotyped through Direct-To-Consumer genetic testing and could be a valuable data source for association studies. However, generally this data is not public for various reasons and thus can’t be used by other scientists. Here we present a survey which evaluates if people are willing to publicly share their genetic information. In the light of those results we also present the web-platform openSNP. It is an open approach which allows participants of Direct-To-Consumer genetic testing to publish their genetic data along with phenotypic information into the public domain. Through this crowdsourced effort of collecting genetic and phenotypic information we create a valuable resource which can be used in a wide array of studies, included Genome-wide association studies. % Please keep the Author Summary between 150 and 200 words % Use first person. PLoS ONE authors please skip this step. % Author Summary not valid for PLoS ONE submissions. \section*{Author Summary} +Missing for now. \section*{Introduction} @@ -211,7 +206,7 @@ \subsubsection*{Sharing genotypic information} Between the start of openSNP on 09/27/2011 and 12/18/2012, 214 people have signed up with openSNP, and 79 genotyping files were made available. The openSNP database lists 69,486,471 SNPs, which are distributed over 1,938,603 unique SNP-IDs. -Figure n depicts the increase of users and genotyping files over time. +Figure \ref{Figure1_label} depicts the increase of users and genotyping files over time. \subsubsection*{Crowdsourcing phenotypes} @@ -227,11 +222,11 @@ \subsubsection*{Crowdsourcing phenotypes} In the same timeframe that was mentioned above, all users combined have entered a total of 675 variations on 47 different phenotypes with those variations being -the different values on a given trait or phenotype. See figure n for the increase of phenotypic information over time. +the different values on a given trait or phenotype. See figure \ref{Figure1_label} for the increase of phenotypic information over time. The mean number of users that have entered their variations for a single phenotype is 14.36 (SD 12.65), the median is 10. The distribution of how many users have -entered their data per phenotype can be seen in figure n+1. The phenotype provided +entered their data per phenotype can be seen in figure \ref{Figure2_label}. The phenotype provided by the most users is the eye color, which has been entered by 54 users. There are two phenotypes which have so far only been provided by a single user: The SAT Writing score and triglyceride-levels. @@ -240,7 +235,7 @@ \subsubsection*{Crowdsourcing phenotypes} \subsubsection*{Connection to external services} In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. A total number of 15,229 documents relevant to the SNP-IDs listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. -Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure n+2). For usability reasons, +Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure \ref{Figure3_label}). For usability reasons, SNPs are ranked by the amount of information gathered by the external services. The external services themselves are ranked by how easily users can access information from these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley results, as those aren't necessary freely available to every user. @@ -362,7 +357,7 @@ \subsection*{Technical realization of the platform} two methods are implemented: \textit{features}, which is used to deliver SNPs located on specific chromosomes or between specific nucleotide positions, based on the user's query. The second method is \textit{sources}, which advertises all DAS-sources for all genotypes present in openSNP. -A flowchart of all services incorporated in openSNP and of all the ways users can upload or access the data is given in figure n+3. The source code of openSNP is +A flowchart of all services incorporated in openSNP and of all the ways users can upload or access the data is given in figure \ref{Figure4_label}. The source code of openSNP is published under Creative Commons BY-SA 3.0 and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. % Do NOT remove this, even if you are not including acknowledgments \section*{Acknowledgments} @@ -373,6 +368,44 @@ \section*{Acknowledgments} \bibliography{papers} \section*{Figure Legends} +\begin{figure}[!ht] + \begin{center} + \includegraphics[scale=0.35]{chart_growth.png} + \end{center} + \caption{ + {\bf Growth of openSNP.} The increase in numbers for users, genotyping-files, phenotypes and their variation from 27.09.2011 to 16.12.2011 is shown.} + \label{Figure1_label} +\end{figure} + +\begin{figure}[!ht] + \begin{center} + \includegraphics[scale=0.40]{histogram_phenotypes.png} + \end{center} + \caption{ + {\bf Histogram of users/phenotype-distribution.} The x-axis shows the minimum number of users who provide information for a phenotype, the y-axis shows how many phenotypes have at least that many users.} + \label{Figure2_label} +\end{figure} + +\begin{figure}[!ht] + \begin{center} + \includegraphics[scale=0.50]{paper_distribution.png} + \end{center} + \caption{ + {\bf Distribution of external information gathered for SNPs in the openSNP-database.} Data on PLoS and SNPedia is openly available for every user. Publications on Mendeley are either Open Access (OA) or Closed Access (CA).} + \label{Figure3_label} +\end{figure} + +\begin{figure}[!ht] + \begin{center} + \includegraphics[scale=0.60]{uml_diagram.png} + \end{center} + \caption{ + {\bf Flow of data inside openSNP.} External databases and user-provided data are used as input. Output of data is done using the website, the \emph{Distributed Annotation System} and a JSON-API.} + \label{Figure4_label} +\end{figure} + + + %\begin{figure}[!ht] %\begin{center} %%\includegraphics[width=4in]{figure_name.2.eps} diff --git a/uml_diagram.png b/uml_diagram.png new file mode 100644 index 0000000000000000000000000000000000000000..4128a0ff35dc539b11f877ebbce8970ad79f8722 GIT binary patch literal 242033 zcmd?RcT`hb(>NRmMFhc!NCypFnh<(b2_RL9G?5}Dfb`x=t_ne#LJ&e#5D<{wdz2;+ ziXy#5kf3x7NQduupGU9v{jJ|x@A}sF-**8494Kxv3iq+{nCdYTP*kTp?%dP{1>l$1O8&5J-gU=RcBwJY_$i zk=aMp)W^^r;p2bb%O2$K?=Rxy=Im{I-@{(S-OC|;U4aDzx(w2Q-?$Tyu{z@);$l>T z-I{B>7bN;I#^)*YRz<|eJ0bl1S6}4hXG)5D#Ve$<*hxS!hMklkX!>p-9#kaJGLXcvG}N#N6&OvaP1(mm^{sIN9ORdn>p(mjX(0sCOC_`skEdh>pvIKX!6Lo5f-V6m zjYRJM{P|vn5-6k!zUa7+y`g$T(o8B-_p>*F^FO^2*JRvnqWyHAppy9;qQh}GzAk~< z)^r1^0*Dy>_{H&BIZOBoos#pg<-2d~XzE*uuf8pRmg-)k3Ly*Nh7OPqyov9F5iwh5 zb$=n$s8a2q!3W|7(#J2DmUj--IEj9)p4oW9t&vtVd0giqjqk(SLOhkKh`b^_II{(Y 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Presenting a survey and an open data-source for personalised medicine +% % Insert Author names, affiliations and corresponding author email. \\ Bastian Greshake$^{1,\ast}$, @@ -84,7 +88,7 @@ \\ \bf{1} Goethe University, Frankfurt am Main, Germany \\ -\bf{2} Bond University, Gold Coast, Australia +\bf{2} University of Queensland, Brisbane, Australia \\ \bf{3} Westf\"alische Wilhelms Universit\"at, M\"unster, Germany \\ @@ -95,7 +99,7 @@ % Please keep the abstract between 250 and 300 words \section*{Abstract} -Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants like Single Nucleotide Polymorphisms. The advent of Direct-To-Consumer genetic testing offers the analysis of Single Nucleotide Polymorphisms for an individual at a low cost without the need to consult a physician or geneticist. Genome-wide association studies compare the genetic variation between two groups to find correlations between membership in a group and the genetic variation. To find significant correlations it is necessary to have a large enough sample size. Over 100.000 people already got themselves genotyped through Direct-To-Consumer genetic testing and could be a valuable data source for association studies. However, generally this data is not public for various reasons and thus can’t be used by other scientists. Here we present a survey which evaluates if people are willing to publicly share their genetic information. In the light of those results we also present the web-platform openSNP. It is an open approach which allows participants of Direct-To-Consumer genetic testing to publish their genetic data along with phenotypic information into the public domain. Through this crowdsourced effort of collecting genetic and phenotypic information we create a valuable resource which can be used in a wide array of studies, included Genome-wide association studies. +Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants like Single Nucleotide Polymorphisms. The advent of Direct-To-Consumer genetic testing offers the analysis of Single Nucleotide Polymorphisms for an individual at a low cost without the need to consult a physician or geneticist. Genome-wide association studies compare the genetic variation between two groups to find correlations between membership in a group and the genetic variation. To find significant correlations it is necessary to have a large enough sample size. Over 100.000 people were already genotyped through Direct-To-Consumer genetic testing and could be a valuable data source for association studies. However, generally this data is not public for various reasons and thus cannot be used by other scientists. It is vital that there is a central open data-repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates if people are willing to publicly share their genetic information. In the light of those results we also present the web-platform openSNP, which is a open database which allows participants of Direct-To-Consumer genetic testing to publish their genetic data along with phenotypic information into the public domain. Through this crowdsourced effort of collecting genetic and phenotypic information we create a valuable resource which can be used in a wide array of studies, included Genome-wide association studies. % Please keep the Author Summary between 150 and 200 words @@ -106,18 +110,19 @@ \section*{Author Summary} \section*{Introduction} -The availabilty of new DNA sequencing techniques has shifted the focus of biological data acuisiton towards new biomedical applications. -Many diseases – for example Alzheimers, Parkinsons or different types of cancers – are at least partly heritable and thus the genome +The availability of new DNA sequencing techniques has shifted the focus of biological data acquisition towards new biomedical applications. +Many diseases – for example Alzheimers, Parkinsons or different types of cancers – are at least partly heritable and thus the genome of patients can be used for diagnostic purposes. Using the genetic information of patients is made possible through the sharp decrease in costs for analysing genetic information. Thus the importance genetic information in biomedicine will/may increase over the next years and may become a standard tool for diagnostics. -The comparison of DNA sequences from individuals in a population can reveal variable sites, which are of major interest in investigating diseases. -A variation of only one nucleotide length at a given site is called a single Nucleotide Polymorphisms (SNP). -The different nucleotides at this site are defined as an allele. -For diploid organisms, as us humans, the genotype at a given site consists of two alleles, which are independently inherited from each parent. -Different methods have been developed to read SNPs in an individual, a -process called genotyping. A widespread used method for genotyping individuals is the use of microarrays. -In comparison to whole genome sequencing only sites which are known to be variable are analysed, making microarrays cheaper and faster \cite{Brown1999}. +%Kicked out the following due to Corpas' argument +%The comparison of DNA sequences from individuals in a population can reveal variable sites, which are of major interest in investigating diseases. +%A variation of only one nucleotide length at a given site is called a single Nucleotide Polymorphisms (SNP). +%The different nucleotides at this site are defined as an allele. +%For diploid organisms, as us humans, the genotype at a given site consists of two alleles, which are independently inherited from each parent. +%Different methods have been developed to read SNPs in an individual, a +%process called genotyping. A widespread used method for genotyping individuals is the use of microarrays. +%In comparison to whole genome sequencing only sites which are known to be variable are analysed, making microarrays cheaper and faster \cite{Brown1999}. If the genotypes for more then one individual are known, the analysis of @@ -149,12 +154,12 @@ \section*{Introduction} This data is scattered and unorganized, making it hard to use this information. While projects like SNPedia try to keep track of all the genotyping files \cite{Cariaso2011}, they do not provide the information necessary to perform GWAS, as the phenotypic information is most often not attached to the genetic information. Projects that attach the phenotype to the genetic information, -like the \textit{Personal Genome Project}, still don't allow for an easy re-use of the data, as they lack an advanced programming interface (API) +like the \textit{Personal Genome Project}, still do not allow for an easy re-use of the data, as they lack an advanced programming interface (API) or other methods by which researchers could download the data and not every customer of DTC genetic testing can participate in it. -We designed an online survey to evaluate the support in the personal genetics community for a crowdsourced online plattform. Here we present the results of the survey in combination with the openSNP project. +We designed an online survey to evaluate the support in the personal genetics community for a crowdsourced online platform. Here we present the results of the survey in combination with the openSNP project. The openSNP project is an online platform, which makes it possible for DTC customers to share genotypic and phenoytypic information and connect it with external data sources. -The data is made available to research via a Creative-Commones-Zero licence. +The data is made available to research via a Creative-Commons-Zero licence. % Results and Discussion can be combined. \section*{Results} @@ -162,12 +167,12 @@ \section*{Results} \subsection*{Survey on Sharing Genetic Information} In total 229 people, 180 with a self-reported chromosomal sex of XY and 56 with a self-reported chromosomal sex of XX participated in our survey on sharing genetic information with the public. The mean age of the participants is 33 (SD = 11,29). 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already -customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% don't plan on +customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% do not plan on becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Cramer's V = 0.077). 67.7 \% of all participants would share their data with their DTC-company without any constraints, 25.8 \% would do so given the company -didn't share the data with third parties. 6.6 \% of the participants would not share their data. Participants self-identified as XX-chromosomal are slightly more likely to answer that DTC companies are allowed to use their results (Cramer's V = 0.221). Those who are a customers of a DTC company or are planning on becoming one in -the future are more likely to share their results, compared to those who don't plan on getting themselves genotyped (Somers-d = 0.331). +didn't share the data with third parties. 6.6 \% of the participants would not share their data. Participants self-identified as XX-chromosomal are slightly more likely to answer that DTC companies are allowed to use their results (Cramer's V = 0.221). Those who are customers of a DTC company or are planning on becoming one in +the future are more likely to share their results, compared to those who do not plan on getting themselves genotyped (Somers-d = 0.331). There are substantial differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with the following @@ -238,13 +243,13 @@ \subsubsection*{Connection to external services} Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure \ref{Figure3_label}). For usability reasons, SNPs are ranked by the amount of information gathered by the external services. The external services themselves are ranked by how easily users can access information from these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of -the Public Library of Science. Lowest values are given to the Mendeley results, as those aren't necessary freely available to every user. +the Public Library of Science. Lowest values are given to the Mendeley results, as those are not necessary freely available to every user. An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. \subsubsection*{Data access} OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypes in a comma-separated table. The genetic data is also -accessible through the Distributed Annotation System, which offers all data for specific chromosomes and specific positions on single chromosomes. +accessible through the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}, which offers all data for specific chromosomes and specific positions on single chromosomes. The data is additionally available over a JSON-API, which allows users to directly access data in the JSON-format. @@ -262,7 +267,7 @@ \subsection*{GWAS and open data} and offer a possibility of getting insight into genetic variations, for example SNPs which are linked to different traits on a population level. -Despite this benefit in terms of cost GWAS can only detect correlations of SNPs with those traits and don't allow +Despite this benefit in terms of cost GWAS can only detect correlations of SNPs with those traits and do not allow inference on the cause for any correlation. Furthermore, GWAS need a large enough sample size, which has to be statistically analysed by using sound methods. Nevertheless, GWAS are still frequently used and new associations are found \cite{10.1371.journal.pone.0031470,10.1371.journal.pone.0030309,10.1371.journal.pone.0029848}. @@ -275,10 +280,10 @@ \subsection*{GWAS and open data} open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation, with users entering data in different measurement systems. While we try to suggest similar entries to the users, -there are some cases where users wont follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. +there are some cases where users will not follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. There are two possible solutions to this problem: One could only allow some (trusted) users to enter new phenotypes or one -could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users can't add +could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users cannot add variations that have not been available from the start. Both methods have a disadvantage: In either case it makes it harder for users to enter their data and thereby raises the bar for participation, which ultimately could lead to less data entered. Facing this trade-off, we decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS @@ -291,14 +296,15 @@ \subsection*{GWAS and open data} With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. -While we don't have collected enough data to perform full scale GWAS yet, this might be possible in the future, as user numbers are rising. +While we do not have collected enough data to perform full scale GWAS yet, this might be possible in the future, as user numbers are rising. -\subsection*{Privacy and health implications and ethical considerations} +\subsection*{Privacy, health implications and ethical considerations} As we have found in the survey on sharing such results, many of these customers are also willing to share their results with scientists and the public to help scientific progress, although people who have taken DTC genetic testing are considering the privacy implications that come with openly sharing those results. -The advent of DTC genetic testing has led to new ethical and social issues. Much of the critique on DTC genetic testing focusses on the practice +%The advent of DTC genetic testing has led to new ethical and social issues. +Much of the critique on DTC genetic testing focusses on the practice of delivering medical information without consulting a physician or genetic counselor to help patients/customers make sense of the information and to put the new knowledge to good use \cite{Hauskeller2011,Hogarth2008,Wasson2009}. Less attention has been paid to the privacy implications that come with this cheap way of obtaining genetical information \cite{Caulfield2011,Joh2011}. @@ -337,8 +343,8 @@ \subsection*{Survey on Sharing Genetic Information} The survey data was analyzed with SPSS 19. -\subsection*{Technical realization of the platform} -The main platform is realized using the web framework Ruby on Rails 3.0.10. Postgres 9.2 is used as the main database backend for Rails. +\subsection*{Technical implementation of the platform} +The main platform is implemented using the web framework Ruby on Rails 3.0.10. Postgres 9.2 is used as the main database backend for Rails. The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, @@ -350,7 +356,7 @@ \subsection*{Technical realization of the platform} Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources are handled using the ruby gem Resque and the standalone server Redis. Search features on the platform itself are implemented using Solr and the ruby gem Sunspot. -Additionally, data can be requested from openSNP using the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}. The required data is stored in a PostgreSQL database. +Additionally, data can be requested from openSNP using the Distributed Annotation System. The required data is stored in a PostgreSQL database. Requested data is delivered in XML-format to facilitate parsing. Additionally, users can request data in the JSON-format, using a system not specified in any standard. OpenSNP only serves as a platform for SNPs, so methods for the delivery of nucleotide sequences as described in the DAS-standard are not implemented. Currently, From d294d5ab2cc9477356e603d979ca028fcbc1780d Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Mon, 26 Mar 2012 09:40:22 +1000 Subject: [PATCH 20/88] Added v2-chip numbers. Removed space in superscript page 2 paragraph 3 (we have to move footnotes to references anyway) Added dbSNP-citation to papers.bib and text --- paper_draft.tex | 6 +- papers.bib | 734 +++++++++++++++++++++++++++++++++--------------- 2 files changed, 511 insertions(+), 229 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index f0e5748..0474521 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -137,7 +137,7 @@ \section*{Introduction} %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. -Those companies use DNA microarrays to screen for around 1 million SNPs spread over the human genome. In return, customers +These companies use DNA microarrays to screen for around 0.5 to 1 million SNPs spread over the human genome. In return, customers get an analysis of the results, as well as a file that includes the customer's raw individual genotypes. In 2011, 23andMe alone had over 100,000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} - the company recognizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about @@ -147,7 +147,7 @@ \section*{Introduction} Although companies like 23andMe are willing to use the data for scientific research, it is not easy for individual scientists to access the data. -The generation of biomedical data by private companies raises concerns about privacy \footnote{https://www.23andme.com/about/privacy/}, +The generation of biomedical data by private companies raises concerns about privacy\footnote{https://www.23andme.com/about/privacy/}, liabilty and consent \cite{Caulfield2011}. Nevertheless, individual customers are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. @@ -351,7 +351,7 @@ \subsection*{Technical implementation of the platform} through querying the content via the MediaWiki API. All databases are queried using the unique identifier of each SNP as the search term. SNPs are catalogued by their unique identifier, which consists of a prefix (mostly \textit{rs}, rarely \textit{i}) and a unique number. This is a common format, -which is employed by the NCBI dbSNP database and is also widely used and easily parsed from different literature sources. Publications from the different databases as +which is employed by the NCBI dbSNP database \cite{Sherry2001} and is also widely used and easily parsed from different literature sources. Publications from the different databases as well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources diff --git a/papers.bib b/papers.bib index cd63bd9..3c36250 100644 --- a/papers.bib +++ b/papers.bib @@ -1,9 +1,49 @@ -% This file was created with JabRef 2.7b. +% This file was created with JabRef 2.7.2. % Encoding: ISO8859_1 +@ARTICLE{Brown1999, + author = {Brown, Patrick O. and Botstein, David}, + title = {{ Exploring the new world of the genomewith DNA microarray}}, + journal = {nature genetics supplement}, + year = {1999}, + volume = {21}, + pages = {33--37} +} + +@ARTICLE{Cariaso2011, + author = {Cariaso, Michael and Lennon, Greg}, + title = {SNPedia: a wiki supporting personal genome annotation, interpretation + and analysis}, + journal = {Nucleic Acids Research}, + year = {2011}, + abstract = {SNPedia (http://www.SNPedia.com) is a wiki resource of the functional + consequences of human genetic variation as published in peer-reviewed + studies. Online since 2006 and freely available for personal use, + SNPedia has focused on the medical, phenotypic and genealogical associations + of single nucleotide polymorphisms. Entries are formatted to allow + associations to be assigned to single genotypes as well as sets of + genotypes (genosets). In this article, we discuss the growth of this + resource and its use by affiliated software to create personal genome + reports.}, + doi = {10.1093/nar/gkr798}, + eprint = {http://nar.oxfordjournals.org/content/early/2011/12/02/nar.gkr798.full.pdf+html}, + url = {http://nar.oxfordjournals.org/content/early/2011/12/02/nar.gkr798.abstract} +} + +@ARTICLE{Caulfield2011, + author = {Caulfield, Timothy and McGuire, Amy L}, + title = {Direct-to-Consumer Genetic Testing: Perceptions, Problems and Policy + Responses}, + journal = {Annual Review of Medicine}, + year = {2011}, + volume = {63}, + pages = {1.1-1.11}, + doi = {10.1146/annurev-med-062110-123753} +} + @ARTICLE{Do2011, author = {Chuong B Do and Joyce Y Tung and Elizabeth Dorfman and Amy K Kiefer - and Emily M Drabant and Uta Francke and Joanna L Mountain and Samuel + and Emily M Drabant and Uta Francke and Joanna L Mountain and Samuel M Goldman and Caroline M Tanner and J. William Langston and Anne Wojcicki and Nicholas Eriksson}, title = {Web-based genome-wide association study identifies two novel loci @@ -14,7 +54,7 @@ @ARTICLE{Do2011 pages = {e1002141}, number = {6}, month = {Jun}, - __markedentry = {[drsnuggles:6]}, + __markedentry = {[drsnuggles:]}, abstract = {Although the causes of Parkinson's disease (PD) are thought to be primarily environmental, recent studies suggest that a number of genes influence susceptibility. Using targeted case recruitment and @@ -23,8 +63,8 @@ @ARTICLE{Do2011 of individuals to date (3,426 cases and 29,624 controls). We discovered two novel, genome-wide significant associations with PD-rs6812193 near SCARB2 (p = 7.6 × 10(-10), OR = 0.84) and rs11868035 near SREBF1/RAI1 - (p = 5.6 × 10(-8), OR = 0.85)-both replicated in an independent cohort. - We also replicated 20 previously discovered genetic associations + (p = 5.6 × 10(-8), OR = 0.85)-both replicated in an independent + cohort. We also replicated 20 previously discovered genetic associations (including LRRK2, GBA, SNCA, MAPT, GAK, and the HLA region), providing support for our novel study design. Relying on a recently proposed method based on genome-wide sharing estimates between distantly related @@ -53,6 +93,37 @@ @ARTICLE{Do2011 url = {http://dx.doi.org/10.1371/journal.pgen.1002141} } +@ARTICLE{Dowell2001, + author = {Dowell, Robin and Jokerst, Rodney and Day, Allen and Eddy, Sean and + Stein, Lincoln}, + title = {The Distributed Annotation System}, + journal = {BMC Bioinformatics}, + year = {2001}, + volume = {2}, + pages = {7}, + number = {1}, + abstract = {BACKGROUND:Currently, most genome annotation is curated by centralized + groups with limited resources. Efforts to share annotations transparently + among multiple groups have not yet been satisfactory.RESULTS:Here + we introduce a concept called the Distributed Annotation System (DAS). + DAS allows sequence annotations to be decentralized among multiple + third-party annotators and integrated on an as-needed basis by client-side + software. The communication between client and servers in DAS is + defined by the DAS XML specification. Annotations are displayed in + layers, one per server. Any client or server adhering to the DAS + XML specification can participate in the system; we describe a simple + prototype client and server example.CONCLUSIONS:The DAS specification + is being used experimentally by Ensembl, WormBase, and the Berkeley + Drosophila Genome Project. Continued success will depend on the readiness + of the research community to adopt DAS and provide annotations. All + components are freely available from the project website http://www.biodas.org/ + webcite.}, + doi = {10.1186/1471-2105-2-7}, + issn = {1471-2105}, + pubmedid = {11667947}, + url = {http://www.biomedcentral.com/1471-2105/2/7} +} + @ARTICLE{Eriksson2010, author = {Nicholas Eriksson and J. Michael Macpherson and Joyce Y Tung and Lawrence S Hon and Brian Naughton and Serge Saxonov and Linda Avey @@ -65,7 +136,6 @@ @ARTICLE{Eriksson2010 pages = {e1000993}, number = {6}, month = {Jun}, - __markedentry = {[drsnuggles:]}, abstract = {Despite the recent rapid growth in genome-wide data, much of human variation remains entirely unexplained. A significant challenge in the pursuit of the genetic basis for variation in common human traits @@ -98,247 +168,459 @@ @ARTICLE{Eriksson2010 url = {http://dx.doi.org/10.1371/journal.pgen.1000993} } -@Article{Johnson2009, -AUTHOR = {Johnson, Andrew and O'Donnell, Christopher}, -TITLE = {An Open Access Database of Genome-wide Association Results}, -JOURNAL = {BMC Medical Genetics}, -VOLUME = {10}, -YEAR = {2009}, -NUMBER = {1}, -PAGES = {6}, -URL = {http://www.biomedcentral.com/1471-2350/10/6}, -DOI = {10.1186/1471-2350-10-6}, -PubMedID = {19161620}, -ISSN = {1471-2350}, -ABSTRACT = {BACKGROUND:The number of genome-wide association studies (GWAS) is growing rapidly leading to the discovery and replication of many new disease loci. Combining results from multiple GWAS datasets may potentially strengthen previous conclusions and suggest new disease loci, pathways or pleiotropic genes. However, no database or centralized resource currently exists that contains anywhere near the full scope of GWAS results.METHODS:We collected available results from 118 GWAS articles into a database of 56,411 significant SNP-phenotype associations and accompanying information, making this database freely available here. In doing so, we met and describe here a number of challenges to creating an open access database of GWAS results. Through preliminary analyses and characterization of available GWAS, we demonstrate the potential to gain new insights by querying a database across GWAS.RESULTS:Using a genomic bin-based density analysis to search for highly associated regions of the genome, positive control loci (e.g., MHC loci) were detected with high sensitivity. Likewise, an analysis of highly repeated SNPs across GWAS identified replicated loci (e.g., APOE, LPL). At the same time we identified novel, highly suggestive loci for a variety of traits that did not meet genome-wide significant thresholds in prior analyses, in some cases with strong support from the primary medical genetics literature (SLC16A7, CSMD1, OAS1), suggesting these genes merit further study. Additional adjustment for linkage disequilibrium within most regions with a high density of GWAS associations did not materially alter our findings. Having a centralized database with standardized gene annotation also allowed us to examine the representation of functional gene categories (gene ontologies) containing one or more associations among top GWAS results. Genes relating to cell adhesion functions were highly over-represented among significant associations (p < 4.6 x 10-14), a finding which was not perturbed by a sensitivity analysis.CONCLUSION:We provide access to a full gene-annotated GWAS database which could be used for further querying, analyses or integration with other genomic information. We make a number of general observations. Of reported associated SNPs, 40% lie within the boundaries of a RefSeq gene and 68% are within 60 kb of one, indicating a bias toward gene-centricity in the findings. We found considerable heterogeneity in information available from GWAS suggesting the wider community could benefit from standardization and centralization of results reporting.}, +@ARTICLE{Finn2007, + author = {Finn, Robert D. and Stalker, James W. and Jackson, David K. and Kulesha, + Eugene and Clements, Jody and Pettett, Roger}, + title = {ProServer: a simple, extensible Perl DAS server}, + journal = {Bioinformatics}, + year = {2007}, + volume = {23}, + pages = {1568-1570}, + number = {12}, + abstract = {Summary: The increasing size and complexity of biological databases + has led to a growing trend to federate rather than duplicate them. + In order to share data between federated databases, protocols for + the exchange mechanism must be developed. One such data exchange + protocol that is widely used is the Distributed Annotation System + (DAS). For example, DAS has enabled small experimental groups to + integrate their data into the Ensembl genome browser. We have developed + ProServer, a simple, lightweight, Perl-based DAS server that does + not depend on a separate HTTP server. The ProServer package is easily + extensible, allowing data to be served from almost any underlying + data model. Recent additions to the DAS protocol have enabled both + structure and alignment (sequence and structural) data to be exchanged. + ProServer allows both of these data types to be served.Availability: + ProServer can be downloaded from http://www.sanger.ac.uk/proserver/ + or CPAN http://search.cpan.org/~rpettett/. Details on the system + requirements and installation of ProServer can be found at http://www.sanger.ac.uk/proserver/.Contact: + rmp@sanger.ac.ukSupplementary Materials: DasClientExamples.pdf}, + doi = {10.1093/bioinformatics/btl650}, + eprint = {http://bioinformatics.oxfordjournals.org/content/23/12/1568.full.pdf+html}, + url = {http://bioinformatics.oxfordjournals.org/content/23/12/1568.abstract} } -@article{Klein2005, -author = {Klein, Robert J. and Zeiss, Caroline and Chew, Emily Y. and Tsai, Jen-Yue and Sackler, Richard S. and Haynes, Chad and Henning, Alice K. and SanGiovanni, John Paul and Mane, Shrikant M. and Mayne, Susan T. and Bracken, Michael B. and Ferris, Frederick L. and Ott, Jurg and Barnstable, Colin and Hoh, Josephine}, -title = {Complement Factor H Polymorphism in Age-Related Macular Degeneration}, -volume = {308}, -number = {5720}, -pages = {385-389}, -year = {2005}, -doi = {10.1126/science.1109557}, -abstract ={Age-related macular degeneration (AMD) is a major cause of blindness in the elderly. We report a genome-wide screen of 96 cases and 50 controls for polymorphisms associated with AMD. Among 116,204 single-nucleotide polymorphisms genotyped, an intronic and common variant in the complement factor H gene (CFH) is strongly associated with AMD (nominal P value <10-7). In individuals homozygous for the risk allele, the likelihood of AMD is increased by a factor of 7.4 (95% confidence interval 2.9 to 19). Resequencing revealed a polymorphism in linkage disequilibrium with the risk allele representing a tyrosine-histidine change at amino acid 402. This polymorphism is in a region of CFH that binds heparin and C-reactive protein. The CFH gene is located on chromosome 1 in a region repeatedly linked to AMD in family-based studies.}, -URL = {http://www.sciencemag.org/content/308/5720/385.abstract}, -eprint = {http://www.sciencemag.org/content/308/5720/385.full.pdf}, -journal = {Science} +@ARTICLE{GelMoreno2011, + author = {Gel Moreno, Bernat and Jenkinson, Andrew and Jimenez, Rafael and + Messeguer Peypoch, Xavier and Hermjakob, Henning}, + title = {easyDAS: Automatic creation of DAS servers}, + journal = {BMC Bioinformatics}, + year = {2011}, + volume = {12}, + pages = {23}, + number = {1}, + abstract = {BACKGROUND:The Distributed Annotation System (DAS) has proven to be + a successful way to publish and share biological data. Although there + are more than 750 active registered servers from around 50 organizations, + setting up a DAS server comprises a fair amount of work, making it + difficult for many research groups to share their biological annotations. + Given the clear advantage that the generalized sharing of relevant + biological data is for the research community it would be desirable + to facilitate the sharing process.RESULTS:Here we present easyDAS, + a web-based system enabling anyone to publish biological annotations + with just some clicks. The system, available at http://www.ebi.ac.uk/panda-srv/easydas + webcite is capable of reading different standard data file formats, + process the data and create a new publicly available DAS source in + a completely automated way. The created sources are hosted on the + EBI systems and can take advantage of its high storage capacity and + network connection, freeing the data provider from any network management + work. easyDAS is an open source project under the GNU LGPL license.CONCLUSIONS:easyDAS + is an automated DAS source creation system which can help many researchers + in sharing their biological data, potentially increasing the amount + of relevant biological data available to the scientific community.}, + doi = {10.1186/1471-2105-12-23}, + issn = {1471-2105}, + pubmedid = {21244646}, + url = {http://www.biomedcentral.com/1471-2105/12/23} } -@article{Cariaso2011, -author = {Cariaso, Michael and Lennon, Greg}, -title = {SNPedia: a wiki supporting personal genome annotation, interpretation and analysis}, -year = {2011}, -doi = {10.1093/nar/gkr798}, -abstract ={SNPedia (http://www.SNPedia.com) is a wiki resource of the functional consequences of human genetic variation as published in peer-reviewed studies. Online since 2006 and freely available for personal use, SNPedia has focused on the medical, phenotypic and genealogical associations of single nucleotide polymorphisms. Entries are formatted to allow associations to be assigned to single genotypes as well as sets of genotypes (genosets). In this article, we discuss the growth of this resource and its use by affiliated software to create personal genome reports.}, -URL = {http://nar.oxfordjournals.org/content/early/2011/12/02/nar.gkr798.abstract}, -eprint = {http://nar.oxfordjournals.org/content/early/2011/12/02/nar.gkr798.full.pdf+html}, -journal = {Nucleic Acids Research} +@ARTICLE{Hauskeller2011, + author = {Hauskeller, C.}, + title = {{Direct to consumer genetic testing}}, + journal = {Bmj}, + year = {2011}, + volume = {342}, + pages = {d2317--d2317}, + number = {apr21 1}, + month = apr, + doi = {10.1136/bmj.d2317}, + file = {:Users/bastian/Documents/Paper/Hauskeller/Bmj/0.pdf:pdf}, + issn = {0959-8138}, + url = {http://www.bmj.com/cgi/doi/10.1136/bmj.d2317} } -@Article{Dowell2001, -AUTHOR = {Dowell, Robin and Jokerst, Rodney and Day, Allen and Eddy, Sean and Stein, Lincoln}, -TITLE = {The Distributed Annotation System}, -JOURNAL = {BMC Bioinformatics}, -VOLUME = {2}, -YEAR = {2001}, -NUMBER = {1}, -PAGES = {7}, -URL = {http://www.biomedcentral.com/1471-2105/2/7}, -DOI = {10.1186/1471-2105-2-7}, -PubMedID = {11667947}, -ISSN = {1471-2105}, -ABSTRACT = {BACKGROUND:Currently, most genome annotation is curated by centralized groups with limited resources. Efforts to share annotations transparently among multiple groups have not yet been satisfactory.RESULTS:Here we introduce a concept called the Distributed Annotation System (DAS). DAS allows sequence annotations to be decentralized among multiple third-party annotators and integrated on an as-needed basis by client-side software. The communication between client and servers in DAS is defined by the DAS XML specification. Annotations are displayed in layers, one per server. Any client or server adhering to the DAS XML specification can participate in the system; we describe a simple prototype client and server example.CONCLUSIONS:The DAS specification is being used experimentally by Ensembl, WormBase, and the Berkeley Drosophila Genome Project. Continued success will depend on the readiness of the research community to adopt DAS and provide annotations. All components are freely available from the project website http://www.biodas.org/ webcite.}, +@ARTICLE{Hindorff2009, + author = {Hindorff, Lucia A. and Sethupathy, Praveen and Junkins, Heather A. + and Ramos, Erin M. and Mehta, Jayashri P. and Collins, Francis S. + and Manolio, Teri A.}, + title = {Potential etiologic and functional implications of genome-wide association + loci for human diseases and traits}, + journal = {Proceedings of the National Academy of Sciences}, + year = {2009}, + volume = {106}, + pages = {9362-9367}, + number = {23}, + abstract = {We have developed an online catalog of SNP-trait associations from + published genome-wide association studies for use in investigating + genomic characteristics of trait/disease-associated SNPs (TASs). + Reported TASs were common [median risk allele frequency 36%, interquartile + range (IQR) 21%−53%] and were associated with modest effect sizes + [median odds ratio (OR) 1.33, IQR 1.20–1.61]. Among 20 genomic + annotation sets, reported TASs were significantly overrepresented + only in nonsynonymous sites [OR = 3.9 (2.2−7.0), p = 3.5 × 10−7] + and 5kb-promoter regions [OR = 2.3 (1.5−3.6), p = 3 × 10−4] + compared to SNPs randomly selected from genotyping arrays. Although + 88% of TASs were intronic (45%) or intergenic (43%), TASs were not + overrepresented in introns and were significantly depleted in intergenic + regions [OR = 0.44 (0.34−0.58), p = 2.0 × 10−9]. Only slightly + more TASs than expected by chance were predicted to be in regions + under positive selection [OR = 1.3 (0.8−2.1), p = 0.2]. This new + online resource, together with bioinformatic predictions of the underlying + functionality at trait/disease-associated loci, is well-suited to + guide future investigations of the role of common variants in complex + disease etiology.}, + doi = {10.1073/pnas.0903103106}, + eprint = {http://www.pnas.org/content/106/23/9362.full.pdf+html}, + url = {http://www.pnas.org/content/106/23/9362.abstract} } -@Article{Jenkinson2008, -AUTHOR = {Jenkinson, Andrew and Albrecht, Mario and Birney, Ewan and Blankenburg, Hagen and Down, Thomas and Finn, Robert and Hermjakob, Henning and Hubbard, Tim and Jimenez, Rafael and Jones, Philip and Kahari, Andreas and Kulesha, Eugene and Macias, Jose and Reeves, Gabrielle and Prlic, Andreas}, -TITLE = {Integrating biological data - the Distributed Annotation System}, -JOURNAL = {BMC Bioinformatics}, -VOLUME = {9}, -YEAR = {2008}, -NUMBER = {Suppl 8}, -PAGES = {S3}, -URL = {http://www.biomedcentral.com/1471-2105/9/S8/S3}, -DOI = {10.1186/1471-2105-9-S8-S3}, -PubMedID = {18673527}, -ISSN = {1471-2105}, -ABSTRACT = {BACKGROUND:The Distributed Annotation System (DAS) is a widely adopted protocol for dynamically integrating a wide range of biological data from geographically diverse sources. DAS continues to expand its applicability and evolve in response to new challenges facing integrative bioinformatics.RESULTS:Here we describe the various infrastructure components of DAS and present a new extended version of the DAS specification. Version 1.53E incorporates several recent developments, including its extension to serve new data types and an ontology for protein features.CONCLUSION:Our extensions to the DAS protocol have facilitated the integration of new data types, and our improvements to the existing DAS infrastructure have addressed recent challenges. The steadily increasing numbers of available data sources demonstrates further adoption of the DAS protocol.}, +@ARTICLE{Hogarth2008, + author = {Hogarth, Stuart and Javitt, Gail and Melzer, David}, + title = {{The current landscape for direct-to-consumer genetic testing: legal, + ethical, and policy issues.}}, + journal = {Annual review of genomics and human genetics}, + year = {2008}, + volume = {9}, + pages = {161--82}, + month = jan, + abstract = {This review surveys the developing market for direct-to-consumer (DTC) + genetic tests and examines the range of companies and tests available, + the regulatory landscape, the concerns raised about DTC testing, + and the calls for enhanced oversight. We provide a comparative overview + of the situation, particularly in the United States and Europe, by + exploring the regulatory frameworks for medical devices and clinical + laboratories. We also discuss a variety of other mechanisms such + as general controls on advertising and consumer law mechanisms.}, + doi = {10.1146/annurev.genom.9.081307.164319}, + file = {:Users/bastian/Documents/Paper/Hogarth, Javitt, Melzer/Annual review of genomics and human genetics/annurev\%2Egenom\%2E9\%2E081307\%2E164319.pdf:pdf}, + issn = {1527-8204}, + keywords = {Advertising as Topic,Europe,Genetic Techniques,Genetic Techniques: + economics,Genetic Techniques: ethics,Genetics, Medical,Genetics, + Medical: economics,Genetics, Medical: ethics,Genetics, Medical: legislation + \& jurisprudence,Humans,Marketing of Health Services,Public Policy,United + States}, + pmid = {18767961}, + url = {http://www.ncbi.nlm.nih.gov/pubmed/18767961} } -@article{Finn2007, -author = {Finn, Robert D. and Stalker, James W. and Jackson, David K. and Kulesha, Eugene and Clements, Jody and Pettett, Roger}, -title = {ProServer: a simple, extensible Perl DAS server}, -volume = {23}, -number = {12}, -pages = {1568-1570}, -year = {2007}, -doi = {10.1093/bioinformatics/btl650}, -abstract ={Summary: The increasing size and complexity of biological databases has led to a growing trend to federate rather than duplicate them. In order to share data between federated databases, protocols for the exchange mechanism must be developed. One such data exchange protocol that is widely used is the Distributed Annotation System (DAS). For example, DAS has enabled small experimental groups to integrate their data into the Ensembl genome browser. We have developed ProServer, a simple, lightweight, Perl-based DAS server that does not depend on a separate HTTP server. The ProServer package is easily extensible, allowing data to be served from almost any underlying data model. Recent additions to the DAS protocol have enabled both structure and alignment (sequence and structural) data to be exchanged. ProServer allows both of these data types to be served.Availability: ProServer can be downloaded from http://www.sanger.ac.uk/proserver/ or CPAN http://search.cpan.org/~rpettett/. Details on the system requirements and installation of ProServer can be found at http://www.sanger.ac.uk/proserver/.Contact: rmp@sanger.ac.ukSupplementary Materials: DasClientExamples.pdf}, -URL = {http://bioinformatics.oxfordjournals.org/content/23/12/1568.abstract}, -eprint = {http://bioinformatics.oxfordjournals.org/content/23/12/1568.full.pdf+html}, -journal = {Bioinformatics} +@ARTICLE{Jenkinson2008, + author = {Jenkinson, Andrew and Albrecht, Mario and Birney, Ewan and Blankenburg, + Hagen and Down, Thomas and Finn, Robert and Hermjakob, Henning and + Hubbard, Tim and Jimenez, Rafael and Jones, Philip and Kahari, Andreas + and Kulesha, Eugene and Macias, Jose and Reeves, Gabrielle and Prlic, + Andreas}, + title = {Integrating biological data - the Distributed Annotation System}, + journal = {BMC Bioinformatics}, + year = {2008}, + volume = {9}, + pages = {S3}, + number = {Suppl 8}, + abstract = {BACKGROUND:The Distributed Annotation System (DAS) is a widely adopted + protocol for dynamically integrating a wide range of biological data + from geographically diverse sources. DAS continues to expand its + applicability and evolve in response to new challenges facing integrative + bioinformatics.RESULTS:Here we describe the various infrastructure + components of DAS and present a new extended version of the DAS specification. + Version 1.53E incorporates several recent developments, including + its extension to serve new data types and an ontology for protein + features.CONCLUSION:Our extensions to the DAS protocol have facilitated + the integration of new data types, and our improvements to the existing + DAS infrastructure have addressed recent challenges. The steadily + increasing numbers of available data sources demonstrates further + adoption of the DAS protocol.}, + doi = {10.1186/1471-2105-9-S8-S3}, + issn = {1471-2105}, + pubmedid = {18673527}, + url = {http://www.biomedcentral.com/1471-2105/9/S8/S3} } -@Article{GelMoreno2011, -AUTHOR = {Gel Moreno, Bernat and Jenkinson, Andrew and Jimenez, Rafael and Messeguer Peypoch, Xavier and Hermjakob, Henning}, -TITLE = {easyDAS: Automatic creation of DAS servers}, -JOURNAL = {BMC Bioinformatics}, -VOLUME = {12}, -YEAR = {2011}, -NUMBER = {1}, -PAGES = {23}, -URL = {http://www.biomedcentral.com/1471-2105/12/23}, -DOI = {10.1186/1471-2105-12-23}, -PubMedID = {21244646}, -ISSN = {1471-2105}, -ABSTRACT = {BACKGROUND:The Distributed Annotation System (DAS) has proven to be a successful way to publish and share biological data. Although there are more than 750 active registered servers from around 50 organizations, setting up a DAS server comprises a fair amount of work, making it difficult for many research groups to share their biological annotations. Given the clear advantage that the generalized sharing of relevant biological data is for the research community it would be desirable to facilitate the sharing process.RESULTS:Here we present easyDAS, a web-based system enabling anyone to publish biological annotations with just some clicks. The system, available at http://www.ebi.ac.uk/panda-srv/easydas webcite is capable of reading different standard data file formats, process the data and create a new publicly available DAS source in a completely automated way. The created sources are hosted on the EBI systems and can take advantage of its high storage capacity and network connection, freeing the data provider from any network management work. easyDAS is an open source project under the GNU LGPL license.CONCLUSIONS:easyDAS is an automated DAS source creation system which can help many researchers in sharing their biological data, potentially increasing the amount of relevant biological data available to the scientific community.}, +@ARTICLE{Joh2011, + author = {Joh, Elizabeth E}, + title = {{Ethics watch: DNA theft: your genetic information at risk.}}, + journal = {Nature reviews. Genetics}, + year = {2011}, + volume = {12}, + pages = {3113}, + number = {October}, + month = oct, + doi = {10.1038/nrg3113}, + file = {:Users/bastian/Documents/Paper/Joh/Nature reviews. Genetics/nrg3113.pdf:pdf}, + issn = {1471-0064}, + pmid = {22025003}, + url = {http://www.ncbi.nlm.nih.gov/pubmed/22025003} } -@article{Hindorff2009, -author = {Hindorff, Lucia A. and Sethupathy, Praveen and Junkins, Heather A. and Ramos, Erin M. and Mehta, Jayashri P. and Collins, Francis S. and Manolio, Teri A.}, -title = {Potential etiologic and functional implications of genome-wide association loci for human diseases and traits}, -volume = {106}, -number = {23}, -pages = {9362-9367}, -year = {2009}, -doi = {10.1073/pnas.0903103106}, -abstract ={We have developed an online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs). Reported TASs were common [median risk allele frequency 36%, interquartile range (IQR) 21%−53%] and were associated with modest effect sizes [median odds ratio (OR) 1.33, IQR 1.20–1.61]. Among 20 genomic annotation sets, reported TASs were significantly overrepresented only in nonsynonymous sites [OR = 3.9 (2.2−7.0), p = 3.5 × 10−7] and 5kb-promoter regions [OR = 2.3 (1.5−3.6), p = 3 × 10−4] compared to SNPs randomly selected from genotyping arrays. Although 88% of TASs were intronic (45%) or intergenic (43%), TASs were not overrepresented in introns and were significantly depleted in intergenic regions [OR = 0.44 (0.34−0.58), p = 2.0 × 10−9]. Only slightly more TASs than expected by chance were predicted to be in regions under positive selection [OR = 1.3 (0.8−2.1), p = 0.2]. This new online resource, together with bioinformatic predictions of the underlying functionality at trait/disease-associated loci, is well-suited to guide future investigations of the role of common variants in complex disease etiology.}, -URL = {http://www.pnas.org/content/106/23/9362.abstract}, -eprint = {http://www.pnas.org/content/106/23/9362.full.pdf+html}, -journal = {Proceedings of the National Academy of Sciences} +@ARTICLE{Johnson2009, + author = {Johnson, Andrew and O'Donnell, Christopher}, + title = {An Open Access Database of Genome-wide Association Results}, + journal = {BMC Medical Genetics}, + year = {2009}, + volume = {10}, + pages = {6}, + number = {1}, + abstract = {BACKGROUND:The number of genome-wide association studies (GWAS) is + growing rapidly leading to the discovery and replication of many + new disease loci. Combining results from multiple GWAS datasets may + potentially strengthen previous conclusions and suggest new disease + loci, pathways or pleiotropic genes. However, no database or centralized + resource currently exists that contains anywhere near the full scope + of GWAS results.METHODS:We collected available results from 118 GWAS + articles into a database of 56,411 significant SNP-phenotype associations + and accompanying information, making this database freely available + here. In doing so, we met and describe here a number of challenges + to creating an open access database of GWAS results. Through preliminary + analyses and characterization of available GWAS, we demonstrate the + potential to gain new insights by querying a database across GWAS.RESULTS:Using + a genomic bin-based density analysis to search for highly associated + regions of the genome, positive control loci (e.g., MHC loci) were + detected with high sensitivity. Likewise, an analysis of highly repeated + SNPs across GWAS identified replicated loci (e.g., APOE, LPL). At + the same time we identified novel, highly suggestive loci for a variety + of traits that did not meet genome-wide significant thresholds in + prior analyses, in some cases with strong support from the primary + medical genetics literature (SLC16A7, CSMD1, OAS1), suggesting these + genes merit further study. Additional adjustment for linkage disequilibrium + within most regions with a high density of GWAS associations did + not materially alter our findings. Having a centralized database + with standardized gene annotation also allowed us to examine the + representation of functional gene categories (gene ontologies) containing + one or more associations among top GWAS results. Genes relating to + cell adhesion functions were highly over-represented among significant + associations (p < 4.6 x 10-14), a finding which was not perturbed + by a sensitivity analysis.CONCLUSION:We provide access to a full + gene-annotated GWAS database which could be used for further querying, + analyses or integration with other genomic information. We make a + number of general observations. Of reported associated SNPs, 40% + lie within the boundaries of a RefSeq gene and 68% are within 60 + kb of one, indicating a bias toward gene-centricity in the findings. + We found considerable heterogeneity in information available from + GWAS suggesting the wider community could benefit from standardization + and centralization of results reporting.}, + doi = {10.1186/1471-2350-10-6}, + issn = {1471-2350}, + pubmedid = {19161620}, + url = {http://www.biomedcentral.com/1471-2350/10/6} } -@article{Caulfield2011, -author = {Caulfield, Timothy and McGuire, Amy L}, -title = {Direct-to-Consumer Genetic Testing: Perceptions, Problems and Policy Responses}, -volume = {63}, -pages = {1.1-1.11}, -year = {2011}, -doi = {10.1146/annurev-med-062110-123753}, -journal = {Annual Review of Medicine} +@ARTICLE{Klein2005, + author = {Klein, Robert J. and Zeiss, Caroline and Chew, Emily Y. and Tsai, + Jen-Yue and Sackler, Richard S. and Haynes, Chad and Henning, Alice + K. and SanGiovanni, John Paul and Mane, Shrikant M. and Mayne, Susan + T. and Bracken, Michael B. and Ferris, Frederick L. and Ott, Jurg + and Barnstable, Colin and Hoh, Josephine}, + title = {Complement Factor H Polymorphism in Age-Related Macular Degeneration}, + journal = {Science}, + year = {2005}, + volume = {308}, + pages = {385-389}, + number = {5720}, + abstract = {Age-related macular degeneration (AMD) is a major cause of blindness + in the elderly. We report a genome-wide screen of 96 cases and 50 + controls for polymorphisms associated with AMD. Among 116,204 single-nucleotide + polymorphisms genotyped, an intronic and common variant in the complement + factor H gene (CFH) is strongly associated with AMD (nominal P value + <10-7). In individuals homozygous for the risk allele, the likelihood + of AMD is increased by a factor of 7.4 (95% confidence interval 2.9 + to 19). Resequencing revealed a polymorphism in linkage disequilibrium + with the risk allele representing a tyrosine-histidine change at + amino acid 402. This polymorphism is in a region of CFH that binds + heparin and C-reactive protein. The CFH gene is located on chromosome + 1 in a region repeatedly linked to AMD in family-based studies.}, + doi = {10.1126/science.1109557}, + eprint = {http://www.sciencemag.org/content/308/5720/385.full.pdf}, + url = {http://www.sciencemag.org/content/308/5720/385.abstract} } - -@article{Hauskeller2011, -author = {Hauskeller, C.}, -doi = {10.1136/bmj.d2317}, -file = {:Users/bastian/Documents/Paper/Hauskeller/Bmj/0.pdf:pdf}, -issn = {0959-8138}, -journal = {Bmj}, -month = apr, -number = {apr21 1}, -pages = {d2317--d2317}, -title = {{Direct to consumer genetic testing}}, -url = {http://www.bmj.com/cgi/doi/10.1136/bmj.d2317}, -volume = {342}, -year = {2011} -} -@article{Hogarth2008, -abstract = {This review surveys the developing market for direct-to-consumer (DTC) genetic tests and examines the range of companies and tests available, the regulatory landscape, the concerns raised about DTC testing, and the calls for enhanced oversight. We provide a comparative overview of the situation, particularly in the United States and Europe, by exploring the regulatory frameworks for medical devices and clinical laboratories. We also discuss a variety of other mechanisms such as general controls on advertising and consumer law mechanisms.}, -author = {Hogarth, Stuart and Javitt, Gail and Melzer, David}, -doi = {10.1146/annurev.genom.9.081307.164319}, -file = {:Users/bastian/Documents/Paper/Hogarth, Javitt, Melzer/Annual review of genomics and human genetics/annurev\%2Egenom\%2E9\%2E081307\%2E164319.pdf:pdf}, -issn = {1527-8204}, -journal = {Annual review of genomics and human genetics}, -keywords = {Advertising as Topic,Europe,Genetic Techniques,Genetic Techniques: economics,Genetic Techniques: ethics,Genetics, Medical,Genetics, Medical: economics,Genetics, Medical: ethics,Genetics, Medical: legislation \& jurisprudence,Humans,Marketing of Health Services,Public Policy,United States}, -month = jan, -pages = {161--82}, -pmid = {18767961}, -title = {{The current landscape for direct-to-consumer genetic testing: legal, ethical, and policy issues.}}, -url = {http://www.ncbi.nlm.nih.gov/pubmed/18767961}, -volume = {9}, -year = {2008} -} -@article{Joh2011, -author = {Joh, Elizabeth E}, -doi = {10.1038/nrg3113}, -file = {:Users/bastian/Documents/Paper/Joh/Nature reviews. Genetics/nrg3113.pdf:pdf}, -issn = {1471-0064}, -journal = {Nature reviews. Genetics}, -month = oct, -number = {October}, -pages = {3113}, -pmid = {22025003}, -title = {{Ethics watch: DNA theft: your genetic information at risk.}}, -url = {http://www.ncbi.nlm.nih.gov/pubmed/22025003}, -volume = {12}, -year = {2011} -} -@article{Wasson2009, -author = {Wasson, Katherine}, -file = {:Users/bastian/Documents/Paper/Wasson/The American Journal of Bioethics/15265160902893965.pdf:pdf}, -journal = {The American Journal of Bioethics}, -number = {6-7}, -pages = {56--58}, -publisher = {Taylor \& Francis}, -title = {{Direct-to-consumer genomics and research ethics: should a more robust informed consent process be included?}}, -url = {http://www.tandfonline.com/doi/abs/10.1080/15265160902893965}, -volume = {9}, -year = {2009} -} -@article{Brown1999, -author = {Brown, Patrick O. and Botstein, David}, -journal = {nature genetics supplement}, -volume = {21}, -year = {1999}, -pages = {33--37}, -title = {{ -Exploring the new world of the genomewith DNA microarray}} +@ARTICLE{10.1371.journal.pone.0031470, + author = {Mei, , Hao AND Chen, , Wei AND Jiang, , Fan AND He, , Jiang AND Srinivasan, + , Sathanur AND Smith, , Erin N. AND Schork, , Nicholas AND Murray, + , Sarah AND Berenson, , Gerald S.}, + title = {Longitudinal Replication Studies of GWAS Risk SNPs Influencing Body + Mass Index over the Course of Childhood and Adulthood}, + journal = {PLoS ONE}, + year = {2012}, + volume = {7}, + pages = {e31470}, + number = {2}, + month = {02}, + abstract = {

