The module provides a foundation for Module 17: MCMC for Genetics.

The use of Bayesian methods in genetics has a long history. This introductory module begins by discussing introductory probability. It then describes Bayesian approaches to binomial proportions, multinomial proportions, two-sample comparisons (binomial, Poisson, normal), the linear model, and Monte Carlo methods of summarization. Advanced topics include hierarchical models, generalized linear models, and missing data.

Illustrative applications will include: Hardy-Weinberg testing and estimation, detection of allele-specific expression, QTL mapping, testing in genome-wide association studies, mixture models, multiple testing in high throughput genomics.