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

Yi Yang, Saonli Basu, Lin Zhang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)724-739
Number of pages16
JournalStatistics in Medicine
Volume39
Issue number6
DOIs
StatePublished - Mar 15 2020

Bibliographical note

Funding Information:
The authors are grateful to the editor and the reviewers for their suggestions and comments. This study is supported in part by grant to S.B. from the National Institutes of Health/National Institute on Drug Abuse R01DA033958 and R21DA046188. This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the WTCCC data is available at www.wtccc.org.uk . Funding for the WTCCC project was provided by the Wellcome Trust under award 076113.

Funding Information:
The authors are grateful to the editor and the reviewers for their suggestions and comments. This study is supported in part by grant to S.B. from the National Institutes of Health/National Institute on Drug Abuse R01DA033958 and R21DA046188. This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the WTCCC data is available at www.wtccc.org.uk. Funding for the WTCCC project was provided by the Wellcome Trust under award 076113.

Publisher Copyright:
© 2019 John Wiley & Sons, Ltd.

Keywords

  • 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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