Genome-wide association studies (GWAS) have identified multiple + common variants associated with body mass index (BMI). In this study, + we tested 23 genotyped GWAS-significant SNPs (p-value<5*10-8) + for longitudinal associations with BMI during childhood (3–17 years) + and adulthood (18–45 years) for 658 subjects. We also proposed + a heuristic forward search for the best joint effect model to explain + the longitudinal BMI variation. After using false discovery rate + (FDR) to adjust for multiple tests, childhood and adulthood BMI were + found to be significantly associated with six SNPs each (q-value<0.05), + with one SNP associated with both BMI measurements: KCTD15 rs29941 + (q-value<7.6*10-4). These 12 SNPs are located at or near genes + either expressed in the brain (BDNF, KCTD15, TMEM18, MTCH2, and FTO) + or implicated in cell apoptosis and proliferation (FAIM2, MAP2K5, + and TFAP2B). The longitudinal effects of FAIM2 rs7138803 on childhood + BMI and MAP2K5 rs2241423 on adulthood BMI decreased as age increased + (q-value<0.05). The FTO candidate SNPs, rs6499640 at the 5 ′-end + and rs1121980 and rs8050136 downstream, were associated with childhood + and adulthood BMI, respectively, and the risk effects of rs6499640 + and rs1121980 increased as birth weight decreased. The best joint + effect model for childhood and adulthood BMI contained 14 and 15 + SNPs each, with 11 in common, and the percentage of explained variance + increased from 0.17% and 9.0*10−6% to 2.22% and 2.71%, + respectively. In summary, this study evidenced the presence of long-term + major effects of genes on obesity development, implicated in pathways + related to neural development and cell metabolism, and different + sets of genes associated with childhood and adulthood BMI, respectively. + The gene effects can vary with age and be modified by prenatal development. + The best joint effect model indicated that multiple variants with + effects that are weak or absent alone can nevertheless jointly exert + a large longitudinal effect on BMI.

}, + doi = {10.1371/journal.pone.0031470}, + publisher = {Public Library of Science}, + url = {http://dx.doi.org/10.1371%2Fjournal.pone.0031470} } -@article{10.1371.journal.pone.0031470, - author = {Mei, , Hao AND Chen, , Wei AND Jiang, , Fan AND He, , Jiang AND Srinivasan, , Sathanur AND Smith, , Erin N. AND Schork, , Nicholas AND Murray, , Sarah AND Berenson, , Gerald S.}, - journal = {PLoS ONE}, - publisher = {Public Library of Science}, - title = {Longitudinal Replication Studies of GWAS Risk SNPs Influencing Body Mass Index over the Course of Childhood and Adulthood}, - year = {2012}, - month = {02}, - volume = {7}, - url = {http://dx.doi.org/10.1371%2Fjournal.pone.0031470}, - pages = {e31470}, - abstract = {

Genome-wide association studies (GWAS) have identified multiple common variants associated with body mass index (BMI). In this study, we tested 23 genotyped GWAS-significant SNPs (p-value<5*10-8) for longitudinal associations with BMI during childhood (3–17 years) and adulthood (18–45 years) for 658 subjects. We also proposed a heuristic forward search for the best joint effect model to explain the longitudinal BMI variation. After using false discovery rate (FDR) to adjust for multiple tests, childhood and adulthood BMI were found to be significantly associated with six SNPs each (q-value<0.05), with one SNP associated with both BMI measurements: KCTD15 rs29941 (q-value<7.6*10-4). These 12 SNPs are located at or near genes either expressed in the brain (BDNF, KCTD15, TMEM18, MTCH2, and FTO) or implicated in cell apoptosis and proliferation (FAIM2, MAP2K5, and TFAP2B). The longitudinal effects of FAIM2 rs7138803 on childhood BMI and MAP2K5 rs2241423 on adulthood BMI decreased as age increased (q-value<0.05). The FTO candidate SNPs, rs6499640 at the 5 ′-end and rs1121980 and rs8050136 downstream, were associated with childhood and adulthood BMI, respectively, and the risk effects of rs6499640 and rs1121980 increased as birth weight decreased. The best joint effect model for childhood and adulthood BMI contained 14 and 15 SNPs each, with 11 in common, and the percentage of explained variance increased from 0.17% and 9.0*10−6% to 2.22% and 2.71%, respectively. In summary, this study evidenced the presence of long-term major effects of genes on obesity development, implicated in pathways related to neural development and cell metabolism, and different sets of genes associated with childhood and adulthood BMI, respectively. The gene effects can vary with age and be modified by prenatal development. The best joint effect model indicated that multiple variants with effects that are weak or absent alone can nevertheless jointly exert a large longitudinal effect on BMI.

}, - number = {2}, - doi = {10.1371/journal.pone.0031470} -} -@article{10.1371.journal.pone.0030309, - author = {Xu, , Bin AND Tong, , Na AND Chen, , Shu-Qiu AND Yang, , Yu AND Zhang, , Xiao-Wen AND Liu, , Jin AND Hu, , Xiang-Nong AND Sha, , Guo-Zhu AND Chen, , Ming}, - journal = {PLoS ONE}, - publisher = {Public Library of Science}, - title = {Contribution of HOGG1 Ser326Cys Polymorphism to the Development of Prostate Cancer in Smokers: Meta-Analysis of 2779 Cases and 3484 Controls}, - year = {2012}, - month = {01}, - volume = {7}, - url = {http://dx.doi.org/10.1371%2Fjournal.pone.0030309}, - pages = {e30309}, - abstract = {

The HOGG1 gene catalyzes the excision of modified bases and removal of DNA damage adducts. It may play an important role in the prevention of carcinogenesis. Ser326Cys polymorphism localizes in exon 7 of the hOGG1 gene. It takes the form of an amino acid substitution, from serine to cysteine, in codon 326. Several epidemiological association studies have been conducted on this polymorphism and its relationship with the risk of prostate cancer. However, results have been conflicting. To resolve this conflict, we conducted a meta-analysis on the association between this polymorphism and prostate cancer, taking into account race, country, sources of controls, and smoking status. A total of nine studies covering 2779 cases and 3484 controls were included in the current meta-analysis. Although no significant association was found between hOGG1 Ser326Cys polymorphism and prostate cancer susceptibility in the pooled analysis, individuals with Ser/Cys+Cys/Cys genotypes were found to have greater risk of prostate cancer if they were also smokers (OR = 2.66, 95% CI = 1.58−4.47) rather than non-smokers (OR = 2.18, 95% CI = 1.13−4.19), compared with those with Ser/Ser genotype. In conclusion, our meta-analysis demonstrates that hOGG1 Ser326Cys polymorphism is a risk factor for prostate cancer in smokers. Further studies are needed to confirm this relationship.

}, - number = {1}, - doi = {10.1371/journal.pone.0030309} -} -@article{10.1371.journal.pone.0029848, - author = {Sebastiani, , Paola AND Solovieff, , Nadia AND DeWan, , Andrew T. AND Walsh, , Kyle M. AND Puca, , Annibale AND Hartley, , Stephen W. AND Melista, , Efthymia AND Andersen, , Stacy AND Dworkis, , Daniel A. AND Wilk, , Jemma B. AND Myers, , Richard H. AND Steinberg, , Martin H. AND Montano, , Monty AND Baldwin, , Clinton T. AND Hoh, , Josephine AND Perls, , Thomas T.}, - journal = {PLoS ONE}, - publisher = {Public Library of Science}, - title = {Genetic Signatures of Exceptional Longevity in Humans}, - year = {2012}, - month = {01}, - volume = {7}, - url = {http://dx.doi.org/10.1371%2Fjournal.pone.0029848}, - pages = {e29848}, - abstract = {

Like most complex phenotypes, exceptional longevity is thought to reflect a combined influence of environmental (e.g., lifestyle choices, where we live) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity in 801 centenarians (median age at death 104 years) and 914 genetically matched healthy controls. Using these data, we built a genetic model that includes 281 single nucleotide polymorphisms (SNPs) and discriminated between cases and controls of the discovery set with 89% sensitivity and specificity, and with 58% specificity and 60% sensitivity in an independent cohort of 341 controls and 253 genetically matched nonagenarians and centenarians (median age 100 years). Consistent with the hypothesis that the genetic contribution is largest with the oldest ages, the sensitivity of the model increased in the independent cohort with older and older ages (71% to classify subjects with an age at death>102 and 85% to classify subjects with an age at death>105). For further validation, we applied the model to an additional, unmatched 60 centenarians (median age 107 years) resulting in 78% sensitivity, and 2863 unmatched controls with 61% specificity. The 281 SNPs include the SNP rs2075650 in TOMM40/APOE that reached irrefutable genome wide significance (posterior probability of association = 1) and replicated in the independent cohort. Removal of this SNP from the model reduced the accuracy by only 1%. Further in-silico analysis suggests that 90% of centenarians can be grouped into clusters characterized by different “genetic signatures†of varying predictive values for exceptional longevity. The correlation between 3 signatures and 3 different life spans was replicated in the combined replication sets. The different signatures may help dissect this complex phenotype into sub-phenotypes of exceptional longevity.

}, - number = {1}, - doi = {10.1371/journal.pone.0029848} -} - - - - - - - +@ARTICLE{10.1371.journal.pone.0029848, + author = {Sebastiani, , Paola AND Solovieff, , Nadia AND DeWan, , Andrew T. + AND Walsh, , Kyle M. AND Puca, , Annibale AND Hartley, , Stephen + W. AND Melista, , Efthymia AND Andersen, , Stacy AND Dworkis, , Daniel + A. AND Wilk, , Jemma B. AND Myers, , Richard H. AND Steinberg, , + Martin H. AND Montano, , Monty AND Baldwin, , Clinton T. AND Hoh, + , Josephine AND Perls, , Thomas T.}, + title = {Genetic Signatures of Exceptional Longevity in Humans}, + journal = {PLoS ONE}, + year = {2012}, + volume = {7}, + pages = {e29848}, + number = {1}, + month = {01}, + abstract = {

Like most complex phenotypes, exceptional longevity is thought + to reflect a combined influence of environmental (e.g., lifestyle + choices, where we live) and genetic factors. To explore the genetic + contribution, we undertook a genome-wide association study of exceptional + longevity in 801 centenarians (median age at death 104 years) and + 914 genetically matched healthy controls. Using these data, we built + a genetic model that includes 281 single nucleotide polymorphisms + (SNPs) and discriminated between cases and controls of the discovery + set with 89% sensitivity and specificity, and with 58% specificity + and 60% sensitivity in an independent cohort of 341 controls and + 253 genetically matched nonagenarians and centenarians (median age + 100 years). Consistent with the hypothesis that the genetic contribution + is largest with the oldest ages, the sensitivity of the model increased + in the independent cohort with older and older ages (71% to classify + subjects with an age at death>102 and 85% to classify subjects + with an age at death>105). For further validation, we applied + the model to an additional, unmatched 60 centenarians (median age + 107 years) resulting in 78% sensitivity, and 2863 unmatched controls + with 61% specificity. The 281 SNPs include the SNP rs2075650 in TOMM40/APOE + that reached irrefutable genome wide significance (posterior probability + of association = 1) and replicated in the independent cohort. + Removal of this SNP from the model reduced the accuracy by only 1%. + Further in-silico analysis suggests that 90% of centenarians can + be grouped into clusters characterized by different “genetic signatures†+ of varying predictive values for exceptional longevity. The correlation + between 3 signatures and 3 different life spans was replicated in + the combined replication sets. The different signatures may help + dissect this complex phenotype into sub-phenotypes of exceptional + longevity.

}, + doi = {10.1371/journal.pone.0029848}, + publisher = {Public Library of Science}, + url = {http://dx.doi.org/10.1371%2Fjournal.pone.0029848} +} +@ARTICLE{Sherry2001, + author = {Sherry, S. T. and Ward, M. H. and Kholodov, M. and Baker, J. and + Phan, L. and Smigielski, E. M. and Sirotkin, K.}, + title = {dbSNP: the NCBI database of genetic variation.}, + journal = {Nucleic Acids Res}, + year = {2001}, + volume = {29}, + pages = {308--311}, + number = {1}, + month = {Jan}, + __markedentry = {[drsnuggles:6]}, + abstract = {In response to a need for a general catalog of genome variation to + address the large-scale sampling designs required by association + studies, gene mapping and evolutionary biology, the National Center + for Biotechnology Information (NCBI) has established the dbSNP database + [S.T.Sherry, M.Ward and K. Sirotkin (1999) Genome Res., 9, 677-679]. + Submissions to dbSNP will be integrated with other sources of information + at NCBI such as GenBank, PubMed, LocusLink and the Human Genome Project + data. The complete contents of dbSNP are available to the public + at website: http://www.ncbi.nlm.nih.gov/SNP. The complete contents + of dbSNP can also be downloaded in multiple formats via anonymous + FTP at ftp://ncbi.nlm.nih.gov/snp/.}, + institution = {National Center for Biotechnology Information, National Library of + Medicine, National Institutes of Health, Bethesda, MD, 20894, USA. + sherry@ncbi.nlm.nih.gov}, + keywords = {Animals; Biotechnology; Databases, Factual; Genetic Variation; Humans; + Information Services; Internet; National Institutes of Health (U.S.); + National Library of Medicine (U.S.); Polymorphism, Single Nucleotide, + genetics; United States}, + language = {eng}, + medline-pst = {ppublish}, + owner = {drsnuggles}, + pmid = {11125122}, + timestamp = {2012.03.26} +} +@ARTICLE{Wasson2009, + author = {Wasson, Katherine}, + title = {{Direct-to-consumer genomics and research ethics: should a more robust + informed consent process be included?}}, + journal = {The American Journal of Bioethics}, + year = {2009}, + volume = {9}, + pages = {56--58}, + number = {6-7}, + file = {:Users/bastian/Documents/Paper/Wasson/The American Journal of Bioethics/15265160902893965.pdf:pdf}, + publisher = {Taylor \& Francis}, + url = {http://www.tandfonline.com/doi/abs/10.1080/15265160902893965} +} +@ARTICLE{10.1371.journal.pone.0030309, + author = {Xu, , Bin AND Tong, , Na AND Chen, , Shu-Qiu AND Yang, , Yu AND Zhang, + , Xiao-Wen AND Liu, , Jin AND Hu, , Xiang-Nong AND Sha, , Guo-Zhu + AND Chen, , Ming}, + title = {Contribution of HOGG1 Ser326Cys Polymorphism to the Development of + Prostate Cancer in Smokers: Meta-Analysis of 2779 Cases and 3484 + Controls}, + journal = {PLoS ONE}, + year = {2012}, + volume = {7}, + pages = {e30309}, + number = {1}, + month = {01}, + abstract = {

The HOGG1 gene catalyzes the excision of modified bases and removal + of DNA damage adducts. It may play an important role in the prevention + of carcinogenesis. Ser326Cys polymorphism localizes in + exon 7 of the hOGG1 gene. It takes the form of an amino acid substitution, + from serine to cysteine, in codon 326. Several epidemiological association + studies have been conducted on this polymorphism and its relationship + with the risk of prostate cancer. However, results have been conflicting. + To resolve this conflict, we conducted a meta-analysis on the association + between this polymorphism and prostate cancer, taking into account + race, country, sources of controls, and smoking status. A total of + nine studies covering 2779 cases and 3484 controls were included + in the current meta-analysis. Although no significant association + was found between hOGG1 Ser326Cys polymorphism and prostate + cancer susceptibility in the pooled analysis, individuals with Ser/Cys+Cys/Cys + genotypes were found to have greater risk of prostate cancer if they + were also smokers (OR = 2.66, 95% CI = 1.58−4.47) rather + than non-smokers (OR = 2.18, 95% CI = 1.13−4.19), compared + with those with Ser/Ser genotype. In conclusion, our meta-analysis + demonstrates that hOGG1 Ser326Cys polymorphism is a risk + factor for prostate cancer in smokers. Further studies are needed + to confirm this relationship.

