SISG MODULE 7

This model is an introduction to Quantitative Genetics, which is the analysis of complex characters where both genetic and environment factors contribute to trait variation. This includes traits such as disease susceptibility, crop yield, growth and reproduction in animals, human and animal behavior, as well as gene expression and other functional genomic data.  A working knowledge of quantitative genetics is critical in diverse fields from plant and animal breeding, human genetics, genomics, behavior, to ecology and evolutionary biology.

The course will cover the fundamentals related to: the genetic basis for complex traits, population genetic assumptions including detection of admixture, Fisher’s variance decomposition, covariance between relatives, calculation of the numerator relationship matrix based on identity-by-descents alleles and an arbitrary pedigree, the genomic relationship matrix, heritability in the broad and narrow sense, inbreeding and cross-breeding, and responses to selection.

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

1. Understand the Fisher variance decomposition..

2. Estimate genetic variances from phenotypic data among known sets of relatives.

3. Compute the expected response to selection on a single quantitative trait. 

4. Compute the expected change in trait means under inbreeding and outcrossing.

5. Estimate genetic correlations and compute the response to a vector of traits under selection.  

6. Understand the basics of, and limitations to, QTL mapping by both linkage and association. 

Bruce Walsh is Professor of Ecology and Evolutionary Biology at the University of Arizona, Tucson. He is broadly interested in using mathematical models to explore the interface of genetics and evolution, with particular focus on the evolution of genome structure and the analysis of complex genetic characters, and is co-author with Mike Lynch of “The Genetic Analysis of Quantitative Traits” and “Evolution and Selection of Quantitative Traits.”

Guilherme Rosa is Professor of Animal and Dairy Science at the Animal and Dairy Sciences at the University of Wisconsin, Madison.  His teaching and research engages quantitative genetics and statistical genomics, including design of experiments and data analysis tools. Some specific areas of interest include mixed effects models, graphical models, Bayesian analysis and Monte Carlo methods, and prediction of complex traits using genomic information.  Learn more about Guilherme’s work here.