A Bayesian hierarchically structured prior for rare-variant association testing

Yi Yang, Saonli Basu, Lin Zhang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Although genome-wide association studies have been widely used to identify associations between complex diseases and genetic variants, standard single-variant analyses often have limited power when applied to rare variants. To overcome this problem, set-based methods have been developed with the aim of boosting power by borrowing strength from multiple rare variants. We propose the adaptive hierarchically structured variable selection (HSVS-A) before test for association of rare variants in a set with continuous or dichotomous phenotypes and to estimate the effect of individual rare variants simultaneously. HSVS-A has the flexibility to integrate a pairwise weighting scheme, which adaptively induces desirable correlations among variants of similar significance such that we can borrow information from potentially causal and noncausal rare variants to boost power. Simulation studies show that for both continuous and dichotomous phenotypes, HSVS-A is powerful when there are multiple causal rare variants, either in the same or opposite direction of effect, with the presence of a large number of noncausal variants. We also apply HSVS-A to the Wellcome Trust Case Control Consortium Crohn's disease data for testing the association of Crohn's disease with rare variants in pathways. HSVS-A identifies two pathways harboring novel protective rare variants for Crohn's disease.

Original languageEnglish (US)
Pages (from-to)413-424
Number of pages12
JournalGenetic epidemiology
Issue number4
Early online dateFeb 10 2021
StatePublished - Jun 2021

Bibliographical note

Funding Information:
This study is supported in part by grants to S. B. from the National Institutes of Health/National Institute on Drug Abuse 5R01DA033958-02 and 1R21DA046188-01A1. 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 from www.wtccc.org.uk. Funding for the WTCCC project was provided by the Wellcome Trust under award 076113.

Publisher Copyright:
© 2021 Wiley Periodicals LLC


  • Bayesian adaptive fused lasso
  • Crohn's disease
  • hierarchical variable selection
  • pairwise weighting scheme
  • rare variants

PubMed: MeSH publication types

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


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