}, + doi = {10.1371/journal.pone.0030309}, + publisher = {Public Library of Science}, + url = {http://dx.doi.org/10.1371%2Fjournal.pone.0030309} +} From d9ce4b241818953ec2fd09409ac153dd8f96ebd0 Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Mon, 26 Mar 2012 09:54:27 +1000 Subject: [PATCH 21/88] Fixed colons and lowercases. Put footnotes into references according to http://blogs.plos.org/mfenner/2011/07/21/how-to-formally-cite-a-blog-post/ --- paper_draft.tex | 12 ++++++------ papers.bib | 17 +++++++++++++++++ 2 files changed, 23 insertions(+), 6 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 0474521..1a3fdd6 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -132,14 +132,14 @@ \section*{Introduction} This method does not allow inference of causal differences but merely identifies correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning, the number of participants in -such studies has been rising: Over 1200 GWAS have been performed \cite{Johnson2009} and over +such studies has been rising. Over 1200 GWAS have been performed \cite{Johnson2009} and over 5000 SNPs have been linked to different diseases and traits in those studies \cite{Hindorff2009}. %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. These companies use DNA microarrays to screen for around 0.5 to 1 million SNPs spread over the human genome. In return, customers get an analysis of the results, as well as a file that includes the customer's raw individual genotypes. In 2011, 23andMe -alone had over 100,000 customers\footnote{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/} +alone had over 100,000 customers \cite{23andMe2011} - the company recognizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer the data is used for association studies. 23andMe has published several articles in which it replicates known findings, but also finds new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. @@ -147,7 +147,7 @@ \section*{Introduction} Although companies like 23andMe are willing to use the data for scientific research, it is not easy for individual scientists to access the data. -The generation of biomedical data by private companies raises concerns about privacy\footnote{https://www.23andme.com/about/privacy/}, +The generation of biomedical data by private companies raises concerns about privacy \cite{23andMe2012}, liabilty and consent \cite{Caulfield2011}. Nevertheless, individual customers are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. @@ -192,7 +192,7 @@ \subsection*{Survey on Sharing Genetic Information} Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the following motivations for not sharing their data when compared to those who would share their data with their DTC company under any circumstances: because they fear discrimination (mean difference = 1.52, SE = 0.322), because they feel it is a breach of their privacy (mean difference = 1.871, SE = 0.324), -because they fear consequences for their family members (mean difference = 1.146, SE = 0.32) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357). In the cases of fear of discrimination and fear of a breach of privacy, substantial differences between all three categories exist: Those who would share their data with their DTC company as long as it did not share the information agree less strongly than those who would not share the data with both fear of discrimination as a motive for not sharing (mean difference = -0.615, SE = 0.345) as well as fear of a breach of privacy (mean difference = -0.668, SE = 0.346). Those who would share their data under any circumstances are even less likely to agree with these motives than those who would share only if their DTC company did not share the information (fear of discrimination: mean value = -0.906, SE = 0.182; breach of privacy: mean difference = -1.203, SE = 0.183). +because they fear consequences for their family members (mean difference = 1.146, SE = 0.32) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357). In the cases of fear of discrimination and fear of a breach of privacy, substantial differences between all three categories exist. Those who would share their data with their DTC company as long as it did not share the information agree less strongly than those who would not share the data with both fear of discrimination as a motive for not sharing (mean difference = -0.615, SE = 0.345) as well as fear of a breach of privacy (mean difference = -0.668, SE = 0.346). Those who would share their data under any circumstances are even less likely to agree with these motives than those who would share only if their DTC company did not share the information (fear of discrimination: mean value = -0.906, SE = 0.182; breach of privacy: mean difference = -1.203, SE = 0.183). \subsection*{openSNP} @@ -234,7 +234,7 @@ \subsubsection*{Crowdsourcing phenotypes} entered their data per phenotype can be seen in figure \ref{Figure2_label}. The phenotype provided by the most users is the eye color, which has been entered by 54 users. There are two phenotypes which have so far only been provided by a single user: -The SAT Writing score and triglyceride-levels. +the SAT Writing score and triglyceride-levels. \subsubsection*{Connection to external services} @@ -282,7 +282,7 @@ \subsection*{GWAS and open data} with users entering data in different measurement systems. While we try to suggest similar entries to the users, there are some cases where users will not follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. -There are two possible solutions to this problem: One could only allow some (trusted) users to enter new phenotypes or one +There are two possible solutions to this problem: one could only allow some (trusted) users to enter new phenotypes or one could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users cannot add variations that have not been available from the start. Both methods have a disadvantage: In either case it makes it harder for users to enter their data and thereby raises the bar for participation, which ultimately could lead to less data entered. diff --git a/papers.bib b/papers.bib index 3c36250..1089588 100644 --- a/papers.bib +++ b/papers.bib @@ -624,3 +624,20 @@ @ARTICLE{10.1371.journal.pone.0030309 url = {http://dx.doi.org/10.1371%2Fjournal.pone.0030309} } +@ARTICLE{23andMe2011, + author = {23andMe}, + title = {{23andMe 2011 State of the Database Address}}, + journal = {The Spittoon}, + year = {2011}, + type = {Blog}, + number = {June 15}, + howpublished = {\url{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/}} +} + +@ARTICLE{23andMe2012, + author = {23andMe}, + title = {{23andMe Privacy Statement}}, + journal = {23andMe Homepage}, + year = {2012}, + howpublished = {\url{http://www.23andme.com/about/privacy/}} +} From 01fef469f3e3cca9787cce5bb5318997b6803d8c Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Tue, 17 Jul 2012 14:35:35 +1000 Subject: [PATCH 22/88] abstract redone --- paper_draft.tex | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 1a3fdd6..1fda6c0 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -99,7 +99,7 @@ % Please keep the abstract between 250 and 300 words \section*{Abstract} -Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants like Single Nucleotide Polymorphisms. The advent of Direct-To-Consumer genetic testing offers the analysis of Single Nucleotide Polymorphisms for an individual at a low cost without the need to consult a physician or geneticist. Genome-wide association studies compare the genetic variation between two groups to find correlations between membership in a group and the genetic variation. To find significant correlations it is necessary to have a large enough sample size. Over 100.000 people were already genotyped through Direct-To-Consumer genetic testing and could be a valuable data source for association studies. However, generally this data is not public for various reasons and thus cannot be used by other scientists. It is vital that there is a central open data-repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates if people are willing to publicly share their genetic information. In the light of those results we also present the web-platform openSNP, which is a open database which allows participants of Direct-To-Consumer genetic testing to publish their genetic data along with phenotypic information into the public domain. Through this crowdsourced effort of collecting genetic and phenotypic information we create a valuable resource which can be used in a wide array of studies, included Genome-wide association studies. +Genome-wide association studies are used to correlate phenotypic traits with genetic variants like Single Nucleotide Polymorphisms (SNPs). Genome-wide association studies compare the genetic variation between two groups to find correlations between membership in a group and the genetic variation. To find significant correlations it is necessary to have a large enough sample size. The advent of Direct-To-Consumer genetic testing offers the analysis of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing. This data could be a valuable source for association studies. However, this data is not public for various reasons and thus cannot be used by other scientists. It seems reasonable to create a central open data-repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates whether people are willing to publicly share their genetic information. In the light of those results we also present the web-platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publish their genetic data along with phenotypic information into the public domain. Through this "crowdsourced" effort of collecting genetic and phenotypic information we create a valuable resource which can be used in a wide array of studies, included Genome-wide association studies. OpenSNP is hosted at www.opensnp.org, and the code is released under MIT-license at github.com/gedankenstuecke/snpr. % Please keep the Author Summary between 150 and 200 words @@ -111,9 +111,8 @@ \section*{Author Summary} \section*{Introduction} The availability of new DNA sequencing techniques has shifted the focus of biological data acquisition towards new biomedical applications. -Many diseases – for example Alzheimers, Parkinsons or different types of cancers – are at least partly heritable and thus the genome +Many diseases – for example Alzheimers, Parkinsons or different types of cancers – are at least partially heritable so the genome of patients can be used for diagnostic purposes. Using the genetic information of patients is made possible through the sharp decrease in costs for analysing genetic information. -Thus the importance genetic information in biomedicine will/may increase over the next years and may become a standard tool for diagnostics. %Kicked out the following due to Corpas' argument %The comparison of DNA sequences from individuals in a population can reveal variable sites, which are of major interest in investigating diseases. @@ -125,7 +124,7 @@ \section*{Introduction} %In comparison to whole genome sequencing only sites which are known to be variable are analysed, making microarrays cheaper and faster \cite{Brown1999}. -If the genotypes for more then one individual are known, the analysis of +If the genotypes for more than one individual are known, the analysis of allele frequencies can be used to associate SNPs with diseases and other inheritable traits. Genome-Wide Assocation Studies (GWAS) make use of statistics to compare the alleles of patients to the alleles of healthy controls. This enables GWAS to find alleles which are significantly overrepresented in patients and associate them with a disease. From 6420495851ecca1f23c6095a797fbe603b7fdea7 Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Tue, 17 Jul 2012 16:09:13 +1000 Subject: [PATCH 23/88] Ironed out sentences, shortened a lot, re-arranged some stuff --- paper_draft.tex | 139 +++++++++++++++++++++--------------------------- 1 file changed, 62 insertions(+), 77 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 1fda6c0..e78f684 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -131,34 +131,32 @@ \section*{Introduction} This method does not allow inference of causal differences but merely identifies correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning, the number of participants in -such studies has been rising. Over 1200 GWAS have been performed \cite{Johnson2009} and over -5000 SNPs have been linked to different diseases and traits in those studies \cite{Hindorff2009}. +such studies has been rising. To date, over 1200 GWAS have been performed \cite{Johnson2009} and over +5000 SNPs have been linked to different diseases and traits \cite{Hindorff2009}. %(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) -Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. +GWAS are not limited to the scientific community. Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. These companies use DNA microarrays to screen for around 0.5 to 1 million SNPs spread over the human genome. In return, customers -get an analysis of the results, as well as a file that includes the customer's raw individual genotypes. In 2011, 23andMe +receive an analysis of the results, as well as a file that includes the customer's raw individual genotypes. In 2011, 23andMe alone had over 100,000 customers \cite{23andMe2011} -- the company recognizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about +- the company realizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer the data is used for association studies. 23andMe has published several articles in which it replicates known findings, but also finds new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. -Over 30,000 23andme-customers have participated in those association studies. +So far, over 30,000 23andme-customers have participated in those association studies, which proves that this data-source has potential for other researchers. -Although companies like 23andMe are willing to use the data for scientific research, -it is not easy for individual scientists to access the data. The generation of biomedical data by private companies raises concerns about privacy \cite{23andMe2012}, -liabilty and consent \cite{Caulfield2011}. -Nevertheless, individual customers are willingly sharing their data. Most do so by uploading their data to +liability and consent \cite{Caulfield2011}. +Nevertheless, in some instances individual customers are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. -This data is scattered and unorganized, making it hard to use this information. While projects like SNPedia try to keep track of all -the genotyping files \cite{Cariaso2011}, they do not provide the information necessary to perform GWAS, as the phenotypic information is -most often not attached to the genetic information. Projects that attach the phenotype to the genetic information, +This data is scattered and unorganized, making it hard to use. While projects like SNPedia try to keep track of all +the publicly available genotyping files \cite{Cariaso2011}, they usually do not provide the information necessary to perform GWAS, as the phenotypic information is +often not attached to the genetic information. Projects that attach the phenotype to the genetic information, like the \textit{Personal Genome Project}, still do not allow for an easy re-use of the data, as they lack an advanced programming interface (API) -or other methods by which researchers could download the data and not every customer of DTC genetic testing can participate in it. +or other methods by which researchers could download the data. Additionally, not every customer of DTC genetic testing can participate in the \textit{Personal Genome Project}. -We designed an online survey to evaluate the support in the personal genetics community for a crowdsourced online platform. Here we present the results of the survey in combination with the openSNP project. -The openSNP project is an online platform, which makes it possible for DTC customers to share genotypic and phenoytypic information and connect it with external data sources. -The data is made available to research via a Creative-Commons-Zero licence. +Here, we present the results of a survey designed to evaluate the support in the personal genetics community for a crowdsourced online platform. +We also present openSNP, an online platform which enables DTC customers to share genotypic and phenoytypic information, as well as receive additional information on their genotypes. +The genotypes are made available to researchers via the open Creative-Commons-Zero licence. % Results and Discussion can be combined. \section*{Results} @@ -195,26 +193,25 @@ \subsection*{Survey on Sharing Genetic Information} \subsection*{openSNP} -The openSNP project (http://opensnp.org) is an open, crowdsourced online platform for both DTC customers and researchers. -DTC customers are encouraged to share their genotyping results and connect them with phenotypes to enable GWAS for researchers, -but the data can also be used for other research and in any other context. +The openSNP project (http://opensnp.org) is an open, crowdsourced online platform for both DTC customers and researchers interested in performing GWAS. +DTC customers are encouraged to share their genotyping results and connect them with phenotypes to enable access for researchers. Users can create a personal profile, discuss SNPs and phenotypes on the platform using a simple commenting system, or send each other private messages. \subsubsection*{Sharing genotypic information} Currently users can upload their genotypings from the companies 23andme, deCODEme and FamilyTreeDNA via a webinterface to the openSNP -project. The uploaded data is published under the Creative Commons Zero-license, +project. There is experimental support for openSNP-exomes. The uploaded data is published under the Creative Commons Zero-license, which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - allows a complete reuse of the data without any constraints. Between the start of openSNP on 09/27/2011 and 12/18/2012, 214 people have signed up with openSNP, and 79 genotyping files were made available. The openSNP -database lists 69,486,471 SNPs, which are distributed over 1,938,603 unique SNP-IDs. +database lists 69,486,471 SNPs of which 1,938,603 are unique SNPs. Figure \ref{Figure1_label} depicts the increase of users and genotyping files over time. \subsubsection*{Crowdsourcing phenotypes} -Beside uploading genotypic data, users may create new phenotypes that are not yet +Users are able to create new phenotypes that are not yet listed by openSNP. The specification of these phenotypes is open and not limited to pre-defined categories. To reduce the amount of manual data curation, openSNP tries to harmonize @@ -224,7 +221,7 @@ \subsubsection*{Crowdsourcing phenotypes} achievement system, rewarding users that upload their data and enter phenotypic information with small badges that are shown on their profile pages. -In the same timeframe that was mentioned above, all users combined have +In the same timeframe as above, all users combined have entered a total of 675 variations on 47 different phenotypes with those variations being the different values on a given trait or phenotype. See figure \ref{Figure1_label} for the increase of phenotypic information over time. @@ -239,10 +236,10 @@ \subsubsection*{Crowdsourcing phenotypes} \subsubsection*{Connection to external services} In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. A total number of 15,229 documents relevant to the SNP-IDs listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. -Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge by every user (Figure \ref{Figure3_label}). For usability reasons, -SNPs are ranked by the amount of information gathered by the external services. The external services themselves are ranked by how easily users can access information -from these sources. The SNPedia entries are given the highest impact, as those are already manually curated, followed by open access publications out of -the Public Library of Science. Lowest values are given to the Mendeley results, as those are not necessary freely available to every user. +Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge (Figure \ref{Figure3_label}). For usability reasons, +SNPs are ranked by the amount of information gathered by the external services. The external services themselves are ranked by how easily non-scientists can understand information +from these sources. The SNPedia entries are given the highest impact, as those are already manually curated and summarized in plain English, followed by open access publications out of +the Public Library of Science. Lowest values are given to the Mendeley results, as those are not necessarily freely available to every user. An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. \subsubsection*{Data access} @@ -251,25 +248,35 @@ \subsubsection*{Data access} accessible through the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}, which offers all data for specific chromosomes and specific positions on single chromosomes. The data is additionally available over a JSON-API, which allows users to directly access data in the JSON-format. - \section*{Discussion} \subsection*{Survey issues} As the survey was taken online by voluntary participants and was mainly spread in the personal genetics community, -the results do not reflect the general population, but overrepresent those people most likely to be interested in +the results do not reflect the general population, but over-represent those people most likely to be interested in a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. -As we wanted to find out about interest in crowdsourcing GWAS and about how people who had purchased DTC genetic -testing felt about sharing their data with third parties, this approach was warranted. -\subsection*{GWAS and open data} -Although prices of exome or even full genome sequencing are dropping rapidly, GWAS tend to stay considerably cheaper -and offer a possibility of getting insight into genetic variations, for example SNPs which are linked to different traits -on a population level. +\subsection*{Privacy, health implications and ethical considerations} + +%The advent of DTC genetic testing has led to new ethical and social issues. +Much of the critique on DTC genetic testing focusses on the practice +of delivering medical information without consulting a physician or genetic counsellor to help patients/customers make sense of the information +and to put the new knowledge to good use \cite{Hauskeller2011,Hogarth2008,Wasson2009}. + +As we have found in the survey on sharing such results, many DTC customers are willing to share their results with the public to help scientific progress, without forgetting about the privacy implications that come with openly sharing genetic information. There is a variety of ethical and privacy implications when it comes to DTC genetic testing\cite{Caulfield2011,Joh2011}. -Despite this benefit in terms of cost GWAS can only detect correlations of SNPs with those traits and do not allow -inference on the cause for any correlation. Furthermore, GWAS need a large enough sample size, -which has to be statistically analysed by using sound methods. -Nevertheless, GWAS are still frequently used and new associations are found \cite{10.1371.journal.pone.0031470,10.1371.journal.pone.0030309,10.1371.journal.pone.0029848}. +Our survey has shown that people are concerned about their privacy and fear that stakeholders like employers, insurance companies, governments +or advertisers might misuse the information. Policy makers start to react to those changes by introducing laws like the +\textit{Genetic Information Non-Discrimination Act} in the United States or the \emph{Gendiagnostikgesetz} in Germany to minimize the impact of +widely available genetic information. DTC genetic testing companies themselves also try to educate their customers about the risks of releasing genetic data. + +OpenSNP openly addresses the problem of privacy implications that come with releasing genetic data twice, during registration for openSNP and during +the upload of the DTC genetic testing results. Users have to confirm that they have read and understood the disclaimer about possible side-effects +that can arise by publishing their data. To further improve this process we are working on implementing an informed consent-processes, +similar to \textit{Consent for Research} (http://www.weconsent.us). + +\subsection*{GWAS and open data} +Although prices of exome or even full genome sequencing are dropping rapidly, GWAS are still considerably cheaper. However, GWAS can only detect correlations of SNPs with those traits and do not allow +inference on the cause for any correlation. Furthermore, for a statistically sound analysis GWAS need a large enough sample size. Nevertheless, GWAS are still frequently used and new associations are found \cite{10.1371.journal.pone.0031470,10.1371.journal.pone.0030309,10.1371.journal.pone.0029848}. One way of bringing down costs for GWAS even further is to make use of already available genotyping results and datasets. Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high @@ -278,46 +285,24 @@ \subsection*{GWAS and open data} By crowdsourcing the acquisition of genetic and phenotypic data, openSNP faces the same problems as any other open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation, -with users entering data in different measurement systems. While we try to suggest similar entries to the users, -there are some cases where users will not follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. +with users entering data in different measurement systems. Another problem with user-entered data is the frequent switching between categorical and continuous phenotypes of users - for example, some users entered the specific value of their height, while other users entered their height according to a category like "150cm to 160cm". + +While we try to suggest similar entries to the users, +there are some cases where users will not follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. There are two possible solutions to this problem: The first one would be to only allow a trusted subset of users to enter new phenotypes. The other one +would be to make users enter all possible variations of a phenotype while creating a new phenotype, so that later users cannot add +variations that have not been available from the start. -There are two possible solutions to this problem: one could only allow some (trusted) users to enter new phenotypes or one -could make users enter all possible variations of a phenotype while creating a new phenotype, so that later users cannot add -variations that have not been available from the start. Both methods have a disadvantage: In either case it makes it harder -for users to enter their data and thereby raises the bar for participation, which ultimately could lead to less data entered. -Facing this trade-off, we decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS -with the data to perform more quality control. +In both cases it makes it harder +for users to enter their data which raises the bar for participation. +We decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS with the data to perform more quality control. -Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP, as we cannot -rule out the possibility that only an unrepresentative subset of people buy DTC genetic testing, from which an even smaller subset -is willing to publish the results, along with phenotypic information of medical relevance. +Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP: only a subset of people buy DTC genetic testing, from which an even smaller subset is willing to publish the results, which can potentially lead to skewed GWAS-results. With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic -data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to be used in GWAS. +data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to use in GWAS. Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. -While we do not have collected enough data to perform full scale GWAS yet, this might be possible in the future, as user numbers are rising. - -\subsection*{Privacy, health implications and ethical considerations} -As we have found in the survey on sharing such results, many of these customers are also willing to share their results with scientists and -the public to help scientific progress, although people who have taken DTC genetic testing are considering the privacy implications that come -with openly sharing those results. - -%The advent of DTC genetic testing has led to new ethical and social issues. -Much of the critique on DTC genetic testing focusses on the practice -of delivering medical information without consulting a physician or genetic counselor to help patients/customers make sense of the information -and to put the new knowledge to good use \cite{Hauskeller2011,Hogarth2008,Wasson2009}. Less attention has been paid to the privacy implications -that come with this cheap way of obtaining genetical information \cite{Caulfield2011,Joh2011}. +While there is not enough data uploaded to perform a statistically sound GWAS yet, this will be possible in the future, as user numbers continue to rise. -With a move towards making one's own genetic and medical information public, it becomes clear that the issue of privacy needs to get a closer look. -Our survey has shown that people are concerned about their privacy and fear that stakeholders like employers, insurance companies, governments -or advertisers might misuse the information. Policy makers start to react to those changes by introducing laws like the -\textit{Genetic Information Non-Discrimination Act} in the United States or the \emph{Gendiagnostikgesetz} in Germany to minimize the impact of -widely available genetic information. DTC genetic testing companies themselves also try to educate their customers about the risks of releasing genetic data. - -We openly address the problem of privacy implications that come with releasing genetic data twice, during registration for openSNP and during -the upload of the DTC genetic testing results. Users have to confirm that they have read and understood the disclaimer about possible side-effects -that can arise by publishing their data. To further improve this process we are working on implementing informed consent-processes, -like the one by \textit{Consent for Research} (http://www.weconsent.us). % You may title this section "Methods" or "Models". % "Models" is not a valid title for PLoS ONE authors. However, PLoS ONE @@ -328,12 +313,12 @@ \subsection*{Survey on Sharing Genetic Information} blogs which focus on genetics and DTC genetic testing and social media like Twitter, Google Plus and Facebook. The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their -(planned) customership of a DTC company. If the participants already were customers, they were also asked if they were already sharing their genetic and phenotypic data. +(planned) customer-ship of a DTC company. If the participants already were customers, they were also asked if they were already sharing their genetic and phenotypic data. All participants were asked if they would share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, but only if they did not share my medical information with anybody else" and No". The survey also asked some scaled questions which measured how strongly participants agreed/disagreed with different reasons for sharing or not sharing their -information with third parties. The scale went from 1 = strongly disagree over 3 = neutral to 5 = strongly agree. Motivations queried for sharing data +information. The scale went from 1 = strongly disagree to 5 = strongly agree. Motivations queried for sharing data were "because I want to help scientists with their research", "because of possible personal benefits (e.g. getting treatments for a disease I have, possibility of new medication, etc.)", "because it may deliver advertising that is relevant to me" and "out of curiosity". Motivations queried for not sharing data were "because advertisers could use the information for targeted campaigns", "because of possible negative consequences for closely related persons", @@ -363,7 +348,7 @@ \subsection*{Technical implementation of the platform} based on the user's query. The second method is \textit{sources}, which advertises all DAS-sources for all genotypes present in openSNP. A flowchart of all services incorporated in openSNP and of all the ways users can upload or access the data is given in figure \ref{Figure4_label}. The source code of openSNP is -published under Creative Commons BY-SA 3.0 and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. +published under the MIT-license and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. % Do NOT remove this, even if you are not including acknowledgments \section*{Acknowledgments} From 9f60df7b1160188855423b8c9853316d61aa4810 Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Tue, 17 Jul 2012 16:13:22 +1000 Subject: [PATCH 24/88] I'm trapped in GitHub, help, I have to live off too-long sentences --- paper_draft.tex | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index e78f684..db982b9 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -141,8 +141,8 @@ \section*{Introduction} alone had over 100,000 customers \cite{23andMe2011} - the company realizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about traits and diseases. With the consent of the customer the data is used for association studies. 23andMe has published several -articles in which it replicates known findings, but also finds new associations for Parkinson's Disease \cite{Eriksson2010, Do2011}. -So far, over 30,000 23andme-customers have participated in those association studies, which proves that this data-source has potential for other researchers. +articles in which known findings are replicated together with new associations disorders like Parkinson's Disease \cite{Eriksson2010, Do2011}. +So far, over 30,000 23andme-customers have participated in 23andme's association studies, which proves that this data-source has a lot of potential for other researchers. The generation of biomedical data by private companies raises concerns about privacy \cite{23andMe2012}, liability and consent \cite{Caulfield2011}. @@ -269,9 +269,9 @@ \subsection*{Privacy, health implications and ethical considerations} \textit{Genetic Information Non-Discrimination Act} in the United States or the \emph{Gendiagnostikgesetz} in Germany to minimize the impact of widely available genetic information. DTC genetic testing companies themselves also try to educate their customers about the risks of releasing genetic data. -OpenSNP openly addresses the problem of privacy implications that come with releasing genetic data twice, during registration for openSNP and during +OpenSNP openly addresses the problem of privacy implications that come with releasing genetic data twice, once during registration for openSNP and once during the upload of the DTC genetic testing results. Users have to confirm that they have read and understood the disclaimer about possible side-effects -that can arise by publishing their data. To further improve this process we are working on implementing an informed consent-processes, +of publishing their data. To further improve this process we are working on implementing an informed consent-processes similar to \textit{Consent for Research} (http://www.weconsent.us). \subsection*{GWAS and open data} @@ -299,7 +299,7 @@ \subsection*{GWAS and open data} Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP: only a subset of people buy DTC genetic testing, from which an even smaller subset is willing to publish the results, which can potentially lead to skewed GWAS-results. With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic -data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to use in GWAS. +data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to freely use in their studies. Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. While there is not enough data uploaded to perform a statistically sound GWAS yet, this will be possible in the future, as user numbers continue to rise. @@ -351,7 +351,7 @@ \subsection*{Technical implementation of the platform} published under the MIT-license and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. % Do NOT remove this, even if you are not including acknowledgments \section*{Acknowledgments} - +We thank Dr. Manuel Corpas for constructive advice in grammar, spelling and structure of this study. %\section*{References} % The bibtex filename From f536d3ca11d42d9b0f4829ab6d26c03cc46c4690 Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Tue, 17 Jul 2012 16:25:16 +1000 Subject: [PATCH 25/88] Minimal mistake fixed, stopping for today --- paper_draft.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper_draft.tex b/paper_draft.tex index db982b9..924bd06 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -285,7 +285,7 @@ \subsection*{GWAS and open data} By crowdsourcing the acquisition of genetic and phenotypic data, openSNP faces the same problems as any other open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation, -with users entering data in different measurement systems. Another problem with user-entered data is the frequent switching between categorical and continuous phenotypes of users - for example, some users entered the specific value of their height, while other users entered their height according to a category like "150cm to 160cm". +with users entering data in different measurement systems. Another problem with user-entered data is the frequent switching between categorical and continuous phenotypes - for example, some users entered the specific value of their height, while other users entered their height according to a category like "150cm to 160cm". While we try to suggest similar entries to the users, there are some cases where users will not follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. There are two possible solutions to this problem: The first one would be to only allow a trusted subset of users to enter new phenotypes. The other one From 38091db50f05e48b76c1833ee643317c9a82259b Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Wed, 18 Jul 2012 10:41:53 +1000 Subject: [PATCH 26/88] Introduced tables for survey-results. Made transition survey/openSNP easier. --- paper_draft.tex | 69 +++++++++++++++++++++++++++++++++++++------------ 1 file changed, 53 insertions(+), 16 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 924bd06..123dd06 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -171,25 +171,62 @@ \subsection*{Survey on Sharing Genetic Information} didn't share the data with third parties. 6.6 \% of the participants would not share their data. Participants self-identified as XX-chromosomal are slightly more likely to answer that DTC companies are allowed to use their results (Cramer's V = 0.221). Those who are customers of a DTC company or are planning on becoming one in the future are more likely to share their results, compared to those who do not plan on getting themselves genotyped (Somers-d = 0.331). -There are substantial differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped -and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with the following -motivations for sharing genotypic information: because they want to help scientists (mean difference = 0.465, SE = 0.128), -because they think of personal benefits (mean difference = 0.448, SE = 0.183) and because they are curious (mean difference = 1.159, SE = 0.193). -On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations -for not sharing their data: because they fear discrimination (mean difference = 1.060, SE = 0.195), because they feel -it is a breach of their privacy (mean difference = 0.821, SE = 0.225), because they fear negative consequences for their -family members (mean difference = 0.733, SE = 0.21) or because they fear personalized advertising (mean difference = 0.848, SE = 0.208). +There are substantial differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped +and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with motivations for sharing genotypic information. On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations +for not sharing their data. For easier comparison both of these groups are compared in table \ref{tab:motivations1}. + +\begin{table} +\begin{tabular}{|l|l|l|} +\hline +& Turkey's HSD & \\ \cline{2-3} +& Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who are already genotyped} & & \\ \hline +... curious & 1.159 & 0.193 \\ +... want to help scientists & 0.465 & 0.128 \\ +... for personal benefits & 0.448 & 0.183 \\ \hline +\textbf{Motivation for not sharing in participants} & & \\ +\textbf{who are not planning to get genotyped} & & \\ \hline +... fear of discrimination & 1.06 & 0.195 \\ +... breach of privacy & 0.821 & 0.225 \\ +... fear of personalized advertising & 0.848 & 0.208 \\ +... negative consequences for family members & 0.733 & 0.21 \\ \hline +\end{tabular} +\caption{Differences in terms of motivation to share genotypings with the public in survey-participants who already received a genotyping compared to participants who are not planning to getting genotyped. } +\label{tab:motivations1} +\end{table} + +\begin{table} +\begin{tabular}{|l|l|l|} +\hline +& Turkey's HSD & \\ \cline{2-3} +& Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who would share with their DTC provider} & & \\ \hline +... curiosity & 1.99 & 0.321 \\ +... want to help science & 1.57 & 0.199 \\ +... for personal benefits & 0.951 & 0.308 \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who would not share with their DTC provider} & & \\ \hline +... fear of discrimination & 1.52 & 0.322 \\ +... fear of consequences for family members & 1.146 & 0.32 \\ +... fear of personalized advertising & 1.112 & 0.357 \\ \hline +\end{tabular} +\caption{Differences in terms of motivations to share genotyping-data, comparison between participants who would share their genotyping data with their DTC provider with participants who would not share their data with their DTC provider.} +\label{tab:motivations2} +\end{table} Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with -the following motivations for sharing than those who would not share their data with their DTC company: because they want to help -scientists (mean difference = 1.57, SE = 0.199), because they think of personal benefits (mean difference = 0.951, SE = 0.308), -and because they are curious (mean difference = 1.99, SE = 0.321). In the case of curiosity as a motive, there is also a substantial difference between those who would share their data with their DTC company under the condition that it did not share the information and those who would not (mean difference = 1.116 SE = 0.344) as well as those who would share under any circumstances (mean difference = -0.874 SE = 0.182). - -Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the following motivations -for not sharing their data when compared to those who would share their data with their DTC company under any circumstances: because they -fear discrimination (mean difference = 1.52, SE = 0.322), because they feel it is a breach of their privacy (mean difference = 1.871, SE = 0.324), -because they fear consequences for their family members (mean difference = 1.146, SE = 0.32) and because they fear personalized advertising (mean difference = 1.112, SE = 0.357). In the cases of fear of discrimination and fear of a breach of privacy, substantial differences between all three categories exist. Those who would share their data with their DTC company as long as it did not share the information agree less strongly than those who would not share the data with both fear of discrimination as a motive for not sharing (mean difference = -0.615, SE = 0.345) as well as fear of a breach of privacy (mean difference = -0.668, SE = 0.346). Those who would share their data under any circumstances are even less likely to agree with these motives than those who would share only if their DTC company did not share the information (fear of discrimination: mean value = -0.906, SE = 0.182; breach of privacy: mean difference = -1.203, SE = 0.183). +the following motivations for sharing than those who would not share their data with their DTC company. +Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the some motivations +for not sharing their data when compared to those who would share their data with their DTC company under any circumstances, for an overview of the motivations of both groups, see table \ref{tab:motivations2}. + +In the case of curiosity as a motive, there is also a substantial difference between those who would share their data with their DTC company under the condition that it did not share the information and those who would not (mean difference = 1.116 SE = 0.344) as well as those who would share under any circumstances (mean difference = -0.874 SE = 0.182). + +In the cases of fear of discrimination and fear of a breach of privacy, substantial differences between all three categories exist. Those who would share their data with their DTC company as long as it did not share the information agree less strongly than those who would not share the data with both fear of discrimination as a motive for not sharing (mean difference = -0.615, SE = 0.345) as well as fear of a breach of privacy (mean difference = -0.668, SE = 0.346). Those who would share their data under any circumstances are even less likely to agree with these motives than those who would share only if their DTC company did not share the information (fear of discrimination: mean value = -0.906, SE = 0.182; breach of privacy: mean difference = -1.203, SE = 0.183). + +These survey-results indicate that there is a definite interest in customers of DTC-companies to share their results with other scientists. We've created the web-platform openSNP to help DTC-customers share their genotyping results with scientists, for free and under the open Creative Commons Zero-license. \subsection*{openSNP} From ddad6484d06373bfa44460605b2ebd77da41c9fe Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Wed, 22 Aug 2012 13:36:56 +0200 Subject: [PATCH 27/88] made supplementary --- supplementary_methods.tex | 201 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 201 insertions(+) create mode 100644 supplementary_methods.tex diff --git a/supplementary_methods.tex b/supplementary_methods.tex new file mode 100644 index 0000000..3de3e0f --- /dev/null +++ b/supplementary_methods.tex @@ -0,0 +1,201 @@ +% Template for PLoS +% Version 1.0 January 2009 +% +% To compile to pdf, run: +% latex plos.template +% bibtex plos.template +% latex plos.template +% latex plos.template +% dvipdf plos.template + +\documentclass[10pt]{article} + +% amsmath package, useful for mathematical formulas +\usepackage{amsmath} +% amssymb package, useful for mathematical symbols +\usepackage{amssymb} + +% graphicx package, useful for including eps and pdf graphics +% include graphics with the command \includegraphics +\usepackage{graphicx} + +% cite package, to clean up citations in the main text. Do not remove. +\usepackage{cite} + +\usepackage{color} + +% Use doublespacing - comment out for single spacing +%\usepackage{setspace} +%\doublespacing + +%FZ: Enable the comment command +\usepackage{verbatim} +% Text layout +\topmargin 0.0cm +\oddsidemargin 0.5cm +\evensidemargin 0.5cm +\textwidth 16cm +\textheight 21cm + +% Bold the 'Figure #' in the caption and separate it with a period +% Captions will be left justified +\usepackage[labelfont=bf,labelsep=period,justification=raggedright]{caption} + +% Use the PLoS provided bibtex style +\bibliographystyle{plos2009} + +% Remove brackets from numbering in List of References +\makeatletter +\renewcommand{\@biblabel}[1]{\quad#1.} +\makeatother + + +% Leave date blank +\date{} + +\pagestyle{myheadings} +%% ** EDIT HERE ** + + +%% ** EDIT HERE ** +%% PLEASE INCLUDE ALL MACROS BELOW +\long\def\authornote#1{% + \leavevmode\unskip\raisebox{-3.5pt}{\rlap{$\scriptstyle\diamond$}}% + \marginpar{\raggedright\hbadness=10000 + \def\baselinestretch{0.8}\tiny + \it #1\par}} +\newcommand{\bastian}[1]{\authornote{BG: #1}} +\newcommand{\fabian}[1]{\authornote{FZ: #1}} +\newcommand{\philipp}[1]{\authornote{PB: #1}} +%% END MACROS SECTION +\begin{document} + +% Title must be 150 characters or less +\begin{flushleft} +{\Large +\textbf{openSNP - Crowdsourcing Genome-Wide Association Studies - Supplementary Methods} +} +% Alternative titles: +% openSNP - a new, open data-source for personalised medicine +% What kind of person would share genotyping-data? Presenting a survey and an open data-source for personalised medicine +% +% Insert Author names, affiliations and corresponding author email. +\\ +Bastian Greshake$^{1,\ast}$, +Philipp Bayer$^{2}$, +Fabian Zimmer$^{3}$, +Julia Reda$^{4}$ +\\ +\bf{1} Goethe University, Frankfurt am Main, Germany +\\ +\bf{2} University of Queensland, Brisbane, Australia +\\ +\bf{3} Westf\"alische Wilhelms Universit\"at, M\"unster, Germany +\\ +\bf{4} Johannes-Gutenberg University, Mainz, Germany +\\ +$\ast$ E-mail: info@opensnp.org +\end{flushleft} + +\section*{Introduction} +To evaluate whether customers of Direct-To-Consumer (DTC) genetic testing would be willing to share their raw data, along with phenotypic annotations, freely on the web a survey on the topic was set up and spread among customers and potential customers of DTC testing. + +\section*{Materials and Methods} +\subsection*{Survey on Sharing Genetic Information} +The survey was performed using \textit{Google Docs} and was distributed to possible participants through the \textit{23andMe }community forums, the \textit{DIYBiology} mailing list, +blogs which focus on genetics and DTC genetic testing and social media like \textit{Twitter}, \textit{Google+} and \textit{Facebook}. + +The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their +(planned) customer-ship with a DTC company. If the participants already were customers, they were also asked if they were already sharing their genetic and phenotypic data. +All participants were asked if they would share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, +but only if they did not share my medical information with anybody else" and No". + +The survey also asked some scaled questions which measured how strongly participants agreed/disagreed with different reasons for sharing or not sharing their +information. The scale went from 1 = strongly disagree to 5 = strongly agree. Motivations queried for sharing data +were "because I want to help scientists with their research", "because of possible personal benefits (e.g. getting treatments for a disease I have, +possibility of new medication, etc.)", "because it may deliver advertising that is relevant to me" and "out of curiosity". Motivations queried for not sharing +data were "because advertisers could use the information for targeted campaigns", "because of possible negative consequences for closely related persons", +"because of the breach of my privacy" and "because of the fear of discrimination (e.g. by the employer, the state, some insurance company)". +Additionally, participants had the possibility of giving their own reasons for sharing or not sharing their data. + +The survey data was analyzed with SPSS 19. + +% Results and Discussion can be combined. +\section*{Results} + +\subsection*{Survey on Sharing Genetic Information} +In total 229 people, 180 with a self-reported chromosomal sex of XY and 56 with a self-reported chromosomal sex of XX participated in our survey on sharing genetic information with the public. +The mean age of the participants is 33 (SD = 11,29). 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already +customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% do not plan on +becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Cramer's V = 0.077). + +67.7 \% of all participants would share their data with their DTC-company without any constraints, 25.8 \% would do so given the company +didn't share the data with third parties. 6.6 \% of the participants would not share their data. Participants self-identified as XX-chromosomal are slightly more likely to answer that DTC companies are allowed to use their results (Cramer's V = 0.221). Those who are customers of a DTC company or are planning on becoming one in +the future are more likely to share their results, compared to those who do not plan on getting themselves genotyped (Somers-d = 0.331). + + +There are substantial differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped +and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with motivations for sharing genotypic information. On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations +for not sharing their data, see table \ref{tab:motivations1}. + +\begin{table} +\begin{tabular}{|l|l|l|} +\hline +& Turkey's HSD & \\ \cline{2-3} +& Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who are already genotyped} & & \\ \hline +... curious & 1.159 & 0.193 \\ +... want to help scientists & 0.465 & 0.128 \\ +... for personal benefits & 0.448 & 0.183 \\ \hline +\textbf{Motivation for not sharing in participants} & & \\ +\textbf{who are not planning to get genotyped} & & \\ \hline +... fear of discrimination & 1.06 & 0.195 \\ +... breach of privacy & 0.821 & 0.225 \\ +... fear of personalized advertising & 0.848 & 0.208 \\ +... negative consequences for family members & 0.733 & 0.21 \\ \hline +\end{tabular} +\caption{Differences in terms of motivation to share genotypings with the public in survey-participants who already received a genotyping compared to participants who are not planning to getting genotyped. } +\label{tab:motivations1} +\end{table} + +\begin{table} +\begin{tabular}{|l|l|l|} +\hline +& Turkey's HSD & \\ \cline{2-3} +& Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who would share with their DTC provider} & & \\ \hline +... curiosity & 1.99 & 0.321 \\ +... want to help science & 1.57 & 0.199 \\ +... for personal benefits & 0.951 & 0.308 \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who would not share with their DTC provider} & & \\ \hline +... fear of discrimination & 1.52 & 0.322 \\ +... fear of consequences for family members & 1.146 & 0.32 \\ +... fear of personalized advertising & 1.112 & 0.357 \\ \hline +\end{tabular} +\caption{Differences in terms of motivations to share genotyping-data, comparison between participants who would share their genotyping data with their DTC provider with participants who would not share their data with their DTC provider.} +\label{tab:motivations2} +\end{table} + +Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with +the following motivations for sharing than those who would not share their data with their DTC company. +Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the some motivations +for not sharing their data when compared to those who would share their data with their DTC company under any circumstances, for an overview of the motivations of both groups, see table \ref{tab:motivations2}. + +In the case of curiosity as a motive, there is also a substantial difference between those who would share their data with their DTC company under the condition that it did not share the information and those who would not (mean difference = 1.116 SE = 0.344) as well as those who would share under any circumstances (mean difference = -0.874 SE = 0.182). + +In the cases of fear of discrimination and fear of a breach of privacy, substantial differences between all three categories exist. Those who would share their data with their DTC company as long as it did not share the information agree less strongly than those who would not share the data with both fear of discrimination as a motive for not sharing (mean difference = -0.615, SE = 0.345) as well as fear of a breach of privacy (mean difference = -0.668, SE = 0.346). Those who would share their data under any circumstances are even less likely to agree with these motives than those who would share only if their DTC company did not share the information (fear of discrimination: mean value = -0.906, SE = 0.182; breach of privacy: mean difference = -1.203, SE = 0.183). + +These survey-results indicate that there is a definite interest in customers of DTC-companies to share their results with other scientists. + +\section*{Discussion} +As the survey was taken online by voluntary participants and was mainly spread in the personal genetics community, +and through social media the results do not necessarily reflect the general population. +Instead it's expected that it over-represents those people most likely to be interested in a project such as openSNP: +Customers of DTC genetic testing companies and people with a high interest in biology. + +Due to those limitations it's not possible to get estimates of how many potential users could be acquired for a platform like openSNP. + +\end{document} From 5d78e7395fa90132f2e7b51725e7d8269d493196 Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Wed, 22 Aug 2012 13:37:43 +0200 Subject: [PATCH 28/88] restructure of paper itself --- paper_draft.tex | 179 ++++++++++++------------------------------------ 1 file changed, 45 insertions(+), 134 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index 123dd06..ddbf23a 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -111,8 +111,8 @@ \section*{Author Summary} \section*{Introduction} The availability of new DNA sequencing techniques has shifted the focus of biological data acquisition towards new biomedical applications. -Many diseases – for example Alzheimers, Parkinsons or different types of cancers – are at least partially heritable so the genome -of patients can be used for diagnostic purposes. Using the genetic information of patients is made possible through the sharp decrease in costs for analysing genetic information. +Many diseases – for example Alzheimers, Parkinsons or different types of cancers (needs citations) – are at least partially heritable so the genome +of patients can be used for diagnostic purposes. Using the genetic information of patients for diagnostics is made possible through the sharp decrease in costs for analysing genetic information \cite{Brown1999}. %Kicked out the following due to Corpas' argument %The comparison of DNA sequences from individuals in a population can reveal variable sites, which are of major interest in investigating diseases. @@ -124,18 +124,18 @@ \section*{Introduction} %In comparison to whole genome sequencing only sites which are known to be variable are analysed, making microarrays cheaper and faster \cite{Brown1999}. -If the genotypes for more than one individual are known, the analysis of -allele frequencies can be used to associate SNPs with diseases and other inheritable traits. Genome-Wide Assocation Studies -(GWAS) make use of statistics to compare the alleles of patients to the alleles of healthy controls. This -enables GWAS to find alleles which are significantly overrepresented in patients and associate them with a disease. +If genetic information for more than one individual is known, the analysis of +allele frequencies of Single Nucleotide Polymorphisms (SNPs) can be used to associate such SNPs with diseases and other inheritable traits. Genome-Wide Assocation Studies +(GWAS) make use of statistics to compare the allele frequencies in patients to the alleles in healthy controls. This +enables GWAS to find SNPs which are significantly overrepresented in patients and associates those SNPs with a trait or disease. This method does not allow inference of causal differences but merely identifies correlations. The first GWAS was published in 2005 and compared age-related macular degeneration in contrast to a healthy control group \cite{Klein2005}. Since the beginning, the number of participants in such studies has been rising. To date, over 1200 GWAS have been performed \cite{Johnson2009} and over -5000 SNPs have been linked to different diseases and traits \cite{Hindorff2009}. -%(http://www.genome.gov/page.cfm?pageid=26525384&clearquery=1#result_table) +5000 SNPs have been linked to different diseases and traits \cite{Hindorff2009}. -GWAS are not limited to the scientific community. Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. +GWAS are not only performed inside the traditional scientific community. +Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. These companies use DNA microarrays to screen for around 0.5 to 1 million SNPs spread over the human genome. In return, customers receive an analysis of the results, as well as a file that includes the customer's raw individual genotypes. In 2011, 23andMe alone had over 100,000 customers \cite{23andMe2011} @@ -148,106 +148,39 @@ \section*{Introduction} liability and consent \cite{Caulfield2011}. Nevertheless, in some instances individual customers are willingly sharing their data. Most do so by uploading their data to their personal website or to open software repositories like \textit{GitHub}. -This data is scattered and unorganized, making it hard to use. While projects like SNPedia try to keep track of all +This data is scattered and unorganized, making it hard to use in studies. While projects like SNPedia try to keep track of all the publicly available genotyping files \cite{Cariaso2011}, they usually do not provide the information necessary to perform GWAS, as the phenotypic information is often not attached to the genetic information. Projects that attach the phenotype to the genetic information, -like the \textit{Personal Genome Project}, still do not allow for an easy re-use of the data, as they lack an advanced programming interface (API) +like the \textit{Personal Genome Project}, still do not allow for an easy re-use of the data, as they lack an application programming interface (API) or other methods by which researchers could download the data. Additionally, not every customer of DTC genetic testing can participate in the \textit{Personal Genome Project}. -Here, we present the results of a survey designed to evaluate the support in the personal genetics community for a crowdsourced online platform. -We also present openSNP, an online platform which enables DTC customers to share genotypic and phenoytypic information, as well as receive additional information on their genotypes. -The genotypes are made available to researchers via the open Creative-Commons-Zero licence. +As a follow up of a survey, which evaluated whether customers of DTC genetic testing would be willing to share their genotypic and phenotypic information (see Supplementary Materials), we designed openSNP, an online platform which enables DTC customers to freely share their genotypic and phenotypic data under a Creative Commons-Zero license. +The phenotypic and genotypic data can be retrieved from openSNP using different methods, which also include different API standards to allow automated processing of the data. +Additionally openSNP also enables users of DTC genetic testing to receive further information on their genotypes, through the crowdsourced resource SNPedia and primary publications, like those of the Public Library of Science journals. % Results and Discussion can be combined. \section*{Results} -\subsection*{Survey on Sharing Genetic Information} -In total 229 people, 180 with a self-reported chromosomal sex of XY and 56 with a self-reported chromosomal sex of XX participated in our survey on sharing genetic information with the public. -The mean age of the participants is 33 (SD = 11,29). 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already -customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% do not plan on -becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Cramer's V = 0.077). - -67.7 \% of all participants would share their data with their DTC-company without any constraints, 25.8 \% would do so given the company -didn't share the data with third parties. 6.6 \% of the participants would not share their data. Participants self-identified as XX-chromosomal are slightly more likely to answer that DTC companies are allowed to use their results (Cramer's V = 0.221). Those who are customers of a DTC company or are planning on becoming one in -the future are more likely to share their results, compared to those who do not plan on getting themselves genotyped (Somers-d = 0.331). - - -There are substantial differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped -and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with motivations for sharing genotypic information. On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations -for not sharing their data. For easier comparison both of these groups are compared in table \ref{tab:motivations1}. - -\begin{table} -\begin{tabular}{|l|l|l|} -\hline -& Turkey's HSD & \\ \cline{2-3} -& Mean difference & SE \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who are already genotyped} & & \\ \hline -... curious & 1.159 & 0.193 \\ -... want to help scientists & 0.465 & 0.128 \\ -... for personal benefits & 0.448 & 0.183 \\ \hline -\textbf{Motivation for not sharing in participants} & & \\ -\textbf{who are not planning to get genotyped} & & \\ \hline -... fear of discrimination & 1.06 & 0.195 \\ -... breach of privacy & 0.821 & 0.225 \\ -... fear of personalized advertising & 0.848 & 0.208 \\ -... negative consequences for family members & 0.733 & 0.21 \\ \hline -\end{tabular} -\caption{Differences in terms of motivation to share genotypings with the public in survey-participants who already received a genotyping compared to participants who are not planning to getting genotyped. } -\label{tab:motivations1} -\end{table} - -\begin{table} -\begin{tabular}{|l|l|l|} -\hline -& Turkey's HSD & \\ \cline{2-3} -& Mean difference & SE \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who would share with their DTC provider} & & \\ \hline -... curiosity & 1.99 & 0.321 \\ -... want to help science & 1.57 & 0.199 \\ -... for personal benefits & 0.951 & 0.308 \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who would not share with their DTC provider} & & \\ \hline -... fear of discrimination & 1.52 & 0.322 \\ -... fear of consequences for family members & 1.146 & 0.32 \\ -... fear of personalized advertising & 1.112 & 0.357 \\ \hline -\end{tabular} -\caption{Differences in terms of motivations to share genotyping-data, comparison between participants who would share their genotyping data with their DTC provider with participants who would not share their data with their DTC provider.} -\label{tab:motivations2} -\end{table} - -Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with -the following motivations for sharing than those who would not share their data with their DTC company. -Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the some motivations -for not sharing their data when compared to those who would share their data with their DTC company under any circumstances, for an overview of the motivations of both groups, see table \ref{tab:motivations2}. - -In the case of curiosity as a motive, there is also a substantial difference between those who would share their data with their DTC company under the condition that it did not share the information and those who would not (mean difference = 1.116 SE = 0.344) as well as those who would share under any circumstances (mean difference = -0.874 SE = 0.182). - -In the cases of fear of discrimination and fear of a breach of privacy, substantial differences between all three categories exist. Those who would share their data with their DTC company as long as it did not share the information agree less strongly than those who would not share the data with both fear of discrimination as a motive for not sharing (mean difference = -0.615, SE = 0.345) as well as fear of a breach of privacy (mean difference = -0.668, SE = 0.346). Those who would share their data under any circumstances are even less likely to agree with these motives than those who would share only if their DTC company did not share the information (fear of discrimination: mean value = -0.906, SE = 0.182; breach of privacy: mean difference = -1.203, SE = 0.183). - -These survey-results indicate that there is a definite interest in customers of DTC-companies to share their results with other scientists. We've created the web-platform openSNP to help DTC-customers share their genotyping results with scientists, for free and under the open Creative Commons Zero-license. - -\subsection*{openSNP} - -The openSNP project (http://opensnp.org) is an open, crowdsourced online platform for both DTC customers and researchers interested in performing GWAS. -DTC customers are encouraged to share their genotyping results and connect them with phenotypes to enable access for researchers. -Users can create a personal profile, discuss SNPs and phenotypes on the -platform using a simple commenting system, or send each other private messages. - - -\subsubsection*{Sharing genotypic information} -Currently users can upload their genotypings from the companies 23andme, deCODEme and FamilyTreeDNA via a webinterface to the openSNP -project. There is experimental support for openSNP-exomes. The uploaded data is published under the Creative Commons Zero-license, -which - in accordance with the Panton Principles (doi:10.1371/journal.pbio.1001195) - +We created the openSNP project (http://opensnp.org) as an open, crowdsourced online platform for DTC customers interested in sharing their raw data and for researchers interested in performing GWAS or perform other types of analysis with the data. +Customers of DTC testing are encouraged to share their genotyping results along with their phenotypic traits to enable easy access for researchers. +Users of openSNP can create a personal profile, discuss SNPs and phenotypes on the platform using a simple commenting system, or send each other private messages. + +People interested in using the data of openSNP can download complete dumps of the genotypic and phenotypic information or use query API endpoints utilizing JavaScript Object Notation (JSON) objects or the Distributed Annotation System (DAS) \cite{Dowell2001}. + + +\subsection*{Sharing genotypic information} +Currently users can upload their genotyping results from the companies \textit{23andMe}, \textit{deCODEme }and \textit{FamilyTreeDNA} via a webinterface to the openSNP +project. There is experimental support for uploading exomes in the VCF format \cite{Danecek01082011}, as \textit{23andMe} recently started exome sequencing for its customers. So far only the SNPs of the exome data sets are visualized on openSNP, but the downloads include all variation found in the exome. +The uploaded data is published under the Creative Commons Zero-license, +which - in accordance with the Panton Principles \cite{10.1371/journal.pbio.1001195} - allows a complete reuse of the data without any constraints. Between the start of openSNP on 09/27/2011 and 12/18/2012, 214 people have signed up with openSNP, and 79 genotyping files were made available. The openSNP -database lists 69,486,471 SNPs of which 1,938,603 are unique SNPs. -Figure \ref{Figure1_label} depicts the increase of users and genotyping files over time. +database lists 69,486,471 genotypes which are distributed over 1,938,603 unique SNPs. +Figure \ref{Figure1_label} depicts the increase of users and genotyping files over time.\bastian{update all numbers} -\subsubsection*{Crowdsourcing phenotypes} +\subsection*{Crowdsourcing phenotypes} Users are able to create new phenotypes that are not yet listed by openSNP. The specification of these phenotypes is open and not limited @@ -267,31 +200,27 @@ \subsubsection*{Crowdsourcing phenotypes} entered their data per phenotype can be seen in figure \ref{Figure2_label}. The phenotype provided by the most users is the eye color, which has been entered by 54 users. There are two phenotypes which have so far only been provided by a single user: -the SAT Writing score and triglyceride-levels. +the SAT Writing score and triglyceride-levels.\bastian{update all numbers and graphs} -\subsubsection*{Connection to external services} +\subsection*{Connection to external services} In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. -A total number of 15,229 documents relevant to the SNP-IDs listed in openSNP could be found in the databases of Mendeley, the Public Library of Science and SNPedia. +A total number of 15,229 documents \bastian{number needs to get updated}relevant to the SNPs listed in openSNP could be found in the publication databases of Mendeley, the Public Library of Science and in the crowdsourced SNPedia. Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge (Figure \ref{Figure3_label}). For usability reasons, -SNPs are ranked by the amount of information gathered by the external services. The external services themselves are ranked by how easily non-scientists can understand information -from these sources. The SNPedia entries are given the highest impact, as those are already manually curated and summarized in plain English, followed by open access publications out of -the Public Library of Science. Lowest values are given to the Mendeley results, as those are not necessarily freely available to every user. +SNPs are ranked by the amount of information gathered through the external services. The external services themselves are ranked by how easily non-scientists can understand information +from these sources and available this information. The SNPedia entries are given the highest impact, as those are already manually curated and summarized in plain English, followed by open access publications out of +the Public Library of Science. Lowest values are given to the Mendeley results, as the publications listed there are for the most part not freely available without subscriptions or one-time payments. An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. -\subsubsection*{Data access} +\subsection*{Data access} OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files -grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypes in a comma-separated table. The genetic data is also +grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypic variation in a comma-separated table. The genetic data is also accessible through the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}, which offers all data for specific chromosomes and specific positions on single chromosomes. -The data is additionally available over a JSON-API, which allows users to directly access data in the JSON-format. +An example of how the DAS can be used can be found on openSNP where users genotypes are visualized inside a genome browser. So far all chromosomal positions are based on the human reference genome NCBI36, as this is the standard reference used by DTC providers right now. \bastian{should this maybe include an image as well?} +The data is additionally available over a JSON-API, which allows users to directly access data in the JSON-format. The methods allow users to programmatically look for the genotypes and annotations at a given SNP as well as for phenotypes for a given user and phenotypic variation for a given phenotype. \section*{Discussion} -\subsection*{Survey issues} -As the survey was taken online by voluntary participants and was mainly spread in the personal genetics community, -the results do not reflect the general population, but over-represent those people most likely to be interested in -a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. - \subsection*{Privacy, health implications and ethical considerations} %The advent of DTC genetic testing has led to new ethical and social issues. @@ -299,7 +228,7 @@ \subsection*{Privacy, health implications and ethical considerations} of delivering medical information without consulting a physician or genetic counsellor to help patients/customers make sense of the information and to put the new knowledge to good use \cite{Hauskeller2011,Hogarth2008,Wasson2009}. -As we have found in the survey on sharing such results, many DTC customers are willing to share their results with the public to help scientific progress, without forgetting about the privacy implications that come with openly sharing genetic information. There is a variety of ethical and privacy implications when it comes to DTC genetic testing\cite{Caulfield2011,Joh2011}. +As we have found in our survey on sharing such results (see supplementary methods), many DTC customers are willing to share their results with the public to help scientific progress, without forgetting about the privacy implications that come with openly sharing genetic information. There is a variety of ethical and privacy implications when it comes to DTC genetic testing\cite{Caulfield2011,Joh2011}. Our survey has shown that people are concerned about their privacy and fear that stakeholders like employers, insurance companies, governments or advertisers might misuse the information. Policy makers start to react to those changes by introducing laws like the @@ -308,10 +237,10 @@ \subsection*{Privacy, health implications and ethical considerations} OpenSNP openly addresses the problem of privacy implications that come with releasing genetic data twice, once during registration for openSNP and once during the upload of the DTC genetic testing results. Users have to confirm that they have read and understood the disclaimer about possible side-effects -of publishing their data. To further improve this process we are working on implementing an informed consent-processes -similar to \textit{Consent for Research} (http://www.weconsent.us). +of publishing their data. To further improve this process we are looking forward to implement an informed consent-processes +by a provider like \textit{Consent for Research} (http://www.weconsent.us). -\subsection*{GWAS and open data} +\subsection*{GWAS and Open Data} Although prices of exome or even full genome sequencing are dropping rapidly, GWAS are still considerably cheaper. However, GWAS can only detect correlations of SNPs with those traits and do not allow inference on the cause for any correlation. Furthermore, for a statistically sound analysis GWAS need a large enough sample size. Nevertheless, GWAS are still frequently used and new associations are found \cite{10.1371.journal.pone.0031470,10.1371.journal.pone.0030309,10.1371.journal.pone.0029848}. @@ -331,38 +260,20 @@ \subsection*{GWAS and open data} In both cases it makes it harder for users to enter their data which raises the bar for participation. -We decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS with the data to perform more quality control. +We decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS with the data to perform additional quality control. Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP: only a subset of people buy DTC genetic testing, from which an even smaller subset is willing to publish the results, which can potentially lead to skewed GWAS-results. With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to freely use in their studies. Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. -While there is not enough data uploaded to perform a statistically sound GWAS yet, this will be possible in the future, as user numbers continue to rise. +While there is not enough data uploaded to perform a statistically sound GWAS yet, this will be possible in the future, as user numbers continue to rise. By including the option of uploading exome data sets the platform already is capable of adjusting for changes in the type of data generated by DTC genetic testing. % You may title this section "Methods" or "Models". % "Models" is not a valid title for PLoS ONE authors. However, PLoS ONE % authors may use "Analysis" \section*{Materials and Methods} -\subsection*{Survey on Sharing Genetic Information} -The survey was performed using Google Docs and was distributed to possible participants through the 23andMe community, the DIYBiology mailing list, -blogs which focus on genetics and DTC genetic testing and social media like Twitter, Google Plus and Facebook. - -The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their -(planned) customer-ship of a DTC company. If the participants already were customers, they were also asked if they were already sharing their genetic and phenotypic data. -All participants were asked if they would share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, -but only if they did not share my medical information with anybody else" and No". - -The survey also asked some scaled questions which measured how strongly participants agreed/disagreed with different reasons for sharing or not sharing their -information. The scale went from 1 = strongly disagree to 5 = strongly agree. Motivations queried for sharing data -were "because I want to help scientists with their research", "because of possible personal benefits (e.g. getting treatments for a disease I have, -possibility of new medication, etc.)", "because it may deliver advertising that is relevant to me" and "out of curiosity". Motivations queried for not sharing -data were "because advertisers could use the information for targeted campaigns", "because of possible negative consequences for closely related persons", -"because of the breach of my privacy" and " because of the fear of discrimination (e.g. by the employer, the state, some insurance company)". -Additionally, participants had the possibility of giving their own reasons for sharing or not sharing their data. - -The survey data was analyzed with SPSS 19. \subsection*{Technical implementation of the platform} The main platform is implemented using the web framework Ruby on Rails 3.0.10. Postgres 9.2 is used as the main database backend for Rails. From a78c861d6db3519183f3588c7db77ec61724fa93 Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Wed, 22 Aug 2012 13:38:08 +0200 Subject: [PATCH 29/88] added new paper --- papers.bib | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/papers.bib b/papers.bib index 1089588..d61e980 100644 --- a/papers.bib +++ b/papers.bib @@ -641,3 +641,32 @@ @ARTICLE{23andMe2012 year = {2012}, howpublished = {\url{http://www.23andme.com/about/privacy/}} } + +@article{Danecek01082011, +author = {Danecek, Petr and Auton, Adam and Abecasis, Goncalo and Albers, Cornelis A. and Banks, Eric and DePristo, Mark A. and Handsaker, Robert E. and Lunter, Gerton and Marth, Gabor T. and Sherry, Stephen T. and McVean, Gilean and Durbin, Richard and 1000 Genomes Project Analysis Group}, +title = {The variant call format and VCFtools}, +volume = {27}, +number = {15}, +pages = {2156-2158}, +year = {2011}, +doi = {10.1093/bioinformatics/btr330}, +abstract ={Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API.Availability: http://vcftools.sourceforge.netContact: rd@sanger.ac.uk}, +URL = {http://bioinformatics.oxfordjournals.org/content/27/15/2156.abstract}, +eprint = {http://bioinformatics.oxfordjournals.org/content/27/15/2156.full.pdf+html}, +journal = {Bioinformatics} +} + +@article{10.1371/journal.pbio.1001195, + author = {Molloy, , Jennifer C.}, + journal = {PLoS Biol}, + publisher = {Public Library of Science}, + title = {The Open Knowledge Foundation: Open Data Means Better Science}, + year = {2011}, + month = {12}, + volume = {9}, + url = {http://dx.doi.org/10.1371%2Fjournal.pbio.1001195}, + pages = {e1001195}, + abstract = {

