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

Authors
  • Yang, Yi1
  • Basu, Saonli1
  • Zhang, Lin1
  • 1 Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota.
Type
Published Article
Journal
Statistics in Medicine
Publisher
Wiley (John Wiley & Sons)
Publication Date
Mar 15, 2020
Volume
39
Issue
6
Pages
724–739
Identifiers
DOI: 10.1002/sim.8442
PMID: 31777110
Source
Medline
Keywords
Language
English
License
Unknown

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 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. © 2019 John Wiley & Sons, Ltd.

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