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

Yi YangSaonli BasuLin Zhang
Published in: Statistics in medicine (2019)
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.
Keyphrases
  • genome wide
  • case control
  • copy number
  • dna methylation
  • genome wide association
  • health information
  • climate change
  • high density
  • genome wide identification
  • social media
  • genome wide analysis