Open data leads to better science, but overcoming the barriers to widespread publication and availability of open scientific data requires a community effort. The Open Knowledge Foundation Open Data in Science Working Group describes their role in this movement.

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l}ZGgrV;$K!84K%>?k1f=5fUBT@m zw-m?!dfOl-94D1ke}8_1X)NmW*t|{o?JUmcAD90EpI&G7(;ua^?f@r&_4w{o+^Ee= z%un<+>E1j~L!PlqXX8xAy3kN;RQN~@WwbDBm9JIQI~d0_w*6)Co1CRq#p(oip4hoM zSDJcGqwskaG^+L7z2-`ZQ|?GA?yjUDfB$}(S;UEeE^4g+CB((BIaZP_ctxh>t>6cV3-7M~jty|6Qo;ONU?L8PYc-o1c>|-a7W>G=ty<-IO!e0_>U?*K9v#MKQEY zAS7d3zTf3^)pPZ@h-4#Awfj_1)%zs`tCHz;bw(j&4Tn_;|iXLUNMpU_AaHC(a$X4MF^k4m*AvJ z0j`=ZnmEVd?|o~6h!-gf->nw>cQGRgJNB`kZl|8QpgtNvp;|i#Pws(d3skTmo~)`| zJ9+Mum4|1Rdh#o=syTnB`0^0Hb(k(kdZr7h@zPPQowa8kOG*@v`i_wVNY6E_2vhoG z>qZ|PxH4j`%JQNzVpJASSp$Jsy{Ex2nfVV)I&^icB_`az+Mm8nW0FZNiX%b0~SE=}RfYs@* zl{cNuy|=f+&p#W`Z&Q+dP|IRlEBPyKdZersJbc6yBYPsSaAASwrGxjf zXbH6hKP0l~1aZ!tx}$m=0Ose#x_tlCQ|%q0R0Yfkqq zQDY;*34WJ2&!C5D+IhK{^V;eXB5ue&;=$V@tUPN)Dr_zYCn=7wkpyC+b4_9yZQESA z5^!4|;^+iEv9dHCroxXD+5;c*&(HZ9&`B@FF7QHjLVroHo$d2P8rBNi&fa@-A;qe_ zv&Im2>~S;8V$U`;nF;Li&8u*j!kE$({(>c*XNL4TFCEDaC5q^Y9}02r&XxNSsAHP< z+V_)PsGYw=|5!Z@u3A%4@%YWJx#?q$k3?Qd_&)KGWPj&Tq1D;$yg22zp-VsR6=Nwu#Q}bOf zz?eZTO!2lrso-^M31_MNvlb-aUeU%Qk44yJJ$f`&8~r*n@>ob4cJ&P|!0%LXmUQYr z+P?h$hn27Ih7jwtf*#i?lyNn$PRHw^f9j{^SoIpYlG|)Lv*~uJdeNEcV z3Wo(GfI!CydqWR|1Pa3cOBuTiP`>`NSp6Cvhqv1+Stk^%TmN09ZZC4v8SCg|4gB+G z!Md}EtBCubW$JrX>3^1~|8rqKfX3Ov$=x0c5+Sezp%9TVcf=~-0rVmo%G!pi$^b1g z27BfGfRL~<2WxvLyaNb<*sFsFS|2(%I>}x)cL2c&Z4!YtjC&6dC_tsYHOA?>BM44h zGEW$ax3B?8NKoue3p7pyjQ=kzmz1eg`{J-3DSzh^yA>mevj^RJCav*F08vcgzn-ciX zkot{;5X1kD5~2>N`{P9PK*;`y1Gqc#Yw!Gwr{6e$uB!hQ2=P4~A|wYQ7EZXj@+)E? z(BJNX6ZcPr{QImzlx+87*-P8|Vip19(?78M-pKHuj{Jj|gDGFr+zRVRAtH-+2J#t@ zZ#B&w2zeR#GY5#MT4NkRSN9%3ki8fMLH67Ng6_p32)MHZ>Opil@54uzlvz)&7Amz*B(#{sD<15P;h( zLI07#V1!U1{DAEKmH`VVmVv>9o%*L99EwCkiN*plQ9>l|*F%9tfza45Ln2WJ^-~W_>!N8?8B7A@hLW~a#h5^^v_K$@_QE)=&?Xw2~qftPd z63I{p^uS2K;C?;8o+yM!28O^z39l0S?ZFXn;EdQWgMd*7u%O_gM3})46by0D9+I#- z_t}F2w-<@^pb#+hz`Q5~g2*3WC{z?iybd@Dj3UM&3Is8+3}~KBJQfByH3 z!!{9Spy@VI41uA*1|`A=@QfmkH82_uKQI=KME}FQK=kgP7r=)+;3G5wNDce-fH@E_ zqICe92u8eT0DCAxe)`iNFd)kuoEK0#sD~ux6b42^;Y6`PkRgeD4#;5OgL*LHy#VN; z4(I{bJ)y+*peO_}FEA(y4JPIf4ueAu%8*bZ{$L1Tvl8P&5LhMR3fSd@H}!q|ArWX% zqS%F@U~uC60?3dMA|JuffRl*rp&{S{Jfi_M;&q@=2sDvBAbUfJ@c}XfaZCbv5(*=V zaUgbq$lAX)fOs%5e*_ufb?X-gP7dbQIIIJOgak-L$Jzr+$oC@Jcs%f805D2;WmdMc z#Dn&ZMZ$v|=&FQ*G*VOsDJzPW7e!0M5HLA_RcU#s0vL{h$%Cb(B|-nciZN)fCA_(V U)807)z=FYG6kJ^L8VVHu2Stu1cK`qY literal 0 HcmV?d00001 From 69f8220fa9ce27f091e7fd9182eacd64cb68bd2a Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Tue, 28 Aug 2012 15:49:42 +1000 Subject: [PATCH 31/88] Reworked abstract a little --- paper_draft.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper_draft.tex b/paper_draft.tex index 123dd06..2a7b3a4 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -99,7 +99,7 @@ % Please keep the abstract between 250 and 300 words \section*{Abstract} -Genome-wide association studies are used to correlate phenotypic traits with genetic variants like Single Nucleotide Polymorphisms (SNPs). Genome-wide association studies compare the genetic variation between two groups to find correlations between membership in a group and the genetic variation. To find significant correlations it is necessary to have a large enough sample size. The advent of Direct-To-Consumer genetic testing offers the analysis of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing. This data could be a valuable source for association studies. However, this data is not public for various reasons and thus cannot be used by other scientists. It seems reasonable to create a central open data-repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates whether people are willing to publicly share their genetic information. In the light of those results we also present the web-platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publish their genetic data along with phenotypic information into the public domain. Through this "crowdsourced" effort of collecting genetic and phenotypic information we create a valuable resource which can be used in a wide array of studies, included Genome-wide association studies. OpenSNP is hosted at www.opensnp.org, and the code is released under MIT-license at github.com/gedankenstuecke/snpr. +Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants, for example Single Nucleotide Polymorphisms (SNPs). These studies compare the genetic variation between two groups to single out certain SNPs that are linked to a phenotypic variation in one of the groups. However, it is necessary to have a large enough sample size to find statistically significant correlations. The advent of Direct-To-Consumer genetic testing offers the analysis of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing companies. This data could be a valuable source for association studies. However, this data is not public for a variety of reasons and thus cannot be used in research. It seems reasonable to create a central open data-repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates whether people are willing to publicly share their genetic information. In the light of those results we present the web-platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publicly publish at no cost their genetic data along with phenotypic information. Through this "crowdsourced" effort of collecting genetic and phenotypic information openSNP has become a valuable resource which can be used in a wide array of studies, including Genome-wide association studies. OpenSNP is hosted at www.opensnp.org, and the code is released under MIT-license at github.com/gedankenstuecke/snpr % Please keep the Author Summary between 150 and 200 words From 37239852ade9c675000e93b29d49187a1e0a7ff3 Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Tue, 28 Aug 2012 16:03:32 +1000 Subject: [PATCH 32/88] Now the abstract flows better --- paper_draft.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper_draft.tex b/paper_draft.tex index 850f516..99a7fd2 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -99,7 +99,7 @@ % Please keep the abstract between 250 and 300 words \section*{Abstract} -Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants, for example Single Nucleotide Polymorphisms (SNPs). These studies compare the genetic variation between two groups to single out certain SNPs that are linked to a phenotypic variation in one of the groups. However, it is necessary to have a large enough sample size to find statistically significant correlations. The advent of Direct-To-Consumer genetic testing offers the analysis of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing companies. This data could be a valuable source for association studies. However, this data is not public for a variety of reasons and thus cannot be used in research. It seems reasonable to create a central open data-repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates whether people are willing to publicly share their genetic information. In the light of those results we present the web-platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publicly publish at no cost their genetic data along with phenotypic information. Through this "crowdsourced" effort of collecting genetic and phenotypic information openSNP has become a valuable resource which can be used in a wide array of studies, including Genome-wide association studies. OpenSNP is hosted at www.opensnp.org, and the code is released under MIT-license at github.com/gedankenstuecke/snpr +Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants. These studies usually compare the genetic variation between two groups to single out certain Single Nucleotide Polymorphisms (SNPs) that are linked to a phenotypic variation in one of the groups. However, it is necessary to have a large enough sample size to find statistically significant correlations. Direct-To-Consumer (DTC) genetic testing can supply additional data: DTC-companies offer the analysis of a large amount of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing companies. However, this data is not public for a variety of reasons and thus cannot be used in research. It seems reasonable to create a central open data-repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates whether people are willing to publicly share their genetic information. In the light of those results we present the web-platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publicly publish at no cost their genetic data along with phenotypic information. Through this "crowdsourced" effort of collecting genetic and phenotypic information openSNP has become a valuable resource which can be used in a wide area of studies, including Genome-wide association studies. OpenSNP is hosted at www.opensnp.org, and the code is released under MIT-license at github.com/gedankenstuecke/snpr % Please keep the Author Summary between 150 and 200 words From 8523346ff8418e98c57b1517a8d850884c3134ec Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Sun, 2 Sep 2012 17:54:06 +1000 Subject: [PATCH 33/88] Added R-code for plots --- timePlot.R | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 timePlot.R diff --git a/timePlot.R b/timePlot.R new file mode 100644 index 0000000..b0dff4a --- /dev/null +++ b/timePlot.R @@ -0,0 +1,10 @@ +svg("NormedUserPhenoPheno.svg") +par(mar=c(5, 4, 4, 6)) +plot(sort(strptime(pheno[,1], "%d.%m.%Y %H:%M")), pheno[,2],type="l", xlab="Time", ylab="Count Phenotypes",xaxt="n") +axis.POSIXct(1,sort(strptime(pheno[,1], "%d.%m.%Y %H:%M")),format="%b-%y") +par(new=T) +plot(sort(strptime(usersp[,1], "%d.%m.%Y %H:%M")), usersp[,2],type="l",xlab="", yaxt="n",col="red",xaxt="n",ylab="") +axis(4,col.axis="red") +mtext("Count User-Phenotypes",side=4,line=3,col="red") +dev.off() + From 005b44655117fc037c1d71c99c22745823dcf55f Mon Sep 17 00:00:00 2001 From: Philipp Bayer Date: Sun, 2 Sep 2012 18:16:05 +1000 Subject: [PATCH 34/88] Added plots --- Genotypings.svg | 182 ++++++++++++++++++++++ NormedUserPhenoPheno.svg | 325 +++++++++++++++++++++++++++++++++++++++ users.svg | 171 ++++++++++++++++++++ 3 files changed, 678 insertions(+) create mode 100644 Genotypings.svg create mode 100644 NormedUserPhenoPheno.svg create mode 100644 users.svg diff --git a/Genotypings.svg b/Genotypings.svg new file mode 100644 index 0000000..bf4ea83 --- /dev/null +++ b/Genotypings.svg @@ -0,0 +1,182 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/NormedUserPhenoPheno.svg b/NormedUserPhenoPheno.svg new file mode 100644 index 0000000..780d4f1 --- /dev/null +++ b/NormedUserPhenoPheno.svg @@ -0,0 +1,325 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/users.svg b/users.svg new file mode 100644 index 0000000..53d55a4 --- /dev/null +++ b/users.svg @@ -0,0 +1,171 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 0a58ebefdcfb8b9a81d039d809e774b4a1294dd4 Mon Sep 17 00:00:00 2001 From: Helge Rausch Date: Sun, 30 Sep 2012 10:13:04 +0300 Subject: [PATCH 35/88] Adding affiliations for Helge Rausch --- paper_draft.tex | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/paper_draft.tex b/paper_draft.tex index 99a7fd2..5e5d355 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -84,7 +84,8 @@ Bastian Greshake$^{1,\ast}$, Philipp Bayer$^{2}$, Fabian Zimmer$^{3}$, -Julia Reda$^{4}$ +Julia Reda$^{4}$, +Helge Rausch$^{5}$ \\ \bf{1} Goethe University, Frankfurt am Main, Germany \\ @@ -94,6 +95,8 @@ \\ \bf{4} Johannes-Gutenberg University, Mainz, Germany \\ +\bf{5} Hochschule für Technik und Wirtschaft, Berlin, Germany +\\ $\ast$ E-mail: info@opensnp.org \end{flushleft} From 534c057806cc322863aeef9b71b2b96b762d24bb Mon Sep 17 00:00:00 2001 From: drsnuggles Date: Thu, 25 Oct 2012 15:04:46 +1000 Subject: [PATCH 36/88] Replaced old graphs by newest versions, added R-code, input-data to reproduce --- 25_10_2012_Graphs/genotypes.svg | 165 + 25_10_2012_Graphs/genotypes.txt | 269 + 25_10_2012_Graphs/phenotypes.txt | 130 + .../phenotypes_vs_userphenotypes.svg | 362 ++ 25_10_2012_Graphs/plotGenotypes.r | 8 + .../plotPhenotypesAndUserPhenotypes.R | 10 + 25_10_2012_Graphs/plotUsers.r | 8 + 25_10_2012_Graphs/user_phenotypes.txt | 4722 +++++++++++++++++ 25_10_2012_Graphs/users.svg | 283 + 25_10_2012_Graphs/users.txt | 627 +++ Genotypings.svg | 182 - NormedUserPhenoPheno.svg | 325 -- timePlot.R | 10 - users.svg | 171 - 14 files changed, 6584 insertions(+), 688 deletions(-) create mode 100644 25_10_2012_Graphs/genotypes.svg create mode 100644 25_10_2012_Graphs/genotypes.txt create mode 100644 25_10_2012_Graphs/phenotypes.txt create mode 100644 25_10_2012_Graphs/phenotypes_vs_userphenotypes.svg create mode 100644 25_10_2012_Graphs/plotGenotypes.r create mode 100644 25_10_2012_Graphs/plotPhenotypesAndUserPhenotypes.R create mode 100644 25_10_2012_Graphs/plotUsers.r create mode 100644 25_10_2012_Graphs/user_phenotypes.txt create mode 100644 25_10_2012_Graphs/users.svg create mode 100644 25_10_2012_Graphs/users.txt delete mode 100644 Genotypings.svg delete mode 100644 NormedUserPhenoPheno.svg delete mode 100644 timePlot.R delete mode 100644 users.svg diff --git a/25_10_2012_Graphs/genotypes.svg b/25_10_2012_Graphs/genotypes.svg new file mode 100644 index 0000000..b1fc6a2 --- /dev/null +++ b/25_10_2012_Graphs/genotypes.svg @@ -0,0 +1,165 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+126 17.10.2012 16:09 +127 19.10.2012 05:46 +128 19.10.2012 05:47 +129 19.10.2012 05:48 +130 21.10.2012 12:29 diff --git a/25_10_2012_Graphs/phenotypes_vs_userphenotypes.svg b/25_10_2012_Graphs/phenotypes_vs_userphenotypes.svg new file mode 100644 index 0000000..16d0c32 --- /dev/null +++ b/25_10_2012_Graphs/phenotypes_vs_userphenotypes.svg @@ -0,0 +1,362 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/25_10_2012_Graphs/plotGenotypes.r b/25_10_2012_Graphs/plotGenotypes.r new file mode 100644 index 0000000..4aa608b --- /dev/null +++ b/25_10_2012_Graphs/plotGenotypes.r @@ -0,0 +1,8 @@ +svg("genotypes.svg") +genotypes <- read.table("genotypes.txt", sep="\t") +plot(strptime(genotypes[,2], format="%d.%m.%Y %H:%M"), genotypes[,1], type="l", ylab="Amount of genotypings",xlab="Time", xaxt="n") +dmin <- min(strptime(genotypes[,2], format="%d.%m.%Y %H:%M")) +dminjan <- as.POSIXct(format(dmin, "%Y-1-1")) +seqYear <- seq(dminjan, by = "1 month", to = max(strptime(genotypes[,2], format="%d.%m.%Y %H:%M")) + (86400 * 365)) +axis(1, at = seqYear, labels = format(seqYear, "%b-%y"), las = 1) +dev.off() diff --git a/25_10_2012_Graphs/plotPhenotypesAndUserPhenotypes.R b/25_10_2012_Graphs/plotPhenotypesAndUserPhenotypes.R new file mode 100644 index 0000000..b36d5ad --- /dev/null +++ b/25_10_2012_Graphs/plotPhenotypesAndUserPhenotypes.R @@ -0,0 +1,10 @@ +svg("phenotypes_vs_userphenotypes.svg") +par(mar=c(5, 4, 4, 6)) +plot(sort(strptime(pheno[,2], "%d.%m.%Y %H:%M")), pheno[,1],type="l", xlab="Time", ylab="Amount of unique phenotypes",xaxt="n") +axis(1, at = seqYear, labels = format(seqYear, "%b-%y"), las = 1) +par(new=T) +plot(sort(strptime(usersp[,2], "%d.%m.%Y %H:%M")), usersp[,1],type="l",xlab="", yaxt="n",col="red",xaxt="n",ylab="") +axis(4,col.axis="red") +mtext("Amount of entered phenotypes", side=4,line=3,col="red") +dev.off() + diff --git a/25_10_2012_Graphs/plotUsers.r b/25_10_2012_Graphs/plotUsers.r new file mode 100644 index 0000000..49b66be --- /dev/null +++ b/25_10_2012_Graphs/plotUsers.r @@ -0,0 +1,8 @@ +svg("users.svg") +users <- read.table("users.txt", sep="\t") +plot(strptime(users[,2], format="%d.%m.%Y %H:%M"), users[,1], type="l", ylab="Amount of user-accounts",xlab="Time", xaxt="n") +dmin <- min(strptime(users[,2], format="%d.%m.%Y %H:%M")) +dminjan <- as.POSIXct(format(dmin, "%Y-1-1")) +seqYear <- seq(dminjan, by = "1 month", to = max(strptime(users[,2], format="%d.%m.%Y %H:%M")) + (86400 * 365)) +axis(1, at = seqYear, labels = format(seqYear, "%b-%y"), las = 1) +dev.off() diff --git a/25_10_2012_Graphs/user_phenotypes.txt b/25_10_2012_Graphs/user_phenotypes.txt new file mode 100644 index 0000000..bbf3bc5 --- /dev/null +++ b/25_10_2012_Graphs/user_phenotypes.txt @@ -0,0 +1,4722 @@ +1 29.09.2011 16:09 +2 29.09.2011 17:00 +3 29.09.2011 20:09 +4 29.09.2011 21:14 +5 29.09.2011 22:38 +6 29.09.2011 22:38 +7 30.09.2011 03:20 +8 30.09.2011 03:20 +9 30.09.2011 03:21 +10 30.09.2011 03:21 +11 30.09.2011 07:25 +12 30.09.2011 07:47 +13 30.09.2011 07:47 +14 30.09.2011 07:47 +15 30.09.2011 07:48 +16 30.09.2011 07:50 +17 30.09.2011 07:51 +18 30.09.2011 07:52 +19 30.09.2011 07:52 +20 30.09.2011 10:26 +21 30.09.2011 11:15 +22 30.09.2011 11:16 +23 30.09.2011 11:16 +24 30.09.2011 11:18 +25 30.09.2011 12:16 +26 30.09.2011 13:33 +27 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18.10.2012 06:38 +605 18.10.2012 08:00 +606 18.10.2012 13:44 +607 18.10.2012 14:59 +608 18.10.2012 19:28 +609 18.10.2012 20:20 +610 19.10.2012 02:55 +611 19.10.2012 05:58 +612 19.10.2012 11:24 +613 19.10.2012 16:44 +614 19.10.2012 16:49 +615 19.10.2012 17:09 +616 20.10.2012 13:42 +617 21.10.2012 06:31 +618 21.10.2012 11:01 +619 21.10.2012 11:31 +620 21.10.2012 22:10 +621 22.10.2012 18:23 +622 23.10.2012 00:19 +623 23.10.2012 08:44 +624 23.10.2012 12:52 +625 23.10.2012 16:06 +626 23.10.2012 19:04 +627 24.10.2012 03:12 diff --git a/Genotypings.svg b/Genotypings.svg deleted file mode 100644 index bf4ea83..0000000 --- a/Genotypings.svg +++ /dev/null @@ -1,182 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/NormedUserPhenoPheno.svg b/NormedUserPhenoPheno.svg deleted file mode 100644 index 780d4f1..0000000 --- a/NormedUserPhenoPheno.svg +++ /dev/null @@ -1,325 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/timePlot.R b/timePlot.R deleted file mode 100644 index b0dff4a..0000000 --- a/timePlot.R +++ /dev/null @@ -1,10 +0,0 @@ -svg("NormedUserPhenoPheno.svg") -par(mar=c(5, 4, 4, 6)) -plot(sort(strptime(pheno[,1], "%d.%m.%Y %H:%M")), pheno[,2],type="l", xlab="Time", ylab="Count Phenotypes",xaxt="n") -axis.POSIXct(1,sort(strptime(pheno[,1], "%d.%m.%Y %H:%M")),format="%b-%y") -par(new=T) -plot(sort(strptime(usersp[,1], "%d.%m.%Y %H:%M")), usersp[,2],type="l",xlab="", yaxt="n",col="red",xaxt="n",ylab="") -axis(4,col.axis="red") -mtext("Count User-Phenotypes",side=4,line=3,col="red") -dev.off() - diff --git a/users.svg b/users.svg deleted file mode 100644 index 53d55a4..0000000 --- a/users.svg +++ /dev/null @@ -1,171 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - From 7a1332bef63bbe65c6e17815e29c848693c60860 Mon Sep 17 00:00:00 2001 From: drsnuggles Date: Thu, 25 Oct 2012 15:25:36 +1000 Subject: [PATCH 37/88] Added acknowledgment to openSNP's users --- paper_draft.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper_draft.tex b/paper_draft.tex index 5e5d355..2f2d16b 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -302,7 +302,7 @@ \subsection*{Technical implementation of the platform} published under the MIT-license and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. % Do NOT remove this, even if you are not including acknowledgments \section*{Acknowledgments} -We thank Dr. Manuel Corpas for constructive advice in grammar, spelling and structure of this study. +We thank Dr. Manuel Corpas and Prof. Dr. Dave Edwards for constructive advice in grammar, spelling and structure of this study. We would also like to thank the users of openSNP.org for their participation, their constructive criticisms and bug-finding abilities and especially for sharing their genotyping and phenotype data. %\section*{References} % The bibtex filename From 8d8b13709bc651f5dd63a0d781959b9a78c5215e Mon Sep 17 00:00:00 2001 From: drsnuggles Date: Fri, 26 Oct 2012 08:28:27 +1000 Subject: [PATCH 38/88] Wrong ylab for genotypes.svg --- 25_10_2012_Graphs/genotypes.svg | 260 ++++++++++++++++++++++---------- 1 file changed, 180 insertions(+), 80 deletions(-) diff --git a/25_10_2012_Graphs/genotypes.svg b/25_10_2012_Graphs/genotypes.svg index b1fc6a2..57223a6 100644 --- a/25_10_2012_Graphs/genotypes.svg +++ b/25_10_2012_Graphs/genotypes.svg @@ -6,63 +6,54 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - @@ -77,52 +68,97 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - + + + + + + + + - - + - - + + - - + + + - - + + + - - - + + + - - - + + + @@ -132,34 +168,98 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 661a835f07c6b001944b7a992a2adc76e2ad2df1 Mon Sep 17 00:00:00 2001 From: drsnuggles Date: Fri, 26 Oct 2012 13:42:01 +1000 Subject: [PATCH 39/88] Re-introduced Survey into main-text --- paper_draft.pdf | Bin 430991 -> 383657 bytes paper_draft.tex | 98 ++++++++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 94 insertions(+), 4 deletions(-) diff --git a/paper_draft.pdf b/paper_draft.pdf index 3a8e11184adb1fa2fab26f0393de6ac9c9b0141b..34af54fc432464596fb8238b30f3763f806452e7 100644 GIT binary patch delta 92261 zcmV)GK)%0^ry8lX7myPa>-v`03S$EV{SRQh}~#(1C2(109wJ#@g}&rd-n4F+4CRr zo2|diqU`4WaFfP~zlqbEO_2LRlHT0!Z$5b8>eu_%&jOwE^!xmWFuMs8KTfiLh?iI= zN$#iAUej86w#6Fld9{v1&ouAfzN6MA_15@&+qEkic-Xsk=t|ohLB2Chdot@^?B0Oz z!nszKC~eW(wgL5hKkTh>tl;%Vf}3?1`r9;v_?Lz2ZNZb`*t_eDtBOynXyX~o+}vdT zb`$43bCdbB@z~G8Y_@&cWiVrZim4Tn5MlVcGQ=~6$d9vZWcU~a@s}zlewve!E`n~M z-n+`y_IyU?Wx;AbuAp?0`_`WG@bBiVJOm`yd%Un`ta@%w~Oqnnjv|5Lr3X`$+fbYKsV;WZ% z&0d#&*;%(M8l(EW7+l|f6?_y6a$@lO!ulK6vy8(k_x@O=S?(3wtu2|Bq+;*HYU|3> zCpT9xR7+su#w&W2{YDJ;$0`dp-q5(-bUN^r?I+-R-Bv}@7`MI~dL!IBoJ4p0>OmwV zJoE}NTl4#$6PtqTN1Qjdt-I%+q1N+1bYQ#}vw@+ppD;Ky`_WQ=U9Yo?Lq8slA+0(X zwprGS044eVfOdhDD+kW~d<32E*QdhPe_O57F!XG5X#Xh~Ojc@KbM{*@9;95OH?Kfk z{&qzF7dv*GvR}m5DD?A4uZcqTgFto?)A}wDemu3^ zxw1|!HjzIDX+RO%(KiI8uZkWm>^#i8dc_Wgc)C}vR(bGwdQwb#Y8fI~{kDy+EZ`U`xt}?gMp0pv|UO?N;nZF92?6#r8*?Yqpy>3 z$V1l|MU(GrXUcx@Fz-2%x9e|-dEgMfPJ*o*-go!k;_R4kxdznf3Fsx=;ttCuv4Q7? 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zPp=37hyDWxg&<)6W$$PH;V?AvG&O(>136700*-+GDMP_dF$hNjYxZ=2$RE7^&NLi_ z#37J>3Nd1*Dj1H2BTm0)02vB;Dot<SA@vQN`wD@ fhzVF)n$*qR!qxrf7X*j^4u_L+bDvYxRwDgBZZi5h diff --git a/paper_draft.tex b/paper_draft.tex index 2f2d16b..c76c68c 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -157,12 +157,78 @@ \section*{Introduction} like the \textit{Personal Genome Project}, still do not allow for an easy re-use of the data, as they lack an application programming interface (API) or other methods by which researchers could download the data. Additionally, not every customer of DTC genetic testing can participate in the \textit{Personal Genome Project}. -As a follow up of a survey, which evaluated whether customers of DTC genetic testing would be willing to share their genotypic and phenotypic information (see Supplementary Materials), we designed openSNP, an online platform which enables DTC customers to freely share their genotypic and phenotypic data under a Creative Commons-Zero license. -The phenotypic and genotypic data can be retrieved from openSNP using different methods, which also include different API standards to allow automated processing of the data. -Additionally openSNP also enables users of DTC genetic testing to receive further information on their genotypes, through the crowdsourced resource SNPedia and primary publications, like those of the Public Library of Science journals. +Here, we present the results of a survey designed to evaluate the support in the personal genetics community for a crowdsourced online platform. We also present openSNP, an online platform which enables DTC customers to share genotypic and phenoytypic information, as well as receive additional information on their genotypes. The genotypes are made available to researchers via the open Creative-Commons-Zero licence. % Results and Discussion can be combined. \section*{Results} +\subsection*{Survey on Sharing Genetic Information} +In total 229 people, 180 with a self-reported chromosomal sex of XY and 56 with a self-reported chromosomal sex of XX participated in our survey on sharing genetic information with the public. +The mean age of the participants is 33 (SD = 11,29). 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already +customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% do not plan on +becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Cramer's V = 0.077). + +67.7 \% of all participants would share their data with their DTC-company without any constraints, 25.8 \% would do so given the company +didn't share the data with third parties. 6.6 \% of the participants would not share their data. Participants self-identified as XX-chromosomal are slightly more likely to answer that DTC companies are allowed to use their results (Cramer's V = 0.221). Those who are customers of a DTC company or are planning on becoming one in +the future are more likely to share their results, compared to those who do not plan on getting themselves genotyped (Somers-d = 0.331). + + +There are substantial differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped +and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with motivations for sharing genotypic information. On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations +for not sharing their data, see table \ref{tab:motivations1}. + +\begin{table} +\begin{tabular}{|l|l|l|} +\hline +& Turkey's HSD & \\ \cline{2-3} +& Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who are already genotyped} & & \\ \hline +... curious & 1.159 & 0.193 \\ +... want to help scientists & 0.465 & 0.128 \\ +... for personal benefits & 0.448 & 0.183 \\ \hline +\textbf{Motivation for not sharing in participants} & & \\ +\textbf{who are not planning to get genotyped} & & \\ \hline +... fear of discrimination & 1.06 & 0.195 \\ +... breach of privacy & 0.821 & 0.225 \\ +... fear of personalized advertising & 0.848 & 0.208 \\ +... negative consequences for family members & 0.733 & 0.21 \\ \hline +\end{tabular} +\caption{Differences in terms of motivation to share genotypings with the public in survey-participants who already received a genotyping compared to participants who are not planning to getting genotyped. } +\label{tab:motivations1} +\end{table} + +\begin{table} +\begin{tabular}{|l|l|l|} +\hline +& Turkey's HSD & \\ \cline{2-3} +& Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who would share with their DTC provider} & & \\ \hline +... curiosity & 1.99 & 0.321 \\ +... want to help science & 1.57 & 0.199 \\ +... for personal benefits & 0.951 & 0.308 \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who would not share with their DTC provider} & & \\ \hline +... fear of discrimination & 1.52 & 0.322 \\ +... fear of consequences for family members & 1.146 & 0.32 \\ +... fear of personalized advertising & 1.112 & 0.357 \\ \hline +\end{tabular} +\caption{Differences in terms of motivations to share genotyping-data, comparison between participants who would share their genotyping data with their DTC provider with participants who would not share their data with their DTC provider.} +\label{tab:motivations2} +\end{table} + +Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with +the following motivations for sharing than those who would not share their data with their DTC company. +Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the some motivations +for not sharing their data when compared to those who would share their data with their DTC company under any circumstances, for an overview of the motivations of both groups, see table \ref{tab:motivations2}. + +In the case of curiosity as a motive, there is also a substantial difference between those who would share their data with their DTC company under the condition that it did not share the information and those who would not (mean difference = 1.116 SE = 0.344) as well as those who would share under any circumstances (mean difference = -0.874 SE = 0.182). + +In the cases of fear of discrimination and fear of a breach of privacy, substantial differences between all three categories exist. Those who would share their data with their DTC company as long as it did not share the information agree less strongly than those who would not share the data with both fear of discrimination as a motive for not sharing (mean difference = -0.615, SE = 0.345) as well as fear of a breach of privacy (mean difference = -0.668, SE = 0.346). Those who would share their data under any circumstances are even less likely to agree with these motives than those who would share only if their DTC company did not share the information (fear of discrimination: mean value = -0.906, SE = 0.182; breach of privacy: mean difference = -1.203, SE = 0.183). + +These survey-results indicate that there is a definite interest in customers of DTC-companies to share their results with other scientists. + +\subsubsection*{Sharing genotypic information} We created the openSNP project (http://opensnp.org) as an open, crowdsourced online platform for DTC customers interested in sharing their raw data and for researchers interested in performing GWAS or perform other types of analysis with the data. Customers of DTC testing are encouraged to share their genotyping results along with their phenotypic traits to enable easy access for researchers. @@ -224,6 +290,11 @@ \subsection*{Data access} The data is additionally available over a JSON-API, which allows users to directly access data in the JSON-format. The methods allow users to programmatically look for the genotypes and annotations at a given SNP as well as for phenotypes for a given user and phenotypic variation for a given phenotype. \section*{Discussion} +\subsection*{Survey issues} + +As the survey was taken online by voluntary participants and was mainly spread in the personal genetics community, the results do not reflect the general population, but over-represent those people most likely to be interested in a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. + + \subsection*{Privacy, health implications and ethical considerations} %The advent of DTC genetic testing has led to new ethical and social issues. @@ -278,6 +349,25 @@ \subsection*{GWAS and Open Data} % authors may use "Analysis" \section*{Materials and Methods} +\subsection*{Survey on Sharing Genetic Information} +The survey was performed using \textit{Google Docs} and was distributed to possible participants through the \textit{23andMe }community forums, the \textit{DIYBiology} mailing list, +blogs which focus on genetics and DTC genetic testing and social media like \textit{Twitter}, \textit{Google+} and \textit{Facebook}. + +The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their +(planned) customer-ship with a DTC company. If the participants already were customers, they were also asked if they were already sharing their genetic and phenotypic data. +All participants were asked if they would share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, +but only if they did not share my medical information with anybody else" and No". + +The survey also asked some scaled questions which measured how strongly participants agreed/disagreed with different reasons for sharing or not sharing their +information. The scale went from 1 = strongly disagree to 5 = strongly agree. Motivations queried for sharing data +were "because I want to help scientists with their research", "because of possible personal benefits (e.g. getting treatments for a disease I have, +possibility of new medication, etc.)", "because it may deliver advertising that is relevant to me" and "out of curiosity". Motivations queried for not sharing +data were "because advertisers could use the information for targeted campaigns", "because of possible negative consequences for closely related persons", +"because of the breach of my privacy" and "because of the fear of discrimination (e.g. by the employer, the state, some insurance company)". +Additionally, participants had the possibility of giving their own reasons for sharing or not sharing their data. + +The survey data was analyzed with SPSS 19. + \subsection*{Technical implementation of the platform} The main platform is implemented using the web framework Ruby on Rails 3.0.10. Postgres 9.2 is used as the main database backend for Rails. The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs @@ -302,7 +392,7 @@ \subsection*{Technical implementation of the platform} published under the MIT-license and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. % Do NOT remove this, even if you are not including acknowledgments \section*{Acknowledgments} -We thank Dr. Manuel Corpas and Prof. Dr. Dave Edwards for constructive advice in grammar, spelling and structure of this study. We would also like to thank the users of openSNP.org for their participation, their constructive criticisms and bug-finding abilities and especially for sharing their genotyping and phenotype data. +We thank Dr. Manuel Corpas and Prof. Dr. Dave Edwards for constructive advice in grammar, spelling and structure of this study. We would especially like to thank the users of openSNP.org for their participation, their constructive criticisms and bug-finding abilities and especially for sharing their genotyping and phenotype data. %\section*{References} % The bibtex filename From 6968636a8945ac035e8db5117e42678a10368d2a Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Fri, 26 Oct 2012 16:53:57 +0200 Subject: [PATCH 40/88] added citations, edited text to accommodate for PGP & genome.gov data --- paper_draft.pdf | Bin 383657 -> 417430 bytes paper_draft.tex | 48 +++++++++++++++--------------- papers.bib | 76 ++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 99 insertions(+), 25 deletions(-) diff --git a/paper_draft.pdf b/paper_draft.pdf index 34af54fc432464596fb8238b30f3763f806452e7..1d695b849138c3b4954023813f197c4898522fb7 100644 GIT binary patch delta 204453 zcmYhiQ*bU^)3qC}*tTukwr$(y9dpIDZQHhO+fG)J{k-2k`Oo^K54vj3s=me;UD1M( z+Jl8irYI^-$3)KtO*X$gvJTCXDvFzAhb%%apI 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Bastian Greshake$^{1,\ast}$, Philipp Bayer$^{2}$, -Fabian Zimmer$^{3}$, -Julia Reda$^{4}$, -Helge Rausch$^{5}$ +Helge Rausch$^{3}$, +Fabian Zimmer$^{4}$, +Julia Reda$^{5}$ \\ -\bf{1} Goethe University, Frankfurt am Main, Germany +\bf{1} Molecular Ecology Group, Goethe University Frankfurt am Main, Germany \\ -\bf{2} University of Queensland, Brisbane, Australia +\bf{2} Applied Bioinformatics Group, School of Agriculture and Food Sciences, The University of Queensland, Australia \\ -\bf{3} Westf\"alische Wilhelms Universit\"at, M\"unster, Germany +\bf{3} Hochschule f\"ur Technik und Wirtschaft Berlin, Germany \\ -\bf{4} Johannes-Gutenberg University, Mainz, Germany +\bf{4} Evolutionary Genomics and Transcriptomics Lab, Department of Genetics, Evolution and Environment, University College London, England \\ -\bf{5} Hochschule für Technik und Wirtschaft, Berlin, Germany +\bf{5} Johannes Gutenberg University Mayence, Germany \\ $\ast$ E-mail: info@opensnp.org \end{flushleft} % Please keep the abstract between 250 and 300 words \section*{Abstract} -Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants. These studies usually compare the genetic variation between two groups to single out certain Single Nucleotide Polymorphisms (SNPs) that are linked to a phenotypic variation in one of the groups. However, it is necessary to have a large enough sample size to find statistically significant correlations. Direct-To-Consumer (DTC) genetic testing can supply additional data: DTC-companies offer the analysis of a large amount of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing companies. However, this data is not public for a variety of reasons and thus cannot be used in research. It seems reasonable to create a central open data-repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates whether people are willing to publicly share their genetic information. In the light of those results we present the web-platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publicly publish at no cost their genetic data along with phenotypic information. Through this "crowdsourced" effort of collecting genetic and phenotypic information openSNP has become a valuable resource which can be used in a wide area of studies, including Genome-wide association studies. OpenSNP is hosted at www.opensnp.org, and the code is released under MIT-license at github.com/gedankenstuecke/snpr +Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants. These studies usually compare the genetic variation between two groups to single out certain Single Nucleotide Polymorphisms (SNPs) that are linked to a phenotypic variation in one of the groups. However, it is necessary to have a large enough sample size to find statistically significant correlations. Direct-To-Consumer (DTC) genetic testing can supply additional data: DTC-companies offer the analysis of a large amount of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing companies. However, this data is not public for a variety of reasons and thus cannot be used in research. It seems reasonable to create a central open data-repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates whether people are willing to publicly share their genetic information. In the light of those results we present the web-platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publicly publish at no cost their genetic data along with phenotypic information. Through this crowdsourced effort of collecting genetic and phenotypic information openSNP has become a valuable resource which can be used in a wide area of studies, including Genome-wide association studies. OpenSNP is hosted at www.opensnp.org, and the code is released under MIT-license at http://github.com/gedankenstuecke/snpr % Please keep the Author Summary between 150 and 200 words % Use first person. PLoS ONE authors please skip this step. % Author Summary not valid for PLoS ONE submissions. \section*{Author Summary} -Missing for now. \section*{Introduction} The availability of new DNA sequencing techniques has shifted the focus of biological data acquisition towards new biomedical applications. -Many diseases – for example Alzheimers, Parkinsons or different types of cancers (needs citations) – are at least partially heritable so the genome +Many diseases – for example Alzheimer's \cite{alzheimer}, Parkinson's \cite{parkinsons} or different types of cancers \cite{breastcancer,prostatecancer} – are at least partially heritable so the genome of patients can be used for diagnostic purposes. Using the genetic information of patients for diagnostics is made possible through the sharp decrease in costs for analysing genetic information \cite{Brown1999}. %Kicked out the following due to Corpas' argument @@ -128,7 +127,7 @@ \section*{Introduction} If genetic information for more than one individual is known, the analysis of -allele frequencies of Single Nucleotide Polymorphisms (SNPs) can be used to associate such SNPs with diseases and other inheritable traits. Genome-Wide Assocation Studies +allele frequencies of Single Nucleotide Polymorphisms (SNPs) can be used to associate such SNPs with diseases and other inheritable traits. Genome-Wide Association Studies (GWAS) make use of statistics to compare the allele frequencies in patients to the alleles in healthy controls. This enables GWAS to find SNPs which are significantly overrepresented in patients and associates those SNPs with a trait or disease. This method does not allow inference of causal differences but merely identifies correlations. @@ -154,10 +153,10 @@ \section*{Introduction} This data is scattered and unorganized, making it hard to use in studies. While projects like SNPedia try to keep track of all the publicly available genotyping files \cite{Cariaso2011}, they usually do not provide the information necessary to perform GWAS, as the phenotypic information is often not attached to the genetic information. Projects that attach the phenotype to the genetic information, -like the \textit{Personal Genome Project}, still do not allow for an easy re-use of the data, as they lack an application programming interface (API) +like the \textit{Personal Genome Project} \cite{Ball24072012}, still do not allow for an easy re-use of the data, as they currently lack an application programming interface (API) or other methods by which researchers could download the data. Additionally, not every customer of DTC genetic testing can participate in the \textit{Personal Genome Project}. -Here, we present the results of a survey designed to evaluate the support in the personal genetics community for a crowdsourced online platform. We also present openSNP, an online platform which enables DTC customers to share genotypic and phenoytypic information, as well as receive additional information on their genotypes. The genotypes are made available to researchers via the open Creative-Commons-Zero licence. +Here, we present the results of a survey designed to evaluate the support in the personal genetics community for a crowdsourced online platform. We also present openSNP, an online platform which enables DTC customers to share genotypic and phenoytypic information, as well as receive additional information on their genotypes. The genotypes are made available to researchers via the open Creative-Commons-Zero license. % Results and Discussion can be combined. \section*{Results} @@ -274,12 +273,12 @@ \subsection*{Crowdsourcing phenotypes} \subsection*{Connection to external services} In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. -A total number of 15,229 documents \bastian{number needs to get updated}relevant to the SNPs listed in openSNP could be found in the publication databases of Mendeley, the Public Library of Science and in the crowdsourced SNPedia. -Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge (Figure \ref{Figure3_label}). For usability reasons, +A total number of 15,229 documents \bastian{number needs to get updated}relevant to the SNPs listed in openSNP could be found in the publication and annotation databases of Mendeley, the Public Library of Science, in the \emph{GET Evidence System} \cite{Ball24072012} and the \emph{NHGRI GWAS Catalog} \cite{Hindorff2009} and in the crowdsourced SNPedia. +Of the primary literature listed on Mendeley \& the Public Library of Science, 25 \% are released in open access journals and can be accessed free of charge (Figure \ref{Figure3_label}). For usability reasons, SNPs are ranked by the amount of information gathered through the external services. The external services themselves are ranked by how easily non-scientists can understand information -from these sources and available this information. The SNPedia entries are given the highest impact, as those are already manually curated and summarized in plain English, followed by open access publications out of -the Public Library of Science. Lowest values are given to the Mendeley results, as the publications listed there are for the most part not freely available without subscriptions or one-time payments. -An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. +from these sources and how available this information is to the public. The SNPedia entries are given the highest impact, as those are already manually curated and summarized in plain English, followed by open access publications out of +the Public Library of Science and the curated databases of the \emph{GET Evidence System} and the \emph{NHGRI GWAS Catalog}. Lowest values are given to the Mendeley results, as the publications listed there are for the most part not freely available without subscriptions or one-time payments. +An entry on SNPedia is valued 2.5 times as high as a PLoS publication or entries in \emph{GET} or the \emph{GWAS Catalog} and 5 times as high as a Mendeley entry. \subsection*{Data access} OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files @@ -311,8 +310,7 @@ \subsection*{Privacy, health implications and ethical considerations} OpenSNP openly addresses the problem of privacy implications that come with releasing genetic data twice, once during registration for openSNP and once during the upload of the DTC genetic testing results. Users have to confirm that they have read and understood the disclaimer about possible side-effects -of publishing their data. To further improve this process we are looking forward to implement an informed consent-processes -by a provider like \textit{Consent for Research} (http://www.weconsent.us). +of publishing their data. Further versions of openSNP may optionally include further consent-processes. \subsection*{GWAS and Open Data} Although prices of exome or even full genome sequencing are dropping rapidly, GWAS are still considerably cheaper. However, GWAS can only detect correlations of SNPs with those traits and do not allow @@ -351,7 +349,7 @@ \section*{Materials and Methods} \subsection*{Survey on Sharing Genetic Information} The survey was performed using \textit{Google Docs} and was distributed to possible participants through the \textit{23andMe }community forums, the \textit{DIYBiology} mailing list, -blogs which focus on genetics and DTC genetic testing and social media like \textit{Twitter}, \textit{Google+} and \textit{Facebook}. +blogs which focus on genetics and DTC genetic testing and social media websites flike \textit{Twitter}, \textit{Google+} and \textit{Facebook}. The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their (planned) customer-ship with a DTC company. If the participants already were customers, they were also asked if they were already sharing their genetic and phenotypic data. @@ -373,14 +371,14 @@ \subsection*{Technical implementation of the platform} The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, -through querying the content via the MediaWiki API. All databases are queried using the unique identifier of each SNP as the search term. +through querying the content via the MediaWiki API. The \emph{NHGRI GWAS Catalog} and the \emph{GET Evidence System} provide complete dumps in plain text formats. Those are regularly downloaded and parsed. All databases are queried or parsed using the unique identifier of each SNP as the search term. SNPs are catalogued by their unique identifier, which consists of a prefix (mostly \textit{rs}, rarely \textit{i}) and a unique number. This is a common format, which is employed by the NCBI dbSNP database \cite{Sherry2001} and is also widely used and easily parsed from different literature sources. Publications from the different databases as well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources -are handled using the ruby gem Resque and the standalone server Redis. Search features on the platform itself are implemented using Solr and the ruby gem Sunspot. +are handled using the ruby gem Resque and the standalone key-value storage server Redis. Search features on the platform itself are implemented using Solr and the ruby gem Sunspot. Additionally, data can be requested from openSNP using the Distributed Annotation System. The required data is stored in a PostgreSQL database. Requested data is delivered in XML-format to facilitate parsing. Additionally, users can request data in the JSON-format, using a system not specified in any standard. @@ -392,7 +390,7 @@ \subsection*{Technical implementation of the platform} published under the MIT-license and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. % Do NOT remove this, even if you are not including acknowledgments \section*{Acknowledgments} -We thank Dr. Manuel Corpas and Prof. Dr. Dave Edwards for constructive advice in grammar, spelling and structure of this study. We would especially like to thank the users of openSNP.org for their participation, their constructive criticisms and bug-finding abilities and especially for sharing their genotyping and phenotype data. +We thank Dr. Manuel Corpas and Prof. Dr. Dave Edwards for constructive advice in grammar, spelling and structure of this study. Further thanks go to Samantha Clark and Dan Bolser for providing valuable feedback and feature ideas during the development and Thomas Down, author of \emph{BioDalliance}, and Rafael Jimenez, author of \emph{MyKaryoView}, for their support on implementing the Distributed Annotation System and the genome browser. For help with their APIs we are grateful to Mike Cariaso of \emph{SNPedia} and the PLOS \& Mendeley API teams. Last but not least: We would especially like to thank the users of openSNP.org for their participation, their constructive criticisms and bug-finding abilities and especially for sharing their genotyping and phenotype data. %\section*{References} % The bibtex filename diff --git a/papers.bib b/papers.bib index d61e980..522bbbc 100644 --- a/papers.bib +++ b/papers.bib @@ -670,3 +670,79 @@ @article{10.1371/journal.pbio.1001195 number = {12}, doi = {10.1371/journal.pbio.1001195} } + + +@article{parkinsons, + author = {Lill, , Christina M. AND Roehr, , Johannes T. AND McQueen, , Matthew B. AND Kavvoura, , Fotini K. AND Bagade, , Sachin AND Schjeide, , Brit-Maren M. AND Schjeide, , Leif M. AND Meissner, , Esther AND Zauft, , Ute AND Allen, , Nicole C. AND Liu, , Tian AND Schilling, , Marcel AND Anderson, , Kari J. AND Beecham, , Gary AND Berg, , Daniela AND Biernacka, , Joanna M. AND Brice, , Alexis AND DeStefano, , Anita L. AND Do, , Chuong B. AND Eriksson, , Nicholas AND Factor, , Stewart A. AND Farrer, , Matthew J. AND Foroud, , Tatiana AND Gasser, , Thomas AND Hamza, , Taye AND Hardy, , John A. AND Heutink, , Peter AND Hill-Burns, , Erin M. AND Klein, , Christine AND Latourelle, , Jeanne C. AND Maraganore, , Demetrius M. AND Martin, , Eden R. AND Martinez, , Maria AND Myers, , Richard H. AND Nalls, , Michael A. AND Pankratz, , Nathan AND Payami, , Haydeh AND Satake, , Wataru AND Scott, , William K. AND Sharma, , Manu AND Singleton, , Andrew B. AND Stefansson, , Kari AND Toda, , Tatsushi AND Tung, , Joyce Y. AND Vance, , Jeffery AND Wood, , Nick W. AND Zabetian, , Cyrus P. AND Young, , Peter AND Tanzi, , Rudolph E. AND Khoury, , Muin J. AND Zipp, , Frauke AND Lehrach, , Hans AND Ioannidis, , John P. A. AND Bertram, , Lars AND 23andMe, The Genetic Epidemiology of Parkinson's Disease (GEO-PD) Consortium, The International Parkinson's Disease Genomics Consortium (IPDGC), The Parkinson's Disease GWAS Consortium, The Wellcome Trust Case Control Consortium 2 (WTCCC2)}, + journal = {PLoS Genet}, + publisher = {Public Library of Science}, + title = {Comprehensive Research Synopsis and Systematic Meta-Analyses in Parkinson's Disease Genetics: The PDGene Database}, + year = {2012}, + month = {03}, + volume = {8}, + url = {http://dx.doi.org/10.1371%2Fjournal.pgen.1002548}, + pages = {e1002548}, + abstract = {Author Summary

