Cancer Biostatistics Monthly Seminar with Meida Wang, PhD
Two novel approaches for jointly analyzing multiple phenotypes in GWAS
Meida Wang, PhD
Clinical Assistant Professor
Division of Epidemiology and Biostatistics
School of Public Health
University of Illinois at Chicago
Abstract:
Traditional genome-wide association studies (GWAS) focus on a single trait and a genetic variant at a time. There is more evidence to show that joint analysis of multiple phenotypes in GWAS can increase statistical power when detecting the association between genetic variants and human complex traits. We developed two novel statistical methods to jointly analyze multiple phenotypes for GWAS: a computationally efficient clustering linear combination approach (ceCLC) and its extension which applicable to GWAS summary statistics (sCLC). A variety of simulation studies demonstrate that the two novel approaches outperform all other methods in most scenarios. An application of UK Biobank GWAS summary statistics from the XIII category shows that sCLC identifies some novel signals that were missed by standard GWAS, which provide new insight into the potential genetic factors of the musculoskeletal system and connective tissue phenotypes.
Meeting ID: 892 2279 0420
Passcode: aHXPCE46
This group meets monthly from Noon – 1 p.m. The meeting agenda includes a presentation, programmatic announcements, and updates from Cancer Center program leaders.