This course covers the fundamental statistical problems in genetics, with an emphasis on human genetics. The content of the course will change over the time. Currently, it focuses on models and methods of gene mapping that utilize linkage and linkage disequilibrium. The major topics to be covered include: introduction to molecular genetics and overview of major research area of statistical genetics, principles of population genetics, segregation analysis, genetic map, parametric and nonparametric linkage analysis, association studies, special topics and computing labs.
This course is for students of biostatistics, epidemiology and statistics with little genetics background but with some knowledge of probability and statistics.
The aim of the course is to prepare students for advanced study and research in the area of statistical genetics.
This course is also a prerequisite for STA4315H-Computational Methods in Statistical Genetics.
This is a graduate course with the following prerequisites: – Statistics at the graduate level or consent of instructor. Knowledge of the following statistical concepts is essential: estimation, hypothesis testing, confidence interval, unbiasedness, sufficiency, likelihood, simple linear models. – Working knowledge of UNIX platform is necessary.There will be some computing involving one or more of the standard computational tools for statistical genetics.