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diff --git a/25_10_2012_Graphs/papers_dotchart.tif b/25_10_2012_Graphs/papers_dotchart.tif
new file mode 100644
index 0000000..76eeeae
Binary files /dev/null and b/25_10_2012_Graphs/papers_dotchart.tif differ
diff --git a/25_10_2012_Graphs/papers_pie.R b/25_10_2012_Graphs/papers_pie.R
new file mode 100644
index 0000000..b4bafb9
--- /dev/null
+++ b/25_10_2012_Graphs/papers_pie.R
@@ -0,0 +1,9 @@
+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()
+
+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..f8601f4
--- /dev/null
+++ b/25_10_2012_Graphs/papers_pie.svg
@@ -0,0 +1,156 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="504pt" height="504pt" viewBox="0 0 504 504" version="1.1">
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diff --git a/25_10_2012_Graphs/phenotypes_vs_userphenotypes.tif b/25_10_2012_Graphs/phenotypes_vs_userphenotypes.tif
new file mode 100644
index 0000000..8279150
Binary files /dev/null and b/25_10_2012_Graphs/phenotypes_vs_userphenotypes.tif differ
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 @@
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diff --git a/25_10_2012_Graphs/users.tif b/25_10_2012_Graphs/users.tif
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index 0000000..02bc75a
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diff --git a/25_10_2012_Graphs/users.txt b/25_10_2012_Graphs/users.txt
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index 0000000..0442772
--- /dev/null
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+++ b/outline.txt
@@ -65,10 +65,10 @@ 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?
-Quality check for phenotypes?
+Polymorphisms to phenotypes, correlation only. done
+Bias in the samples collected by openSNP? done
+Sample size to small? done
+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
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+++ b/paper_draft.tex
@@ -15,6 +15,10 @@
 % amssymb package, useful for mathematical symbols
 \usepackage{amssymb}
 
+% for checkmarks
+\usepackage{pifont}% http://ctan.org/pkg/pifont
+\newcommand{\cmark}{\ding{51}}%
+\newcommand{\xmark}{\ding{55}}%
 % graphicx package, useful for including eps and pdf graphics
 % include graphics with the command \includegraphics
 \usepackage{graphicx}
@@ -28,7 +32,8 @@
 %\usepackage{setspace} 
 %\doublespacing
 
-
+%FZ: Enable the comment command
+\usepackage{verbatim}
 % Text layout
 \topmargin 0.0cm
 \oddsidemargin 0.5cm
@@ -58,119 +63,394 @@
 
 %% ** 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}}
+\usepackage[utf8x]{inputenc}
+\DeclareUnicodeCharacter{2717}{✗}
+%\usepackage{newunicodechar}
+%\newunicodechar{✓}{\checkmark}
+%\newunicodechar{✘}{\xmark}
 
 %% END MACROS SECTION
-
 \begin{document}
 
 % Title must be 150 characters or less
 \begin{flushleft}
 {\Large
-\textbf{openSNP - Crowdsourcing Genome Wide Association Studies}
+\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$^{2}$, 
-Fabian Zimmer$^{3}$,
-Julia Reda$^{4}$
+Bastian Greshake$^{1,2,\ast}$, 
+Philipp E. Bayer$^{3,4}$, 
+Helge Rausch$^{5}$,
+Julia Reda$^{6}$
+\\
+\bf{1} Molecular Ecology Group, Biodiversity \& Climate Research Centre, Frankfurt am Main, Germany
+\\
+\bf{2} Department for Applied Bioinformatics, Institute for Cell Biology and Neuroscience, Goethe University, Frankfurt am Main, Germany 
 \\
-\bf{1} Goethe University, Frankfurt am Main, Germany
+\bf{3} School of Land, Crop and Food Sciences and Australian Centre for Plant Functional Genomics, University of Queensland, Brisbane, Australia
 \\
-\bf{2} Bond University, Gold Coast, Australia
+\bf{4} Australian Centre for Plant Functional Genomics, School of Agriculture and Food Sciences, University of Queensland, Brisbane, Australia
 \\
-\bf{3} Westf\"alische Wilhelms Universit\"at, M\"unster, Germany
+\bf{5} Hochschule f\"ur Technik und Wirtschaft, Berlin, Germany 
 \\
-\bf{4} Johannes-Gutenberg University, Mainz, Germany
+\bf{6} Johannes Gutenberg University, Mainz, Germany
 \\
 $\ast$ E-mail: info@opensnp.org
 \end{flushleft}
 
 % Please keep the abstract between 250 and 300 words
 \section*{Abstract}
-Genome wide association studies (GWAS) are a cheap and quick way to assess health risks by comparing Single Nucleotide Polymorphisms (SNPs) between groups of participants. 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 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.
+%we present a survey which evaluates whether people are willing to publicly share their genetic information. In the light of those results 
+Here we present the web platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publish their genetic data at no cost along with phenotypic information. Through this crowdsourced effort of collecting genetic and phenotypic information, openSNP has become a 
+%valuable 
+resource for 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
 
+\section*{Introduction}
 
-% 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}
+The availability of new DNA sequencing techniques has shifted the focus of biological data acquisition towards new biomedical applications.
+Many illnesses - 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 illnesses 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 illness.
+While the method does not allow inference of causal differences but merely identifies correlations, it can serve as a valuable tool for the unbiased discovery of candidate loci, which then can be checked up in functional follow-up studies \cite{10.1371/journal.pgen.1002584}, leading to a deeper understanding of diseases and thus potentially to new drug targets.  
+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 illnesses 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 raw file that includes the customer's individual genotypes. In 2011, 23andMe 
+alone had over 100,000 customers \cite{23andMe2011}. The company realizes the potential of performing GWAS with this amount of data by using surveys to ask their customers about 
+traits and illnesses. With the consent of the customer, the data is used for association studies. 23andMe has published several 
+studies in which known findings are replicated together with new associations for 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}, as their consent forms only allow residents of the United States to apply.  
+
+%Enter here: definition, benefits of sourcing
+Crowdsourcing, giving a task into the hands of a potentially large number of -- mainly intrinsically motivated -- people, has become a widely used practice in the internet age and is getting adopted in the realm of science as well. One of the main benefits of crowdsourcing is that small contributions to a project pile up to create a larger work, which would have been virtually impossible to create otherwise. This approach especially benefits scientists who might not have enough funding or time to create data, or in cases where the amounts of data are too large to be analyzed by researchers alone.
+ Galaxy Zoo and FoldIt \cite{Eiben2012, GalaxyZoo} are two of the best known examples. 
+Galaxy Zoo enables amateur astronomers to walk through telescopic images to categorize the shown objects at a rate which could not have been matched by the efforts of professional astronomers.
+Similarly, crowdsourcing can not only be applied to analyzing data, but also to collecting data. This approach has been shown to work when it comes to tracking bird migration \cite{CrowdsourcingReview2010}. With the advent of DTC genetic testing and the internet, a similar approach can now be applied to human genetics.
+
+There have been studies investigating how likely customers of such companies are to share their data. \cite{Darst2013} investigated the likelihood of 2,024 individuals to share their test results with their health-care providers and found that 26.5\% (540 individuals) did share their results with their physician or health-care provider. Those that shared were older, had a higher income and were less concerned about testing or the privacy implications of sharing their data compared to customers that didn't share their data.
+
+Other studies have shown that DTC customers see themselves as being well-informed. Interviews with early adopters have shown that these customers are better informed and more skeptical about the capabilities of genotyping than expected \cite{McGowan2010}. However, in another study of early adopters, 32\% of customers had misperceptions about personal genomic testing \cite{Gollust2012}. Of these participants, 92\% intended to share their results with physicians in order to receive medical recommendations. In both studies, participants generally chose this technology to be better informed about genetic risks and to satisfy their own curiosity. 
+
+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.
 
-\section*{Introduction}
+% 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 several motivations 
+%for not sharing their data (for an overview of these motivations, 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. 
 
-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).
+\subsubsection*{Sharing genotypic information}
 
-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.  
+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.
 
-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.  
+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}. 
 
-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. 
 
-% Results and Discussion can be combined.
-\section*{Results}
+\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. Due to space constraints on the database level, openSNP currently only displays the SNPs of the exome data sets on the website but the whole VCF files can be downloaded.
+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 launch 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. As of 10/27/2012, 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 since September 2011.
 
