Quantitative Genomics and Genetics
A rigorous treatment of analysis techniques used to understand complex genetic systems. This course covers both the fundamentals and advances in statistical methodology used to analyze disease and agriculturally relevant and evolutionarily important phenotypes. Topics include mapping quantitative trait loci (QTLs), application of microarray and related genomic data to gene mapping, and evolutionary quantitative genetics. Analysis techniques include association mapping, interval mapping, and analysis of pedigrees for both single and multiple QTL models. Application of classical inference and Bayesian analysis approaches is covered and there is an emphasis on computational methods.
Topics
- Students will learn a statistical modeling strategy that is both basic and general, as well as how to apply this strategy to learn information about biological systems when analyzing genome-wide data. More specifically, students will learn the mathematics and interpretation of linear statistical models.
- Students will learn what these models can be used to infer when applied to genome-wide genetic and related data.
- Students will learn how to effectively and efficiently analyze large-scale genomic data and how to program in R for this purpose.
- Students will learn the limits of interpretation when applying these statistical models to genomic data when inferring information about a biological system.
- Students will learn a statistical modeling strategy that is both basic and general, as well as how to apply this strategy to learn information about biological systems when analyzing genome-wide data. More specifically, students will learn the mathematics and interpretation of linear statistical models.
Official roster learning outcomes
Prerequisites & course information
STSCI 3080; STSCI 2100 or STSCI 2150 or STSCI 2200; or equivalents.
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- Quantitative Genomics and Genetics · 4.0 · Undergraduate