Foundations of Statistical Genomics

This course establishes a foundation in the application of statistics to problems in genetics and genomics through lectures, homework sets, and class discussions of publications. Statistical topics may include probabilistic modeling, likelihood based inference, Bayesian inference, bootstrap, EM algorithm, regularization, statistical modeling, principal components analysis, multiple hypothesis testing, and causality. There is an emphasis on applications in population genetics, gene expression, and human genomics. The statistical programming language R is extensively used to explore methods and analyze data.