The genetic basis of Parkinson's disease is complex, i.e. it is determined by a number of different disease-causing and disease-predisposing genes. Especially the latter have proven difficult to find, evidenced by more than 800 published genetic association studies, typically showing discrepant results. To facilitate the interpretation of this large and continuously increasing body of data, we have created a freely available online database (“PDGeneâ€: http://www.pdgene.org) which provides an exhaustive account of all published genetic association studies in PD. One particularly useful feature is the calculation and display of up-to-date summary statistics of published data for overlapping DNA sequence variants (polymorphisms). These meta-analyses revealed eleven gene loci that showed a statistically very significant (P<5×10−8; a.k.a. genome-wide significance) association with risk for PD: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, SYT11/RAB25. In addition and purely by data-mining, we identified one novel PD susceptibility locus in a gene called ITGA8 (rs7077361, P = 1.3×10−8). We note that our continuously updated database represents the most comprehensive research synopsis of genetic association studies in PD to date. In addition to vastly facilitating the work of other PD geneticists, our approach may serve as a valuable example for other complex diseases.

}, + number = {3}, + doi = {10.1371/journal.pgen.1002548} +} + +@article{alzheimer, +author = {Tanzi, Rudolph E.}, +title = {The Genetics of Alzheimer Disease}, +volume = {2}, +number = {10}, +year = {2012}, +doi = {10.1101/cshperspect.a006296}, +abstract ={Family history is the second strongest risk factor for Alzheimer disease (AD) following advanced age. Twin and family studies indicate that genetic factors are estimated to play a role in at least 80% of AD cases. The inheritance of AD exhibits a dichotomous pattern. On one hand, rare mutations in APP, PSEN1, and PSEN2 virtually guarantee early-onset (<60 years) familial AD, which represents ∼5% of AD. On the other hand, common gene polymorphisms, such as the ε4 and ε2 variants of the APOE gene, can influence susceptibility for ∼50% of the common late-onset AD. These four genes account for 30%–50% of the inheritability of AD. Genome-wide association studies have recently led to the identification of 11 additional AD candidate genes. This paper reviews the past, present, and future attempts to elucidate the complex and heterogeneous genetic underpinnings of AD.}, +URL = {http://perspectivesinmedicine.cshlp.org/content/2/10/a006296.abstract}, +eprint = {http://perspectivesinmedicine.cshlp.org/content/2/10/a006296.full.pdf+html}, +journal = {Cold Spring Harbor Perspectives in Medicine} +} + +@article {breastcancer, +author = {Njiaju, Uchenna O. and Olopade, Olufunmilayo I.}, +title = {Genetic Determinants of Breast Cancer Risk: A Review of Current Literature and Issues Pertaining to Clinical Application}, +journal = {The Breast Journal}, +volume = {18}, +number = {5}, +publisher = {Blackwell Publishing Ltd}, +issn = {1524-4741}, +url = {http://dx.doi.org/10.1111/j.1524-4741.2012.01274.x}, +doi = {10.1111/j.1524-4741.2012.01274.x}, +pages = {436--442}, +keywords = {breast cancer, genetic risk, risk reduction}, +year = {2012}, +} + +@article{prostatecancer, +author = {Abate-Shen, Cory and Shen, Michael M.}, +title = {Molecular genetics of prostate cancer}, +volume = {14}, +number = {19}, +pages = {2410-2434}, +year = {2000}, +doi = {10.1101/gad.819500}, +URL = {http://genesdev.cshlp.org/content/14/19/2410.short}, +eprint = {http://genesdev.cshlp.org/content/14/19/2410.full.pdf+html}, +journal = {Genes & Development} +} + +@article{Ball24072012, +author = {Ball, Madeleine P. and Thakuria, Joseph V. and Zaranek, Alexander Wait and Clegg, Tom and Rosenbaum, Abraham M. and Wu, Xiaodi and Angrist, Misha and Bhak, Jong and Bobe, Jason and Callow, Matthew J. and Cano, Carlos and Chou, Michael F. and Chung, Wendy K. and Douglas, Shawn M. and Estep, Preston W. and Gore, Athurva and Hulick, Peter and Labarga, Alberto and Lee, Je-Hyuk and Lunshof, Jeantine E. and Kim, Byung Chul and Kim, Jong-Il and Li, Zhe and Murray, Michael F. and Nilsen, Geoffrey B. and Peters, Brock A. and Raman, Anugraha M. and Rienhoff, Hugh Y. and Robasky, Kimberly and Wheeler, Matthew T. and Vandewege, Ward and Vorhaus, Daniel B. and Yang, Joyce L. and Yang, Luhan and Aach, John and Ashley, Euan A. and Drmanac, Radoje and Kim, Seong-Jin and Li, Jin Billy and Peshkin, Leonid and Seidman, Christine E. and Seo, Jeong-Sun and Zhang, Kun and Rehm, Heidi L. and Church, George M.}, +title = {A public resource facilitating clinical use of genomes}, +volume = {109}, +number = {30}, +pages = {11920-11927}, +year = {2012}, +doi = {10.1073/pnas.1201904109}, +abstract ={Rapid advances in DNA sequencing promise to enable new diagnostics and individualized therapies. Achieving personalized medicine, however, will require extensive research on highly reidentifiable, integrated datasets of genomic and health information. To assist with this, participants in the Personal Genome Project choose to forgo privacy via our institutional review board- approved “open consent†process. The contribution of public data and samples facilitates both scientific discovery and standardization of methods. We present our findings after enrollment of more than 1,800 participants, including whole-genome sequencing of 10 pilot participant genomes (the PGP-10). We introduce the Genome-Environment-Trait Evidence (GET-Evidence) system. This tool automatically processes genomes and prioritizes both published and novel variants for interpretation. In the process of reviewing the presumed healthy PGP-10 genomes, we find numerous literature references implying serious disease. Although it is sometimes impossible to rule out a late-onset effect, stringent evidence requirements can address the high rate of incidental findings. To that end we develop a peer production system for recording and organizing variant evaluations according to standard evidence guidelines, creating a public forum for reaching consensus on interpretation of clinically relevant variants. Genome analysis becomes a two-step process: using a prioritized list to record variant evaluations, then automatically sorting reviewed variants using these annotations. Genome data, health and trait information, participant samples, and variant interpretations are all shared in the public domain—we invite others to review our results using our participant samples and contribute to our interpretations. We offer our public resource and methods to further personalized medical research.}, +URL = {http://www.pnas.org/content/109/30/11920.abstract}, +eprint = {http://www.pnas.org/content/109/30/11920.full.pdf+html}, +journal = {Proceedings of the National Academy of Sciences} +} + + + + + From e9a30181f1f6da4730131d45ba6650c4f0e9c7dd Mon Sep 17 00:00:00 2001 From: drsnuggles Date: Sat, 27 Oct 2012 18:23:23 +1000 Subject: [PATCH 41/88] PNG-versions of the graphs, updated most stats (except # UserSNPs, still counting), moved tables, inserted new graphs --- 25_10_2012_Graphs/genotypes.png | Bin 0 -> 24602 bytes .../phenotypes_vs_userphenotypes.png | Bin 0 -> 35542 bytes 25_10_2012_Graphs/users.png | Bin 0 -> 25711 bytes paper_draft.pdf | Bin 383657 -> 186113 bytes paper_draft.tex | 125 +++++++++--------- 5 files changed, 60 insertions(+), 65 deletions(-) create mode 100644 25_10_2012_Graphs/genotypes.png create mode 100644 25_10_2012_Graphs/phenotypes_vs_userphenotypes.png create mode 100644 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zz3=p0yt67bH{h8}IlZzPwj%Ll5!}ntbNcZZuD%YV+BdRk81^Y**3CU@J}+xW>`0rH zpOf)ouH1~M-$L=dlPfGKi$2&)0V5#|*tmgL9wc%wrum@o$;M|^Z|n)lBV)LX$D%<%A{VG z`^+Nt=HZLUgCV5wnw2U?YnaQ()v?5(FGzk*@8EZEyZPqfMua&zrLN`gbFmyNlI_rZ zBc%~Oc0C5gu<)cJ*}6T2DRtU`9nQjJqMYLgeT!T~t?9QV>zlyhJDwg|HE# zeIRs%-cCpu!!?QPp0@5D4sNazH!3Z0j%MJVSaJzET{qq7t zAdpDH?f`@zk3}PY{}I3-QCJi;1_A(r8be|bf7T%|m_O=3@XtCp2>Vfo|5p`7Fa`$x zz8D5WgD@(A6bwNv5C%hl7|NBoAJ2bXQoaYmsn-H9Eb>=@6n+>CfMJmTsQGs>g#-or zQ#A@jEf7Fr5mdx50EzjN7@)}hT_6m=An*ukVgLjGary7Z^Is=00EB^*zp(v=!Qm9u z)EIz8Q;DXKAmIRY9f(H%>;nE@5;%xLQ;UJa0W_6kN?WniS%4t`IEJc@q8LC>JI25< z$lpX0z7IhT;X&B1d-B63rHTpp`70S6n{%;5YBpd~&c7mjYfw~JImU4tqk$_mtA0(7?V5mX}px~6l^VhYM{sSlk z22D+ZLIQs{MuDh5h*6YmQM*HBPuT%{wI@l#%~c;g9`AQL^+0 zMj0`};3!1af?^4{pg=i0K$H@QlE=U(@07Di2?4^Author Summary

The genetic basis of Parkinson's + disease is complex, i.e. it is determined by a number of different + disease-causing and disease-predisposing genes. Especially the latter + have proven difficult to find, evidenced by more than 800 published + genetic association studies, typically showing discrepant results. + To facilitate the interpretation of this large and continuously increasing + body of data, we have created a freely available online database + (“PDGeneâ€: http://www.pdgene.org) + which provides an exhaustive account of all published genetic association + studies in PD. One particularly useful feature is the calculation + and display of up-to-date summary statistics of published data for + overlapping DNA sequence variants (polymorphisms). These meta-analyses + revealed eleven gene loci that showed a statistically very significant + (P<5×10−8; a.k.a. genome-wide significance) + association with risk for PD: BST1, CCDC62/HIP1R, + DGKQ/GAK, GBA, LRRK2, + MAPT, MCCC1/LAMP3, PARK16, SNCA, + STK39, SYT11/RAB25. In addition + and purely by data-mining, we identified one novel PD susceptibility + locus in a gene called ITGA8 (rs7077361, P = 1.3×10−8). + We note that our continuously updated database represents the most + comprehensive research synopsis of genetic association studies in + PD to date. In addition to vastly facilitating the work of other + PD geneticists, our approach may serve as a valuable example for + other complex diseases.

}, + doi = {10.1371/journal.pgen.1002548}, + publisher = {Public Library of Science}, + url = {http://dx.doi.org/10.1371%2Fjournal.pgen.1002548} +} + @ARTICLE{10.1371.journal.pone.0031470, author = {Mei, , Hao AND Chen, , Wei AND Jiang, , Fan AND He, , Jiang AND Srinivasan, , Sathanur AND Smith, , Erin N. AND Schork, , Nicholas AND Murray, @@ -487,6 +661,40 @@ @ARTICLE{10.1371.journal.pone.0031470 url = {http://dx.doi.org/10.1371%2Fjournal.pone.0031470} } +@ARTICLE{10.1371/journal.pbio.1001195, + author = {Molloy, , Jennifer C.}, + title = {The Open Knowledge Foundation: Open Data Means Better Science}, + journal = {PLoS Biol}, + year = {2011}, + volume = {9}, + pages = {e1001195}, + number = {12}, + month = {12}, + abstract = {

Open data leads to better science, but overcoming the barriers + to widespread publication and availability of open scientific data + requires a community effort. The Open Knowledge Foundation Open Data + in Science Working Group describes their role in this movement.

}, + doi = {10.1371/journal.pbio.1001195}, + publisher = {Public Library of Science}, + url = {http://dx.doi.org/10.1371%2Fjournal.pbio.1001195} +} + +@ARTICLE{breastcancer, + author = {Njiaju, Uchenna O. and Olopade, Olufunmilayo I.}, + title = {Genetic Determinants of Breast Cancer Risk: A Review of Current Literature + and Issues Pertaining to Clinical Application}, + journal = {The Breast Journal}, + year = {2012}, + volume = {18}, + pages = {436--442}, + number = {5}, + doi = {10.1111/j.1524-4741.2012.01274.x}, + issn = {1524-4741}, + keywords = {breast cancer, genetic risk, risk reduction}, + publisher = {Blackwell Publishing Ltd}, + url = {http://dx.doi.org/10.1111/j.1524-4741.2012.01274.x} +} + @ARTICLE{10.1371.journal.pone.0029848, author = {Sebastiani, , Paola AND Solovieff, , Nadia AND DeWan, , Andrew T. AND Walsh, , Kyle M. AND Puca, , Annibale AND Hartley, , Stephen @@ -545,7 +753,7 @@ @ARTICLE{Sherry2001 pages = {308--311}, number = {1}, month = {Jan}, - __markedentry = {[drsnuggles:6]}, + __markedentry = {[drsnuggles:]}, abstract = {In response to a need for a general catalog of genome variation to address the large-scale sampling designs required by association studies, gene mapping and evolutionary biology, the National Center @@ -571,6 +779,32 @@ @ncbi.nlm.nih.gov timestamp = {2012.03.26} } +@ARTICLE{alzheimer, + author = {Tanzi, Rudolph E.}, + title = {The Genetics of Alzheimer Disease}, + journal = {Cold Spring Harbor Perspectives in Medicine}, + year = {2012}, + volume = {2}, + number = {10}, + abstract = {Family history is the second strongest risk factor for Alzheimer disease + (AD) following advanced age. Twin and family studies indicate that + genetic factors are estimated to play a role in at least 80% of AD + cases. The inheritance of AD exhibits a dichotomous pattern. On one + hand, rare mutations in APP, PSEN1, and PSEN2 virtually guarantee + early-onset (<60 years) familial AD, which represents ∼5% of AD. + On the other hand, common gene polymorphisms, such as the ε4 and + ε2 variants of the APOE gene, can influence susceptibility for ∼50% + of the common late-onset AD. These four genes account for 30%–50% + of the inheritability of AD. Genome-wide association studies have + recently led to the identification of 11 additional AD candidate + genes. This paper reviews the past, present, and future attempts + to elucidate the complex and heterogeneous genetic underpinnings + of AD.}, + doi = {10.1101/cshperspect.a006296}, + eprint = {http://perspectivesinmedicine.cshlp.org/content/2/10/a006296.full.pdf+html}, + url = {http://perspectivesinmedicine.cshlp.org/content/2/10/a006296.abstract} +} + @ARTICLE{Wasson2009, author = {Wasson, Katherine}, title = {{Direct-to-consumer genomics and research ethics: should a more robust @@ -624,125 +858,3 @@ @ARTICLE{10.1371.journal.pone.0030309 url = {http://dx.doi.org/10.1371%2Fjournal.pone.0030309} } -@ARTICLE{23andMe2011, - author = {23andMe}, - title = {{23andMe 2011 State of the Database Address}}, - journal = {The Spittoon}, - year = {2011}, - type = {Blog}, - number = {June 15}, - howpublished = {\url{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/}} -} - -@ARTICLE{23andMe2012, - author = {23andMe}, - title = {{23andMe Privacy Statement}}, - journal = {23andMe Homepage}, - year = {2012}, - howpublished = {\url{http://www.23andme.com/about/privacy/}} -} - -@article{Danecek01082011, -author = {Danecek, Petr and Auton, Adam and Abecasis, Goncalo and Albers, Cornelis A. and Banks, Eric and DePristo, Mark A. and Handsaker, Robert E. and Lunter, Gerton and Marth, Gabor T. and Sherry, Stephen T. and McVean, Gilean and Durbin, Richard and 1000 Genomes Project Analysis Group}, -title = {The variant call format and VCFtools}, -volume = {27}, -number = {15}, -pages = {2156-2158}, -year = {2011}, -doi = {10.1093/bioinformatics/btr330}, -abstract ={Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API.Availability: http://vcftools.sourceforge.netContact: rd@sanger.ac.uk}, -URL = {http://bioinformatics.oxfordjournals.org/content/27/15/2156.abstract}, -eprint = {http://bioinformatics.oxfordjournals.org/content/27/15/2156.full.pdf+html}, -journal = {Bioinformatics} -} - -@article{10.1371/journal.pbio.1001195, - author = {Molloy, , Jennifer C.}, - journal = {PLoS Biol}, - publisher = {Public Library of Science}, - title = {The Open Knowledge Foundation: Open Data Means Better Science}, - year = {2011}, - month = {12}, - volume = {9}, - url = {http://dx.doi.org/10.1371%2Fjournal.pbio.1001195}, - pages = {e1001195}, - abstract = {

Open data leads to better science, but overcoming the barriers to widespread publication and availability of open scientific data requires a community effort. The Open Knowledge Foundation Open Data in Science Working Group describes their role in this movement.

}, - number = {12}, - doi = {10.1371/journal.pbio.1001195} -} - - -@article{parkinsons, - author = {Lill, , Christina M. AND Roehr, , Johannes T. AND McQueen, , Matthew B. AND Kavvoura, , Fotini K. AND Bagade, , Sachin AND Schjeide, , Brit-Maren M. AND Schjeide, , Leif M. AND Meissner, , Esther AND Zauft, , Ute AND Allen, , Nicole C. AND Liu, , Tian AND Schilling, , Marcel AND Anderson, , Kari J. AND Beecham, , Gary AND Berg, , Daniela AND Biernacka, , Joanna M. AND Brice, , Alexis AND DeStefano, , Anita L. AND Do, , Chuong B. AND Eriksson, , Nicholas AND Factor, , Stewart A. AND Farrer, , Matthew J. AND Foroud, , Tatiana AND Gasser, , Thomas AND Hamza, , Taye AND Hardy, , John A. AND Heutink, , Peter AND Hill-Burns, , Erin M. AND Klein, , Christine AND Latourelle, , Jeanne C. AND Maraganore, , Demetrius M. AND Martin, , Eden R. AND Martinez, , Maria AND Myers, , Richard H. AND Nalls, , Michael A. AND Pankratz, , Nathan AND Payami, , Haydeh AND Satake, , Wataru AND Scott, , William K. AND Sharma, , Manu AND Singleton, , Andrew B. AND Stefansson, , Kari AND Toda, , Tatsushi AND Tung, , Joyce Y. AND Vance, , Jeffery AND Wood, , Nick W. AND Zabetian, , Cyrus P. AND Young, , Peter AND Tanzi, , Rudolph E. AND Khoury, , Muin J. AND Zipp, , Frauke AND Lehrach, , Hans AND Ioannidis, , John P. A. AND Bertram, , Lars AND 23andMe, The Genetic Epidemiology of Parkinson's Disease (GEO-PD) Consortium, The International Parkinson's Disease Genomics Consortium (IPDGC), The Parkinson's Disease GWAS Consortium, The Wellcome Trust Case Control Consortium 2 (WTCCC2)}, - journal = {PLoS Genet}, - publisher = {Public Library of Science}, - title = {Comprehensive Research Synopsis and Systematic Meta-Analyses in Parkinson's Disease Genetics: The PDGene Database}, - year = {2012}, - month = {03}, - volume = {8}, - url = {http://dx.doi.org/10.1371%2Fjournal.pgen.1002548}, - pages = {e1002548}, - abstract = {Author Summary

The genetic basis of Parkinson's disease is complex, i.e. it is determined by a number of different disease-causing and disease-predisposing genes. Especially the latter have proven difficult to find, evidenced by more than 800 published genetic association studies, typically showing discrepant results. To facilitate the interpretation of this large and continuously increasing body of data, we have created a freely available online database (“PDGeneâ€: http://www.pdgene.org) which provides an exhaustive account of all published genetic association studies in PD. One particularly useful feature is the calculation and display of up-to-date summary statistics of published data for overlapping DNA sequence variants (polymorphisms). These meta-analyses revealed eleven gene loci that showed a statistically very significant (P<5×10−8; a.k.a. genome-wide significance) association with risk for PD: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, SYT11/RAB25. In addition and purely by data-mining, we identified one novel PD susceptibility locus in a gene called ITGA8 (rs7077361, P = 1.3×10−8). We note that our continuously updated database represents the most comprehensive research synopsis of genetic association studies in PD to date. In addition to vastly facilitating the work of other PD geneticists, our approach may serve as a valuable example for other complex diseases.

