25th Summer Institute in Statistical Genetics (SISG)

Module 9: Quantitative Genetics

Mon, July 20 to Wed, July 22

Module dates/times: Monday, July 20; Tuesday, July 21, and Wednesday, July 22. Live sessions will start no earlier than 8 a.m. Pacific and end no later than 2:30 p.m. Pacific, except for Wednesdays. For modules that end on Wednesday, live sessions will end by 11 a.m. Pacific. For modules that start on Wednesday, live sessions will begin no earlier than 11:30 a.m.

This module assumes the material in Modules 1 and 5 and it 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, such as 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, 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. Suggested pairing: All later modules.

Access 2019 course materials.

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.