-\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). 
 
-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). 
+\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.
 
-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). 
+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). 
 
-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).
 
-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). 
+\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 (Figure \ref{Figure3_label}).
+Of the primary literature listed on Mendeley, the \emph{NHGRI GWAS Catalog} \& the Public Library of Science, about 20 \% are released in open access journals and can be accessed free of charge (Figure \ref{oa_label}), although probably not all publications on Mendeley are correctly flagged and the \emph{NHGRI GWAS Catalog} does not give details on whether a publication is open access or not. So the total number of open access publications might be higher. 
 
-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). 
+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*{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. 
+Users are also able to link their Fitbit\cite{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.
 
-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.
+\subsection*{Data access}
+openSNP offers complete 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. For privacy reasons, openSNP does not log any IPs. 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. All chromosomal positions are based on the human reference genome NCBI37, as this is the standard reference used by DTC providers right now.
 
-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.
+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.
 
-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. 
+\section*{Discussion}
 
-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. 
+%\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. 
+%
+Here, we present openSNP, a crowdsourced resource that enables customers of DTC testing companies to share their genotypings with researchers and receive new annotations for their genetic variants. Through a of number of active users already present on openSNP, we have shown that at least some customers of DTC companies are willing to share their data at no cost to researchers around the world and are willing to annotate their data with phenotypes.
 
-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. 
+\subsection*{Comparing openSNP to other crowdsourcing platforms}
+Projects similar to openSNP are the SNPedia, the Personal Genome Project and PatientsLikeMe.com (see table \ref{tab:platforms} for an overview). The focus of the SNPedia is the aggregation and summary of primary scientific literature on SNPs. The project uses a Wiki to store and display the data collected by volunteers contributing to the project. The data is mainly organized by the unique Rs-ID, as given by dbSNP. If Rs-IDs are missing, the identifiers given by the DTC testing companies may be used, similar to the way openSNP stores the data. For individual SNPs, pages may list scientific literature and summaries on the found impact can be given. As those pages are largely created manually and not automatically through database access, these summaries may not be complete. openSNP utilizes the SNPedia by crawling their data for SNPs, the summary of the impact and the magnitude a SNP has. While they offer a page listing download-URLs, the SNPedia does not offer any uploading capabilities for genetic data and has no APIs to easily access SNPs or data subsets in the different data sets. Similarly, there is no way for users of SNPedia to share their phenotypes in a machine readable format. 
 
-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 Personal Genome Project (PGP) has its focus on collecting and hosting genetic as well as phenotypic data. Unlike openSNP, they do not offer a completely open enrollment. For each participant of the PGP, eligibility has to be established and participants have to give IRB approved informed consent. This allows for an easier re-use of the data, but at the same time makes it impossible for many people to enroll (e.g. non-US citizens). Depending on the specific use one has for the data, the PGPs enrollment policy might be preferable to the open approach openSNP takes. While the PGP stores genotyping data as well as exome and genome data sets, it is currently impossible to access this data through an API, instead data has to be manually extracted from their database. The annotation database of the PGP is not aimed at delivering specific publications, but instead focuses more on specific traits. The annotation data stored by the PGP is incorporated into openSNP as well. 
 
-\section*{Discussion}
+PatientsLikeMe is a community for patients with life-changing illnesses to track and share the development of their illness with other patients with similar illnesses \cite{Wicks2010}.  This helps patients in gaining a better understanding of their illnesses -- 72\% of surveyed participants found the site "moderately" or "very helpful", for example when it comes to starting a new medication (37\% found the site helpful), or when it comes to changing the medication (27\%). Some subsets of data stored in PatientsLikeMe are open to the public and have been shown to be useful for research, for example in Multiple Sclerosis \cite{Bove2013}. Alternatively, access to the data they store can be licensed by researchers for a fee. 
 
+There are some projects that use gamification to let players work with crowdsourced scientific data. For example, FoldIt is a puzzle game that lets players fold protein structures in order to achieve optimal structures. Players of Foldit have been able to identify protein structures and were even able to improve the activity of existing protein structures \cite{Eiben2012}. Another example is Galaxy Zoo \cite{GalaxyZoo}, which allows everyone to perform classification tasks for galaxies based on images collected by the Sloan Digital Sky Survey. 
 
+Unlike PatientsLikeMe, the PGP, or openSNP -- which give the task of collecting the data into the hands of the crowd -- Foldit and Galaxy Zoo limit themselves to analyzing data which was previously collected by scientists.  
 
-% 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 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. 
+\subsection*{Privacy, health implications and ethical considerations}
 
-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 advent of DTC genetic testing has led to new ethical and social issues. 
+Much of the criticism of DTC genetic testing focuses 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}.  
 
-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.
+%As we have found in our survey on sharing such results (see supplementary methods), many 
+There is a variety of ethical and privacy implications when it comes to DTC genetic testing \cite{Caulfield2011,Joh2011,Joly2013}. Nevertheless, studies show that DTC customers are willing to share their results given the right circumstances and personal benefits gained through the sharing, while being aware that sharing genetic data can lead to misuse of the data and consequences such as genetic discrimination  \cite{Darst2013}. 
 
-The data of the survey was analyzed with SPSS 19. 
+%Our survey has shown that 
+As people are concerned about their privacy and fear that stakeholders like employers, insurance companies, governments or advertisers might misuse the information \cite{Wolinsky2005}, policy makers start to react to those changes by having introduced laws like the 
+\textit{Genetic Information Non-Discrimination Act} (GINA) in the United States or the \emph{Gendiagnostikgesetz} (GenDG) in Germany to minimize the impact of
+widely available genetic information. DTC genetic testing companies themselves also try to create online communities - like the 23andMe community forums - that help in educating their customers about the risks of releasing genetic data \cite{Lee2009}. Neither GINA nor the GenDG offer complete protection from genetic discrimination, as certain areas, such as life insurances, are not covered by those laws.  
 
-\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. 
+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 include further consent processes.
 
-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. 
+For users of openSNP, the biggest potential problem is legal genetic discrimination, in fields not covered by laws such as GINA or GenDG, once their public data is re-identified. As the genetic information itself is highly personalized the anonymous sharing of genetic data is impossible. And while users can register pseudonymously, this should not be seen as ultimate protection against re-identification. 
+A recent study once again showed that metadata, potentially attached to genetic profiles, such as date of birth, gender and postal code, can be be used to re-identify individuals on a name basis \cite{Sweeney2013}. 
+A similar approach utilized genetic markers on the Y chromosome along with genealogical databases and metadata such as age and state to infer surnames and from there on the individuals \cite{Gymrek18012013}. 
+Thus users need to be aware of the potential of re-identification through providing metadata along with their genetic information and the genetic discrimination that could follow.
 
-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.  
+\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, which often is not easy to obtain. Either because generating the needed amount of data still is a cost factor or because it is hard to find enough participants for the case conditions, for example if rare diseases are to be studied. Nevertheless, GWAS are still frequently used and new associations are still being discovered \cite{10.1371.journal.pone.0031470,10.1371.journal.pone.0030309,10.1371.journal.pone.0029848}. 
 
