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Quantitative Issues in Cancer Research Working Seminar

October 17 @ 1:00 pm - 1:50 pm

Virtual
10-17-2022_Cancer Working Group Seminar Flyer

Luke Benz
Doctoral Student, Department of Biostatistics, Harvard University

“mixWAS: A Federated Algorithm for Testing Variant Level Associations Across Mixed Type Phenotypes”

ABSTRACT: Methods that leverage cross-phenotype associations or pleiotropy in risk prediction have been shown to achieve improved performance compared to single-phenotype analyses. We introduce mixWAS, a new cross-phenotype association test for mixed data type phenotypes tailored to work with data in a federated setting, when multiple sites can not share individual level data due to privacy restrictions. Given the wide range of possible forms pleiotropy may take, mixWAS is designed to be powerful against both dense alternatives, where many phenotypes are associated with the SNP in question, as well as sparse alternatives, where the majority of phenotypes are not associated with the SNP. In this talk, we present background and motivation for the method, power simulations comparing mixWAS to existing PheWAS methods, and discuss preliminary results of applying mixWAS to real EHR data from eMERGE to identify pleiotropic SNPs.

Details

Date: October 17
Time: 1:00 pm - 1:50 pm
Calendars: Lecture / Seminar

Venue

Virtual