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

Yi Yang, Saonli Basu, Lin Zhang

Research output: Contribution to journalArticle


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.

Original languageEnglish (US)
Pages (from-to)724-739
Number of pages16
JournalStatistics in Medicine
Issue number6
StatePublished - Mar 15 2020


  • generalized fused lasso
  • group lasso
  • hierarchical variable selection
  • pathway-based GWAS
  • summary statistics

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

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