-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. 
+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 who are willing to pay for the genotyping by themselves. 
+
+openSNP tries to enable and facilitate the re-use of this already generated data by offering a platform where customers of DTC genetic testing can publish their results into the public domain. Allowing interested parties to use the data for their own research allows scientists to perform studies without the need to generate genetic data sets on their own. Additionally the data can be used to enrich other data sets in order to overcome limited sample sizes, which is especially of interest for rare diseases. 
+
+In 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 three possible solutions to this problem: The first one would be to only allow a trusted subset of users to enter new phenotypes. The second 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. The third one is to exclude users from the phenotype-creation process by allowing users to select their phenotypes from a pre-given set of possible variations.
+
+In the first two cases it makes it harder 
+for users to enter their data which raises the bar for participation, and the third case doesn't let users participate at all.
+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. 21 people, mainly from underrepresented demographics, have been offered free genotypings using funding provided by the Wikimedia Germany association in order to mitigate this bias. 
+
+Furthermore, it is impossible to verify whether users who have uploaded data are actually the sources of that data. This opens the venue to potentially malicious usage, as genotypings from strangers can be uploaded, as well as misinformation about phenotypes can be entered. The openSNP project has currently no means of verifying the validity of data uploaded by users. Of course, users can always delete their data or contact the team to delete stored data. Old backups of the database are deleted so that at any given time, there are only two backups. This means that deleted data disappears from the webpage immediately and will disappear after two months in the backend where it isn't accessible to the public.
+
+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 is already capable of adjusting for changes in the type of data generated by DTC genetic testing. Future improvements made on openSNP will address interoperability with other platforms and tools in Personal Genomics, amongst others: The standardization of phenotypes, the inclusion of further annotation sources and support for a wider range of data sets, including full genome data.
+
+ 
+% 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*{Ethics Statement}
+In line with the German regulations and the ethics approval system for biomedical studies \cite{Pinkerton2002} we contacted the ethics commission of the Goethe University Frankfurt am Main, Germany teaching hospital. Its director confirmed that this study does not fall within their remit.
+
+%\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 3.2.13. 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. 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. 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. David Edwards for constructive advice in grammar, spelling and structure of this study. Further thanks go to Samantha Clark, Fabian Zimmer 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. Thanks go to Misha Angrist and the anonymous reviewers for their helpful comments on this manuscript. 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}
+	%	\includegraphics[scale=0.6]{25_10_2012_Graphs/new_pie.png}
+	%\end{center}
+	\caption{
+	{\bf Ratio of open access Publications.} Green pieces are open access. The \emph{NHGRI GWAS Catalog} doesn't give information about the open access status.}
+	\label{oa_label}
+\end{figure}
+
+\begin{figure}[!ht]
+	%\begin{center}
+	%  \includegraphics[scale=0.3]{latest_uml.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}
@@ -185,6 +465,63 @@ \section*{Figure Legends}
 
 
 \section*{Tables}
+\begin{table}
+\caption{Comparison of crowdsourced genetics platforms. N/A = Not Applicable, x = Present, - = Absent *SNPedia only provides an API to webpages of individual SNPs, not access to genetic data of individuals}
+\begin{tabular}{|l|l|l|l|l|l|l|}
+\hline
+Name & Provides  & Provides & Open  & API & IRB approval & License \\ 
+& annotation & data & enrollment & & & \\
+\hline
+SNPedia & (x*) & - & N/A & x & N/A & CC-NC-SA 3.0 \\ 
+\hline
+PGP & x & x & - & - & x & CC-BY 1.0, CC0 \\
+\hline
+PatientsLikeMe & - & x & x & - & x & Closed, CC-BY-SA 3.0 \\
+\hline
+openSNP & x & x & x & x & - & CC-BY 3.0 \\
+\hline
+\end{tabular}
+\label{tab:platforms}
+\end{table}
+%\begin{table}
+%\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. }  
+%\begin{tabular}{|p{7cm}|p{2cm}|p{2cm}|p{2cm}|p{2cm}|}
+%\hline
+%& Mean \emph{Genotyped} & Mean \emph{Not Genotyped} & Mean Difference & Standard Error\\ 
+%\hline
+%\textbf{Motivation for sharing data in participants who are already genotyped} & & & & \\
+%\hline 
+%... curious & 3.82 & 2.66 & 1.159 & 0.193 \\ \hline % checked
+%... want to help scientists & 4.64 & 4.18  & 0.465 & 0.128 \\ \hline % checked
+%... for personal benefits & 3.77 & 3.32  & 0.448 & 0.183 \\ \hline % checked
+%\textbf{Motivation for not sharing in participants who are not planning to get genotyped} & & & & \\ \hline
+%... fear of discrimination & 3.09 & 4.15  & 1.06 & 0.195 \\ \hline % checked
+%... breach of privacy & 3.01 & 3.68  & 0.666 & 0.211 \\ \hline % checked
+%... fear of personalized advertising & 3.03 & 3.88  & 0.848 & 0.208 \\ \hline % checked
+%... negative consequences for family members & 2.93 & 3.57  & 0.639 & 0.197 \\ \hline 
+%\end{tabular}
+%\label{tab:motivations1}
+%\end{table}
+%
+%\begin{table}
+%\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.}
+%\begin{tabular}{|p{7cm}|p{2cm}|p{2cm}|p{2cm}|p{2cm}|}
+%\hline
+%& Mean {Sharing} & Mean {Not Sharing} & Mean Difference & Standard Error \\ 
+%\hline
+%\textbf{Motivation for sharing genotypings in participants who would share} & & & & \\
+%\hline
+%... curiosity & 3.86 & 1.87 & 1.99 & 0.321 \\ \hline % checked
+%... want to help science & 4.70 & 3.13 & 1.57 & 0.199 \\ \hline % checked
+%... for personal benefits & 3.68 & 2.73 & 0.951 & 0.308 \\ \hline % checked
+%\textbf{Motivation for sharing genotypings in participants who would not share} & & & & \\
+%\hline
+%... fear of discrimination & 3.21 & 4.73 & 1.52 & 0.322 \\ \hline % checked
+%... fear of consequences for family members & 2.79 & 3.93 & 1.146 & 0.32 \\ \hline % checked
+%... fear of personalized advertising & 3.17 & 4.29 & 1.112 & 0.357 \\ \hline % checked
+%\end{tabular}
+%\label{tab:motivations2}
+%\end{table}
 %\begin{table}[!ht]
 %\caption{
 %\bf{Table title}}
@@ -197,4 +534,3 @@ \section*{Tables}
 % \end{table}
 
 \end{document}
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+% 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 several motivations 
+for not sharing their data (for an overview of these motivations, 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 since September 2011.
+
+
+\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 (Figure \ref{Figure3_label}).
+Of the primary literature listed on Mendeley, the \emph{NHGRI GWAS Catalog} \& the Public Library of Science, about 20 \% are released in open access journals and can be accessed free of charge (Figure \ref{oa_label}), although probably not all publications on Mendeley are correctly flagged and the \emph{NHGRI GWAS Catalog} does not give details on whether a publication is Open Access or not. So the total number of Open Access publications might be higher. 
+
+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\cite{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.
+
+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 focuses 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}.  
+
+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. 21 people, mainly from underrepresented demographics, have been offered free genotyping using funding provided by the Wikimedia Germany association in order to mitigate this bias.
+
+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*{Ethics Statement}
+The survey was taken anonymously by the participants and analyzed anonymously, thus IRB approval was deemed unnecessary according to US regulations in \textit{45 CFR 46.101(b)} and in accordance the Hessian data protection officer (http://www.datenschutz.hessen.de/wf001.htm\#entry2223).
+
+\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. 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. 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. David 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}
+	\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}
+	\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}
+	\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 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 Ratio of Open Access Publications.} Green pieces are Open Access. The \emph{NHGRI GWAS Catalog} doesn't give information about the Open Access status.}
+	\label{oa_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}
+\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. }  
+\begin{tabular}{|p{7cm}|p{2cm}|p{2cm}|p{2cm}|p{2cm}|}
+\hline
+& Mean \emph{Genotyped} & Mean \emph{Not Genotyped} & Mean Difference & Standard Error\\ 
+\hline
+\textbf{Motivation for sharing data in participants who are already genotyped} & & & & \\
+\hline 
+... curious & 3.82 & 2.66 & 1.159 & 0.193 \\ \hline % checked
+... want to help scientists & 4.64 & 4.18  & 0.465 & 0.128 \\ \hline % checked
+... for personal benefits & 3.77 & 3.32  & 0.448 & 0.183 \\ \hline % checked
+\textbf{Motivation for not sharing in participants who are not planning to get genotyped} & & & & \\ \hline
+... fear of discrimination & 3.09 & 4.15  & 1.06 & 0.195 \\ \hline % checked
+... breach of privacy & 3.01 & 3.68  & 0.666 & 0.211 \\ \hline % checked
+... fear of personalized advertising & 3.03 & 3.88  & 0.848 & 0.208 \\ \hline % checked
+... negative consequences for family members & 2.93 & 3.57  & 0.639 & 0.197 \\ \hline 
+\end{tabular}
+\label{tab:motivations1}
+\end{table}
+
+\begin{table}
+\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.}
+\begin{tabular}{|p{7cm}|p{2cm}|p{2cm}|p{2cm}|p{2cm}|}
+\hline
+& Mean {Sharing} & Mean {Not Sharing} & Mean Difference & Standard Error \\ 
+\hline
+\textbf{Motivation for sharing genotypings in participants who would share} & & & & \\
+\hline
+... curiosity & 3.86 & 1.87 & 1.99 & 0.321 \\ \hline % checked
+... want to help science & 4.70 & 3.13 & 1.57 & 0.199 \\ \hline % checked
+... for personal benefits & 3.68 & 2.73 & 0.951 & 0.308 \\ \hline % checked
+\textbf{Motivation for sharing genotypings in participants who would not share} & & & & \\
+\hline
+... fear of discrimination & 3.21 & 4.73 & 1.52 & 0.322 \\ \hline % checked
+... fear of consequences for family members & 2.79 & 3.93 & 1.146 & 0.32 \\ \hline % checked
+... fear of personalized advertising & 3.17 & 4.29 & 1.112 & 0.357 \\ \hline % checked
+\end{tabular}
+\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}
diff --git a/papers.bib b/papers.bib
index 462f5e0..85f530d 100644
--- a/papers.bib
+++ b/papers.bib
@@ -1,6 +1,270 @@
-% This file was created with JabRef 2.7b.
+% This file was created with JabRef 2.8.
 % Encoding: ISO8859_1
 
