SISG MODULE 15

This module focuses on statistical theory as well as applied methodology for the genetics and analysis of quantitative traits in human populations, with emphasis on estimation and prediction analysis using genetic markers. 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.  Students are encouraged to have taken Module 11, Quantitative Genetics and to be familiar with basic R programming.  Module 19, Association Mapping, picks up on more advanced topics in GWAS.

Learning Objectives: After attending this module, participants will be able to: 

1. Understand the theory behind partitioning of variance components in genetic data, appreciating the difference between the Kempthorne and Falconer perspectives. 

2. Be able to relate population genetic processes to the architecture of complex traits. 

3. Use PLINK to perform GWAS on quantitative traits. 

4. Be familiar with the use of GCTA to estimate genome-wide SNP heritability.

5. Estimate genome-wide identity by descent. 

6. Understand the principles and pitfalls of genetic prediction from genetic marker data.

Loic Yengo is Associate Professor of Statistical Genetics in the Institute for Molecular Bioscience at the University of Queensland in Brisbane, Australia. His group develops and applies novel statistical methods to analyse large volumes of genomic data. He has also contributed to improving understanding of the genetic and phenotypic consequences of non-random mating (inbreeding and assortative mating) in human populations. Learn more about Loic’s work here.

David Cutler is a Professor in the Department of Human Genetics at Emory University in Atlanta. He is a theoretical quantitative and population geneticist, with interests in high performance computational approaches to genome data analysis, often from the perspective of haplotypes. Access Dave’s publications here.