Module dates/times: Wednesday, July 22; Thursday, July 23, and Friday, July 24. 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 serves as a foundation for many of the later modules. It includes:
- A unified treatment for the analysis of discrete genetic data, starting with estimates and sample variances of allele frequencies to illustrate genetic vs statistical sampling and Bayesian approaches.
- A detailed look at Hardy-Weinberg and linkage disequilibrium, including the use of exact tests with mid-p-values and a new look at X-chromosome Hardy-Weinberg testing.
- A new characterization of population structure with F-statistics, based on allelic matching within and between populations with individual inbreeding and relationship estimation as a special case.
- Analyses illustrated with applications to forensic science and association mapping, with particular reference to rare variants.
Concepts illustrated with R exercises. Suggested pairing: Modules 6, 8 and all later modules.
Access 2019 course materials.
Learning Objectives: After attending this module, participants will be able to:
- Estimate allele frequencies from genotype counts, including the case of allelic dominance, and estimate the within-population inbreeding coefficient.
- Determine the sample size needed to detect a specified level of within-population inbreeding with a goodness-of-fit test for Hardy-Weinberg equilibrium.
- Use publicly-available software to conduct an exact test for Hardy-Weinberg equilibrium.
- Predict kinship levels for pairs of individuals in simple pedigrees, and identify reference data to estimate kinship from SNP genotype profiles. Identify estimation methods used by publicly-available software and select methods appropriate for particular study.
- Calculate allele-matching proportions within and between individuals and populations at single loci from genotype counts in order to estimate population-structure parameters. Evaluate the population-structure estimation methods used in publicly-available software.