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Activity Number: 245
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #320902 View Presentation
Title: A Bayesian Hierarchical Model for Pathway Analysis with Simultaneous Inference on Pathway-Gene-SNP Structure
Author(s): Lei Zhang* and Swati Biswas and Pankaj Choudhary
Companies: The University of Texas at Dallas and The University of Texas at Dallas and The University of Texas at Dallas
Keywords: bayesian hierarchical model ; hierarchical prior ; joint modeling ; high dimensionality ; hierarchical multiplicity adjustment

Pathway analysis jointly tests the combined effects of all single nucleotide polymorphisms (SNPs) in all genes belonging to a molecular pathway. It is usually more powerful than single-SNP analyses if multiple associated variants of modest effects exist in a pathway. We develop a Bayesian hierarchical model that fully models the natural three level hierarchy, namely SNP-gene-pathway, unlike the current methods that use ad hoc ways of combining such information. The joint modeling allows detecting not only the associated pathways but also testing for association with genes and SNPs within significant pathways and genes in a hierarchical manner. Appropriate priors are used to regularize the effects and hierarchical FDR is used for multiplicity adjustment of the entire inference procedure. To study the proposed approach, we conducted simulations with samples generated under realistic linkage disequilibrium patterns obtained from the HapMap project. We find that our method has higher power than some current approaches for identifying pathways with multiple modest-sized variants. Moreover, in some settings, it can detect associated genes and SNPs, a feature unavailable in other methods.

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

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