Module dates/times: Monday, July 22; 8:30 a.m. -5 p.m.; Tuesday, July 23, 8:30 a.m.-5 p.m., and Wednesday, July 24, 8:30 a.m.-Noon
This module will provide students with the basic tools to carry out genetic association analysis within the context of genome wide association studies (GWAS) and next-generation sequencing studies with considerable emphasis on hands-on learning.
Topics covered include: case-control (disease) association testing; quantitative trait analysis; quality control processes in GWAS; multi-locus testing using gene and pathway information; population structure and ancestry inference; association testing in the presence of population structure and/or relatedness; gene-environment and gene-gene interactions; basic rare variant association analysis in sequencing studies; advanced rare variant methods; sequence kernel association tests (SKAT); meta analysis; design considerations; and other emerging topics.
An important component of this module is in-class software exercises which will provide students with hands-on experience analyzing real data using state-of-the-art analysis tools for GWAS and next generation sequencing data.
Assumes basic familiarity with R. Other public domain software that will be used includes PLINK.
Suggested pairing: Modules 13 and 17.
Access 2018 course materials through the Summer Institutes archives.
Timothy Thornton is Associate Professor of Biostatistics at the University of Washington. His research interest is in the area of statistical genetics, with an emphasis on statistical methodology for genetic association studies of complex traits in samples with relatedness, ancestry admixture, and/or population structure. He recently published “Admixture mapping in the Hispanic Community Health Study/Study of Latinos reveals regions of genetic associations with blood pressure traits.” PLoS One 12:e0188400, 2017.
Michael Wu is an Associate Member in the Biostatistics and Biomathematics Program at the Fred Hutchinson Cancer Research Center. The major thrust of his research lies in the development and application of statistical methods for translational science and particularly for analysis of high-dimensional genomic data within the broader context of clinical trials as well as population-based genetic, genomic, epigenetic, and microbiome studies. He recently published “A fast small-sample kernel independence test with application to microbiome association studies.” Biometrics 73:1453-1463,2017.
Thornton and Wu recently jointly published “Powerful genetic association analysis for common or rare variants with high-dimensional structured traits.” Genetics 206:1779-1790, 2017.