}, - number = {3}, - doi = {10.1371/journal.pgen.1002548} -} - -@article{alzheimer, -author = {Tanzi, Rudolph E.}, -title = {The Genetics of Alzheimer Disease}, -volume = {2}, -number = {10}, -year = {2012}, -doi = {10.1101/cshperspect.a006296}, -abstract ={Family history is the second strongest risk factor for Alzheimer disease (AD) following advanced age. Twin and family studies indicate that genetic factors are estimated to play a role in at least 80% of AD cases. The inheritance of AD exhibits a dichotomous pattern. On one hand, rare mutations in APP, PSEN1, and PSEN2 virtually guarantee early-onset (<60 years) familial AD, which represents ∼5% of AD. On the other hand, common gene polymorphisms, such as the ε4 and ε2 variants of the APOE gene, can influence susceptibility for ∼50% of the common late-onset AD. These four genes account for 30%–50% of the inheritability of AD. Genome-wide association studies have recently led to the identification of 11 additional AD candidate genes. This paper reviews the past, present, and future attempts to elucidate the complex and heterogeneous genetic underpinnings of AD.}, -URL = {http://perspectivesinmedicine.cshlp.org/content/2/10/a006296.abstract}, -eprint = {http://perspectivesinmedicine.cshlp.org/content/2/10/a006296.full.pdf+html}, -journal = {Cold Spring Harbor Perspectives in Medicine} -} - -@article {breastcancer, -author = {Njiaju, Uchenna O. and Olopade, Olufunmilayo I.}, -title = {Genetic Determinants of Breast Cancer Risk: A Review of Current Literature and Issues Pertaining to Clinical Application}, -journal = {The Breast Journal}, -volume = {18}, -number = {5}, -publisher = {Blackwell Publishing Ltd}, -issn = {1524-4741}, -url = {http://dx.doi.org/10.1111/j.1524-4741.2012.01274.x}, -doi = {10.1111/j.1524-4741.2012.01274.x}, -pages = {436--442}, -keywords = {breast cancer, genetic risk, risk reduction}, -year = {2012}, -} - -@article{prostatecancer, -author = {Abate-Shen, Cory and Shen, Michael M.}, -title = {Molecular genetics of prostate cancer}, -volume = {14}, -number = {19}, -pages = {2410-2434}, -year = {2000}, -doi = {10.1101/gad.819500}, -URL = {http://genesdev.cshlp.org/content/14/19/2410.short}, -eprint = {http://genesdev.cshlp.org/content/14/19/2410.full.pdf+html}, -journal = {Genes & Development} -} - -@article{Ball24072012, -author = {Ball, Madeleine P. and Thakuria, Joseph V. and Zaranek, Alexander Wait and Clegg, Tom and Rosenbaum, Abraham M. and Wu, Xiaodi and Angrist, Misha and Bhak, Jong and Bobe, Jason and Callow, Matthew J. and Cano, Carlos and Chou, Michael F. and Chung, Wendy K. and Douglas, Shawn M. and Estep, Preston W. and Gore, Athurva and Hulick, Peter and Labarga, Alberto and Lee, Je-Hyuk and Lunshof, Jeantine E. and Kim, Byung Chul and Kim, Jong-Il and Li, Zhe and Murray, Michael F. and Nilsen, Geoffrey B. and Peters, Brock A. and Raman, Anugraha M. and Rienhoff, Hugh Y. and Robasky, Kimberly and Wheeler, Matthew T. and Vandewege, Ward and Vorhaus, Daniel B. and Yang, Joyce L. and Yang, Luhan and Aach, John and Ashley, Euan A. and Drmanac, Radoje and Kim, Seong-Jin and Li, Jin Billy and Peshkin, Leonid and Seidman, Christine E. and Seo, Jeong-Sun and Zhang, Kun and Rehm, Heidi L. and Church, George M.}, -title = {A public resource facilitating clinical use of genomes}, -volume = {109}, -number = {30}, -pages = {11920-11927}, -year = {2012}, -doi = {10.1073/pnas.1201904109}, -abstract ={Rapid advances in DNA sequencing promise to enable new diagnostics and individualized therapies. Achieving personalized medicine, however, will require extensive research on highly reidentifiable, integrated datasets of genomic and health information. To assist with this, participants in the Personal Genome Project choose to forgo privacy via our institutional review board- approved “open consent†process. The contribution of public data and samples facilitates both scientific discovery and standardization of methods. We present our findings after enrollment of more than 1,800 participants, including whole-genome sequencing of 10 pilot participant genomes (the PGP-10). We introduce the Genome-Environment-Trait Evidence (GET-Evidence) system. This tool automatically processes genomes and prioritizes both published and novel variants for interpretation. In the process of reviewing the presumed healthy PGP-10 genomes, we find numerous literature references implying serious disease. Although it is sometimes impossible to rule out a late-onset effect, stringent evidence requirements can address the high rate of incidental findings. To that end we develop a peer production system for recording and organizing variant evaluations according to standard evidence guidelines, creating a public forum for reaching consensus on interpretation of clinically relevant variants. Genome analysis becomes a two-step process: using a prioritized list to record variant evaluations, then automatically sorting reviewed variants using these annotations. Genome data, health and trait information, participant samples, and variant interpretations are all shared in the public domain—we invite others to review our results using our participant samples and contribute to our interpretations. We offer our public resource and methods to further personalized medical research.}, -URL = {http://www.pnas.org/content/109/30/11920.abstract}, -eprint = {http://www.pnas.org/content/109/30/11920.full.pdf+html}, -journal = {Proceedings of the National Academy of Sciences} -} - - - - - From d1aa1b44b3e46eecee32e626db32638190bab9c5 Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Sun, 28 Oct 2012 19:40:40 +0100 Subject: [PATCH 44/88] proofreading --- paper_draft.tex | 924 ++++++++++++++++++++++++------------------------ 1 file changed, 461 insertions(+), 463 deletions(-) diff --git a/paper_draft.tex b/paper_draft.tex index a2d1e4b..8a3cdf4 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -1,463 +1,461 @@ -% Template for PLoS -% Version 1.0 January 2009 -% -% To compile to pdf, run: -% latex plos.template -% bibtex plos.template -% latex plos.template -% latex plos.template -% dvipdf plos.template - -\documentclass[10pt]{article} - -% amsmath package, useful for mathematical formulas -\usepackage{amsmath} -% amssymb package, useful for mathematical symbols -\usepackage{amssymb} - -% graphicx package, useful for including eps and pdf graphics -% include graphics with the command \includegraphics -\usepackage{graphicx} - -% cite package, to clean up citations in the main text. Do not remove. -\usepackage{cite} - -\usepackage{color} - -% Use doublespacing - comment out for single spacing -%\usepackage{setspace} -%\doublespacing - -%FZ: Enable the comment command -\usepackage{verbatim} -% Text layout -\topmargin 0.0cm -\oddsidemargin 0.5cm -\evensidemargin 0.5cm -\textwidth 16cm -\textheight 21cm - -% Bold the 'Figure #' in the caption and separate it with a period -% Captions will be left justified -\usepackage[labelfont=bf,labelsep=period,justification=raggedright]{caption} - -% Use the PLoS provided bibtex style -\bibliographystyle{plos2009} - -% Remove brackets from numbering in List of References -\makeatletter -\renewcommand{\@biblabel}[1]{\quad#1.} -\makeatother - - -% Leave date blank -\date{} - -\pagestyle{myheadings} -%% ** EDIT HERE ** - - -%% ** EDIT HERE ** -%% PLEASE INCLUDE ALL MACROS BELOW -\long\def\authornote#1{% - \leavevmode\unskip\raisebox{-3.5pt}{\rlap{$\scriptstyle\diamond$}}% - \marginpar{\raggedright\hbadness=10000 - \def\baselinestretch{0.8}\tiny - \it #1\par}} -\newcommand{\bastian}[1]{\authornote{BG: #1}} -\newcommand{\fabian}[1]{\authornote{FZ: #1}} -\newcommand{\philipp}[1]{\authornote{PB: #1}} -%% END MACROS SECTION -\begin{document} - -% Title must be 150 characters or less -\begin{flushleft} -{\Large -\textbf{openSNP - Crowdsourcing Genome-Wide Association Studies} -} -% Alternative titles: -% openSNP - a new, open data-source for personalised medicine -% What kind of person would share genotyping-data? Presenting a survey and an open data-source for personalised medicine -% -% Insert Author names, affiliations and corresponding author email. -\\ -Bastian Greshake$^{1,\ast}$, -Philipp Bayer$^{2}$, -Helge Rausch$^{3}$, -Fabian Zimmer$^{4}$, -Julia Reda$^{5}$ -\\ -\bf{1} Molecular Ecology Group, Goethe University Frankfurt am Main, Germany -\\ -\bf{2} Applied Bioinformatics Group, School of Agriculture and Food Sciences, The University of Queensland, Australia -\\ -\bf{3} Hochschule f\"ur Technik und Wirtschaft Berlin, Germany -\\ -\bf{4} Evolutionary Genomics and Transcriptomics Lab, Department of Genetics, Evolution and Environment, University College London, England -\\ -\bf{5} Johannes Gutenberg University Mayence, Germany -\\ -$\ast$ E-mail: info@opensnp.org -\end{flushleft} - -% Please keep the abstract between 250 and 300 words -\section*{Abstract} -Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants. These studies usually compare the genetic variation between two groups to single out certain Single Nucleotide Polymorphisms (SNPs) that are linked to a phenotypic variation in one of the groups. However, it is necessary to have a large enough sample size to find statistically significant correlations. Direct-To-Consumer (DTC) genetic testing can supply additional data: DTC-companies offer the analysis of a large amount of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing companies. However, this data is not public for a variety of reasons and thus cannot be used in research. It seems reasonable to create a central open data-repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates whether people are willing to publicly share their genetic information. In the light of those results we present the web-platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publicly publish at no cost their genetic data along with phenotypic information. Through this crowdsourced effort of collecting genetic and phenotypic information openSNP has become a valuable resource which can be used in a wide area of studies, including Genome-wide association studies. OpenSNP is hosted at www.opensnp.org, and the code is released under MIT-license at http://github.com/gedankenstuecke/snpr - - -% Please keep the Author Summary between 150 and 200 words -% Use first person. PLoS ONE authors please skip this step. -% Author Summary not valid for PLoS ONE submissions. -\section*{Author Summary} - -\section*{Introduction} - -The availability of new DNA sequencing techniques has shifted the focus of biological data acquisition towards new biomedical applications. -Many diseases – for example Alzheimer's \cite{alzheimer}, Parkinson's \cite{parkinsons} or different types of cancers \cite{breastcancer,prostatecancer} – are at least partially heritable so the genome -of patients can be used for diagnostic purposes. Using the genetic information of patients for diagnostics is made possible through the sharp decrease in costs for analysing genetic information \cite{Brown1999}. - -%Kicked out the following due to Corpas' argument -%The comparison of DNA sequences from individuals in a population can reveal variable sites, which are of major interest in investigating diseases. -%A variation of only one nucleotide length at a given site is called a single Nucleotide Polymorphisms (SNP). -%The different nucleotides at this site are defined as an allele. -%For diploid organisms, as us humans, the genotype at a given site consists of two alleles, which are independently inherited from each parent. -%Different methods have been developed to read SNPs in an individual, a -%process called genotyping. A widespread used method for genotyping individuals is the use of microarrays. -%In comparison to whole genome sequencing only sites which are known to be variable are analysed, making microarrays cheaper and faster \cite{Brown1999}. - - -If genetic information for more than one individual is known, the analysis of -allele frequencies of Single Nucleotide Polymorphisms (SNPs) can be used to associate such SNPs with diseases and other inheritable traits. Genome-Wide Association Studies -(GWAS) make use of statistics to compare the allele frequencies in patients to the alleles in healthy controls. This -enables GWAS to find SNPs which are significantly overrepresented in patients and associates those SNPs with a trait or disease. -This method does not allow inference of causal differences but merely identifies correlations. -The first GWAS was published in 2005 and compared age-related macular degeneration in contrast -to a healthy control group \cite{Klein2005}. Since the beginning, the number of participants in -such studies has been rising. To date, over 1200 GWAS have been performed \cite{Johnson2009} and over -5000 SNPs have been linked to different diseases and traits \cite{Hindorff2009}. - -GWAS are not only performed inside the traditional scientific community. -Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. -These companies use DNA microarrays to screen for around 0.5 to 1 million SNPs spread over the human genome. In return, customers -receive an analysis of the results, as well as a file that includes the customer's raw individual genotypes. In 2011, 23andMe -alone had over 100,000 customers \cite{23andMe2011} -- the company realizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about -traits and diseases. With the consent of the customer the data is used for association studies. 23andMe has published several -articles in which known findings are replicated together with new associations disorders like Parkinson's Disease \cite{Eriksson2010, Do2011}. -So far, over 30,000 23andme-customers have participated in 23andme's association studies, which proves that this data-source has a lot of potential for other researchers. - -The generation of biomedical data by private companies raises concerns about privacy \cite{23andMe2012}, -liability and consent \cite{Caulfield2011}. -Nevertheless, in some instances individual customers are willingly sharing their data. Most do so by uploading their data to -their personal website or to open software repositories like \textit{GitHub}. -This data is scattered and unorganized, making it hard to use in studies. While projects like SNPedia try to keep track of all -the publicly available genotyping files \cite{Cariaso2011}, they usually do not provide the information necessary to perform GWAS, as the phenotypic information is -often not attached to the genetic information. Projects that attach the phenotype to the genetic information, -like the \textit{Personal Genome Project} \cite{Ball24072012}, still do not allow for an easy re-use of the data, as they currently lack an application programming interface (API) -or other methods by which researchers could download the data. Additionally, not every customer of DTC genetic testing can participate in the \textit{Personal Genome Project}. - -Here, we present the results of a survey designed to evaluate the support in the personal genetics community for a crowdsourced online platform. We also present openSNP, an online platform which enables DTC customers to share genotypic and phenoytypic information, as well as receive additional information on their genotypes. The genotypes are made available to researchers via the open Creative-Commons-Zero license. - -% Results and Discussion can be combined. -\section*{Results} -\subsection*{Survey on Sharing Genetic Information} -In total 229 people, 180 with a self-reported chromosomal sex of XY and 56 with a self-reported chromosomal sex of XX participated in our survey on sharing genetic information with the public. -The mean age of the participants is 33 (SD = 11,29). 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already -customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% do not plan on -becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Cramer's V = 0.077). - -67.7 \% of all participants would share their data with their DTC-company without any constraints, 25.8 \% would do so given the company -didn't share the data with third parties. 6.6 \% of the participants would not share their data. Participants self-identified as XX-chromosomal are slightly more likely to answer that DTC companies are allowed to use their results (Cramer's V = 0.221). Those who are customers of a DTC company or are planning on becoming one in -the future are more likely to share their results, compared to those who do not plan on getting themselves genotyped (Somers-d = 0.331). - - -There are substantial differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped -and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with motivations for sharing genotypic information. On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations -for not sharing their data, see table \ref{tab:motivations1}. - -\begin{table} -\begin{tabular}{|l|l|l|} -\hline -& Turkey's HSD & \\ \cline{2-3} -& Mean difference & SE \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who are already genotyped} & & \\ \hline -... curious & 1.159 & 0.193 \\ -... want to help scientists & 0.465 & 0.128 \\ -... for personal benefits & 0.448 & 0.183 \\ \hline -\textbf{Motivation for not sharing in participants} & & \\ -\textbf{who are not planning to get genotyped} & & \\ \hline -... fear of discrimination & 1.06 & 0.195 \\ -... breach of privacy & 0.821 & 0.225 \\ -... fear of personalized advertising & 0.848 & 0.208 \\ -... negative consequences for family members & 0.733 & 0.21 \\ \hline -\end{tabular} -\caption{Differences in terms of motivation to share genotypings with the public in survey-participants who already received a genotyping compared to participants who are not planning to getting genotyped. } -\label{tab:motivations1} -\end{table} - -\begin{table} -\begin{tabular}{|l|l|l|} -\hline -& Turkey's HSD & \\ \cline{2-3} -& Mean difference & SE \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who would share with their DTC provider} & & \\ \hline -... curiosity & 1.99 & 0.321 \\ -... want to help science & 1.57 & 0.199 \\ -... for personal benefits & 0.951 & 0.308 \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who would not share with their DTC provider} & & \\ \hline -... fear of discrimination & 1.52 & 0.322 \\ -... fear of consequences for family members & 1.146 & 0.32 \\ -... fear of personalized advertising & 1.112 & 0.357 \\ \hline -\end{tabular} -\caption{Differences in terms of motivations to share genotyping-data, comparison between participants who would share their genotyping data with their DTC provider with participants who would not share their data with their DTC provider.} -\label{tab:motivations2} -\end{table} - -Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with -the following motivations for sharing than those who would not share their data with their DTC company. -Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the some motivations -for not sharing their data when compared to those who would share their data with their DTC company under any circumstances, for an overview of the motivations of both groups, see table \ref{tab:motivations2}. - -In the case of curiosity as a motive, there is also a substantial difference between those who would share their data with their DTC company under the condition that it did not share the information and those who would not (mean difference = 1.116 SE = 0.344) as well as those who would share under any circumstances (mean difference = -0.874 SE = 0.182). - -In the cases of fear of discrimination and fear of a breach of privacy, substantial differences between all three categories exist. Those who would share their data with their DTC company as long as it did not share the information agree less strongly than those who would not share the data with both fear of discrimination as a motive for not sharing (mean difference = -0.615, SE = 0.345) as well as fear of a breach of privacy (mean difference = -0.668, SE = 0.346). Those who would share their data under any circumstances are even less likely to agree with these motives than those who would share only if their DTC company did not share the information (fear of discrimination: mean value = -0.906, SE = 0.182; breach of privacy: mean difference = -1.203, SE = 0.183). - -These survey-results indicate that there is a definite interest in customers of DTC-companies to share their results with other scientists. - -\subsubsection*{Sharing genotypic information} - -We created the openSNP project (http://opensnp.org) as an open, crowdsourced online platform for DTC customers interested in sharing their raw data and for researchers interested in performing GWAS or perform other types of analysis with the data. -Customers of DTC testing are encouraged to share their genotyping results along with their phenotypic traits to enable easy access for researchers. -Users of openSNP can create a personal profile, discuss SNPs and phenotypes on the platform using a simple commenting system, or send each other private messages. - -People interested in using the data of openSNP can download complete dumps of the genotypic and phenotypic information or use query API endpoints utilizing JavaScript Object Notation (JSON) objects or the Distributed Annotation System (DAS) \cite{Dowell2001}. - - -\subsection*{Sharing genotypic information} -Currently users can upload their genotyping results from the companies \textit{23andMe}, \textit{deCODEme }and \textit{FamilyTreeDNA} via a webinterface to the openSNP -project. There is experimental support for uploading exomes in the VCF format \cite{Danecek01082011}, as \textit{23andMe} recently started exome sequencing for its customers. So far only the SNPs of the exome data sets are visualized on openSNP, but the downloads include all variation found in the exome. -The uploaded data is published under the Creative Commons Zero-license, -which - in accordance with the Panton Principles \cite{10.1371/journal.pbio.1001195} - -allows a complete reuse of the data without any constraints. -Between the start of openSNP on 09/27/2011 and 12/18/2012, 214 people have signed -up with openSNP, and 79 genotyping files were made available. The openSNP -database lists 69,486,471 genotypes which are distributed over 1,938,603 unique SNPs. -Figure \ref{Figure1_label} depicts the increase of users and genotyping files over time.\bastian{update all numbers} - - -\subsection*{Crowdsourcing phenotypes} -Users are able to create new phenotypes that are not yet -listed by openSNP. -The specification of these phenotypes is open and not limited -to pre-defined categories. To reduce the amount of manual data curation, openSNP tries to harmonize -the expression and spelling of the same phenotype or variation. We implemented an -autocompletion-feature, which helps users to reuse already entered phenotypes. -Users are encouraged to list as many phenotypes as possible through a simple -achievement system, rewarding users that upload their data and enter phenotypic -information with small badges that are shown on their profile pages. - -In the same timeframe as above, all users combined have -entered a total of 675 variations on 47 different phenotypes with those variations being -the different values on a given trait or phenotype. See figure \ref{Figure1_label} for the increase of phenotypic information over time. - -The mean number of users that have entered their variations for a single phenotype -is 14.36 (SD 12.65), the median is 10. The distribution of how many users have -entered their data per phenotype can be seen in figure \ref{Figure2_label}. The phenotype provided -by the most users is the eye color, which has been entered by 54 users. There are -two phenotypes which have so far only been provided by a single user: -the SAT Writing score and triglyceride-levels.\bastian{update all numbers and graphs} - - -\subsection*{Connection to external services} -In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. -A total number of 15,229 documents \bastian{number needs to get updated}relevant to the SNPs listed in openSNP could be found in the publication and annotation databases of Mendeley, the Public Library of Science, in the \emph{GET Evidence System} \cite{Ball24072012} and the \emph{NHGRI GWAS Catalog} \cite{Hindorff2009} and in the crowdsourced SNPedia. -Of the primary literature listed on Mendeley \& the Public Library of Science, 25 \% are released in open access journals and can be accessed free of charge (Figure \ref{Figure3_label}). For usability reasons, -SNPs are ranked by the amount of information gathered through the external services. The external services themselves are ranked by how easily non-scientists can understand information -from these sources and how available this information is to the public. The SNPedia entries are given the highest impact, as those are already manually curated and summarized in plain English, followed by open access publications out of -the Public Library of Science and the curated databases of the \emph{GET Evidence System} and the \emph{NHGRI GWAS Catalog}. Lowest values are given to the Mendeley results, as the publications listed there are for the most part not freely available without subscriptions or one-time payments. -An entry on SNPedia is valued 2.5 times as high as a PLoS publication or entries in \emph{GET} or the \emph{GWAS Catalog} and 5 times as high as a Mendeley entry. - -\subsection*{Data access} -OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files -grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypic variation in a comma-separated table. The genetic data is also -accessible through the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}, which offers all data for specific chromosomes and specific positions on single chromosomes. -An example of how the DAS can be used can be found on openSNP where users genotypes are visualized inside a genome browser. So far all chromosomal positions are based on the human reference genome NCBI36, as this is the standard reference used by DTC providers right now. \bastian{should this maybe include an image as well?} - -The data is additionally available over a JSON-API, which allows users to directly access data in the JSON-format. The methods allow users to programmatically look for the genotypes and annotations at a given SNP as well as for phenotypes for a given user and phenotypic variation for a given phenotype. -\section*{Discussion} - -\subsection*{Survey issues} - -As the survey was taken online by voluntary participants and was mainly spread in the personal genetics community, the results do not reflect the general population, but over-represent those people most likely to be interested in a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. - - -\subsection*{Privacy, health implications and ethical considerations} - -%The advent of DTC genetic testing has led to new ethical and social issues. -Much of the critique on DTC genetic testing focusses on the practice -of delivering medical information without consulting a physician or genetic counsellor to help patients/customers make sense of the information -and to put the new knowledge to good use \cite{Hauskeller2011,Hogarth2008,Wasson2009}. - -As we have found in our survey on sharing such results (see supplementary methods), many DTC customers are willing to share their results with the public to help scientific progress, without forgetting about the privacy implications that come with openly sharing genetic information. There is a variety of ethical and privacy implications when it comes to DTC genetic testing\cite{Caulfield2011,Joh2011}. - -Our survey has shown that people are concerned about their privacy and fear that stakeholders like employers, insurance companies, governments -or advertisers might misuse the information. Policy makers start to react to those changes by introducing laws like the -\textit{Genetic Information Non-Discrimination Act} in the United States or the \emph{Gendiagnostikgesetz} in Germany to minimize the impact of -widely available genetic information. DTC genetic testing companies themselves also try to educate their customers about the risks of releasing genetic data. - -OpenSNP openly addresses the problem of privacy implications that come with releasing genetic data twice, once during registration for openSNP and once during -the upload of the DTC genetic testing results. Users have to confirm that they have read and understood the disclaimer about possible side-effects -of publishing their data. Further versions of openSNP may optionally include further consent-processes. - -\subsection*{GWAS and Open Data} -Although prices of exome or even full genome sequencing are dropping rapidly, GWAS are still considerably cheaper. However, GWAS can only detect correlations of SNPs with those traits and do not allow -inference on the cause for any correlation. Furthermore, for a statistically sound analysis GWAS need a large enough sample size. Nevertheless, GWAS are still frequently used and new associations are found \cite{10.1371.journal.pone.0031470,10.1371.journal.pone.0030309,10.1371.journal.pone.0029848}. - -One way of bringing down costs for GWAS even further is to make use of already available genotyping results and datasets. -Data produced by DTC genetic testing companies is a promising source for such data, as such companies already have high -numbers of customers which are willing to pay for the genotyping by themselves. - -By crowdsourcing the acquisition of genetic and phenotypic data, openSNP faces the same problems as any other -open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. -Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation, -with users entering data in different measurement systems. Another problem with user-entered data is the frequent switching between categorical and continuous phenotypes - for example, some users entered the specific value of their height, while other users entered their height according to a category like "150cm to 160cm". - -While we try to suggest similar entries to the users, -there are some cases where users will not follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. There are two possible solutions to this problem: The first one would be to only allow a trusted subset of users to enter new phenotypes. The other one -would be to make users enter all possible variations of a phenotype while creating a new phenotype, so that later users cannot add -variations that have not been available from the start. - -In both cases it makes it harder -for users to enter their data which raises the bar for participation. -We decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS with the data to perform additional quality control. - -Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP: only a subset of people buy DTC genetic testing, from which an even smaller subset is willing to publish the results, which can potentially lead to skewed GWAS-results. - -With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic -data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to freely use in their studies. -Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. -While there is not enough data uploaded to perform a statistically sound GWAS yet, this will be possible in the future, as user numbers continue to rise. By including the option of uploading exome data sets the platform already is capable of adjusting for changes in the type of data generated by DTC genetic testing. - - -% You may title this section "Methods" or "Models". -% "Models" is not a valid title for PLoS ONE authors. However, PLoS ONE -% authors may use "Analysis" -\section*{Materials and Methods} - -\subsection*{Survey on Sharing Genetic Information} -The survey was performed using \textit{Google Docs} and was distributed to possible participants through the \textit{23andMe }community forums, the \textit{DIYBiology} mailing list, -blogs which focus on genetics and DTC genetic testing and social media websites flike \textit{Twitter}, \textit{Google+} and \textit{Facebook}. - -The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their -(planned) customer-ship with a DTC company. If the participants already were customers, they were also asked if they were already sharing their genetic and phenotypic data. -All participants were asked if they would share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, -but only if they did not share my medical information with anybody else" and No". - -The survey also asked some scaled questions which measured how strongly participants agreed/disagreed with different reasons for sharing or not sharing their -information. The scale went from 1 = strongly disagree to 5 = strongly agree. Motivations queried for sharing data -were "because I want to help scientists with their research", "because of possible personal benefits (e.g. getting treatments for a disease I have, -possibility of new medication, etc.)", "because it may deliver advertising that is relevant to me" and "out of curiosity". Motivations queried for not sharing -data were "because advertisers could use the information for targeted campaigns", "because of possible negative consequences for closely related persons", -"because of the breach of my privacy" and "because of the fear of discrimination (e.g. by the employer, the state, some insurance company)". -Additionally, participants had the possibility of giving their own reasons for sharing or not sharing their data. - -The survey data was analyzed with SPSS 19. - -\subsection*{Technical implementation of the platform} -The main platform is implemented using the web framework Ruby on Rails 3.0.10. Postgres 9.2 is used as the main database backend for Rails. -The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs -which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of -the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, -through querying the content via the MediaWiki API. The \emph{NHGRI GWAS Catalog} and the \emph{GET Evidence System} provide complete dumps in plain text formats. Those are regularly downloaded and parsed. All databases are queried or parsed using the unique identifier of each SNP as the search term. - -SNPs are catalogued by their unique identifier, which consists of a prefix (mostly \textit{rs}, rarely \textit{i}) and a unique number. This is a common format, -which is employed by the NCBI dbSNP database \cite{Sherry2001} and is also widely used and easily parsed from different literature sources. Publications from the different databases as -well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. - -Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources -are handled using the ruby gem Resque and the standalone key-value storage server Redis. Search features on the platform itself are implemented using Solr and the ruby gem Sunspot. -Additionally, data can be requested from openSNP using the Distributed Annotation System. The required data is stored in a PostgreSQL database. -Requested data is delivered in XML-format to facilitate parsing. Additionally, users can request data in the JSON-format, using a system not specified in any standard. - -OpenSNP only serves as a platform for SNPs, so methods for the delivery of nucleotide sequences as described in the DAS-standard are not implemented. Currently, -two methods are implemented: \textit{features}, which is used to deliver SNPs located on specific chromosomes or between specific nucleotide positions, -based on the user's query. The second method is \textit{sources}, which advertises all DAS-sources for all genotypes present in openSNP. - -A flowchart of all services incorporated in openSNP and of all the ways users can upload or access the data is given in figure \ref{Figure4_label}. The source code of openSNP is -published under the MIT-license and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. -% Do NOT remove this, even if you are not including acknowledgments -\section*{Acknowledgments} -We thank Dr. Manuel Corpas and Prof. Dr. Dave Edwards for constructive advice in grammar, spelling and structure of this study. Further thanks go to Samantha Clark and Dan Bolser for providing valuable feedback and feature ideas during the development and Thomas Down, author of \emph{BioDalliance}, and Rafael Jimenez, author of \emph{MyKaryoView}, for their support on implementing the Distributed Annotation System and the genome browser. For help with their APIs we are grateful to Mike Cariaso of \emph{SNPedia} and the PLOS \& Mendeley API teams. Last but not least: We would especially like to thank the users of openSNP.org for their participation, their constructive criticisms and bug-finding abilities and especially for sharing their genotyping and phenotype data. - -%\section*{References} -% The bibtex filename -\bibliography{papers} - -\section*{Figure Legends} -\begin{figure}[!ht] - \begin{center} - \includegraphics[scale=0.35]{chart_growth.png} - \end{center} - \caption{ - {\bf Growth of openSNP.} The increase in numbers for users, genotyping-files, phenotypes and their variation from 27.09.2011 to 16.12.2011 is shown.} - \label{Figure1_label} -\end{figure} - -\begin{figure}[!ht] - \begin{center} - \includegraphics[scale=0.40]{histogram_phenotypes.png} - \end{center} - \caption{ - {\bf Histogram of users/phenotype-distribution.} The x-axis shows the minimum number of users who provide information for a phenotype, the y-axis shows how many phenotypes have at least that many users.} - \label{Figure2_label} -\end{figure} - -\begin{figure}[!ht] - \begin{center} - \includegraphics[scale=0.50]{paper_distribution.png} - \end{center} - \caption{ - {\bf Distribution of external information gathered for SNPs in the openSNP-database.} Data on PLoS and SNPedia is openly available for every user. Publications on Mendeley are either Open Access (OA) or Closed Access (CA).} - \label{Figure3_label} -\end{figure} - -\begin{figure}[!ht] - \begin{center} - \includegraphics[scale=0.60]{uml_diagram.png} - \end{center} - \caption{ - {\bf Flow of data inside openSNP.} External databases and user-provided data are used as input. Output of data is done using the website, the \emph{Distributed Annotation System} and a JSON-API.} - \label{Figure4_label} -\end{figure} - - - -%\begin{figure}[!ht] -%\begin{center} -%%\includegraphics[width=4in]{figure_name.2.eps} -%\end{center} -%\caption{ -%{\bf Bold the first sentence.} Rest of figure 2 caption. Caption -%should be left justified, as specified by the options to the caption -%package. -%} -%\label{Figure_label} -%\end{figure} - - -\section*{Tables} -%\begin{table}[!ht] -%\caption{ -%\bf{Table title}} -%\begin{tabular}{|c|c|c|} -%table information -%\end{tabular} -%\begin{flushleft}Table caption -%\end{flushleft} -%\label{tab:label} -% \end{table} - -\end{document} +% Template for PLoS +% Version 1.0 January 2009 +% +% To compile to pdf, run: +% latex plos.template +% bibtex plos.template +% latex plos.template +% latex plos.template +% dvipdf plos.template + +\documentclass[10pt]{article} + +% amsmath package, useful for mathematical formulas +\usepackage{amsmath} +% amssymb package, useful for mathematical symbols +\usepackage{amssymb} + +% graphicx package, useful for including eps and pdf graphics +% include graphics with the command \includegraphics +\usepackage{graphicx} + +% cite package, to clean up citations in the main text. Do not remove. +\usepackage{cite} + +\usepackage{color} + +% Use doublespacing - comment out for single spacing +%\usepackage{setspace} +%\doublespacing + +%FZ: Enable the comment command +\usepackage{verbatim} +% Text layout +\topmargin 0.0cm +\oddsidemargin 0.5cm +\evensidemargin 0.5cm +\textwidth 16cm +\textheight 21cm + +% Bold the 'Figure #' in the caption and separate it with a period +% Captions will be left justified +\usepackage[labelfont=bf,labelsep=period,justification=raggedright]{caption} + +% Use the PLoS provided bibtex style +\bibliographystyle{plos2009} + +% Remove brackets from numbering in List of References +\makeatletter +\renewcommand{\@biblabel}[1]{\quad#1.} +\makeatother + + +% Leave date blank +\date{} + +\pagestyle{myheadings} +%% ** EDIT HERE ** + + +%% ** EDIT HERE ** +%% PLEASE INCLUDE ALL MACROS BELOW +\long\def\authornote#1{% + \leavevmode\unskip\raisebox{-3.5pt}{\rlap{$\scriptstyle\diamond$}}% + \marginpar{\raggedright\hbadness=10000 + \def\baselinestretch{0.8}\tiny + \it #1\par}} +\newcommand{\bastian}[1]{\authornote{BG: #1}} +\newcommand{\fabian}[1]{\authornote{FZ: #1}} +\newcommand{\philipp}[1]{\authornote{PB: #1}} +%% END MACROS SECTION +\begin{document} + +% Title must be 150 characters or less +\begin{flushleft} +{\Large +\textbf{openSNP - a crowdsourced web resource for personal genomics} +} +% Alternative titles: +% openSNP - a new, open data-source for personalised medicine +% What kind of person would share genotyping-data? Presenting a survey and an open data-source for personalised medicine +% +% Insert Author names, affiliations and corresponding author email. +\\ +Bastian Greshake$^{1,\ast}$, +Philipp Bayer$^{2}$, +Helge Rausch$^{3}$, +Fabian Zimmer$^{4}$, +Julia Reda$^{5}$ +\\ +\bf{1} Molecular Ecology Group, Goethe University Frankfurt am Main, Germany +\\ +\bf{2} Applied Bioinformatics Group, School of Agriculture and Food Sciences, The University of Queensland, Australia +\\ +\bf{3} Hochschule f\"ur Technik und Wirtschaft Berlin, Germany +\\ +\bf{4} Evolutionary Genomics and Transcriptomics Lab, Department of Genetics, Evolution and Environment, University College London, England +\\ +\bf{5} Johannes Gutenberg University Mayence, Germany +\\ +$\ast$ E-mail: info@opensnp.org +\end{flushleft} + +% Please keep the abstract between 250 and 300 words +\section*{Abstract} +Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants. These studies usually compare the genetic variation between two groups to single out certain Single Nucleotide Polymorphisms (SNPs) that are linked to a phenotypic variation in one of the groups. However, it is necessary to have a large enough sample size to find statistically significant correlations. Direct-To-Consumer (DTC) genetic testing can supply additional data: DTC-companies offer the analysis of a large amount of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing companies. However, this data is not public for a variety of reasons and thus cannot be used in research. It seems reasonable to create a central open data repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates whether people are willing to publicly share their genetic information. In the light of those results we present the web platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publish at no cost their genetic data along with phenotypic information. Through this crowdsourced effort of collecting genetic and phenotypic information, openSNP has become a valuable resource, which can be used in a wide area of studies, including Genome-wide association studies. OpenSNP is hosted at http://www.opensnp.org, and the code is released under MIT-license at http://github.com/gedankenstuecke/snpr + + +% Please keep the Author Summary between 150 and 200 words +% Use first person. PLoS ONE authors please skip this step. +% Author Summary not valid for PLoS ONE submissions. +\section*{Author Summary} + +\section*{Introduction} + +The availability of new DNA sequencing techniques has shifted the focus of biological data acquisition towards new biomedical applications. +Many diseases - for example Alzheimer's \cite{alzheimer}, Parkinson's \cite{parkinsons} or different types of cancers \cite{breastcancer,prostatecancer} - are at least partially heritable, so the genome +of patients can be used for diagnostic purposes. Using the genetic information of patients for diagnostics is made possible through the sharp decrease in costs for analysing genetic information \cite{Brown1999}. + +%Kicked out the following due to Corpas' argument +%The comparison of DNA sequences from individuals in a population can reveal variable sites, which are of major interest in investigating diseases. +%A variation of only one nucleotide length at a given site is called a single Nucleotide Polymorphisms (SNP). +%The different nucleotides at this site are defined as an allele. +%For diploid organisms, as us humans, the genotype at a given site consists of two alleles, which are independently inherited from each parent. +%Different methods have been developed to read SNPs in an individual, a +%process called genotyping. A widespread used method for genotyping individuals is the use of microarrays. +%In comparison to whole genome sequencing only sites which are known to be variable are analysed, making microarrays cheaper and faster \cite{Brown1999}. + + +If genetic information on more than one individual is known, the analysis of +allele frequencies of Single Nucleotide Polymorphisms (SNPs) can be used to associate such SNPs with diseases and other inheritable traits. Genome-Wide Association Studies +(GWAS) make use of statistics to compare the allele frequencies in patients to the alleles in healthy controls. This +enables GWAS to find SNPs which are significantly overrepresented in patients and associates those SNPs with a trait or disease. +This method does not allow inference of causal differences but merely identifies correlations. +The first GWAS was published in 2005 and compared age-related macular degeneration in contrast +to a healthy control group \cite{Klein2005}. Since the beginning, the number of participants in +such studies has been rising. To date, over 1200 GWAS have been performed \cite{Johnson2009} and over +5000 SNPs have been linked to different diseases and traits \cite{Hindorff2009}. + +GWAS are not only performed inside the traditional scientific community. +Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. +These companies use DNA microarrays to screen for around 0.5 to 1 million SNPs spread over the human genome. In return, customers +receive an analysis of the results, as well as a file that includes the customer's raw individual genotypes. In 2011, 23andMe +alone had over 100,000 customers \cite{23andMe2011} +- the company realizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about +traits and diseases. With the consent of the customer, the data is used for association studies. 23andMe has published several +articles in which known findings are replicated together with new associations disorders like Parkinson's Disease \cite{Eriksson2010, Do2011}. +So far, over 30,000 23andMe-customers have participated in 23andMe's association studies, which proves that this data source has a lot of potential for other researchers. + +The generation of biomedical data by private companies raises concerns about privacy \cite{23andMe2012}, +liability and consent \cite{Caulfield2011}. +Nevertheless, in some instances individual customers are willingly sharing their data. Most do so by uploading their data to +their personal website or to open software repositories like \textit{GitHub}. +This data is scattered and unorganized, making it hard to use in studies. While projects like SNPedia try to keep track of all +the publicly available genotyping files \cite{Cariaso2011}, they usually do not provide the information necessary to perform GWAS, as the phenotypic information is +often not attached to the genetic information. Projects that attach the phenotype to the genetic information, +like the \textit{Personal Genome Project} \cite{Ball24072012}, still do not allow for an easy re-use of the data, as they currently lack an application programming interface (API) +or other methods by which researchers could download the data. Additionally, not every customer of DTC genetic testing can participate in the \textit{Personal Genome Project}. + +Here, we present the results of a survey designed to evaluate the support in the personal genetics community for a crowdsourced online platform. We also present openSNP, an online platform which enables DTC customers to share genotypic and phenoytypic information, as well as receive additional information on their genotypes. The genotypes are made available to researchers via the open Creative Commons Zero license. + +% Results and Discussion can be combined. +\section*{Results} +\subsection*{Survey on Sharing Genetic Information} +In total 229 people, 180 with a self-reported chromosomal sex of XY and 56 with a self-reported chromosomal sex of XX, participated in our survey on sharing genetic information with the public. +The mean age of the participants is 33 (SD = 11,29). 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already +customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% do not plan on +becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Cramer's V = 0.077). + +67.7 \% of all participants would share their data with their DTC company without any constraints, 25.8 \% would do so given the company +didn't share the data with third parties. 6.6 \% of the participants would not share their data. Participants self-identified as XX-chromosomal are slightly more likely to answer that DTC companies are allowed to use their results (Cramer's V = 0.221). Those who are customers of a DTC company or are planning on becoming one in +the future are more likely to share their results, compared to those who do not plan on getting themselves genotyped (Somers-d = 0.331). + + +There are substantial differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped +and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with motivations for sharing genotypic information. On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations +for not sharing their data, see table \ref{tab:motivations1}. + +Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with +the following motivations for sharing than those who would not share their data with their DTC company. +Those participants who are not willing to share data with their DTC company are likely to agree more strongly with some motivations +for not sharing their data when compared to those who would share their data with their DTC company under any circumstances. For an overview of the motivations of both groups, see table \ref{tab:motivations2}. + +In the case of curiosity as a motive, there is also a substantial difference between those who would share their data with their DTC company under the condition that it did not share the information and those who would not (mean difference = 1.116 SE = 0.344) as well as those who would share under any circumstances (mean difference = -0.874 SE = 0.182). + +In the cases of fear of discrimination and fear of a breach of privacy, substantial differences between all three categories exist. Those who would share their data with their DTC company as long as it did not share the information agree less strongly than those who would not share the data with both fear of discrimination as a motive for not sharing (mean difference = -0.615, SE = 0.345) as well as fear of a breach of privacy (mean difference = -0.668, SE = 0.346). Those who would share their data under any circumstances are even less likely to agree with these motives than those who would share only if their DTC company did not share the information (fear of discrimination: mean value = -0.906, SE = 0.182; breach of privacy: mean difference = -1.203, SE = 0.183). + +These survey results indicate that there is a definite interest in customers of DTC companies to share their results with other scientists. + +\subsubsection*{Sharing genotypic information} + +We created the openSNP project (http://opensnp.org) as an open, crowdsourced online platform for DTC customers interested in sharing their raw data and for researchers interested in performing GWAS or other types of analysis with the data. +Customers of DTC testing are encouraged to share their genotyping results along with their phenotypic traits to enable easy access for researchers. +Users of openSNP can create a personal profile, discuss SNPs and phenotypes on the platform using a simple commenting system, or send each other private messages. + +People interested in using the data of openSNP can download complete dumps of the genotypic and phenotypic information or use query API endpoints utilizing JavaScript Object Notation (JSON) objects or the Distributed Annotation System (DAS) \cite{Dowell2001}. + + +\subsection*{Sharing genotypic information} +Currently users can upload their genotyping results from the companies \textit{23andMe}, \textit{deCODEme }and \textit{FamilyTreeDNA} via a web interface to the openSNP +project. There is experimental support for uploading exomes in the VCF format \cite{Danecek01082011}, as \textit{23andMe} recently started exome sequencing for its customers. So far only the SNPs of the exome data sets are visualized on openSNP, but the downloads include all variation found in the exome. +The uploaded data is published under the Creative Commons Zero license, +which - in accordance with the Panton Principles \cite{10.1371/journal.pbio.1001195} - +allows a complete re-use of the data without any constraints. +Between the start of openSNP on 09/27/2011 and 10/27/2012, 633 people have signed +up with openSNP, and 270 genetic datasets have been made available. The openSNP +database lists 215,546,685 genotypes which are distributed over 2,140,643 unique SNPs. +Figures \ref{Figure1_label} and \ref{Figure2_label} depict the increase in users and genotyping files over time. + + +\subsection*{Crowdsourcing phenotypes} +Users are able to create new phenotypes that are not yet +listed by openSNP. +The specification of these phenotypes is open and not limited +to pre-defined categories. To reduce the amount of manual data curation, openSNP tries to harmonize +the expression and spelling of the same phenotype or variation. We implemented an +autocompletion feature, which helps users reuse already entered phenotypes. +Users are encouraged to list as many phenotypes as possible through a simple +achievement system, rewarding users that upload their data and enter phenotypic +information with badges that are shown on their profile pages. + +In the same timeframe mentioned above, all users combined have +entered a total of 4743 variations on 130 different phenotypes with those variations being +the different values on a given trait or phenotype. The mean number of users that have entered their variations for a single phenotype +is 36.48. The distribution of how many users have +entered their data per phenotype, compared to the amount of unique phenotypes, can be seen in figure \ref{pheno}. The phenotype provided by the most users is "eye color", for which 207 users entered their phenotype (retrieved 10/27/2012). + + +\subsection*{Connection to external services} +In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. +A total number of 21,134 documents relevant to the SNPs listed in openSNP could be found in the publication and annotation databases of Mendeley, the Public Library of Science, in the \emph{GET Evidence System} \cite{Ball24072012} and the \emph{NHGRI GWAS Catalog} \cite{Hindorff2009} and in the crowdsourced SNPedia. +Of the primary literature listed on Mendeley \& the Public Library of Science, 25 \% are released in open access journals and can be accessed free of charge (Figure \ref{Figure3_label}). + +For usability reasons, +SNPs are ranked by the amount of information gathered through the external services. The external services themselves are ranked by how easily non-scientists can understand information +from these sources and how available this information is to the public. The SNPedia entries are given the highest impact, as those are already manually curated and summarized in plain English, followed by open access publications out of +the Public Library of Science and the curated databases of the \emph{GET Evidence System} and the \emph{NHGRI GWAS Catalog}. Lowest values are given to the Mendeley results, as the publications listed there are for the most part not freely available without subscriptions or one-time payments. +An entry on SNPedia is valued 2.5 times as high as a PLoS publication or entries in \emph{GET} or the \emph{GWAS Catalog} and 5 times as high as a Mendeley entry. + +Users are also able to link their Fitbit accounts to their user-accounts. Fitbit is a commercial service which lets its customers track their BMI, movement and sleep data. This data can be linked to openSNP to give interested researchers an automatically maintained dataset of body and sleep developments over time. + +\subsection*{Data access} +OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files +grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypic variation in a comma-separated table. The genetic data is also +accessible through the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}, which offers all data for specific chromosomes and specific positions on single chromosomes. +An example of how the DAS can be used is implemented on openSNP, where users' genotypes are visualized inside a genome browser. So far, all chromosomal positions are based on the human reference genome NCBI36, as this is the standard reference used by DTC providers right now. \bastian{should this maybe include an image as well?