Course: STAT 26300=STAT 35490
Title: Introduction to Statistical Genetics
Instructor(s): Mary Sara McPeek
Teaching Assistant(s): Huanlin Zhou
Class Schedule: Section 1: MW 1:50 PM–3:10 PM (Remote)
Textbook(s): None
Description: As a result of technological advances over the past few decades, there is a tremendous wealth of genetic data currently being collected. These data have the potential to shed light on the genetic factors influencing traits and diseases, as well as on questions of ancestry and population history. The aim of this course is to develop a thorough understanding of probabilistic models and statistical theory and methods underlying analysis of genetic data, focusing on problems in complex trait mapping, with some coverage of population genetics. Although the case studies are all in the area of statistical genetics, the statistical inference topics, which will include likelihood-based inference, linear mixed models, and restricted maximum likelihood, among others, are widely applicable to other areas. No biological background is needed, but a strong foundation in linear algebra, as well as probability and statistics at the level of STAT 24400-STAT 24500 or higher is assumed.
Prerequisite(s): STAT 24500 or STAT 24510