+@ARTICLE{23andMe2012,
+  author = {23andMe},
+  title = {{23andMe Privacy Statement http://www.23andme.com/about/privacy/}},
+  journal = {23andMe Homepage},
+  year = {2012},
+  howpublished = {\url{http://www.23andme.com/about/privacy/}}
+}
+
+@ARTICLE{23andMe2011,
+  author = {23andMe},
+  title = {{23andMe 2011 State of the Database Address}},
+  journal = {The Spittoon http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/},
+  year = {2011},
+  number = {June 15},
+  howpublished = {\url{http://spittoon.23andme.com/2011/06/15/23andme-2011-state-of-the-database-address/}},
+  type = {Blog}
+}
+
+@ARTICLE{prostatecancer,
+  author = {Abate-Shen, Cory and Shen, Michael M.},
+  title = {Molecular genetics of prostate cancer},
+  journal = {Genes \& Development},
+  year = {2000},
+  volume = {14},
+  pages = {2410-2434},
+  number = {19},
+  doi = {10.1101/gad.819500},
+  eprint = {http://genesdev.cshlp.org/content/14/19/2410.full.pdf+html},
+  url = {http://genesdev.cshlp.org/content/14/19/2410.short}
+}
+
+@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},
+  journal = {Proceedings of the National Academy of Sciences},
+  year = {2012},
+  volume = {109},
+  pages = {11920-11927},
+  number = {30},
+  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.},
+  doi = {10.1073/pnas.1201904109},
+  eprint = {http://www.pnas.org/content/109/30/11920.full.pdf+html},
+  url = {http://www.pnas.org/content/109/30/11920.abstract}
+}
+
+@ARTICLE{Bove2013,
+  author = {Bove, Riley and Secor, Elizabeth and Healy, Brian C. and Musallam,
+	Alexander and Vaughan, Timothy and Glanz, Bonnie I. and Greeke, Emily
+	and Weiner, Howard L. and Chitnis, Tanuja and Wicks, Paul and {De
+	Jager}, Philip L.},
+  title = {Evaluation of an online platform for multiple sclerosis research:
+	patient description, validation of severity scale, and exploration
+	of BMI effects on disease course.},
+  journal = {PLoS One},
+  year = {2013},
+  volume = {8},
+  pages = {e59707},
+  number = {3},
+  abstract = {To assess the potential of an online platform, PatientsLikeMe.com
+	(PLM), for research in multiple sclerosis (MS). An investigation
+	of the role of body mass index (BMI) on MS disease course was conducted
+	to illustrate the utility of the platform.First, we compared the
+	demographic characteristics of subjects from PLM and from a regional
+	MS center. Second, we validated PLM's patient-reported outcome measure
+	(MS Rating Scale, MSRS) against standard physician-rated tools. Finally,
+	we analyzed the relation of BMI to the MSRS measure.Compared with
+	4,039 MS Center patients, the 10,255 PLM members were younger, more
+	educated, and less often male and white. Disease course was more
+	often relapsing remitting, with younger symptom onset and shorter
+	disease duration. Differences were significant because of large sample
+	sizes but small in absolute terms. MSRS scores for 121 MS Center
+	patients revealed acceptable agreement between patient-derived and
+	physician-derived composite scores (weighted kappa = 0.46). The Walking
+	domain showed the highest weighted kappa (0.73) and correlation (rs
+	= 0.86) between patient and physician scores. Additionally, there
+	were good correlations between the patient-reported MSRS composite
+	and walking scores and physician-derived measures: Expanded Disability
+	Status Scale (composite rs = 0.61, walking rs = 0.74), Timed 25 Foot
+	Walk (composite rs = 0.70, walking rs = 0.69), and Ambulation Index
+	(composite rs = 0.81, walking rs = 0.84). Finally, using PLM data,
+	we found a modest correlation between BMI and cross-sectional MSRS
+	(rho = 0.17) and no association between BMI and disease course.The
+	PLM population is comparable to a clinic population, and its patient-reported
+	MSRS is correlated with existing clinical instruments. Thus, this
+	online platform may provide a venue for MS investigations with unique
+	strengths (frequent data collection, large sample sizes). To illustrate
+	its applicability, we assessed the role of BMI in MS disease course
+	but did not find a clinically meaningful role for BMI in this setting.},
+  doi = {10.1371/journal.pone.0059707},
+  institution = {Program in Translational NeuroPsychiatric Genomics, Department of
+	Neurology, Institute for the Neurosciences, Brigham and Women's Hospital,
+	Boston, Massachusetts, United States of America.},
+  language = {eng},
+  medline-pst = {ppublish},
+  owner = {drsnuggles},
+  pii = {PONE-D-12-37775},
+  pmid = {23527256},
+  timestamp = {2013.05.30},
+  url = {http://dx.doi.org/10.1371/journal.pone.0059707}
+}
+
+@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{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},
+  journal = {Bioinformatics},
+  year = {2011},
+  volume = {27},
+  pages = {2156-2158},
+  number = {15},
+  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},
+  doi = {10.1093/bioinformatics/btr330},
+  eprint = {http://bioinformatics.oxfordjournals.org/content/27/15/2156.full.pdf+html},
+  url = {http://bioinformatics.oxfordjournals.org/content/27/15/2156.abstract}
+}
+
+@ARTICLE{Darst2013,
+  author = {Darst, Burcu F. and Madlensky, Lisa and Schork, Nicholas J. and Topol,
+	Eric J. and Bloss, Cinnamon S.},
+  title = {Characteristics of Genomic Test Consumers Who Spontaneously Share
+	Results With Their Health Care Provider.},
+  journal = {Health Commun},
+  year = {2013},
+  month = {Feb},
+  abstract = {The purpose of this study was to evaluate the characteristics of direct-to-consumer
+	(DTC) genomic test consumers who spontaneously shared their test
+	results with their health care provider. Utilizing data from the
+	Scripps Genomic Health Initiative, we compared demographic, behavioral,
+	and attitudinal characteristics of DTC genomic test consumers who
+	shared their results with their physician or health care provider
+	versus those who did not share. We also compared genomic risk estimates
+	between the two groups. Of 2,024 individuals assessed at approximately
+	6 months post testing, 540 individuals (26.5\%) reported sharing
+	their results with their physician or health care provider. Those
+	who shared were older (p < .001), had a higher income (p = .01),
+	were more likely to be married (p = .005), and were more likely to
+	identify with a religion (p = .004). As assessed prior to undergoing
+	testing, sharers also reported higher levels of exercise (p = .003),
+	lower fat intake (p = .02), fewer overall concerns about testing
+	(p = .001), and fewer concerns related to the privacy of their genomic
+	information (p = .03). The genomic disease risk estimates disclosed
+	were not associated with sharing. Thus, in a DTC genomic testing
+	context, physicians and other health care providers may be more likely
+	to encounter patients who are more health conscious and have fewer
+	concerns about the privacy of their genomic information. Genomic
+	risk itself does not appear to be a primary determinant of sharing
+	behavior among consumers.},
+  doi = {10.1080/10410236.2012.717216},
+  institution = {a Scripps Genomic Medicine , Scripps Translational Science Institute,
+	Scripps Health.},
+  language = {eng},
+  medline-pst = {aheadofprint},
+  owner = {drsnuggles},
+  pmid = {23384116},
+  timestamp = {2013.05.30},
+  url = {http://dx.doi.org/10.1080/10410236.2012.717216}
+}
+
+@ARTICLE{CrowdsourcingReview2010,
+  author = {Dickinson, Janis L. and Zuckerberg, Benjamin and Bonter, David N.},
+  title = {{Citizen Science as an Ecological Research Tool: Challenges and Benefits}},
+  journal = {The Annual Review of Ecology, Evolution, and Systematics},
+  year = {2010},
+  volume = {41},
+  pages = {149--172},
+  citeulike-article-id = {10239704},
+  keywords = {citizen, nrc, science},
+  posted-at = {2012-01-18 15:45:27},
+  priority = {2}
+}
+
 @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
@@ -14,7 +278,6 @@ @ARTICLE{Do2011
   pages = {e1002141},
   number = {6},
   month = {Jun},
-  __markedentry = {[drsnuggles:6]},
   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
@@ -22,9 +285,9 @@ @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.
-	We also replicated 20 previously discovered genetic associations
+	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
 	method based on genome-wide sharing estimates between distantly related
@@ -53,6 +316,81 @@ @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{Eiben2012,
+  author = {Eiben, Christopher B. and Siegel, Justin B. and Bale, Jacob B. and
+	Cooper, Seth and Khatib, Firas and Shen, Betty W. and Players, Foldit
+	and Stoddard, Barry L. and Popovic, Zoran and Baker, David},
+  title = {Increased Diels-Alderase activity through backbone remodeling guided
+	by Foldit players.},
+  journal = {Nat Biotechnol},
+  year = {2012},
+  volume = {30},
+  pages = {190--192},
+  number = {2},
+  month = {Feb},
+  abstract = {Computational enzyme design holds promise for the production of renewable
+	fuels, drugs and chemicals. De novo enzyme design has generated catalysts
+	for several reactions, but with lower catalytic efficiencies than
+	naturally occurring enzymes. Here we report the use of game-driven
+	crowdsourcing to enhance the activity of a computationally designed
+	enzyme through the functional remodeling of its structure. Players
+	of the online game Foldit were challenged to remodel the backbone
+	of a computationally designed bimolecular Diels-Alderase to enable
+	additional interactions with substrates. Several iterations of design
+	and characterization generated a 24-residue helix-turn-helix motif,
+	including a 13-residue insertion, that increased enzyme activity
+	>18-fold. X-ray crystallography showed that the large insertion adopts
+	a helix-turn-helix structure positioned as in the Foldit model. These
+	results demonstrate that human creativity can extend beyond the macroscopic
+	challenges encountered in everyday life to molecular-scale design
+	problems.},
+  doi = {10.1038/nbt.2109},
+  institution = {Department of Biochemistry, University of Washington, Seattle, Washington,
+	USA.},
+  keywords = {Algorithms; Catalysis; Computational Biology; Crystallography, X-Ray;
+	Enzymes, chemical synthesis/chemistry; Helix-Turn-Helix Motifs; Humans;
+	Models, Molecular; Protein Engineering, methods; Structure-Activity
+	Relationship; Substrate Specificity; Video Games},
+  language = {eng},
+  medline-pst = {epublish},
+  owner = {drsnuggles},
+  pii = {nbt.2109},
+  pmid = {22267011},
+  timestamp = {2013.06.01},
+  url = {http://dx.doi.org/10.1038/nbt.2109}
+}
+
 @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 +403,6 @@ @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
@@ -98,3 +435,935 @@ @ARTICLE{Eriksson2010
   url = {http://dx.doi.org/10.1371/journal.pgen.1000993}
 }
 
