Statistics in Medicine

A Bayesian hierarchical variable selection prior for pathway‐based GWAS using summary statistics

Journal Article

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 a moderate effect size 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 that uses the generalized fused hierarchical structured variable selection 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 that involves complex gene‐gene interactions. We apply our method to the Wellcome Trust Case Control Consortium Crohn's disease GWAS data, demonstrating its practical application to real data.

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Published features on are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.