Module dates/times: Wednesday, July 25, 1:30-5 p.m.; Thursday, July 26, 8:30 a.m.-5 p.m., and Friday, July 27, 8:30 a.m.-5 p.m.
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.
Ken Rice is Professor of Biostatistics at the University of Washington. His research focuses primarily on developing and applying statistical methods for complex disease epidemiology, notably cardiovascular disease. He leads the Analysis Committee for the CHARGE consortium, a large group of investigators studying genetic determinants of heart and aging outcomes. He recently published “Large-scale genome-wide analysis identifies genetic variants associated with cardiac structure and function.” J. Clinical Investigation 127:1798-1812.
Jon Wakefield is Professor of Statistics and Biostatistics at the University of Washington. His research interests include spatial epidemiology, space-time models for infectious disease data, small area estimation, hierarchical models for survey data, estimating national and subnational disease burden, ecological inference for non-infectious and infectious disease data, genome-wide association studies, analysis of next generation RNAseq data and the links between Bayes and frequentist procedures. He recently published “Impacts of Neanderthal-introgressed sequences on the landscape of human gene expression.” Cell 168:916, 2017.