+@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{fitbit,
+  author = {FitbitInc},
+  title = {{Fitbit http://www.fitbit.com}},
+  journal = {Fitbit Homepage},
+  year = {2012},
+  howpublished = {\url{http://www.23andme.com/about/privacy/}}
+}
+
+@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{Gollust2012,
+  author = {Gollust, S. E. and Gordon, E. S. and Zayac, C. and Griffin, G. and
+	Christman, M. F. and Pyeritz, R. E. and Wawak, L. and Bernhardt,
+	B. A.},
+  title = {Motivations and perceptions of early adopters of personalized genomics:
+	perspectives from research participants.},
+  journal = {Public Health Genomics},
+  year = {2012},
+  volume = {15},
+  pages = {22--30},
+  number = {1},
+  abstract = {To predict the potential public health impact of personal genomics,
+	empirical research on public perceptions of these services is needed.
+	In this study, 'early adopters' of personal genomics were surveyed
+	to assess their motivations, perceptions and intentions.Participants
+	were recruited from everyone who registered to attend an enrollment
+	event for the Coriell Personalized Medicine Collaborative, a United
+	States-based (Camden, N.J.) research study of the utility of personalized
+	medicine, between March 31, 2009 and April 1, 2010 (n = 369). Participants
+	completed an Internet-based survey about their motivations, awareness
+	of personalized medicine, perceptions of study risks and benefits,
+	and intentions to share results with health care providers.Respondents
+	were motivated to participate for their own curiosity and to find
+	out their disease risk to improve their health. Fewer than 10\% expressed
+	deterministic perspectives about genetic risk, but 32\% had misperceptions
+	about the research study or personal genomic testing. Most respondents
+	perceived the study to have health-related benefits. Nearly all (92\%)
+	intended to share their results with physicians, primarily to request
+	specific medical recommendations.Early adopters of personal genomics
+	are prospectively enthusiastic about using genomic profiling information
+	to improve their health, in close consultation with their physicians.
+	This suggests that early users (i.e. through direct-to-consumer companies
+	or research) may follow up with the health care system. Further research
+	should address whether intentions to seek care match actual behaviors.},
+  doi = {10.1159/000327296},
+  institution = {Division of Health Policy and Management, University of Minnesota
+	School of Public Health, Minneapolis, MN, USA. sgollust@umn.edu},
+  keywords = {Adolescent; Adult; Aged; Consumer Participation; Female; Genetic Predisposition
+	to Disease; Genomics; Humans; Individualized Medicine; Information
+	Dissemination; Male; Middle Aged; Motivation; Perception; Prospective
+	Studies; Young Adult},
+  language = {eng},
+  medline-pst = {ppublish},
+  owner = {drsnuggles},
+  pii = {000327296},
+  pmid = {21654153},
+  timestamp = {2013.05.30},
+  url = {http://dx.doi.org/10.1159/000327296}
+}
+
+@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{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{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{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{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{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{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{Lee2009,
+  author = {Lee, Sandra Soo-Jin and Crawley, LaVera},
+  title = {Research 2.0: social networking and direct-to-consumer (DTC) genomics.},
+  journal = {Am J Bioeth},
+  year = {2009},
+  volume = {9},
+  pages = {35--44},
+  number = {6-7},
+  __markedentry = {[drsnuggles:6]},
+  abstract = {The convergence of increasingly efficient high throughput sequencing
+	technology and ubiquitous Internet use by the public has fueled the
+	proliferation of companies that provide personal genetic information
+	(PGI) direct-to-consumers. Companies such as 23andme (Mountain View,
+	CA) and Navigenics (Foster City, CA) are emblematic of a growing
+	market for PGI that some argue represents a paradigm shift in how
+	the public values this information and incorporates it into how they
+	behave and plan for their futures. This new class of social networking
+	business ventures that market the science of the personal genome
+	illustrates the new trend in collaborative science. In addition to
+	fostering a consumer empowerment movement, it promotes the trend
+	of democratizing information--openly sharing of data with all interested
+	parties, not just the biomedical researcher--for the purposes of
+	pooling data (increasing statistical power) and escalating the innovation
+	process. This target article discusses the need for new approaches
+	to studying DTC genomics using social network analysis to identify
+	the impact of obtaining, sharing, and using PGI. As a locus of biosociality,
+	DTC personal genomics forges social relationships based on beliefs
+	of common genetic susceptibility that links risk, disease, and group
+	identity. Ethical issues related to the reframing of DTC personal
+	genomic consumers as advocates and research subjects and the creation
+	of new social formations around health research may be identified
+	through social network analysis.},
+  doi = {10.1080/15265160902874452},
+  institution = {Stanford Center for Biomedical Ethics, Stanford University Medical
+	School, Palo Alto, CA 94304, USA. sandra.lee@stanford.edu},
+  keywords = {Biotechnology, ethics/trends; Consumer Participation, trends; Databases,
+	Genetic; Decision Making, ethics; Genetic Predisposition to Disease;
+	Genetic Privacy, ethics; Genetic Testing, ethics; Genome, Human;
+	Genomics, ethics; Humans; Internet; Marketing of Health Services,
+	ethics/methods; Private Sector, ethics/trends; Public Opinion; Public
+	Policy, trends; Social Support},
+  language = {eng},
+  medline-pst = {ppublish},
+  owner = {drsnuggles},
+  pii = {911997739},
+  pmid = {19998112},
+  timestamp = {2013.06.03},
+  url = {http://dx.doi.org/10.1080/15265160902874452}
+}
+
+@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)},
+  title = {Comprehensive Research Synopsis and Systematic Meta-Analyses in Parkinson's
+	Disease Genetics: The PDGene Database},
+  journal = {PLoS Genet},
+  year = {2012},
+  volume = {8},
+  pages = {e1002548},
+  number = {3},
+  month = {03},
+  abstract = {<title>Author Summary</title> <p>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
+	(“PDGene”: <ext-link xmlns:xlink="http://www.w3.org/1999/xlink"
+	ext-link-type="uri" xlink:href="http://www.pdgene.org" xlink:type="simple">http://www.pdgene.org</ext-link>)
+	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
+	(<italic>P</italic>&lt;5×10<sup>−8</sup>; a.k.a. genome-wide significance)
+	association with risk for PD: <italic>BST1</italic>, <italic>CCDC62/HIP1R</italic>,
+	<italic>DGKQ/GAK</italic>, <italic>GBA</italic>, <italic>LRRK2</italic>,
+	<italic>MAPT</italic>, <italic>MCCC1/LAMP3</italic>, PARK16, <italic>SNCA</italic>,
+	<italic>STK39</italic>, <italic>SYT11/RAB25</italic>. In addition
+	and purely by data-mining, we identified one novel PD susceptibility
+	locus in a gene called <italic>ITGA8</italic> (rs7077361, <italic>P</italic> = 1.3×10<sup>−8</sup>).
