The use of Bayesian methods in genetics has a long history. In this introductory module we will begin by discussing introductory probability. We will then describe Bayesian approaches to binomial proportions, multinomial proportions, two-sample comparisons (binomial, Poisson, normal), the linear model, and Monte Carlo methods of summarization. Advanced topics will be touched on, including 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.

Background Reading: P.D. Hoff (2009). A First Course in Bayesian Statistical Methods. Springer-Verlag.