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Statistics Seminar Series: Jingshu Wang - Estimating Causal Relationship for Complex Traits with Weak and Heterogeneous Genetic Effects

When: Wednesday, February 26, 2020
11:00 AM - 12:00 PM Central

Where: 2006 Sheridan Road, B02, Evanston, IL 60208 map it

Audience: Faculty/Staff - Student - Post Docs/Docs - Graduate Students

Contact: Kisa Kowal   (847) 491-3974

Group: Department of Statistics

Category: Academic


Department of Statistics Winter 2020 Seminar Series

Talk Title: Estimating Causal Relationship for Complex Traits with Weak and Heterogeneous Genetic Effects

Speaker: Jingshu Wang, Assistant Professor, University of Illinois at Chicago

Time: 11:00am

Abstract: Genetic association signals tend to be spread across the whole genome for complex traits. The recently proposed ``omnigenic'' model indicates that, when the risk factor is a complex trait, most genetic variants can weakly affect the risk factor while also easily affecting a common disease not through the risk factor. Existing methods in Mendelian Randomization (MR) are not ideal under such pervasive pleiotropy. We propose a comprehensive framework GRAPPLE (Genome-wide mR Analysis under Pervasive PLEiotropy) for MR, utilizing both strongly and weakly associated genetic variant, and can detect the existence of multiple pleiotropic pathways. We show that GRAPPLE is a comprehensive tool that can detect and adjust for pleiotropy, simultaneously estimate the causal effect of multiple risk factors and can determine the causal relationship direction using three-sample GWAS summary statistics datasets. With GRAPPLE, we conduct a screening for the lipid traits (HDL-C, LDL-C and triglycerides) with around 30 common diseases to understand their roles as risk factors and detect potential pleiotropic pathways.  

Location: Department of Statistics, Room B02, 2006 Sheridan Rd, Evanston 60208

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