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About

Our group is interested in the analysis and prediction of complex traits and diseases using genetic (integrating pedigrees, genomics, and other omics) and environmental information. Our research involves methods, software development, and applications in human health, plant and animal breeding. Most of us are affiliated with the Department of Epidemiology and Biostatistics at Michigan State University.

Projects

Genomic Analysis and Prediction of Complex Traits. Development and evaluation of methods and software for analysis and prediction of complex traits using high-dimensional genomic data (e.g., SNPs, genotyping by sequencing, and other types of sequence data). Our research in this area has focused on the use of shrinkage and variable selection in parametric models, as well as on the use of some semi-parametric methods (e.g., RKHS).

Genomics x Environment. Development of methods for integrating high-dimensional genomic and environmental data in a unified framework. We have developed methods that can model interactions between high-dimensional marker panels and high-dimensional environmental covariates. These methods were originally developed and tested with data from wheat trails. We are currently extending some of these methods for analysis of complex human traits and diseases.

Integration of Data from Multiple Omics Layers. Development of models and software for integrating high-dimensional multi-layer omics data. Our focus is on methods that can integrate whole-omics profiles and can model interactions between two or more high-dimensional predictor sets (e.g., genome-by-methylome interactions). We are currently working on using these methods for prediction of breast cancer outcomes and in plant omics applications.

Software development for analysis of big omics data. We have developed several R packages for genetic analysis using pedigrees, genomes and other omics (see software below for further details).

Genomic Analysis of Obesity and Response to Exercise. We maintain an active collaboration with researchers from the TIGER (Training Interventions and Genetics of Exercise Response) study, developing and implementing methods for the identification of genetic factors influencing Body Composition and Response to Exercise Intervention.

Software

BGLR. The Bayesian Generalized Linear Regression R package implements a variety of shrinkage and variable selection methods. The package can be used with whole-genome data (e.g., SNPs, gene expression or other omics), pedigrees and non-genetic covariates, including high-dimensional environmental data. [Article] [CRAN] [Source Code]

BGData. A suite of R packages to enable analysis of extremely large genomic data sets (potentially millions of individuals and millions of molecular markers). [Article] [CRAN] [Source Code]

pedigreemm. An R package for analysis of complex traits and diseases using generalized linear mixed models using likelihood methods. [Article] [Documentation] [CRAN]

pedigreeR. R functions related to pedigrees. [Source Code]

MTM. Implements a Bayesian Multi-Trait Gaussian models with user defined-(co)variance structures. [Documentation] [Source Code]

Activities

People

Agustín González Reymúndez

  • Title: PhD Student
  • Email: [email protected]
  • Areas of Interest: Genomic tools for QTL mapping and genomic prediction, with applications in human genetics and plant breeding

Alexa Lupi

  • Title: PhD Student
  • Email: [email protected]
  • Areas of Interest: Biostatistics, Statistical Genetics, Epidemiology

Alexander Grueneberg

Ana I. Vazquez

Anirban Samaddar

  • Title: PhD Student
  • Email: [email protected]
  • Areas of Interest: Bayesian Statistics, Time Series, Statistical Genetics

Fernando Aguate

  • Title: Postdoc
  • Email: [email protected]
  • Areas of Interest: Biostatistics, Statistical Genetics, Plant Breeding, Software Development
  • Links: Website, GitHub, Google Scholar
  • Also joined us as a visitor in 2016 while at Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba (Argentina)

Filipe Couto

  • Title: Postdoc
  • Email: [email protected]
  • Areas of Interest: Biostatistics, plant Breeding, genome-wide association studies and prediction of complex traits in plants

Gabriel Rovere

  • Title: Postdoc
  • Email: [email protected]
  • Areas of Interest: Animal Breeding. Livestock Genetic Evaluations, Horse Breeding, Breeding Goals.