} + +The data is additionally available over a JSON API, which allows users to directly access data in the JSON format. The methods allow users to programmatically look for the genotypes and annotations at a given SNP as well as for phenotypes for a given user and phenotypic variation for a given phenotype. + +\section*{Discussion} + +\subsection*{Survey issues} + +As the survey was taken online by voluntary participants and was mainly spread in the personal genetics community, the results do not reflect the general population, but over-represent those people most likely to be interested in a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. + + +\subsection*{Privacy, health implications and ethical considerations} + +%The advent of DTC genetic testing has led to new ethical and social issues. +Much of the criticism of DTC genetic testing focusses on the practice +of delivering medical information without consulting a physician or genetic counsellor to help patients/customers make sense of the information +and to put the new knowledge to good use \cite{Hauskeller2011,Hogarth2008,Wasson2009}. + +As we have found in our survey on sharing such results (see supplementary methods), many DTC customers are willing to share their results with the public to help scientific progress, without forgetting about the privacy implications that come with openly sharing genetic information. There is a variety of ethical and privacy implications when it comes to DTC genetic testing\cite{Caulfield2011,Joh2011}. + +Our survey has shown that people are concerned about their privacy and fear that stakeholders like employers, insurance companies, governments +or advertisers might misuse the information. Policy makers start to react to those changes by introducing laws like the +\textit{Genetic Information Non-Discrimination Act} in the United States or the \emph{Gendiagnostikgesetz} in Germany to minimize the impact of +widely available genetic information. DTC genetic testing companies themselves also try to educate their customers about the risks of releasing genetic data. + +OpenSNP openly addresses the problem of privacy implications that come with releasing genetic data twice, once during registration for openSNP and once during +the upload of the DTC genetic testing results. Users have to confirm that they have read and understood the disclaimer about possible side-effects +of publishing their data. Further versions of openSNP may optionally include further consent processes. + +\subsection*{GWAS and Open Data} +Although prices of exome or even full genome sequencing are dropping rapidly, GWAS are still considerably cheaper. However, GWAS can only detect correlations of SNPs with those traits and do not allow +inference on the cause for any correlation. Furthermore, for a statistically sound analysis, GWAS need a large enough sample size. Nevertheless, GWAS are still frequently used and new associations are found \cite{10.1371.journal.pone.0031470,10.1371.journal.pone.0030309,10.1371.journal.pone.0029848}. + +One way of bringing down costs for GWAS even further is to make use of already available genotyping results and datasets. +Data produced by DTC genetic testing companies is a promising source for such results, as those companies already have high +numbers of customers which are willing to pay for the genotyping by themselves. + +By crowdsourcing the acquisition of genetic and phenotypic data, openSNP faces the same problems as any other +open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. +Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation, +with users entering data in different measurement systems. Another problem with user-entered data is the frequent switching between categorical and continuous phenotypes - for example, some users entered the specific value of their height, while other users entered their height according to a category like "150cm to 160cm". + +While we try to suggest similar entries to the users, +there are some cases where users will not follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. There are two possible solutions to this problem: The first one would be to only allow a trusted subset of users to enter new phenotypes. The other one +would be to make users enter all possible variations of a phenotype while creating a new phenotype, so that later users cannot add +variations that have not been available from the start. + +In both cases it makes it harder +for users to enter their data which raises the bar for participation. +We decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS with the data to perform additional quality control. + +Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP: only a subset of people buy DTC genetic testing, from which an even smaller subset is willing to publish the results, which can potentially lead to skewed GWAS-results. + +With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic +data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to freely use in their studies. +Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. +While there is not enough data uploaded to perform a statistically sound GWAS yet, this will be possible in the future, as user numbers continue to rise. By including the option of uploading exome data sets the platform already is capable of adjusting for changes in the type of data generated by DTC genetic testing. + + +% You may title this section "Methods" or "Models". +% "Models" is not a valid title for PLoS ONE authors. However, PLoS ONE +% authors may use "Analysis" +\section*{Materials and Methods} + +\subsection*{Survey on Sharing Genetic Information} +The survey was performed using \textit{Google Docs} and was distributed to possible participants through the \textit{23andMe }community forums, the \textit{DIYBiology} mailing list, +blogs which focus on genetics and DTC genetic testing and social media websites like \textit{Twitter}, \textit{Google+} and \textit{Facebook}. + +The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their +(planned) customership with a DTC company. If the participants already were customers, they were also asked if they were already sharing their genetic and phenotypic data. +All participants were asked if they would be willing to share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, +but only if they did not share my medical information with anybody else" and No". + +The survey also asked some scaled questions, which measured how strongly participants agreed/disagreed with different reasons for sharing or not sharing their +information. The scale went from 1 = strongly disagree to 5 = strongly agree. Motivations queried for sharing data +were "because I want to help scientists with their research", "because of possible personal benefits (e.g. getting treatments for a disease I have, +possibility of new medication, etc.)", "because it may deliver advertising that is relevant to me" and "out of curiosity". Motivations queried for not sharing +data were "because advertisers could use the information for targeted campaigns", "because of possible negative consequences for closely related persons", +"because of the breach of my privacy" and "because of the fear of discrimination (e.g. by the employer, the state, some insurance company)". +Additionally, participants had the possibility of giving their own reasons for sharing or not sharing their data. + +The survey data was analyzed with SPSS 19. + +\subsection*{Technical implementation of the platform} +The main platform is implemented using the web framework Ruby on Rails 3.0.10. Postgres 9.2 is used as the main database backend for Rails. +The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs +which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of +the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, +through querying the content via the MediaWiki API. The \emph{NHGRI GWAS Catalog} and the \emph{GET Evidence System} provide complete dumps in plain text formats. Those are regularly downloaded and parsed. All databases are queried or parsed using the unique identifier of each SNP as the search term. + +SNPs are catalogued by their unique identifier, which consists of a prefix (mostly \textit{rs}, rarely \textit{i}) and a unique number. This is a common format, +which is employed by the NCBI dbSNP database \cite{Sherry2001} and is also widely used and easily parsed from different literature sources. Publications from the different databases as +well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. + +Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources +are handled using the ruby gem Resque and the standalone key-value storage server Redis. Search features on the platform itself are implemented using Solr and the ruby gem Sunspot. +Additionally, data can be requested from openSNP using the Distributed Annotation System. The required data is stored in a PostgreSQL database. +Requested data is delivered in XML-format to facilitate parsing. Additionally, users can request data in the JSON-format, using a system not specified in any standard. + +OpenSNP only serves as a platform for SNPs, so methods for the delivery of nucleotide sequences as described in the DAS-standard are not implemented. Currently, +two methods are implemented: firstly \textit{features}, which is used to deliver SNPs located on specific chromosomes or between specific nucleotide positions, +based on the user's query. The second method is \textit{sources}, which advertises all DAS sources for all genotypes present in openSNP. + +A flowchart of all services incorporated in openSNP and of all the ways users can upload or access the data is given in figure \ref{Figure4_label}. The source code of openSNP is +published under the MIT license and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. +% Do NOT remove this, even if you are not including acknowledgments +\section*{Acknowledgments} +We thank Dr. Manuel Corpas and Prof. Dr. Dave Edwards for constructive advice in grammar, spelling and structure of this study. Further thanks go to Samantha Clark and Dan Bolser for providing valuable feedback and feature ideas during the development and Thomas Down, author of \emph{BioDalliance}, and Rafael Jimenez, author of \emph{MyKaryoView}, for their support on implementing the Distributed Annotation System and the genome browser. For help with their APIs we are grateful to Mike Cariaso of \emph{SNPedia} and the PLOS \& Mendeley API teams. We would especially like to thank the users of openSNP.org for their participation, their constructive criticism and bug-finding abilities and especially for sharing their genotyping and phenotype data. + +%\section*{References} +% The bibtex filename +\bibliography{papers} + +\section*{Figure Legends} +\begin{figure}[!ht] + \begin{center} + \includegraphics[scale=0.35]{25_10_2012_Graphs/users.png} + \end{center} + \caption{ + {\bf Growth of openSNP-user-accounts.} The increase in numbers for users from 27.09.2011 to 27.10.2012 is shown.} + \label{Figure1_label} +\end{figure} +\begin{figure}[!ht] + \begin{center} + \includegraphics[scale=0.35]{25_10_2012_Graphs/genotypes.png} + \end{center} + \caption{ + {\bf Growth of available genotypings.} The increase in numbers for genotyping-files from 27.09.2011 to 27.10.2012 is shown.} + \label{Figure2_label} +\end{figure} +\begin{figure}[!ht] + \begin{center} + \includegraphics[scale=0.40]{25_10_2012_Graphs/phenotypes_vs_userphenotypes.png} + \end{center} + \caption{ + {\bf Development of unique phenotypes and phenotypic information over time.} The x-axis shows the time-frame from start of the project until October 2012, the left y-axis shows how many unique phenotypes have been entered, and the right y-axis shows the amount of phenotypes users entered.} + \label{pheno} +\end{figure} + + +\begin{figure}[!ht] + \begin{center} + + \end{center} + \caption{ + {\bf Flow of data inside openSNP.} External databases and user-provided data are used as input. Output of data is done using the website, the \emph{Distributed Annotation System} and a JSON-API.} + \label{Figure4_label} +\end{figure} + + + +%\begin{figure}[!ht] +%\begin{center} +%%\includegraphics[width=4in]{figure_name.2.eps} +%\end{center} +%\caption{ +%{\bf Bold the first sentence.} Rest of figure 2 caption. Caption +%should be left justified, as specified by the options to the caption +%package. +%} +%\label{Figure_label} +%\end{figure} + + +\section*{Tables} +\begin{table} +\begin{tabular}{|l|l|l|} +\hline +& Turkey's HSD & \\ \cline{2-3} +& Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who are already genotyped} & & \\ \hline +... curious & 1.159 & 0.193 \\ +... want to help scientists & 0.465 & 0.128 \\ +... for personal benefits & 0.448 & 0.183 \\ \hline +\textbf{Motivation for not sharing in participants} & & \\ +\textbf{who are not planning to get genotyped} & & \\ \hline +... fear of discrimination & 1.06 & 0.195 \\ +... breach of privacy & 0.821 & 0.225 \\ +... fear of personalized advertising & 0.848 & 0.208 \\ +... negative consequences for family members & 0.733 & 0.21 \\ \hline +\end{tabular} +\caption{Differences in terms of motivation to share genotypings with the public in survey-participants who already received a genotyping compared to participants who are not planning to getting genotyped. } +\label{tab:motivations1} +\end{table} + +\begin{table} +\begin{tabular}{|l|l|l|} +\hline +& Turkey's HSD & \\ \cline{2-3} +& Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who would share with their DTC provider} & & \\ \hline +... curiosity & 1.99 & 0.321 \\ +... want to help science & 1.57 & 0.199 \\ +... for personal benefits & 0.951 & 0.308 \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who would not share with their DTC provider} & & \\ \hline +... fear of discrimination & 1.52 & 0.322 \\ +... fear of consequences for family members & 1.146 & 0.32 \\ +... fear of personalized advertising & 1.112 & 0.357 \\ \hline +\end{tabular} +\caption{Differences in terms of motivations to share genotyping-data, comparison between participants who would share their genotyping data with their DTC provider with participants who would not share their data with their DTC provider.} +\label{tab:motivations2} +\end{table} +%\begin{table}[!ht] +%\caption{ +%\bf{Table title}} +%\begin{tabular}{|c|c|c|} +%table information +%\end{tabular} +%\begin{flushleft}Table caption +%\end{flushleft} +%\label{tab:label} +% \end{table} + +\end{document} From bb4f69809c44dab58f11e565c9bea1a44ac9d29e Mon Sep 17 00:00:00 2001 From: drsnuggles Date: Mon, 29 Oct 2012 14:25:27 +1000 Subject: [PATCH 45/88] Added piechart and dotchart on distribution of annotational sources. --- 25_10_2012_Graphs/papers_dotchart.svg | 237 ++++++++++++++++++++++++++ 25_10_2012_Graphs/papers_pie.R | 9 + 25_10_2012_Graphs/papers_pie.svg | 144 ++++++++++++++++ 3 files changed, 390 insertions(+) create mode 100644 25_10_2012_Graphs/papers_dotchart.svg create mode 100644 25_10_2012_Graphs/papers_pie.R create mode 100644 25_10_2012_Graphs/papers_pie.svg diff --git a/25_10_2012_Graphs/papers_dotchart.svg b/25_10_2012_Graphs/papers_dotchart.svg new file mode 100644 index 0000000..84ebb01 --- /dev/null +++ b/25_10_2012_Graphs/papers_dotchart.svg @@ -0,0 +1,237 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/25_10_2012_Graphs/papers_pie.R b/25_10_2012_Graphs/papers_pie.R new file mode 100644 index 0000000..c984f51 --- /dev/null +++ b/25_10_2012_Graphs/papers_pie.R @@ -0,0 +1,9 @@ +names <- c("SNPedia", "PLOS", "Mendeley", "PGP", "Genome.gov") +numbers <- c(8257, 1771, 11116, 289, 3095) +svg("papers_pie.svg") +pie(numbers, labels=names) +dev.off() + +svg("papers_dotchart.svg") +dotchart(numbers, labels=names, xlab="Amount of annotational entries", xlim=c(0, 12000)) +dev.off() diff --git a/25_10_2012_Graphs/papers_pie.svg b/25_10_2012_Graphs/papers_pie.svg new file mode 100644 index 0000000..eb29f58 --- /dev/null +++ b/25_10_2012_Graphs/papers_pie.svg @@ -0,0 +1,144 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From fc2c432644ae34711ae275d96f6e98b38afd5a09 Mon Sep 17 00:00:00 2001 From: drsnuggles Date: Mon, 29 Oct 2012 15:42:38 +1000 Subject: [PATCH 46/88] Changed my name slightly (because PB is all over pubmed, PEB is not.). 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a/25_10_2012_Graphs/papers_pie.R +++ b/25_10_2012_Graphs/papers_pie.R @@ -1,5 +1,5 @@ -names <- c("SNPedia", "PLOS", "Mendeley", "PGP", "Genome.gov") -numbers <- c(8257, 1771, 11116, 289, 3095) +numbers <- c(289, 1771, 3095, 8257, 11116) +names <- c("PGP", "PLOS", "NHGRI Catalog", "SNPedia", "Mendeley") svg("papers_pie.svg") pie(numbers, labels=names) dev.off() diff --git a/25_10_2012_Graphs/papers_pie.svg b/25_10_2012_Graphs/papers_pie.svg index eb29f58..f8601f4 100644 --- a/25_10_2012_Graphs/papers_pie.svg +++ b/25_10_2012_Graphs/papers_pie.svg @@ -18,45 +18,54 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + + + + + + + + + + @@ -76,7 +85,7 @@ - + @@ -102,16 +111,19 @@ - - - - - - - - - - + + + + + + + + + + + + + @@ -119,26 +131,26 @@ - + - - - - + + + + - - - - - - - - + + + + + + + + diff --git a/paper_draft.pdf b/paper_draft.pdf index 47fe073ec0ca328888895fc4f8bdc52efd4be46a..aadd7194903185a3bb5a9bbf2ed30445492c390d 100644 GIT binary patch delta 48720 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Do not remove. -\usepackage{cite} - -\usepackage{color} - -% Use doublespacing - comment out for single spacing -%\usepackage{setspace} -%\doublespacing - -%FZ: Enable the comment command -\usepackage{verbatim} -% Text layout -\topmargin 0.0cm -\oddsidemargin 0.5cm -\evensidemargin 0.5cm -\textwidth 16cm -\textheight 21cm - -% Bold the 'Figure #' in the caption and separate it with a period -% Captions will be left justified -\usepackage[labelfont=bf,labelsep=period,justification=raggedright]{caption} - -% Use the PLoS provided bibtex style -\bibliographystyle{plos2009} - -% Remove brackets from numbering in List of References -\makeatletter -\renewcommand{\@biblabel}[1]{\quad#1.} -\makeatother - - -% Leave date blank -\date{} - -\pagestyle{myheadings} -%% ** EDIT HERE ** - - -%% ** EDIT HERE ** -%% PLEASE INCLUDE ALL MACROS BELOW -\long\def\authornote#1{% - \leavevmode\unskip\raisebox{-3.5pt}{\rlap{$\scriptstyle\diamond$}}% - \marginpar{\raggedright\hbadness=10000 - \def\baselinestretch{0.8}\tiny - \it #1\par}} -\newcommand{\bastian}[1]{\authornote{BG: #1}} -\newcommand{\fabian}[1]{\authornote{FZ: #1}} -\newcommand{\philipp}[1]{\authornote{PB: #1}} -%% END MACROS SECTION -\begin{document} - -% Title must be 150 characters or less -\begin{flushleft} -{\Large -\textbf{openSNP - a crowdsourced web resource for personal genomics} -} -% Alternative titles: -% openSNP - a new, open data-source for personalised medicine -% What kind of person would share genotyping-data? Presenting a survey and an open data-source for personalised medicine -% -% Insert Author names, affiliations and corresponding author email. -\\ -Bastian Greshake$^{1,\ast}$, -Philipp Bayer$^{2}$, -Helge Rausch$^{3}$, -Fabian Zimmer$^{4}$, -Julia Reda$^{5}$ -\\ -\bf{1} Molecular Ecology Group, Goethe University Frankfurt am Main, Germany -\\ -\bf{2} Applied Bioinformatics Group, School of Agriculture and Food Sciences, The University of Queensland, Australia -\\ -\bf{3} Hochschule f\"ur Technik und Wirtschaft Berlin, Germany -\\ -\bf{4} Evolutionary Genomics and Transcriptomics Lab, Department of Genetics, Evolution and Environment, University College London, England -\\ -\bf{5} Johannes Gutenberg University Mayence, Germany -\\ -$\ast$ E-mail: info@opensnp.org -\end{flushleft} - -% Please keep the abstract between 250 and 300 words -\section*{Abstract} -Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants. These studies usually compare the genetic variation between two groups to single out certain Single Nucleotide Polymorphisms (SNPs) that are linked to a phenotypic variation in one of the groups. However, it is necessary to have a large enough sample size to find statistically significant correlations. Direct-To-Consumer (DTC) genetic testing can supply additional data: DTC-companies offer the analysis of a large amount of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing companies. However, this data is not public for a variety of reasons and thus cannot be used in research. It seems reasonable to create a central open data repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates whether people are willing to publicly share their genetic information. In the light of those results we present the web platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publish at no cost their genetic data along with phenotypic information. Through this crowdsourced effort of collecting genetic and phenotypic information, openSNP has become a valuable resource, which can be used in a wide area of studies, including Genome-wide association studies. OpenSNP is hosted at http://www.opensnp.org, and the code is released under MIT-license at http://github.com/gedankenstuecke/snpr - - -% Please keep the Author Summary between 150 and 200 words -% Use first person. PLoS ONE authors please skip this step. -% Author Summary not valid for PLoS ONE submissions. -\section*{Author Summary} - -\section*{Introduction} - -The availability of new DNA sequencing techniques has shifted the focus of biological data acquisition towards new biomedical applications. -Many diseases - for example Alzheimer's \cite{alzheimer}, Parkinson's \cite{parkinsons} or different types of cancers \cite{breastcancer,prostatecancer} - are at least partially heritable, so the genome -of patients can be used for diagnostic purposes. Using the genetic information of patients for diagnostics is made possible through the sharp decrease in costs for analysing genetic information \cite{Brown1999}. - -%Kicked out the following due to Corpas' argument -%The comparison of DNA sequences from individuals in a population can reveal variable sites, which are of major interest in investigating diseases. -%A variation of only one nucleotide length at a given site is called a single Nucleotide Polymorphisms (SNP). -%The different nucleotides at this site are defined as an allele. -%For diploid organisms, as us humans, the genotype at a given site consists of two alleles, which are independently inherited from each parent. -%Different methods have been developed to read SNPs in an individual, a -%process called genotyping. A widespread used method for genotyping individuals is the use of microarrays. -%In comparison to whole genome sequencing only sites which are known to be variable are analysed, making microarrays cheaper and faster \cite{Brown1999}. - - -If genetic information on more than one individual is known, the analysis of -allele frequencies of Single Nucleotide Polymorphisms (SNPs) can be used to associate such SNPs with diseases and other inheritable traits. Genome-Wide Association Studies -(GWAS) make use of statistics to compare the allele frequencies in patients to the alleles in healthy controls. This -enables GWAS to find SNPs which are significantly overrepresented in patients and associates those SNPs with a trait or disease. -This method does not allow inference of causal differences but merely identifies correlations. -The first GWAS was published in 2005 and compared age-related macular degeneration in contrast -to a healthy control group \cite{Klein2005}. Since the beginning, the number of participants in -such studies has been rising. To date, over 1200 GWAS have been performed \cite{Johnson2009} and over -5000 SNPs have been linked to different diseases and traits \cite{Hindorff2009}. - -GWAS are not only performed inside the traditional scientific community. -Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. -These companies use DNA microarrays to screen for around 0.5 to 1 million SNPs spread over the human genome. In return, customers -receive an analysis of the results, as well as a file that includes the customer's raw individual genotypes. In 2011, 23andMe -alone had over 100,000 customers \cite{23andMe2011} -- the company realizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about -traits and diseases. With the consent of the customer, the data is used for association studies. 23andMe has published several -articles in which known findings are replicated together with new associations disorders like Parkinson's Disease \cite{Eriksson2010, Do2011}. -So far, over 30,000 23andMe-customers have participated in 23andMe's association studies, which proves that this data source has a lot of potential for other researchers. - -The generation of biomedical data by private companies raises concerns about privacy \cite{23andMe2012}, -liability and consent \cite{Caulfield2011}. -Nevertheless, in some instances individual customers are willingly sharing their data. Most do so by uploading their data to -their personal website or to open software repositories like \textit{GitHub}. -This data is scattered and unorganized, making it hard to use in studies. While projects like SNPedia try to keep track of all -the publicly available genotyping files \cite{Cariaso2011}, they usually do not provide the information necessary to perform GWAS, as the phenotypic information is -often not attached to the genetic information. Projects that attach the phenotype to the genetic information, -like the \textit{Personal Genome Project} \cite{Ball24072012}, still do not allow for an easy re-use of the data, as they currently lack an application programming interface (API) -or other methods by which researchers could download the data. Additionally, not every customer of DTC genetic testing can participate in the \textit{Personal Genome Project}. - -Here, we present the results of a survey designed to evaluate the support in the personal genetics community for a crowdsourced online platform. We also present openSNP, an online platform which enables DTC customers to share genotypic and phenoytypic information, as well as receive additional information on their genotypes. The genotypes are made available to researchers via the open Creative Commons Zero license. - -% Results and Discussion can be combined. -\section*{Results} -\subsection*{Survey on Sharing Genetic Information} -In total 229 people, 180 with a self-reported chromosomal sex of XY and 56 with a self-reported chromosomal sex of XX, participated in our survey on sharing genetic information with the public. -The mean age of the participants is 33 (SD = 11,29). 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already -customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% do not plan on -becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Cramer's V = 0.077). - -67.7 \% of all participants would share their data with their DTC company without any constraints, 25.8 \% would do so given the company -didn't share the data with third parties. 6.6 \% of the participants would not share their data. Participants self-identified as XX-chromosomal are slightly more likely to answer that DTC companies are allowed to use their results (Cramer's V = 0.221). Those who are customers of a DTC company or are planning on becoming one in -the future are more likely to share their results, compared to those who do not plan on getting themselves genotyped (Somers-d = 0.331). - - -There are substantial differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped -and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with motivations for sharing genotypic information. On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations -for not sharing their data, see table \ref{tab:motivations1}. - -Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with -the following motivations for sharing than those who would not share their data with their DTC company. -Those participants who are not willing to share data with their DTC company are likely to agree more strongly with some motivations -for not sharing their data when compared to those who would share their data with their DTC company under any circumstances. For an overview of the motivations of both groups, see table \ref{tab:motivations2}. - -In the case of curiosity as a motive, there is also a substantial difference between those who would share their data with their DTC company under the condition that it did not share the information and those who would not (mean difference = 1.116 SE = 0.344) as well as those who would share under any circumstances (mean difference = -0.874 SE = 0.182). - -In the cases of fear of discrimination and fear of a breach of privacy, substantial differences between all three categories exist. Those who would share their data with their DTC company as long as it did not share the information agree less strongly than those who would not share the data with both fear of discrimination as a motive for not sharing (mean difference = -0.615, SE = 0.345) as well as fear of a breach of privacy (mean difference = -0.668, SE = 0.346). Those who would share their data under any circumstances are even less likely to agree with these motives than those who would share only if their DTC company did not share the information (fear of discrimination: mean value = -0.906, SE = 0.182; breach of privacy: mean difference = -1.203, SE = 0.183). - -These survey results indicate that there is a definite interest in customers of DTC companies to share their results with other scientists. - -\subsubsection*{Sharing genotypic information} - -We created the openSNP project (http://opensnp.org) as an open, crowdsourced online platform for DTC customers interested in sharing their raw data and for researchers interested in performing GWAS or other types of analysis with the data. -Customers of DTC testing are encouraged to share their genotyping results along with their phenotypic traits to enable easy access for researchers. -Users of openSNP can create a personal profile, discuss SNPs and phenotypes on the platform using a simple commenting system, or send each other private messages. - -People interested in using the data of openSNP can download complete dumps of the genotypic and phenotypic information or use query API endpoints utilizing JavaScript Object Notation (JSON) objects or the Distributed Annotation System (DAS) \cite{Dowell2001}. - - -\subsection*{Sharing genotypic information} -Currently users can upload their genotyping results from the companies \textit{23andMe}, \textit{deCODEme }and \textit{FamilyTreeDNA} via a web interface to the openSNP -project. There is experimental support for uploading exomes in the VCF format \cite{Danecek01082011}, as \textit{23andMe} recently started exome sequencing for its customers. So far only the SNPs of the exome data sets are visualized on openSNP, but the downloads include all variation found in the exome. -The uploaded data is published under the Creative Commons Zero license, -which - in accordance with the Panton Principles \cite{10.1371/journal.pbio.1001195} - -allows a complete re-use of the data without any constraints. -Between the start of openSNP on 09/27/2011 and 10/27/2012, 633 people have signed -up with openSNP, and 270 genetic datasets have been made available. The openSNP -database lists 215,546,685 genotypes which are distributed over 2,140,643 unique SNPs. -Figures \ref{Figure1_label} and \ref{Figure2_label} depict the increase in users and genotyping files over time. - - -\subsection*{Crowdsourcing phenotypes} -Users are able to create new phenotypes that are not yet -listed by openSNP. -The specification of these phenotypes is open and not limited -to pre-defined categories. To reduce the amount of manual data curation, openSNP tries to harmonize -the expression and spelling of the same phenotype or variation. We implemented an -autocompletion feature, which helps users reuse already entered phenotypes. -Users are encouraged to list as many phenotypes as possible through a simple -achievement system, rewarding users that upload their data and enter phenotypic -information with badges that are shown on their profile pages. - -In the same timeframe mentioned above, all users combined have -entered a total of 4743 variations on 130 different phenotypes with those variations being -the different values on a given trait or phenotype. The mean number of users that have entered their variations for a single phenotype -is 36.48. The distribution of how many users have -entered their data per phenotype, compared to the amount of unique phenotypes, can be seen in figure \ref{pheno}. The phenotype provided by the most users is "eye color", for which 207 users entered their phenotype (retrieved 10/27/2012). - - -\subsection*{Connection to external services} -In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. -A total number of 21,134 documents relevant to the SNPs listed in openSNP could be found in the publication and annotation databases of Mendeley, the Public Library of Science, in the \emph{GET Evidence System} \cite{Ball24072012} and the \emph{NHGRI GWAS Catalog} \cite{Hindorff2009} and in the crowdsourced SNPedia. -Of the primary literature listed on Mendeley \& the Public Library of Science, 25 \% are released in open access journals and can be accessed free of charge (Figure \ref{Figure3_label}). - -For usability reasons, -SNPs are ranked by the amount of information gathered through the external services. The external services themselves are ranked by how easily non-scientists can understand information -from these sources and how available this information is to the public. The SNPedia entries are given the highest impact, as those are already manually curated and summarized in plain English, followed by open access publications out of -the Public Library of Science and the curated databases of the \emph{GET Evidence System} and the \emph{NHGRI GWAS Catalog}. Lowest values are given to the Mendeley results, as the publications listed there are for the most part not freely available without subscriptions or one-time payments. -An entry on SNPedia is valued 2.5 times as high as a PLoS publication or entries in \emph{GET} or the \emph{GWAS Catalog} and 5 times as high as a Mendeley entry. - -Users are also able to link their Fitbit accounts to their user-accounts. Fitbit is a commercial service which lets its customers track their BMI, movement and sleep data. This data can be linked to openSNP to give interested researchers an automatically maintained dataset of body and sleep developments over time. - -\subsection*{Data access} -OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files -grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypic variation in a comma-separated table. The genetic data is also -accessible through the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}, which offers all data for specific chromosomes and specific positions on single chromosomes. -An example of how the DAS can be used is implemented on openSNP, where users' genotypes are visualized inside a genome browser. So far, all chromosomal positions are based on the human reference genome NCBI36, as this is the standard reference used by DTC providers right now. \bastian{should this maybe include an image as well?} - -The data is additionally available over a JSON API, which allows users to directly access data in the JSON format. The methods allow users to programmatically look for the genotypes and annotations at a given SNP as well as for phenotypes for a given user and phenotypic variation for a given phenotype. - -\section*{Discussion} - -\subsection*{Survey issues} - -As the survey was taken online by voluntary participants and was mainly spread in the personal genetics community, the results do not reflect the general population, but over-represent those people most likely to be interested in a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. - - -\subsection*{Privacy, health implications and ethical considerations} - -%The advent of DTC genetic testing has led to new ethical and social issues. -Much of the criticism of DTC genetic testing focusses on the practice -of delivering medical information without consulting a physician or genetic counsellor to help patients/customers make sense of the information -and to put the new knowledge to good use \cite{Hauskeller2011,Hogarth2008,Wasson2009}. - -As we have found in our survey on sharing such results (see supplementary methods), many DTC customers are willing to share their results with the public to help scientific progress, without forgetting about the privacy implications that come with openly sharing genetic information. There is a variety of ethical and privacy implications when it comes to DTC genetic testing\cite{Caulfield2011,Joh2011}. - -Our survey has shown that people are concerned about their privacy and fear that stakeholders like employers, insurance companies, governments -or advertisers might misuse the information. Policy makers start to react to those changes by introducing laws like the -\textit{Genetic Information Non-Discrimination Act} in the United States or the \emph{Gendiagnostikgesetz} in Germany to minimize the impact of -widely available genetic information. DTC genetic testing companies themselves also try to educate their customers about the risks of releasing genetic data. - -OpenSNP openly addresses the problem of privacy implications that come with releasing genetic data twice, once during registration for openSNP and once during -the upload of the DTC genetic testing results. Users have to confirm that they have read and understood the disclaimer about possible side-effects -of publishing their data. Further versions of openSNP may optionally include further consent processes. - -\subsection*{GWAS and Open Data} -Although prices of exome or even full genome sequencing are dropping rapidly, GWAS are still considerably cheaper. However, GWAS can only detect correlations of SNPs with those traits and do not allow -inference on the cause for any correlation. Furthermore, for a statistically sound analysis, GWAS need a large enough sample size. Nevertheless, GWAS are still frequently used and new associations are found \cite{10.1371.journal.pone.0031470,10.1371.journal.pone.0030309,10.1371.journal.pone.0029848}. - -One way of bringing down costs for GWAS even further is to make use of already available genotyping results and datasets. -Data produced by DTC genetic testing companies is a promising source for such results, as those companies already have high -numbers of customers which are willing to pay for the genotyping by themselves. - -By crowdsourcing the acquisition of genetic and phenotypic data, openSNP faces the same problems as any other -open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. -Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation, -with users entering data in different measurement systems. Another problem with user-entered data is the frequent switching between categorical and continuous phenotypes - for example, some users entered the specific value of their height, while other users entered their height according to a category like "150cm to 160cm". - -While we try to suggest similar entries to the users, -there are some cases where users will not follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. There are two possible solutions to this problem: The first one would be to only allow a trusted subset of users to enter new phenotypes. The other one -would be to make users enter all possible variations of a phenotype while creating a new phenotype, so that later users cannot add -variations that have not been available from the start. - -In both cases it makes it harder -for users to enter their data which raises the bar for participation. -We decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS with the data to perform additional quality control. - -Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP: only a subset of people buy DTC genetic testing, from which an even smaller subset is willing to publish the results, which can potentially lead to skewed GWAS-results. - -With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic -data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to freely use in their studies. -Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. -While there is not enough data uploaded to perform a statistically sound GWAS yet, this will be possible in the future, as user numbers continue to rise. By including the option of uploading exome data sets the platform already is capable of adjusting for changes in the type of data generated by DTC genetic testing. - - -% You may title this section "Methods" or "Models". -% "Models" is not a valid title for PLoS ONE authors. However, PLoS ONE -% authors may use "Analysis" -\section*{Materials and Methods} - -\subsection*{Survey on Sharing Genetic Information} -The survey was performed using \textit{Google Docs} and was distributed to possible participants through the \textit{23andMe }community forums, the \textit{DIYBiology} mailing list, -blogs which focus on genetics and DTC genetic testing and social media websites like \textit{Twitter}, \textit{Google+} and \textit{Facebook}. - -The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their -(planned) customership with a DTC company. If the participants already were customers, they were also asked if they were already sharing their genetic and phenotypic data. -All participants were asked if they would be willing to share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, -but only if they did not share my medical information with anybody else" and No". - -The survey also asked some scaled questions, which measured how strongly participants agreed/disagreed with different reasons for sharing or not sharing their -information. The scale went from 1 = strongly disagree to 5 = strongly agree. Motivations queried for sharing data -were "because I want to help scientists with their research", "because of possible personal benefits (e.g. getting treatments for a disease I have, -possibility of new medication, etc.)", "because it may deliver advertising that is relevant to me" and "out of curiosity". Motivations queried for not sharing -data were "because advertisers could use the information for targeted campaigns", "because of possible negative consequences for closely related persons", -"because of the breach of my privacy" and "because of the fear of discrimination (e.g. by the employer, the state, some insurance company)". -Additionally, participants had the possibility of giving their own reasons for sharing or not sharing their data. - -The survey data was analyzed with SPSS 19. - -\subsection*{Technical implementation of the platform} -The main platform is implemented using the web framework Ruby on Rails 3.0.10. Postgres 9.2 is used as the main database backend for Rails. -The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs -which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of -the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, -through querying the content via the MediaWiki API. The \emph{NHGRI GWAS Catalog} and the \emph{GET Evidence System} provide complete dumps in plain text formats. Those are regularly downloaded and parsed. All databases are queried or parsed using the unique identifier of each SNP as the search term. - -SNPs are catalogued by their unique identifier, which consists of a prefix (mostly \textit{rs}, rarely \textit{i}) and a unique number. This is a common format, -which is employed by the NCBI dbSNP database \cite{Sherry2001} and is also widely used and easily parsed from different literature sources. Publications from the different databases as -well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. - -Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources -are handled using the ruby gem Resque and the standalone key-value storage server Redis. Search features on the platform itself are implemented using Solr and the ruby gem Sunspot. -Additionally, data can be requested from openSNP using the Distributed Annotation System. The required data is stored in a PostgreSQL database. -Requested data is delivered in XML-format to facilitate parsing. Additionally, users can request data in the JSON-format, using a system not specified in any standard. - -OpenSNP only serves as a platform for SNPs, so methods for the delivery of nucleotide sequences as described in the DAS-standard are not implemented. Currently, -two methods are implemented: firstly \textit{features}, which is used to deliver SNPs located on specific chromosomes or between specific nucleotide positions, -based on the user's query. The second method is \textit{sources}, which advertises all DAS sources for all genotypes present in openSNP. - -A flowchart of all services incorporated in openSNP and of all the ways users can upload or access the data is given in figure \ref{Figure4_label}. The source code of openSNP is -published under the MIT license and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. -% Do NOT remove this, even if you are not including acknowledgments -\section*{Acknowledgments} -We thank Dr. Manuel Corpas and Prof. Dr. Dave Edwards for constructive advice in grammar, spelling and structure of this study. Further thanks go to Samantha Clark and Dan Bolser for providing valuable feedback and feature ideas during the development and Thomas Down, author of \emph{BioDalliance}, and Rafael Jimenez, author of \emph{MyKaryoView}, for their support on implementing the Distributed Annotation System and the genome browser. For help with their APIs we are grateful to Mike Cariaso of \emph{SNPedia} and the PLOS \& Mendeley API teams. We would especially like to thank the users of openSNP.org for their participation, their constructive criticism and bug-finding abilities and especially for sharing their genotyping and phenotype data. - -%\section*{References} -% The bibtex filename -\bibliography{papers} - -\section*{Figure Legends} -\begin{figure}[!ht] - \begin{center} - \includegraphics[scale=0.35]{25_10_2012_Graphs/users.png} - \end{center} - \caption{ - {\bf Growth of openSNP-user-accounts.} The increase in numbers for users from 27.09.2011 to 27.10.2012 is shown.} - \label{Figure1_label} -\end{figure} -\begin{figure}[!ht] - \begin{center} - \includegraphics[scale=0.35]{25_10_2012_Graphs/genotypes.png} - \end{center} - \caption{ - {\bf Growth of available genotypings.} The increase in numbers for genotyping-files from 27.09.2011 to 27.10.2012 is shown.} - \label{Figure2_label} -\end{figure} -\begin{figure}[!ht] - \begin{center} - \includegraphics[scale=0.40]{25_10_2012_Graphs/phenotypes_vs_userphenotypes.png} - \end{center} - \caption{ - {\bf Development of unique phenotypes and phenotypic information over time.} The x-axis shows the time-frame from start of the project until October 2012, the left y-axis shows how many unique phenotypes have been entered, and the right y-axis shows the amount of phenotypes users entered.} - \label{pheno} -\end{figure} - - -\begin{figure}[!ht] - \begin{center} - - \end{center} - \caption{ - {\bf Flow of data inside openSNP.} External databases and user-provided data are used as input. Output of data is done using the website, the \emph{Distributed Annotation System} and a JSON-API.} - \label{Figure4_label} -\end{figure} - - - -%\begin{figure}[!ht] -%\begin{center} -%%\includegraphics[width=4in]{figure_name.2.eps} -%\end{center} -%\caption{ -%{\bf Bold the first sentence.} Rest of figure 2 caption. Caption -%should be left justified, as specified by the options to the caption -%package. -%} -%\label{Figure_label} -%\end{figure} - - -\section*{Tables} -\begin{table} -\begin{tabular}{|l|l|l|} -\hline -& Turkey's HSD & \\ \cline{2-3} -& Mean difference & SE \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who are already genotyped} & & \\ \hline -... curious & 1.159 & 0.193 \\ -... want to help scientists & 0.465 & 0.128 \\ -... for personal benefits & 0.448 & 0.183 \\ \hline -\textbf{Motivation for not sharing in participants} & & \\ -\textbf{who are not planning to get genotyped} & & \\ \hline -... fear of discrimination & 1.06 & 0.195 \\ -... breach of privacy & 0.821 & 0.225 \\ -... fear of personalized advertising & 0.848 & 0.208 \\ -... negative consequences for family members & 0.733 & 0.21 \\ \hline -\end{tabular} -\caption{Differences in terms of motivation to share genotypings with the public in survey-participants who already received a genotyping compared to participants who are not planning to getting genotyped. } -\label{tab:motivations1} -\end{table} - -\begin{table} -\begin{tabular}{|l|l|l|} -\hline -& Turkey's HSD & \\ \cline{2-3} -& Mean difference & SE \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who would share with their DTC provider} & & \\ \hline -... curiosity & 1.99 & 0.321 \\ -... want to help science & 1.57 & 0.199 \\ -... for personal benefits & 0.951 & 0.308 \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who would not share with their DTC provider} & & \\ \hline -... fear of discrimination & 1.52 & 0.322 \\ -... fear of consequences for family members & 1.146 & 0.32 \\ -... fear of personalized advertising & 1.112 & 0.357 \\ \hline -\end{tabular} -\caption{Differences in terms of motivations to share genotyping-data, comparison between participants who would share their genotyping data with their DTC provider with participants who would not share their data with their DTC provider.