+	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.</p>},
+  doi = {10.1371/journal.pgen.1002548},
+  publisher = {Public Library of Science},
+  url = {http://dx.doi.org/10.1371%2Fjournal.pgen.1002548}
+}
+
+@ARTICLE{GalaxyZoo,
+  author = {Lintott, Chris J. and Schawinski, Kevin and Slosar, Anže and Land,
+	Kate and Bamford, Steven and Thomas, Daniel and Raddick, M. Jordan
+	and Nichol, Robert C. and Szalay, Alex and Andreescu, Dan and Murray,
+	Phil and Vandenberg, Jan},
+  title = {Galaxy Zoo: morphologies derived from visual inspection of galaxies
+	from the Sloan Digital Sky Survey},
+  journal = {Monthly Notices of the Royal Astronomical Society},
+  year = {2008},
+  volume = {389},
+  pages = {1179-1189},
+  number = {3},
+  abstract = {In order to understand the formation and subsequent evolution of galaxies
+	one must first distinguish between the two main morphological classes
+	of massive systems: spirals and early-type systems. This paper introduces
+	a project, Galaxy Zoo, which provides visual morphological classifications
+	for nearly one million galaxies, extracted from the Sloan Digital
+	Sky Survey (SDSS). This achievement was made possible by inviting
+	the general public to visually inspect and classify these galaxies
+	via the internet. The project has obtained more than 4 × 107 individual
+	classifications made by ∼105 participants. We discuss the motivation
+	and strategy for this project, and detail how the classifications
+	were performed and processed. We find that Galaxy Zoo results are
+	consistent with those for subsets of SDSS galaxies classified by
+	professional astronomers, thus demonstrating that our data provide
+	a robust morphological catalogue. Obtaining morphologies by direct
+	visual inspection avoids introducing biases associated with proxies
+	for morphology such as colour, concentration or structural parameters.
+	In addition, this catalogue can be used to directly compare SDSS
+	morphologies with older data sets. The colour–magnitude diagrams
+	for each morphological class are shown, and we illustrate how these
+	distributions differ from those inferred using colour alone as a
+	proxy for morphology.},
+  doi = {10.1111/j.1365-2966.2008.13689.x},
+  eprint = {http://mnras.oxfordjournals.org/content/389/3/1179.full.pdf+html},
+  url = {http://mnras.oxfordjournals.org/content/389/3/1179.abstract}
+}
+
+@ARTICLE{McGowan2010,
+  author = {McGowan, Michelle L. and Fishman, Jennifer R. and Lambrix, Marcie
+	A.},
+  title = {Personal genomics and individual identities: motivations and moral
+	imperatives of early users.},
+  journal = {New Genet Soc},
+  year = {2010},
+  volume = {29},
+  pages = {261--290},
+  number = {3},
+  month = {Sep},
+  abstract = {Since 2007, consumer genomics companies have marketed personal genome
+	scanning services to assess users' genetic predispositions to a variety
+	of complex diseases and traits. This study investigates early users'
+	reasons for utilizing personal genome services, their evaluation
+	of the technology, how they interpret the results, and how they incorporate
+	the results into health-related decision-making. The analysis contextualizes
+	early users' relationships to the technology, the knowledge generated
+	by it, and how it mediates their relationship to their own health
+	and to biomedicine more broadly. The results reveal that early users
+	approach personal genome scanning with both optimism for genomic
+	research and scepticism about the technology's current capabilities,
+	which runs contrary to concerns that consumers may be ill equipped
+	to interpret and understand genome scan results. These findings provide
+	important qualitative insight into early users' conceptualizations
+	of personal genomic risk assessment and illuminate their involvement
+	in configuring this technology in the making.},
+  doi = {10.1080/14636778.2010.507485},
+  institution = {Department of Bioethics, Case Western Reserve University, Cleveland,
+	US.},
+  language = {eng},
+  medline-pst = {ppublish},
+  owner = {drsnuggles},
+  pmid = {21076647},
+  timestamp = {2013.05.30},
+  url = {http://dx.doi.org/10.1080/14636778.2010.507485}
+}
+
+@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 = {<p>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&lt;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&lt;0.05),
+	with one SNP associated with both BMI measurements: KCTD15 rs29941
+	(q-value&lt;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&lt;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<sup>−6</sup>% 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.</p>},
+  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.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 = {<p>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.</p>},
+  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
+	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 = {<p>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&gt;102 and 85% to classify subjects
+	with an age at death&gt;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 <italic>TOMM40/APOE</italic>
+	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.</p>},
+  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},
+  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{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
+	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{Wicks2010,
+  author = {Wicks, Paul and Massagli, Michael and Frost, Jeana and Brownstein,
+	Catherine and Okun, Sally and Vaughan, Timothy and Bradley, Richard
+	and Heywood, James},
+  title = {Sharing health data for better outcomes on PatientsLikeMe.},
+  journal = {J Med Internet Res},
+  year = {2010},
+  volume = {12},
+  pages = {e19},
+  number = {2},
+  abstract = {PatientsLikeMe is an online quantitative personal research platform
+	for patients with life-changing illnesses to share their experience
+	using patient-reported outcomes, find other patients like them matched
+	on demographic and clinical characteristics, and learn from the aggregated
+	data reports of others to improve their outcomes. The goal of the
+	website is to help patients answer the question: "Given my status,
+	what is the best outcome I can hope to achieve, and how do I get
+	there?"Using a cross-sectional online survey, we sought to describe
+	the potential benefits of PatientsLikeMe in terms of treatment decisions,
+	symptom management, clinical management, and outcomes.Almost 7,000
+	members from six PatientsLikeMe communities (amyotrophic lateral
+	sclerosis [ALS], Multiple Sclerosis [MS], Parkinson's Disease, human
+	immunodeficiency virus [HIV], fibromyalgia, and mood disorders) were
+	sent a survey invitation using an internal survey tool (PatientsLikeMe
+	Lens).Complete responses were received from 1323 participants (19\%
+	of invited members). Between-group demographics varied according
+	to disease community. Users perceived the greatest benefit in learning
+	about a symptom they had experienced; 72\% (952 of 1323) rated the
+	site "moderately" or "very helpful." Patients also found the site
+	helpful for understanding the side effects of their treatments (n
+	= 757, 57\%). Nearly half of patients (n = 559, 42\%) agreed that
+	the site had helped them find another patient who had helped them