Guanqi Lu

  • Title: PhD Student
  • Email: [email protected]
  • Areas of Interest: Statistical Genetics, Biostatistics

Gustavo de los Campos

Harold Wu

  • Title: PhD Student
  • Email: [email protected]
  • Areas of Interest: Statistical Genetics, Statistical Modeling, Clinical Trials

Marco López-Cruz

  • Title: PhD Student
  • Email: [email protected]
  • Areas of Interest: Biostatistics, Statistical Genetics, Plant Breeding

Paulino Pérez

Wesley Bird

Past Members

C. Austin Pickens

  • Title: Doctoral Candidate
  • Email: [email protected]
  • Areas of Interest: Novel biomarker discovery using mass spectrometry-based lipidomics and disease prediction
  • Links: GitHub, ResearchGate

Deniz Akdemir

  • Title: Postdoc
  • Email: [email protected]
  • Areas of Interest: Data Mining, Multivariate Statistics, Statistical Genetics, Animal and Plant Breeding

Felix Enciso

  • Title: PhD Candidate
  • Email: [email protected]
  • Areas of Interest: Genome-wide association and genome selection studies for complex traits in potato, genetic engineering in potato using CRIPRS/Cas9 technology
  • Links: GitHub, Publications

Hank Wu

Hwasoon Kim

Lian Lian

  • Title: Postdoc
  • Email: [email protected]
  • Areas of Interest: Statistical Genetics, Plant Breeding

Mengying Sun

Michael P. Behring

  • Title: PhD Candidate
  • Email: [email protected]
  • Areas of Interest: Epidemiology, Genetics of Cancer

Paige Duren

Raka Mandal

  • Title: PhD Student
  • Email: [email protected]
  • Areas of Interest: Biostatistics, Statistical Learning, Bayesian Statistics

Scott Funkhouser

Shyamali Mukerjee

  • Title: Master Student
  • Email: [email protected]
  • Areas of Interest: Statistical Genetics, Application of Statistical Methods to Public Health Issues

Siddharth Avadhanam

  • Title: Master Student
  • Email: [email protected]
  • Areas of Interest: Statistical Genetics, Biostatistics, Bioinformatics

Yeni Liliana Bernal Rubio

Yogasudha Veturi

  • Title: PhD Candidate
  • Email: [email protected]
  • Areas of Interest: Biostatistics, Statistical Genetics, Plant Breeding

Visitors

2018

Cecilia Salvoro

  • Email: [email protected]
  • Affiliation: Department of Biology, University of Padova, Padova, Italy
  • Areas of Interest: Human Genetics, Next-generation Sequencing, Genetic Mapping of Diseases, Prediction of Eye Color
  • Links: ResearchGate

Maria Martinez Castillero

  • Email: [email protected]
  • Affiliation: University of Padova (Italy)
  • Areas of Interest: Quantitative genetics, programming, animal science
  • Links: LinkedIn

Pernille Bjarup Hansen

  • Email: [email protected]
  • Affiliation: Department of Molecular Biology and Genetics, Aarhus University, Flakkebjerg, Denmark
  • Areas of interest: Plant genetics, quantitative genetics, abiotic stress and plant breeding

2017

Muhammad Yasir Nawaz

  • Email: [email protected]
  • Areas of Interest: Genomic prediction, Livestock breeding, Application of statistical methods to public and animal health issues

2016

Hugo O. Toledo Alvarado

  • Email: [email protected]
  • Affiliation: Università degli studi di Padova (Italy)
  • Project: The use of Fourier-Transform Infrared (FTIR) Spectra as an innovative tool for predicting fertility traits in dairy cattle

M. Angeles Pérez-Cabal

  • Email: [email protected]
  • Affiliation: Complutense of University of Madrid (Spain)
  • Areas of Interest: Animal Breeding

2015

Juan Pablo Gutierrez Garcia

  • Email: [email protected]
  • Affiliation: Complutense of University of Madrid (Spain)
  • Areas of Interest: Animal Breeding and Conservation Genetics
  • Links: Website

2014

Christina Lehermeier

Swetlana Berger

  • Email: [email protected]
  • Affiliation: Georg-August-Universität Göttingen (Germany)
  • Areas of Interest: Scale effects in genomic modelling and prediction