} -\label{tab:motivations2} -\end{table} -%\begin{table}[!ht] -%\caption{ -%\bf{Table title}} -%\begin{tabular}{|c|c|c|} -%table information -%\end{tabular} -%\begin{flushleft}Table caption -%\end{flushleft} -%\label{tab:label} -% \end{table} - -\end{document} +% Template for PLoS +% Version 1.0 January 2009 +% +% To compile to pdf, run: +% latex plos.template +% bibtex plos.template +% latex plos.template +% latex plos.template +% dvipdf plos.template + +\documentclass[10pt]{article} + +% amsmath package, useful for mathematical formulas +\usepackage{amsmath} +% amssymb package, useful for mathematical symbols +\usepackage{amssymb} + +% graphicx package, useful for including eps and pdf graphics +% include graphics with the command \includegraphics +\usepackage{graphicx} + +% cite package, to clean up citations in the main text. Do not remove. +\usepackage{cite} + +\usepackage{color} + +% Use doublespacing - comment out for single spacing +%\usepackage{setspace} +%\doublespacing + +%FZ: Enable the comment command +\usepackage{verbatim} +% Text layout +\topmargin 0.0cm +\oddsidemargin 0.5cm +\evensidemargin 0.5cm +\textwidth 16cm +\textheight 21cm + +% Bold the 'Figure #' in the caption and separate it with a period +% Captions will be left justified +\usepackage[labelfont=bf,labelsep=period,justification=raggedright]{caption} + +% Use the PLoS provided bibtex style +\bibliographystyle{plos2009} + +% Remove brackets from numbering in List of References +\makeatletter +\renewcommand{\@biblabel}[1]{\quad#1.} +\makeatother + + +% Leave date blank +\date{} + +\pagestyle{myheadings} +%% ** EDIT HERE ** + + +%% ** EDIT HERE ** +%% PLEASE INCLUDE ALL MACROS BELOW +\long\def\authornote#1{% + \leavevmode\unskip\raisebox{-3.5pt}{\rlap{$\scriptstyle\diamond$}}% + \marginpar{\raggedright\hbadness=10000 + \def\baselinestretch{0.8}\tiny + \it #1\par}} +\newcommand{\bastian}[1]{\authornote{BG: #1}} +\newcommand{\fabian}[1]{\authornote{FZ: #1}} +\newcommand{\philipp}[1]{\authornote{PB: #1}} +%% END MACROS SECTION +\begin{document} + +% Title must be 150 characters or less +\begin{flushleft} +{\Large +\textbf{openSNP - a crowdsourced web resource for personal genomics} +} +% Alternative titles: +% openSNP - a new, open data-source for personalised medicine +% What kind of person would share genotyping-data? Presenting a survey and an open data-source for personalised medicine +% +% Insert Author names, affiliations and corresponding author email. +\\ +Bastian Greshake$^{1,\ast}$, +Philipp E. Bayer$^{2}$, +Helge Rausch$^{3}$, +Fabian Zimmer$^{4}$, +Julia Reda$^{5}$ +\\ +\bf{1} Molecular Ecology Group, Goethe University Frankfurt am Main, Germany +\\ +\bf{2} Applied Bioinformatics Group, School of Agriculture and Food Sciences, The University of Queensland, Australia +\\ +\bf{3} Hochschule f\"ur Technik und Wirtschaft Berlin, Germany +\\ +\bf{4} Evolutionary Genomics and Transcriptomics Lab, Department of Genetics, Evolution and Environment, University College London, England +\\ +\bf{5} Johannes Gutenberg University Mayence, Germany +\\ +$\ast$ E-mail: info@opensnp.org +\end{flushleft} + +% Please keep the abstract between 250 and 300 words +\section*{Abstract} +Genome-wide association studies are widely used to correlate phenotypic traits with genetic variants. These studies usually compare the genetic variation between two groups to single out certain Single Nucleotide Polymorphisms (SNPs) that are linked to a phenotypic variation in one of the groups. However, it is necessary to have a large enough sample size to find statistically significant correlations. Direct-To-Consumer (DTC) genetic testing can supply additional data: DTC-companies offer the analysis of a large amount of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing companies. However, this data is not public for a variety of reasons and thus cannot be used in research. It seems reasonable to create a central open data repository for such data, but it was previously unknown if and how people would submit their data to such a repository. Here we present a survey which evaluates whether people are willing to publicly share their genetic information. In the light of those results we present the web platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publish at no cost their genetic data along with phenotypic information. Through this crowdsourced effort of collecting genetic and phenotypic information, openSNP has become a valuable resource, which can be used in a wide area of studies, including Genome-wide association studies. OpenSNP is hosted at http://www.opensnp.org, and the code is released under MIT-license at http://github.com/gedankenstuecke/snpr + + +% Please keep the Author Summary between 150 and 200 words +% Use first person. PLoS ONE authors please skip this step. +% Author Summary not valid for PLoS ONE submissions. +\section*{Author Summary} + +\section*{Introduction} + +The availability of new DNA sequencing techniques has shifted the focus of biological data acquisition towards new biomedical applications. +Many diseases - for example Alzheimer's \cite{alzheimer}, Parkinson's \cite{parkinsons} or different types of cancers \cite{breastcancer,prostatecancer} - are at least partially heritable, so the genome +of patients can be used for diagnostic purposes. Using the genetic information of patients for diagnostics is made possible through the sharp decrease in costs for analysing genetic information \cite{Brown1999}. + +%Kicked out the following due to Corpas' argument +%The comparison of DNA sequences from individuals in a population can reveal variable sites, which are of major interest in investigating diseases. +%A variation of only one nucleotide length at a given site is called a single Nucleotide Polymorphisms (SNP). +%The different nucleotides at this site are defined as an allele. +%For diploid organisms, as us humans, the genotype at a given site consists of two alleles, which are independently inherited from each parent. +%Different methods have been developed to read SNPs in an individual, a +%process called genotyping. A widespread used method for genotyping individuals is the use of microarrays. +%In comparison to whole genome sequencing only sites which are known to be variable are analysed, making microarrays cheaper and faster \cite{Brown1999}. + + +If genetic information on more than one individual is known, the analysis of +allele frequencies of Single Nucleotide Polymorphisms (SNPs) can be used to associate such SNPs with diseases and other inheritable traits. Genome-Wide Association Studies +(GWAS) make use of statistics to compare the allele frequencies in patients to the alleles in healthy controls. This +enables GWAS to find SNPs which are significantly overrepresented in patients and associates those SNPs with a trait or disease. +This method does not allow inference of causal differences but merely identifies correlations. +The first GWAS was published in 2005 and compared age-related macular degeneration in contrast +to a healthy control group \cite{Klein2005}. Since the beginning, the number of participants in +such studies has been rising. To date, over 1200 GWAS have been performed \cite{Johnson2009} and over +5000 SNPs have been linked to different diseases and traits \cite{Hindorff2009}. + +GWAS are not only performed inside the traditional scientific community. +Since 2006, companies like 23andMe, deCODEme or FamilyTreeDNA have been offering Direct-To-Consumer (DTC) genetic testing. +These companies use DNA microarrays to screen for around 0.5 to 1 million SNPs spread over the human genome. In return, customers +receive an analysis of the results, as well as a file that includes the customer's raw individual genotypes. In 2011, 23andMe +alone had over 100,000 customers \cite{23andMe2011} +- the company realizes the potential to perform GWAS with this amount of data by using surveys to ask their customers about +traits and diseases. With the consent of the customer, the data is used for association studies. 23andMe has published several +articles in which known findings are replicated together with new associations disorders like Parkinson's Disease \cite{Eriksson2010, Do2011}. +So far, over 30,000 23andMe-customers have participated in 23andMe's association studies, which proves that this data source has a lot of potential for other researchers. + +The generation of biomedical data by private companies raises concerns about privacy \cite{23andMe2012}, +liability and consent \cite{Caulfield2011}. +Nevertheless, in some instances individual customers are willingly sharing their data. Most do so by uploading their data to +their personal website or to open software repositories like \textit{GitHub}. +This data is scattered and unorganized, making it hard to use in studies. While projects like SNPedia try to keep track of all +the publicly available genotyping files \cite{Cariaso2011}, they usually do not provide the information necessary to perform GWAS, as the phenotypic information is +often not attached to the genetic information. Projects that attach the phenotype to the genetic information, +like the \textit{Personal Genome Project} \cite{Ball24072012}, still do not allow for an easy re-use of the data, as they currently lack an application programming interface (API) +or other methods by which researchers could download the data. Additionally, not every customer of DTC genetic testing can participate in the \textit{Personal Genome Project}. + +Here, we present the results of a survey designed to evaluate the support in the personal genetics community for a crowdsourced online platform. We also present openSNP, an online platform which enables DTC customers to share genotypic and phenoytypic information, as well as receive additional information on their genotypes. The genotypes are made available to researchers via the open Creative Commons Zero license. + +% Results and Discussion can be combined. +\section*{Results} +\subsection*{Survey on Sharing Genetic Information} +In total 229 people, 180 with a self-reported chromosomal sex of XY and 56 with a self-reported chromosomal sex of XX, participated in our survey on sharing genetic information with the public. +The mean age of the participants is 33 (SD = 11,29). 81.7 \% reported their ethnicity as caucasian. 39.7 \% of the participants are already +customers of at least one DTC genetic testing company and further 30.1 \% of them plan on becoming one in the future. 29.7 \% do not plan on +becoming a DTC customer. There is no significant difference in the usage of DTC companies between chromosomal sexes (Cramer's V = 0.077). + +67.7 \% of all participants would share their data with their DTC company without any constraints, 25.8 \% would do so given the company +didn't share the data with third parties. 6.6 \% of the participants would not share their data. Participants self-identified as XX-chromosomal are slightly more likely to answer that DTC companies are allowed to use their results (Cramer's V = 0.221). Those who are customers of a DTC company or are planning on becoming one in +the future are more likely to share their results, compared to those who do not plan on getting themselves genotyped (Somers-d = 0.331). + + +There are substantial differences in terms of motivation, tested by Tukey's HSD test, between those people who have already been genotyped +and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with motivations for sharing genotypic information. On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations +for not sharing their data, see table \ref{tab:motivations1}. + +Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with +the following motivations for sharing than those who would not share their data with their DTC company. +Those participants who are not willing to share data with their DTC company are likely to agree more strongly with some motivations +for not sharing their data when compared to those who would share their data with their DTC company under any circumstances. For an overview of the motivations of both groups, see table \ref{tab:motivations2}. + +In the case of curiosity as a motive, there is also a substantial difference between those who would share their data with their DTC company under the condition that it did not share the information and those who would not (mean difference = 1.116 SE = 0.344) as well as those who would share under any circumstances (mean difference = -0.874 SE = 0.182). + +In the cases of fear of discrimination and fear of a breach of privacy, substantial differences between all three categories exist. Those who would share their data with their DTC company as long as it did not share the information agree less strongly than those who would not share the data with both fear of discrimination as a motive for not sharing (mean difference = -0.615, SE = 0.345) as well as fear of a breach of privacy (mean difference = -0.668, SE = 0.346). Those who would share their data under any circumstances are even less likely to agree with these motives than those who would share only if their DTC company did not share the information (fear of discrimination: mean value = -0.906, SE = 0.182; breach of privacy: mean difference = -1.203, SE = 0.183). + +These survey results indicate that there is a definite interest in customers of DTC companies to share their results with other scientists. + +\subsubsection*{Sharing genotypic information} + +We created the openSNP project (http://opensnp.org) as an open, crowdsourced online platform for DTC customers interested in sharing their raw data and for researchers interested in performing GWAS or other types of analysis with the data. +Customers of DTC testing are encouraged to share their genotyping results along with their phenotypic traits to enable easy access for researchers. +Users of openSNP can create a personal profile, discuss SNPs and phenotypes on the platform using a simple commenting system, or send each other private messages. + +People interested in using the data of openSNP can download complete dumps of the genotypic and phenotypic information or use query API endpoints utilizing JavaScript Object Notation (JSON) objects or the Distributed Annotation System (DAS) \cite{Dowell2001}. + + +\subsection*{Sharing genotypic information} +Currently users can upload their genotyping results from the companies \textit{23andMe}, \textit{deCODEme }and \textit{FamilyTreeDNA} via a web interface to the openSNP +project. There is experimental support for uploading exomes in the VCF format \cite{Danecek01082011}, as \textit{23andMe} recently started exome sequencing for its customers. So far only the SNPs of the exome data sets are visualized on openSNP, but the downloads include all variation found in the exome. +The uploaded data is published under the Creative Commons Zero license, +which - in accordance with the Panton Principles \cite{10.1371/journal.pbio.1001195} - +allows a complete re-use of the data without any constraints. +Between the start of openSNP on 09/27/2011 and 10/27/2012, 633 people have signed +up with openSNP, and 270 genetic datasets have been made available. The openSNP +database lists 215,546,685 genotypes which are distributed over 2,140,643 unique SNPs. +Figures \ref{Figure1_label} and \ref{Figure2_label} depict the increase in users and genotyping files over time. + + +\subsection*{Crowdsourcing phenotypes} +Users are able to create new phenotypes that are not yet +listed by openSNP. +The specification of these phenotypes is open and not limited +to pre-defined categories. To reduce the amount of manual data curation, openSNP tries to harmonize +the expression and spelling of the same phenotype or variation. We implemented an +autocompletion feature, which helps users reuse already entered phenotypes. +Users are encouraged to list as many phenotypes as possible through a simple +achievement system, rewarding users that upload their data and enter phenotypic +information with badges that are shown on their profile pages. + +In the same timeframe mentioned above, all users combined have +entered a total of 4743 variations on 130 different phenotypes with those variations being +the different values on a given trait or phenotype. The mean number of users that have entered their variations for a single phenotype +is 36.48. The distribution of how many users have +entered their data per phenotype, compared to the amount of unique phenotypes, can be seen in figure \ref{pheno}. The phenotype provided by the most users is "eye color", for which 207 users entered their phenotype (retrieved 10/27/2012). + + +\subsection*{Connection to external services} +In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. +A total number of 21,134 documents relevant to the SNPs listed in openSNP could be found in the publication and annotation databases of Mendeley, the Public Library of Science, in the \emph{GET Evidence System} \cite{Ball24072012} and the \emph{NHGRI GWAS Catalog} \cite{Hindorff2009} and in the crowdsourced SNPedia. +Of the primary literature listed on Mendeley \& the Public Library of Science, 25 \% are released in open access journals and can be accessed free of charge (Figure \ref{Figure3_label}). + +For usability reasons, +SNPs are ranked by the amount of information gathered through the external services. The external services themselves are ranked by how easily non-scientists can understand information +from these sources and how available this information is to the public. The SNPedia entries are given the highest impact, as those are already manually curated and summarized in plain English, followed by open access publications out of +the Public Library of Science and the curated databases of the \emph{GET Evidence System} and the \emph{NHGRI GWAS Catalog}. Lowest values are given to the Mendeley results, as the publications listed there are for the most part not freely available without subscriptions or one-time payments. +An entry on SNPedia is valued 2.5 times as high as a PLoS publication or entries in \emph{GET} or the \emph{GWAS Catalog} and 5 times as high as a Mendeley entry. + +Users are also able to link their Fitbit accounts to their user-accounts. Fitbit is a commercial service which lets its customers track their BMI, movement and sleep data. This data can be linked to openSNP to give interested researchers an automatically maintained dataset of body and sleep developments over time. + +\subsection*{Data access} +OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files +grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypic variation in a comma-separated table. The genetic data is also +accessible through the Distributed Annotation System \cite{Dowell2001,Jenkinson2008}, which offers all data for specific chromosomes and specific positions on single chromosomes. +An example of how the DAS can be used is implemented on openSNP, where users' genotypes are visualized inside a genome browser. So far, all chromosomal positions are based on the human reference genome NCBI36, as this is the standard reference used by DTC providers right now. \bastian{should this maybe include an image as well?} + +The data is additionally available over a JSON API, which allows users to directly access data in the JSON format. The methods allow users to programmatically look for the genotypes and annotations at a given SNP as well as for phenotypes for a given user and phenotypic variation for a given phenotype. + +\section*{Discussion} + +\subsection*{Survey issues} + +As the survey was taken online by voluntary participants and was mainly spread in the personal genetics community, the results do not reflect the general population, but over-represent those people most likely to be interested in a project such as openSNP: customers of DTC genetic testing companies and people with a high interest in biology. + + +\subsection*{Privacy, health implications and ethical considerations} + +%The advent of DTC genetic testing has led to new ethical and social issues. +Much of the criticism of DTC genetic testing focusses on the practice +of delivering medical information without consulting a physician or genetic counsellor to help patients/customers make sense of the information +and to put the new knowledge to good use \cite{Hauskeller2011,Hogarth2008,Wasson2009}. + +As we have found in our survey on sharing such results (see supplementary methods), many DTC customers are willing to share their results with the public to help scientific progress, without forgetting about the privacy implications that come with openly sharing genetic information. There is a variety of ethical and privacy implications when it comes to DTC genetic testing\cite{Caulfield2011,Joh2011}. + +Our survey has shown that people are concerned about their privacy and fear that stakeholders like employers, insurance companies, governments +or advertisers might misuse the information. Policy makers start to react to those changes by introducing laws like the +\textit{Genetic Information Non-Discrimination Act} in the United States or the \emph{Gendiagnostikgesetz} in Germany to minimize the impact of +widely available genetic information. DTC genetic testing companies themselves also try to educate their customers about the risks of releasing genetic data. + +OpenSNP openly addresses the problem of privacy implications that come with releasing genetic data twice, once during registration for openSNP and once during +the upload of the DTC genetic testing results. Users have to confirm that they have read and understood the disclaimer about possible side-effects +of publishing their data. Further versions of openSNP may optionally include further consent processes. + +\subsection*{GWAS and Open Data} +Although prices of exome or even full genome sequencing are dropping rapidly, GWAS are still considerably cheaper. However, GWAS can only detect correlations of SNPs with those traits and do not allow +inference on the cause for any correlation. Furthermore, for a statistically sound analysis, GWAS need a large enough sample size. Nevertheless, GWAS are still frequently used and new associations are found \cite{10.1371.journal.pone.0031470,10.1371.journal.pone.0030309,10.1371.journal.pone.0029848}. + +One way of bringing down costs for GWAS even further is to make use of already available genotyping results and datasets. +Data produced by DTC genetic testing companies is a promising source for such results, as those companies already have high +numbers of customers which are willing to pay for the genotyping by themselves. + +By crowdsourcing the acquisition of genetic and phenotypic data, openSNP faces the same problems as any other +open platform on the Internet, namely the need to trust users regarding the data they upload and enter on openSNP. +Additionally, the quality of the data varies, especially in terms of accuracy on the phenotypic variation, +with users entering data in different measurement systems. Another problem with user-entered data is the frequent switching between categorical and continuous phenotypes - for example, some users entered the specific value of their height, while other users entered their height according to a category like "150cm to 160cm". + +While we try to suggest similar entries to the users, +there are some cases where users will not follow those suggestions, so duplicates or similar phenotypes or variations in traits may arise. There are two possible solutions to this problem: The first one would be to only allow a trusted subset of users to enter new phenotypes. The other one +would be to make users enter all possible variations of a phenotype while creating a new phenotype, so that later users cannot add +variations that have not been available from the start. + +In both cases it makes it harder +for users to enter their data which raises the bar for participation. +We decided to keep data entry as easy as possible, at the cost of forcing users who want to perform GWAS with the data to perform additional quality control. + +Another risk regarding data quality that should be kept in mind is a possible bias in data availability on openSNP: only a subset of people buy DTC genetic testing, from which an even smaller subset is willing to publish the results, which can potentially lead to skewed GWAS-results. + +With openSNP, we have built a platform that can be used by customers of DTC genetic testing to easily share their genetic and phenotypic +data with a wide audience, as well as by scientists and interested citizens who are looking for datasets to freely use in their studies. +Customers of DTC genetic testing also benefit from an easy access to primary literature on SNPs and genetic variations they carry. +While there is not enough data uploaded to perform a statistically sound GWAS yet, this will be possible in the future, as user numbers continue to rise. By including the option of uploading exome data sets the platform already is capable of adjusting for changes in the type of data generated by DTC genetic testing. + + +% You may title this section "Methods" or "Models". +% "Models" is not a valid title for PLoS ONE authors. However, PLoS ONE +% authors may use "Analysis" +\section*{Materials and Methods} + +\subsection*{Survey on Sharing Genetic Information} +The survey was performed using \textit{Google Docs} and was distributed to possible participants through the \textit{23andMe }community forums, the \textit{DIYBiology} mailing list, +blogs which focus on genetics and DTC genetic testing and social media websites like \textit{Twitter}, \textit{Google+} and \textit{Facebook}. + +The survey included demographics such as age, chromosomal sex and ethnicity of the participants. Furthermore, it included questions on their +(planned) customership with a DTC company. If the participants already were customers, they were also asked if they were already sharing their genetic and phenotypic data. +All participants were asked if they would be willing to share their genetical or phenotypic information with their DTC company, possible answers were "Yes", "Yes, +but only if they did not share my medical information with anybody else" and No". + +The survey also asked some scaled questions, which measured how strongly participants agreed/disagreed with different reasons for sharing or not sharing their +information. The scale went from 1 = strongly disagree to 5 = strongly agree. Motivations queried for sharing data +were "because I want to help scientists with their research", "because of possible personal benefits (e.g. getting treatments for a disease I have, +possibility of new medication, etc.)", "because it may deliver advertising that is relevant to me" and "out of curiosity". Motivations queried for not sharing +data were "because advertisers could use the information for targeted campaigns", "because of possible negative consequences for closely related persons", +"because of the breach of my privacy" and "because of the fear of discrimination (e.g. by the employer, the state, some insurance company)". +Additionally, participants had the possibility of giving their own reasons for sharing or not sharing their data. + +The survey data was analyzed with SPSS 19. + +\subsection*{Technical implementation of the platform} +The main platform is implemented using the web framework Ruby on Rails 3.0.10. Postgres 9.2 is used as the main database backend for Rails. +The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs +which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of +the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, +through querying the content via the MediaWiki API. The \emph{NHGRI GWAS Catalog} and the \emph{GET Evidence System} provide complete dumps in plain text formats. Those are regularly downloaded and parsed. All databases are queried or parsed using the unique identifier of each SNP as the search term. + +SNPs are catalogued by their unique identifier, which consists of a prefix (mostly \textit{rs}, rarely \textit{i}) and a unique number. This is a common format, +which is employed by the NCBI dbSNP database \cite{Sherry2001} and is also widely used and easily parsed from different literature sources. Publications from the different databases as +well as the users' genotypes are associated with individual SNPs by the Rs-ID. Allele and genotype frequencies are updated regularly, based on the data present in openSNP. + +Processes with a longer runtime, such as parsing the genotyping results, creating archives of results which are to be mailed to users and queries to external resources +are handled using the ruby gem Resque and the standalone key-value storage server Redis. Search features on the platform itself are implemented using Solr and the ruby gem Sunspot. +Additionally, data can be requested from openSNP using the Distributed Annotation System. The required data is stored in a PostgreSQL database. +Requested data is delivered in XML-format to facilitate parsing. Additionally, users can request data in the JSON-format, using a system not specified in any standard. + +OpenSNP only serves as a platform for SNPs, so methods for the delivery of nucleotide sequences as described in the DAS-standard are not implemented. Currently, +two methods are implemented: firstly \textit{features}, which is used to deliver SNPs located on specific chromosomes or between specific nucleotide positions, +based on the user's query. The second method is \textit{sources}, which advertises all DAS sources for all genotypes present in openSNP. + +A flowchart of all services incorporated in openSNP and of all the ways users can upload or access the data is given in figure \ref{Figure4_label}. The source code of openSNP is +published under the MIT license and can be downloaded at http://github.com/gedankenstuecke/snpr. The genetical and phenotypical data is licensed under Creative Commons Zero. +% Do NOT remove this, even if you are not including acknowledgments +\section*{Acknowledgments} +We thank Dr. Manuel Corpas and Prof. Dr. Dave Edwards for constructive advice in grammar, spelling and structure of this study. Further thanks go to Samantha Clark and Dan Bolser for providing valuable feedback and feature ideas during the development and Thomas Down, author of \emph{BioDalliance}, and Rafael Jimenez, author of \emph{MyKaryoView}, for their support on implementing the Distributed Annotation System and the genome browser. For help with their APIs we are grateful to Mike Cariaso of \emph{SNPedia} and the PLOS \& Mendeley API teams. We would especially like to thank the users of openSNP.org for their participation, their constructive criticism and bug-finding abilities and especially for sharing their genotyping and phenotype data. + +%\section*{References} +% The bibtex filename +\bibliography{papers} + +\section*{Figure Legends} +\begin{figure}[!ht] + \begin{center} + \includegraphics[scale=0.35]{25_10_2012_Graphs/users.png} + \end{center} + \caption{ + {\bf Growth of openSNP-user-accounts.} The increase in numbers for users from 27.09.2011 to 27.10.2012 is shown.} + \label{Figure1_label} +\end{figure} +\begin{figure}[!ht] + \begin{center} + \includegraphics[scale=0.35]{25_10_2012_Graphs/genotypes.png} + \end{center} + \caption{ + {\bf Growth of available genotypings.} The increase in numbers for genotyping-files from 27.09.2011 to 27.10.2012 is shown.} + \label{Figure2_label} +\end{figure} +\begin{figure}[!ht] + \begin{center} + \includegraphics[scale=0.40]{25_10_2012_Graphs/phenotypes_vs_userphenotypes.png} + \end{center} + \caption{ + {\bf Development of unique phenotypes and phenotypic information over time.} The x-axis shows the time-frame from start of the project until October 2012, the left y-axis shows how many unique phenotypes have been entered, and the right y-axis shows the amount of phenotypes users entered.} + \label{pheno} +\end{figure} + +\begin{figure}[!ht] + \begin{center} + \includegraphics[scale=0.5]{25_10_2012_Graphs/papers_dotchart.png} + \end{center} + \caption{ + {\bf Distribution of annotation-sources at openSNP.} Currently, SNP-annotations from SNPedia, PLOS, Mendeley, the \emph{GET Evidence System} and the \emph{NHGRI GWAS Catalog} are being collected.} + \label{Figure3_label} +\end{figure} + + +\begin{figure}[!ht] + \begin{center} + \end{center} + \caption{ + {\bf Flow of data inside openSNP.} External databases and user-provided data are used as input. Output of data is done using the website, the \emph{Distributed Annotation System} and a JSON-API.} + \label{Figure4_label} +\end{figure} + + + +%\begin{figure}[!ht] +%\begin{center} +%%\includegraphics[width=4in]{figure_name.2.eps} +%\end{center} +%\caption{ +%{\bf Bold the first sentence.} Rest of figure 2 caption. Caption +%should be left justified, as specified by the options to the caption +%package. +%} +%\label{Figure_label} +%\end{figure} + + +\section*{Tables} +\begin{table} +\begin{tabular}{|l|l|l|} +\hline +& Turkey's HSD & \\ \cline{2-3} +& Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who are already genotyped} & & \\ \hline +... curious & 1.159 & 0.193 \\ +... want to help scientists & 0.465 & 0.128 \\ +... for personal benefits & 0.448 & 0.183 \\ \hline +\textbf{Motivation for not sharing in participants} & & \\ +\textbf{who are not planning to get genotyped} & & \\ \hline +... fear of discrimination & 1.06 & 0.195 \\ +... breach of privacy & 0.821 & 0.225 \\ +... fear of personalized advertising & 0.848 & 0.208 \\ +... negative consequences for family members & 0.733 & 0.21 \\ \hline +\end{tabular} +\caption{Differences in terms of motivation to share genotypings with the public in survey-participants who already received a genotyping compared to participants who are not planning to getting genotyped. } +\label{tab:motivations1} +\end{table} + +\begin{table} +\begin{tabular}{|l|l|l|} +\hline +& Turkey's HSD & \\ \cline{2-3} +& Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who would share with their DTC provider} & & \\ \hline +... curiosity & 1.99 & 0.321 \\ +... want to help science & 1.57 & 0.199 \\ +... for personal benefits & 0.951 & 0.308 \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who would not share with their DTC provider} & & \\ \hline +... fear of discrimination & 1.52 & 0.322 \\ +... fear of consequences for family members & 1.146 & 0.32 \\ +... fear of personalized advertising & 1.112 & 0.357 \\ \hline +\end{tabular} +\caption{Differences in terms of motivations to share genotyping-data, comparison between participants who would share their genotyping data with their DTC provider with participants who would not share their data with their DTC provider.} +\label{tab:motivations2} +\end{table} +%\begin{table}[!ht] +%\caption{ +%\bf{Table title}} +%\begin{tabular}{|c|c|c|} +%table information +%\end{tabular} +%\begin{flushleft}Table caption +%\end{flushleft} +%\label{tab:label} +% \end{table} + +\end{document} From b5509e904ca6e89004ce4acba314a53f91c3ce97 Mon Sep 17 00:00:00 2001 From: drsnuggles Date: Mon, 29 Oct 2012 15:47:22 +1000 Subject: [PATCH 47/88] Added note on insufficiently evaluated PGP-SNPs not being stored --- paper_draft.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper_draft.tex b/paper_draft.tex index fb97f19..ea3d9f7 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -330,7 +330,7 @@ \subsection*{Technical implementation of the platform} The database stores genotyping results, users' phenotypic information, literature results from Mendeley and the Public Library of Science as well as summaries on SNPs which can be found in SNPedia. The literature database of Mendeley is queried using the REST API, which delivers results in JSON. The literature database of the Public Library of Science is queried using the respective REST API, which delivers results in an XML-format. Summaries on SNPs are provided by SNPedia, -through querying the content via the MediaWiki API. The \emph{NHGRI GWAS Catalog} and the \emph{GET Evidence System} provide complete dumps in plain text formats. Those are regularly downloaded and parsed. All databases are queried or parsed using the unique identifier of each SNP as the search term. +through querying the content via the MediaWiki API. The \emph{NHGRI GWAS Catalog} and the \emph{GET Evidence System} provide complete dumps in plain text formats. Those are regularly downloaded and parsed. SNPs that are described as 'Insufficiently evaluated' in the \emph{GET Evidence System} are not stored. All databases are queried or parsed using the unique identifier of each SNP as the search term. SNPs are catalogued by their unique identifier, which consists of a prefix (mostly \textit{rs}, rarely \textit{i}) and a unique number. This is a common format, which is employed by the NCBI dbSNP database \cite{Sherry2001} and is also widely used and easily parsed from different literature sources. 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zV|y3_hz$6!9YNj&0)H=$|BB^ac0Vrsj1MAXfec|2`e5E&3May--n{+yGx*4oC9VeAPfCxBkg*jfL6?4g&n zu&@REF`7fZr$xaA7Jxs->H7ykfGQ_D6Of;sUFZ!PBfk)kO@Ix^%+Dae!ottQ&&tRu m#LUDE_}^930o>eAY;3iyZT}eaAW)f^S)aUm^+sCg$^Qdvs1qds diff --git a/paper_draft.tex b/paper_draft.tex index ea3d9f7..764bdef 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -416,43 +416,43 @@ \section*{Figure Legends} \section*{Tables} \begin{table} -\begin{tabular}{|l|l|l|} +\begin{tabular}{|l|l|l|l|l|} \hline -& Turkey's HSD & \\ \cline{2-3} -& Mean difference & SE \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who are already genotyped} & & \\ \hline -... curious & 1.159 & 0.193 \\ -... want to help scientists & 0.465 & 0.128 \\ -... for personal benefits & 0.448 & 0.183 \\ \hline -\textbf{Motivation for not sharing in participants} & & \\ -\textbf{who are not planning to get genotyped} & & \\ \hline -... fear of discrimination & 1.06 & 0.195 \\ -... breach of privacy & 0.821 & 0.225 \\ -... fear of personalized advertising & 0.848 & 0.208 \\ -... negative consequences for family members & 0.733 & 0.21 \\ \hline +& Mean & Mean & Tukey's HSD & \\ +& \emph{Genotyped} & \emph{Not Genotyped} & Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & & & \\ +\textbf{who are already genotyped} & & & & \\ \hline +... curious & 3.82 & 2.66 & 1.159 & 0.193 \\ +... want to help scientists & 4.64 & 4.18 & 0.465 & 0.128 \\ +... for personal benefits & 3.77 & 3.32 & 0.448 & 0.183 \\ \hline +\textbf{Motivation for not sharing in participants} & & & & \\ +\textbf{who are not planning to get genotyped} & & & & \\ \hline +... fear of discrimination & 3.09 & 4.15 & 1.06 & 0.195 \\ +... breach of privacy & 3.01 & 3.68 & 0.821 & 0.225 \\ +... fear of personalized advertising & 3.03 & 3.88 & 0.848 & 0.208 \\ +... negative consequences for family members & 2.93 & 3.57 & 0.733 & 0.21 \\ \hline \end{tabular} \caption{Differences in terms of motivation to share genotypings with the public in survey-participants who already received a genotyping compared to participants who are not planning to getting genotyped. } \label{tab:motivations1} \end{table} \begin{table} -\begin{tabular}{|l|l|l|} +\begin{tabular}{|l|l|l|l|l|} \hline -& Turkey's HSD & \\ \cline{2-3} -& Mean difference & SE \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who would share with their DTC provider} & & \\ \hline -... curiosity & 1.99 & 0.321 \\ -... want to help science & 1.57 & 0.199 \\ -... for personal benefits & 0.951 & 0.308 \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who would not share with their DTC provider} & & \\ \hline -... fear of discrimination & 1.52 & 0.322 \\ -... fear of consequences for family members & 1.146 & 0.32 \\ -... fear of personalized advertising & 1.112 & 0.357 \\ \hline +& Mean & Mean & Tukey's HSD & \\ +& \emph{Sharing} & \emph{Not Sharing} & Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & & & \\ +\textbf{who would share} & & & & \\ \hline +... curiosity & 3.86 & 1.87 & 1.99 & 0.321 \\ +... want to help science & 4.44 & 3.19 & 1.57 & 0.199 \\ +... for personal benefits & 3.68 & 2.73 & 0.951 & 0.308 \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & & & \\ +\textbf{who would not share} & & & & \\ \hline +... fear of discrimination & 3.21 & 4.73 & 1.52 & 0.322 \\ +... fear of consequences for family members & 2.79 & 3.93 & 1.146 & 0.32 \\ +... fear of personalized advertising & 3.17 & 4.29 & 1.112 & 0.357 \\ \hline \end{tabular} -\caption{Differences in terms of motivations to share genotyping-data, comparison between participants who would share their genotyping data with their DTC provider with participants who would not share their data with their DTC provider.} +\caption{Differences in terms of motivations to share genotyping-data, comparison between participants who would share their genotyping data with participants who would not share their data.} \label{tab:motivations2} \end{table} %\begin{table}[!ht] From 362eb98309fa8494fae5bebfb3743af8e18bda92 Mon Sep 17 00:00:00 2001 From: Bastian Greshake Date: Mon, 29 Oct 2012 21:32:41 +0100 Subject: [PATCH 49/88] finished tables & graphs --- 25_10_2012_Graphs/percent_oa.png | Bin 0 -> 342456 bytes 25_10_2012_Graphs/percent_oa.tiff | Bin 0 -> 64006158 bytes latest_uml.png | Bin 0 -> 403882 bytes paper_draft.pdf | Bin 240231 -> 890726 bytes paper_draft.tex | 82 ++++++++++++++++-------------- papers.bib | 12 ++++- 6 files changed, 55 insertions(+), 39 deletions(-) create mode 100644 25_10_2012_Graphs/percent_oa.png create mode 100644 25_10_2012_Graphs/percent_oa.tiff create mode 100644 latest_uml.png diff --git a/25_10_2012_Graphs/percent_oa.png b/25_10_2012_Graphs/percent_oa.png new file mode 100644 index 0000000000000000000000000000000000000000..7db5a060e76e842691a65b537080fa10e3ebee4e 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zg)8ei)!!Id-k@DqqE2O}D8mxb<xX>_^VZASYeu zfg}id=6|yl!r^e68{Mx~LpVGdPcIrr#L|xr!VwAJFHZlQKZHl43IChP-y^}Jq0OE4 zHxfKZ+_HJ$F=zrkF#*|L=<5+697x{?fkYt?=t+nmZp)03*#Ck1ZB_et**d$Dz1WnL zPzpxQfn*dB&8A>@{=5&0fb@&}b}DGQJDx{vPVwi(F_gIyR>g`yK=w6NWFOPeP{pa@ zRfym*P)$u$9l{<{QBhGwA(;OMVu?~#X7l#3_44ueB0I7nn<9}!Vv~?iKdHg?AExto AEdT%j diff --git a/paper_draft.tex b/paper_draft.tex index c76c68c..6c49e14 100644 --- a/paper_draft.tex +++ b/paper_draft.tex @@ -176,47 +176,6 @@ \subsection*{Survey on Sharing Genetic Information} and those who are not planning on getting genotyped. The first group is likely to agree more strongly, on a five-point scale, with motivations for sharing genotypic information. On the other hand, those people who are not planning on getting genotyped are more likely to agree with the following motivations for not sharing their data, see table \ref{tab:motivations1}. -\begin{table} -\begin{tabular}{|l|l|l|} -\hline -& Turkey's HSD & \\ \cline{2-3} -& Mean difference & SE \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who are already genotyped} & & \\ \hline -... curious & 1.159 & 0.193 \\ -... want to help scientists & 0.465 & 0.128 \\ -... for personal benefits & 0.448 & 0.183 \\ \hline -\textbf{Motivation for not sharing in participants} & & \\ -\textbf{who are not planning to get genotyped} & & \\ \hline -... fear of discrimination & 1.06 & 0.195 \\ -... breach of privacy & 0.821 & 0.225 \\ -... fear of personalized advertising & 0.848 & 0.208 \\ -... negative consequences for family members & 0.733 & 0.21 \\ \hline -\end{tabular} -\caption{Differences in terms of motivation to share genotypings with the public in survey-participants who already received a genotyping compared to participants who are not planning to getting genotyped. } -\label{tab:motivations1} -\end{table} - -\begin{table} -\begin{tabular}{|l|l|l|} -\hline -& Turkey's HSD & \\ \cline{2-3} -& Mean difference & SE \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who would share with their DTC provider} & & \\ \hline -... curiosity & 1.99 & 0.321 \\ -... want to help science & 1.57 & 0.199 \\ -... for personal benefits & 0.951 & 0.308 \\ \hline -\textbf{Motivation for sharing genotypings in participants} & & \\ -\textbf{who would not share with their DTC provider} & & \\ \hline -... fear of discrimination & 1.52 & 0.322 \\ -... fear of consequences for family members & 1.146 & 0.32 \\ -... fear of personalized advertising & 1.112 & 0.357 \\ \hline -\end{tabular} -\caption{Differences in terms of motivations to share genotyping-data, comparison between participants who would share their genotyping data with their DTC provider with participants who would not share their data with their DTC provider.} -\label{tab:motivations2} -\end{table} - Similarly, those people who would share data with their DTC provider under any circumstances are likely to agree more strongly with the following motivations for sharing than those who would not share their data with their DTC company. Those participants who are not willing to share data with their DTC company are likely to agree more strongly with the some motivations @@ -243,10 +202,10 @@ \subsection*{Sharing genotypic information} The uploaded data is published under the Creative Commons Zero-license, which - in accordance with the Panton Principles \cite{10.1371/journal.pbio.1001195} - allows a complete reuse of the data without any constraints. -Between the start of openSNP on 09/27/2011 and 12/18/2012, 214 people have signed -up with openSNP, and 79 genotyping files were made available. The openSNP -database lists 69,486,471 genotypes which are distributed over 1,938,603 unique SNPs. -Figure \ref{Figure1_label} depicts the increase of users and genotyping files over time.\bastian{update all numbers} +Between the start of openSNP on 09/27/2011 and 10/27/2012, 633 people have signed +up with openSNP, and 270 genetic datasets were made available. The openSNP +database lists X genotypes which are distributed over 2,140,643 unique SNPs. +Figures \ref{Figure1_label} and \ref{Figure2_label} depicts the increase of users and genotyping files over time.\bastian{update all numbers} \subsection*{Crowdsourcing phenotypes} @@ -261,26 +220,23 @@ \subsection*{Crowdsourcing phenotypes} information with small badges that are shown on their profile pages. In the same timeframe as above, all users combined have -entered a total of 675 variations on 47 different phenotypes with those variations being -the different values on a given trait or phenotype. See figure \ref{Figure1_label} for the increase of phenotypic information over time. - -The mean number of users that have entered their variations for a single phenotype -is 14.36 (SD 12.65), the median is 10. The distribution of how many users have -entered their data per phenotype can be seen in figure \ref{Figure2_label}. The phenotype provided -by the most users is the eye color, which has been entered by 54 users. There are -two phenotypes which have so far only been provided by a single user: -the SAT Writing score and triglyceride-levels.\bastian{update all numbers and graphs} +entered a total of 4743 variations on 130 different phenotypes with those variations being +the different values on a given trait or phenotype. The mean number of users that have entered their variations for a single phenotype +is 36.48. The distribution of how many users have +entered their data per phenotype , compared to the amount of unique phenotypes, can be seen in figure \ref{pheno}. The phenotype provided by the most users is "eye color", for which 207 users entered their phenotype. \subsection*{Connection to external services} In order to provide users with relevant information on their respective genotypes, openSNP scans databases of the scientific literature for specific SNPs. A total number of 15,229 documents \bastian{number needs to get updated}relevant to the SNPs listed in openSNP could be found in the publication databases of Mendeley, the Public Library of Science and in the crowdsourced SNPedia. -Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge (Figure \ref{Figure3_label}). For usability reasons, +Of the primary literature, 25 \% are released in open access journals and can be accessed free of charge. For usability reasons, SNPs are ranked by the amount of information gathered through the external services. The external services themselves are ranked by how easily non-scientists can understand information from these sources and available this information. The SNPedia entries are given the highest impact, as those are already manually curated and summarized in plain English, followed by open access publications out of the Public Library of Science. Lowest values are given to the Mendeley results, as the publications listed there are for the most part not freely available without subscriptions or one-time payments. An entry on SNPedia is valued 2.5 times as high as a PLoS publication and 5 times as high as a Mendeley entry. +Users are also able to link their Fitbit-accounts to their user-accounts. Fitbit is a commercial service which lets their customers track their BMI, movement-data and sleep data. This data can be linked to openSNP to give interested researchers an automatically maintained dataset of body- and sleep-developments over time. + \subsection*{Data access} OpenSNP offers extensive access to the data uploaded by users. Anyone can download single genotyping files for specific users, get archives of multiple genotyping files grouped by phenotypic variation, or access a single download that includes all genotyping files and all phenotypic variation in a comma-separated table. The genetic data is also @@ -401,34 +357,33 @@ \section*{Acknowledgments} \section*{Figure Legends} \begin{figure}[!ht] \begin{center} - \includegraphics[scale=0.35]{chart_growth.png} + \includegraphics[scale=0.35]{25_10_2012_Graphs/users.png} \end{center} \caption{ - {\bf Growth of openSNP.} The increase in numbers for users, genotyping-files, phenotypes and their variation from 27.09.2011 to 16.12.2011 is shown.} + {\bf Growth of openSNP-user-accounts.} The increase in numbers for users from 27.09.2011 to 27.10.2012 is shown.} \label{Figure1_label} \end{figure} - \begin{figure}[!ht] \begin{center} - \includegraphics[scale=0.40]{histogram_phenotypes.png} + \includegraphics[scale=0.35]{25_10_2012_Graphs/genotypes.png} \end{center} \caption{ - {\bf Histogram of users/phenotype-distribution.} The x-axis shows the minimum number of users who provide information for a phenotype, the y-axis shows how many phenotypes have at least that many users.} + {\bf Growth of available genotypings.} The increase in numbers for genotyping-files from 27.09.2011 to 27.10.2012 is shown.} \label{Figure2_label} \end{figure} - \begin{figure}[!ht] \begin{center} - \includegraphics[scale=0.50]{paper_distribution.png} + \includegraphics[scale=0.40]{25_10_2012_Graphs/phenotypes_vs_userphenotypes.png} \end{center} \caption{ - {\bf Distribution of external information gathered for SNPs in the openSNP-database.} Data on PLoS and SNPedia is openly available for every user. Publications on Mendeley are either Open Access (OA) or Closed Access (CA).} - \label{Figure3_label} + {\bf Development of unique phenotypes and phenotypic information over time.} The x-axis shows the time-frame from start of the project until October 2012, the left y-axis shows how many unique phenotypes have been entered, and the right y-axis shows the amount of phenotypes users entered.} + \label{pheno} \end{figure} + \begin{figure}[!ht] \begin{center} - \includegraphics[scale=0.60]{uml_diagram.png} + \end{center} \caption{ {\bf Flow of data inside openSNP.} External databases and user-provided data are used as input. Output of data is done using the website, the \emph{Distributed Annotation System} and a JSON-API.} @@ -451,6 +406,46 @@ \section*{Figure Legends} \section*{Tables} +\begin{table} +\begin{tabular}{|l|l|l|} +\hline +& Turkey's HSD & \\ \cline{2-3} +& Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who are already genotyped} & & \\ \hline +... curious & 1.159 & 0.193 \\ +... want to help scientists & 0.465 & 0.128 \\ +... for personal benefits & 0.448 & 0.183 \\ \hline +\textbf{Motivation for not sharing in participants} & & \\ +\textbf{who are not planning to get genotyped} & & \\ \hline +... fear of discrimination & 1.06 & 0.195 \\ +... breach of privacy & 0.821 & 0.225 \\ +... fear of personalized advertising & 0.848 & 0.208 \\ +... negative consequences for family members & 0.733 & 0.21 \\ \hline +\end{tabular} +\caption{Differences in terms of motivation to share genotypings with the public in survey-participants who already received a genotyping compared to participants who are not planning to getting genotyped. } +\label{tab:motivations1} +\end{table} + +\begin{table} +\begin{tabular}{|l|l|l|} +\hline +& Turkey's HSD & \\ \cline{2-3} +& Mean difference & SE \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who would share with their DTC provider} & & \\ \hline +... curiosity & 1.99 & 0.321 \\ +... want to help science & 1.57 & 0.199 \\ +... for personal benefits & 0.951 & 0.308 \\ \hline +\textbf{Motivation for sharing genotypings in participants} & & \\ +\textbf{who would not share with their DTC provider} & & \\ \hline +... fear of discrimination & 1.52 & 0.322 \\ +... fear of consequences for family members & 1.146 & 0.32 \\ +... fear of personalized advertising & 1.112 & 0.357 \\ \hline +\end{tabular} +\caption{Differences in terms of motivations to share genotyping-data, comparison between participants who would share their genotyping data with their DTC provider with participants who would not share their data with their DTC provider.} +\label{tab:motivations2} +\end{table} %\begin{table}[!ht] %\caption{ %\bf{Table title}} From 450d6f993fb29f1b14547c0cdd18ebe405f572b8 Mon Sep 17 00:00:00 2001 From: drsnuggles Date: Sat, 27 Oct 2012 18:53:53 +1000 Subject: [PATCH 42/88] Added some more stats, changed title, only missing thing is the user-snps-counter --- paper_draft.pdf | Bin 186113 -> 188122 bytes paper_draft.tex | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/paper_draft.pdf b/paper_draft.pdf index 056c3c5996d201a33c3c28c825e2e4c7b561ab45..9b021df571d97709cf8f8eadda264b6fa6a555a3 100644 GIT binary patch delta 64650 zcmV($K;yrGt_#||3y>oNGBq%g9VvfWOOxA14!-+W%x$Tv5zV)x>|u8tJ5Exu<6TcG zRh#6{qFPdyDNhjpn>qrg32FkMBRwXq5*`d<;Hi+bn4PVdtB^E$t4_2Zq@#CauL9X8Wea zKnb_LsZ9mY!Pd>mm7e9kK3{aRjN@>V7m(_e@trd@?Ml<&EipKnPm5#~SnOQg6yau- 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