+	understand what it was like to take a specific treatment for their
+	condition. More patients found the site helpful with decisions to
+	start a medication (n = 496, 37\%) than to change a medication (n
+	= 359, 27\%), change a dosage (n = 336, 25\%), or stop a medication
+	(n = 290, 22\%). Almost all participants (n = 1,249, 94\%) were diagnosed
+	when they joined the site. Most (n = 824, 62\%) experienced no change
+	in their confidence in that diagnosis or had an increased level of
+	confidence (n = 456, 34\%). Use of the site was associated with increasing
+	levels of comfort in sharing personal health information among those
+	who had initially been uncomfortable. Overall, 12\% of patients (n
+	= 151 of 1320) changed their physician as a result of using the site;
+	this figure was doubled in patients with fibromyalgia (21\%, n =
+	33 of 150). Patients reported community-specific benefits: 41\% of
+	HIV patients (n = 72 of 177) agreed they had reduced risky behaviors
+	and 22\% of mood disorders patients (n = 31 of 141) agreed they needed
+	less inpatient care as a result of using the site. Analysis of the
+	Web access logs showed that participants who used more features of
+	the site (eg, posted in the online forum) perceived greater benefit.We
+	have established that members of the community reported a range of
+	benefits, and that these may be related to the extent of site use.
+	Third party validation and longitudinal evaluation is an important
+	next step in continuing to evaluate the potential of online data-sharing
+	platforms.},
+  doi = {10.2196/jmir.1549},
+  institution = { Development, 155 2nd Street, Cambridge, MA 02141, USA. pwicks@patientslikeme.com},
+  keywords = {Adult; Consumer Participation; Cross-Sectional Studies; Data Display;
+	Decision Support Techniques; Disease Management; Female; Health Records,
+	Personal; Humans; Information Dissemination, methods; Internet; Male;
+	Middle Aged; Online Systems; Physician-Patient Relations; Population
+	Surveillance; Rare Diseases, diagnosis/therapy; Self Care, methods;
+	Self-Help Groups; Social Support},
+  language = {eng},
+  medline-pst = {epublish},
+  owner = {drsnuggles},
+  pii = {v12i2e19},
+  pmid = {20542858},
+  timestamp = {2013.05.30},
+  url = {http://dx.doi.org/10.2196/jmir.1549}
+}
+
+@ARTICLE{Wolinsky2005,
+  author = {Wolinsky, Howard},
+  title = {Do-it-yourself diagnosis.},
+  journal = {EMBO Rep},
+  year = {2005},
+  volume = {6},
+  pages = {805--807},
+  number = {9},
+  month = {Sep},
+  __markedentry = {[drsnuggles:]},
+  doi = {10.1038/sj.embor.7400508},
+  keywords = {Genetic Predisposition to Disease; Genetic Testing, economics/legislation
+	/&/ jurisprudence/methods/standards/trends; Humans},
+  language = {eng},
+  medline-pst = {ppublish},
+  owner = {drsnuggles},
+  pii = {7400508},
+  pmid = {16138089},
+  timestamp = {2013.06.03},
+  url = {http://dx.doi.org/10.1038/sj.embor.7400508}
+}
+
+@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 = {<p>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. Ser<sup>326</sup>Cys 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 Ser<sup>326</sup>Cys 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 Ser<sup>326</sup>Cys polymorphism is a risk
+	factor for prostate cancer in smokers. Further studies are needed
+	to confirm this relationship.</p>},
+  doi = {10.1371/journal.pone.0030309},
+  publisher = {Public Library of Science},
+  url = {http://dx.doi.org/10.1371%2Fjournal.pone.0030309}
+}
+
+@article{Gymrek18012013,
+author = {Gymrek, Melissa and McGuire, Amy L. and Golan, David and Halperin, Eran and Erlich, Yaniv}, 
+title = {Identifying Personal Genomes by Surname Inference},
+volume = {339}, 
+number = {6117}, 
+pages = {321-324}, 
+year = {2013}, 
+doi = {10.1126/science.1229566}, 
+abstract ={Sharing sequencing data sets without identifiers has become a common practice in genomics. Here, we report that surnames can be recovered from personal genomes by profiling short tandem repeats on the Y chromosome (Y-STRs) and querying recreational genetic genealogy databases. We show that a combination of a surname with other types of metadata, such as age and state, can be used to triangulate the identity of the target. A key feature of this technique is that it entirely relies on free, publicly accessible Internet resources. We quantitatively analyze the probability of identification for U.S. males. We further demonstrate the feasibility of this technique by tracing back with high probability the identities of multiple participants in public sequencing projects.}, 
+URL = {http://www.sciencemag.org/content/339/6117/321.abstract}, 
+eprint = {http://www.sciencemag.org/content/339/6117/321.full.pdf}, 
+journal = {Science} 
+}
+
+@ARTICLE{Sweeney2013,
+author = {Sweeney, Latanya and Abu, Akua and Winn, Julia},
+    title = "{Identifying Participants in the Personal Genome Project by Name (A Re-identification Experiment)}",
+  journal = {ArXiv},
+archivePrefix = "arXiv",
+   eprint = {1304.7605},
+ primaryClass = "cs.CY",
+ keywords = {Computer Science - Computers and Society, K.4.1, K.6.5, J.3, H.1.2, H.2.0, H.2.7, H.3.5},
+     year = 2013,
+    month = apr,
+   adsurl = {http://adsabs.harvard.edu/abs/2013arXiv1304.7605S},
+  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+
+@Article{Joly2013,
+AUTHOR = {Joly, Yann and Ngueng Feze, Ida and Simard, Jacques},
+TITLE = {Genetic discrimination and life insurance: a systematic review of the evidence},
+JOURNAL = {BMC Medicine},
+VOLUME = {11},
+YEAR = {2013},
+NUMBER = {1},
+PAGES = {25},
+URL = {http://www.biomedcentral.com/1741-7015/11/25},
+DOI = {10.1186/1741-7015-11-25},
+PubMedID = {23369270},
+ISSN = {1741-7015},
+ABSTRACT = {BACKGROUND:Since the late 1980s, genetic discrimination has remained one of the major concerns associated with genetic research and clinical genetics. Europe has adopted a plethora of laws and policies, both at the regional and national levels, to prevent insurers from having access to genetic information for underwriting. Legislators from the United States and the United Kingdom have also felt compelled to adopt protective measures specifically addressing genetics and insurance. But does the available evidence really confirm the popular apprehension about genetic discrimination and the subsequent genetic exceptionalism?METHODS:This paper presents the results of a systematic, critical review of over 20 years of genetic discrimination studies in the context of life insurance.RESULTS:The available data clearly document the existence of individual cases of genetic discrimination. The significance of this initial finding is, however, greatly diminished by four observations. First, the methodology used in most of the studies is not sufficiently robust to clearly establish either the prevalence or the impact of discriminatory practices. Second, the current body of evidence was mostly developed around a small number of 'classic' genetic conditions. Third, the heterogeneity and small scope of most of the studies prevents formal statistical analysis of the aggregate results. Fourth, the small number of reported genetic discrimination cases in some studies could indicate that these incidents took place due to occasional errors, rather than the voluntary or planned choice, of the insurers.CONCLUSION:Important methodological limitations and inconsistencies among the studies considered make it extremely difficult, at the moment, to justify policy action taken on the basis of evidence alone. Nonetheless, other empirical and theoretical factors have emerged (for example, the prevalence and impact of the fear of genetic discrimination among patients and research participants, the (un)importance of genetic information for the commercial viability of the private life insurance industry, and the need to develop more equitable schemes of access to life insurance) that should be considered along with the available evidence of genetic discrimination for a more holistic view of the debate.},
