Bayesian Generalized Fused Hierarchical Structured Variable Selection Prior for Pathway-Based GWAS Using Summary Statistics

• Activity Number: 237 - SPEED:Statistical Methods for GWAs, Genetics, Genomics, and Other Omics Studies, Part 1
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Yi Yang* and Saonli Basu and Lin Zhang
• Companies: University of Minnesota and University of Minnesota, Biostatistics SPH and Division of Biostatistics, University of Minnesota
• Keywords: Generalized fused lasso; Group lasso; Hierarchical variable selection; Pathway-based GWAS; Summary statistics

Abstract

While genome-wide association studies (GWASs) have been widely used to uncover associations between diseases and genetic variants, standard SNP-level GWASs often lack the power to identify SNPs that individually have moderate effect sizes but jointly contribute to the disease. To overcome this problem, pathway-based GWASs methods have been developed as an alternative strategy that complements SNP-level approaches. We propose a Bayesian method which utilizes the generalized fused hierarchical structured variable selection (HSVS) prior to identify pathways associated with the disease using SNP-level summary statistics. Our prior has the flexibility to take in pathway structural information so that it can model the gene-level correlation based on prior biological knowledge, an important feature that makes it appealing compared to existing pathway-based methods. Using simulations, we show that our method outperforms competing methods in various scenarios, particularly when we have pathway structural information which involves complex gene-gene interactions. We apply our method to the Wellcome Trust Case Control Consortium (WTCCC) Crohn's disease GWAS data.