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Activity Number: 85 - SPEED: An Ensemble of Advances in Genomics and Genetics
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 5:05 PM to 5:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #332965
Title: An Integrative Bayesian Approach to Dissect Complex Trait Etiology
Author(s): Corbin Quick*
Companies: University of Michigan
Keywords: GWAS; summary statistics; empirical Bayes

Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with hundreds of complex traits. However, the biological mechanisms underlying these associations are often poorly understood, particularly for non-coding associations. Large regulatory genomics datasets (e.g., from ENCODE/Roadmap and GTEx) present new opportunities to gain insight into complex trait etiology, as well as new computational and statistical challenges. We present a novel Bayesian approach to identify molecular traits (e.g., gene expression, protein functionality) potentially underlying genetic associations with complex traits by integrating GWAS and functional genomics data. We use an approximate E-M algorithm for efficient empirical Bayes estimation. We discuss an application to lipid and diabetes-related traits using publicly available GWAS association statistics.

Authors who are presenting talks have a * after their name.

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