This module assumes the material in Module 1: Probability and Statistical Inference and Module 4: Regression and Analysis of Variance, and provides a foundation for many later modules.
Quantitative Genetics is the analysis of complex characters where both genetic and environment factors contribute to trait variation. Since this includes most traits of interest — disease susceptibility, crop yield, growth and reproduction in animals, human and animal behavior, and all gene expression data (transcriptome and proteome) — a working knowledge of quantitative genetics is critical in diverse fields from plant and animal breeding, human genetics, genomics, and behavior, to ecology and evolutionary biology.
The course will cover the basics of quantitative genetics including: 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 IBD alleles and an arbitrary pedigree, the genomic relationship matrix based on AIS alleles, heritability in the broad and narrow sense, inbreeding and cross-breeding, and response to selection.
The module also includes an introduction to advanced topics such as: Mixed Models, Best Linear Unbiased Prediction (BLUP), Genomic selection (GBLUP), Genome Wide Association Analysis (GWAS), QTL mapping, detection of selection from genomic data, correlated characters; and the multivariate response to selection.