+}
+
+
+@article{10.1371/journal.pgen.1002584,
+    author = {Pattaro, , Cristian AND Köttgen, , Anna AND Teumer, , Alexander AND Garnaas, , Maija AND Böger, , Carsten A. AND Fuchsberger, , Christian AND Olden, , Matthias AND Chen, , Ming-Huei AND Tin, , Adrienne AND Taliun, , Daniel AND Li, , Man AND Gao, , Xiaoyi AND Gorski, , Mathias AND Yang, , Qiong AND Hundertmark, , Claudia AND Foster, , Meredith C. AND O'Seaghdha, , Conall M. AND Glazer, , Nicole AND Isaacs, , Aaron AND Liu, , Ching-Ti AND Smith, , Albert V. AND O'Connell, , Jeffrey R. AND Struchalin, , Maksim AND Tanaka, , Toshiko AND Li, , Guo AND Johnson, , Andrew D. AND Gierman, , Hinco J. AND Feitosa, , Mary AND Hwang, , Shih-Jen AND Atkinson, , Elizabeth J. AND Lohman, , Kurt AND Cornelis, , Marilyn C. AND Johansson, , Åsa AND Tönjes, , Anke AND Dehghan, , Abbas AND Chouraki, , Vincent AND Holliday, , Elizabeth G. AND Sorice, , Rossella AND Kutalik, , Zoltan AND Lehtimäki, , Terho AND Esko, , Tõnu AND Deshmukh, , Harshal AND Ulivi, , Sheila AND Chu, , Audrey Y. AND Murgia, , Federico AND Trompet, , Stella AND Imboden, , Medea AND Kollerits, , Barbara AND Pistis, , Giorgio AND Harris, , Tamara B. AND Launer, , Lenore J. AND Aspelund, , Thor AND Eiriksdottir, , Gudny AND Mitchell, , Braxton D. AND Boerwinkle, , Eric AND Schmidt, , Helena AND Cavalieri, , Margherita AND Rao, , Madhumathi AND Hu, , Frank B. AND Demirkan, , Ayse AND Oostra, , Ben A. AND de Andrade, , Mariza AND Turner, , Stephen T. AND Ding, , Jingzhong AND Andrews, , Jeanette S. AND Freedman, , Barry I. AND Koenig, , Wolfgang AND Illig, , Thomas AND Döring, , Angela AND Wichmann, , H.-Erich AND Kolcic, , Ivana AND Zemunik, , Tatijana AND Boban, , Mladen AND Minelli, , Cosetta AND Wheeler, , Heather E. AND Igl, , Wilmar AND Zaboli, , Ghazal AND Wild, , Sarah H. AND Wright, , Alan F. AND Campbell, , Harry AND Ellinghaus, , David AND Nöthlings, , Ute AND Jacobs, , Gunnar AND Biffar, , Reiner AND Endlich, , Karlhans AND Ernst, , Florian AND Homuth, , Georg AND Kroemer, , Heyo K. AND Nauck, , Matthias AND Stracke, , Sylvia AND Völker, , Uwe AND Völzke, , Henry AND Kovacs, , Peter AND Stumvoll, , Michael AND Mägi, , Reedik AND Hofman, , Albert AND Uitterlinden, , Andre G. AND Rivadeneira, , Fernando AND Aulchenko, , Yurii S. AND Polasek, , Ozren AND Hastie, , Nick AND Vitart, , Veronique AND Helmer, , Catherine AND Wang, , Jie Jin AND Ruggiero, , Daniela AND Bergmann, , Sven AND Kähönen, , Mika AND Viikari, , Jorma AND Nikopensius, , Tiit AND Province, , Michael AND Ketkar, , Shamika AND Colhoun, , Helen AND Doney, , Alex AND Robino, , Antonietta AND Giulianini, , Franco AND Krämer, , Bernhard K. AND Portas, , Laura AND Ford, , Ian AND Buckley, , Brendan M. AND Adam, , Martin AND Thun, , Gian-Andri AND Paulweber, , Bernhard AND Haun, , Margot AND Sala, , Cinzia AND Metzger, , Marie AND Mitchell, , Paul AND Ciullo, , Marina AND Kim, , Stuart K. AND Vollenweider, , Peter AND Raitakari, , Olli AND Metspalu, , Andres AND Palmer, , Colin AND Gasparini, , Paolo AND Pirastu, , Mario AND Jukema, , J. Wouter AND Probst-Hensch, , Nicole M. AND Kronenberg, , Florian AND Toniolo, , Daniela AND Gudnason, , Vilmundur AND Shuldiner, , Alan R. AND Coresh, , Josef AND Schmidt, , Reinhold AND Ferrucci, , Luigi AND Siscovick, , David S. AND van Duijn, , Cornelia M. AND Borecki, , Ingrid AND Kardia, , Sharon L. R. AND Liu, , Yongmei AND Curhan, , Gary C. AND Rudan, , Igor AND Gyllensten, , Ulf AND Wilson, , James F. AND Franke, , Andre AND Pramstaller, , Peter P. AND Rettig, , Rainer AND Prokopenko, , Inga AND Witteman, , Jacqueline C. M. AND Hayward, , Caroline AND Ridker, , Paul AND Parsa, , Afshin AND Bochud, , Murielle AND Heid, , Iris M. AND Goessling, , Wolfram AND Chasman, , Daniel I. AND Kao, , W. H. Linda AND Fox, , Caroline S. AND CARDIoGRAM Consortium, ICBP Consortium, CARe Consortium, Wellcome Trust Case Control Consortium 2 (WTCCC2)},
+    journal = {PLoS Genet},
+    publisher = {Public Library of Science},
+    title = {Genome-Wide Association and Functional Follow-Up Reveals New Loci for Kidney Function},
+    year = {2012},
+    month = {03},
+    volume = {8},
+    url = {http://dx.doi.org/10.1371%2Fjournal.pgen.1002584},
+    pages = {e1002584},
+    abstract = {<title>Author Summary</title><p>Chronic kidney disease (CKD) is an important public health problem with a hereditary component. We performed a new genome-wide association study in up to 130,600 European ancestry individuals to identify genes that may influence kidney function, specifically genes that may influence kidney function differently depending on sex, age, hypertension, and diabetes status of individuals. We uncovered 6 new loci associated with estimated glomerular filtration rate (eGFR), the primary measure of renal function, in or near <italic>MPPED2</italic>, <italic>DDX1</italic>, <italic>SLC47A1</italic>, <italic>CDK12</italic>, <italic>CASP9</italic>, and <italic>INO80</italic>. <italic>CDK12</italic> effect was stronger in younger and absent in older individuals. <italic>MPPED2</italic>, <italic>DDX1</italic>, <italic>SLC47A1</italic>, and <italic>CDK12</italic> loci were associated with eGFR in African ancestry samples as well, highlighting the cross-ethnicity validity of our findings. Using the zebrafish model, we performed morpholino knockdown of <italic>mpped2</italic> and <italic>casp9</italic> in zebrafish embryos and revealed podocyte and tubular abnormalities with altered dextran clearance, suggesting a role for these genes in renal function. These results further our understanding of the pathogenesis of CKD and provide insights into potential novel mechanisms of disease.</p>},
+    number = {3},
+    doi = {10.1371/journal.pgen.1002584}
+}        
+
+@article{Pinkerton2002,
+title = "Ethical approval for multicentre clinical trials in children: contrasting systems in three European countries ",
+journal = "European Journal of Cancer ",
+volume = "38",
+number = "8",
+pages = "1051 - 1058",
+year = "2002",
+note = "",
+issn = "0959-8049",
+doi = "http://dx.doi.org/10.1016/S0959-8049(02)00021-7",
+url = "http://www.sciencedirect.com/science/article/pii/S0959804902000217",
+author = "C.R. Pinkerton and S. Ablett and J. Boos and T. Philip",
+keywords = "Childhood cancer",
+keywords = "Ethical approval",
+keywords = "Phase I/II studies",
+keywords = "European collaboration "
+}
+
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@@ -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}
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