QuantGen Group

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 Excercise. 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] [download]

BGData. Memory mapped matrices for R. [documentation] [source]

pedigreemm. An R package for analysis of complex traits and diseases using generalided linear mixed models using likelihood methods. [article] [documentation] [download]

pedigreeR. R functions related to pedigrees. [source]

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

Activities

People

Agustín González Reymúndez

Agustín González Reymúndez PhD Student

  • Areas of Interest: Genomic tools for QTL mapping and genomic prediction, with applications in human genetics and plant breeding
Alexa Lupi

Alexa Lupi PhD Student

  • Areas of Interest: Biostatistics, Statistical Genetics, Epidemiology
Alexander Grueneberg

Alexander Grueneberg Programmer

Ana I. Vazquez

Ana I. Vazquez Assistant Professor

Felix Enciso

Felix Enciso PhD Candidate

  • 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
Filipe Couto

Filipe Couto Postdoc

  • Areas of Interest: Biostatistics, plant Breeding, genome-wide association studies and prediction of complex traits in plants
Gabriel Rovere

Gabriel Rovere Postdoc

  • Areas of Interest: Animal Breeding. Livestock Genetic Evaluations, Horse Breeding, Breeding Goals.
Gustavo de los Campos

Gustavo de los Campos Associate Professor

Marco López-Cruz

Marco López-Cruz PhD Student

  • Areas of Interest: Biostatistics, Statistical Genetics, Plant Breeding
Mengying Sun

Mengying Sun Research Assistant

Paige Duren

Paige Duren

  • Areas of Interest: Nursing
Paulino Pérez

Paulino Pérez Associate Professor

Scott Funkhouser

Scott Funkhouser PhD Student

  • Areas of Interest: Software development for multiple omics layers, genomic prediction
  • Links: Website, GitHub
  • Software: editTools

Past Members

C. Austin Pickens

C. Austin Pickens Doctoral Candidate

  • Areas of Interest: Novel biomarker discovery using mass spectrometry-based lipidomics and disease prediction
  • Links: GitHub, ResearchGate
Deniz Akdemir

Deniz Akdemir Postdoc

  • Areas of Interest: Data Mining, Multivariate Statistics, Statistical Genetics, Animal and Plant Breeding
Hank Wu

Hank Wu Master Student

  • Areas of Interest: Biostatistics
Hwasoon Kim

Hwasoon Kim Postdoc

  • Areas of Interest: Biostatistics, Clinical Trials
  • Links: Website, GitHub
Lian Lian

Lian Lian Postdoc

  • Areas of Interest: Statistical Genetics, Plant Breeding
Michael P. Behring

Michael P. Behring PhD Candidate

  • Areas of Interest: Epidemiology, Genetics of Cancer
Raka Mandal

Raka Mandal PhD Student

  • Areas of Interest: Biostatistics, Statistical Learning, Bayesian Statistics
Shyamali Mukerjee

Shyamali Mukerjee Master Student

  • Areas of Interest: Statistical Genetics, Application of Statistical Methods to Public Health Issues
Siddharth Avadhanam

Siddharth Avadhanam Master Student

  • Areas of Interest: Statistical Genetics, Biostatistics, Bioinformatics
Yeni Liliana Bernal Rubio

Yeni Liliana Bernal Rubio Postdoc

Yogasudha Veturi

Yogasudha Veturi PhD Candidate

  • Areas of Interest: Biostatistics, Statistical Genetics, Plant Breeding

Visitors

2018

Cecilia Salvoro

Cecilia Salvoro

  • 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

Maria Martinez Castillero

  • Affiliation: University of Padova (Italy)
  • Areas of Interest: Quantitative genetics, programming, animal science
  • Links: LinkedIn

2017

Muhammad Yasir Nawaz

Muhammad Yasir Nawaz

  • Areas of Interest: Genomic prediction, Livestock breeding, Application of statistical methods to public and animal health issues

2016

Fernando Aguate

Fernando Aguate

  • Affiliation: Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba (Argentina)
  • Areas of Interest: Biostatistics, Statistical Genetics, Plant Breeding, Software Development
Hugo O. Toledo Alvarado

Hugo O. Toledo Alvarado

  • 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

M. Angeles Pérez-Cabal

  • Affiliation: Complutense of University of Madrid (Spain)
  • Areas of Interest: Animal Breeding

2015

Juan Pablo Gutierrez Garcia

Juan Pablo Gutierrez Garcia

  • Affiliation: Complutense of University of Madrid (Spain)
  • Areas of Interest: Animal Breeding and Conservation Genetics
  • Links: Website

2014

Christina Lehermeier

Christina Lehermeier

  • Affiliation: Plant Breeding, Technische Universität München (Germany)
  • Areas of Interest: Statistics, Quantitative Genetics, Plant Breeding
  • Links: Google Scholar, TUM Plant Breeding
Swetlana Berger

Swetlana Berger

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