SISG 2018 Modules

23rd Summer Institute in Statistical Genetics (SISG)

Module 14: Advanced Quantitative Genetics

Session 5: Mon Jul 23 to Wed Jul 25

Module dates/times: Monday, July 23; 8:30 a.m. -5 p.m.; Tuesday, July 24, 8:30 a.m.-5 p.m., and Wednesday, July 25, 8:30 a.m.-Noon

This module focuses on the genetics and analysis of quantitative traits in human populations, with emphasis on estimation and prediction analysis using genetic markers. It is a good match with Module 18: Statistical & Quantitative Genetics of Disease that deals with similar topics but with a focus on disease (binary) outcomes.

Topics include: the resemblance between relatives; estimation of genetic variance associated with genome-wide identity by descent; GWAS for quantitative traits; the use of GWAS data to estimate and partition genetic variation; principles and pitfalls of prediction analyses using genetic markers.

A series of computer exercises will provide hands-on experience of implementing a variety of approaches using R, the Merlin suite of software, PLINK and GCTA ( is external)).

Matthew Robinson is Professor of Computational Biology at the University of Lausanne. He develops and applies statistical methodology for large human phenotype-genotype datasets to address questions in population, quantitative, and medical genetics. His current work focuses on improved testing for sex-, age-, or environment-specific genetic effects, quantifying maternal genetic and social genetic effects, and investigating the role of genetic interactions between microbial and host genotype in shaping phenotype in the human population. He has recently published “Genotype-covariate interaction effects and the heritability of adult body mass index.” Nature Genetics 49:1174, 2017.

Peter Visscher is Professor and Chair of Quantitative Genetics at the University of Queensland. His research focuses on understanding individual differences between people in traits that are important for health outcomes and aging. A better understanding of the genes that underlie variation in risk to diseases may lead to better treatments. The traits he studies include gene expression, gene methylation, height and body- mass-index, psychiatric disease and neurogenetic conditions. He recently published “Concepts, estimation and interpretation of SNP-based heritability.” Nature Genetics 49:1304, 2017.

Access 2017 Course Materials (2018 materials will be uploaded to this page prior to the start of the module)