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GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies

Author

Listed:
  • Zihuai He

    (Stanford University
    Stanford University)

  • Linxi Liu

    (University of Pittsburgh)

  • Michael E. Belloy

    (Stanford University)

  • Yann Guen

    (Stanford University
    Institut du Cerveau - Paris Brain Institute - ICM)

  • Aaron Sossin

    (Stanford University)

  • Xiaoxia Liu

    (Stanford University)

  • Xinran Qi

    (Stanford University)

  • Shiyang Ma

    (Columbia University)

  • Prashnna K. Gyawali

    (Stanford University)

  • Tony Wyss-Coray

    (Stanford University)

  • Hua Tang

    (Stanford University)

  • Chiara Sabatti

    (Stanford University)

  • Emmanuel Candès

    (Stanford University
    Stanford University)

  • Michael D. Greicius

    (Stanford University)

  • Iuliana Ionita-Laza

    (Columbia University)

Abstract

Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) a meta-analysis for Alzheimer’s disease comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies and (2) analysis of 1403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests.

Suggested Citation

  • Zihuai He & Linxi Liu & Michael E. Belloy & Yann Guen & Aaron Sossin & Xiaoxia Liu & Xinran Qi & Shiyang Ma & Prashnna K. Gyawali & Tony Wyss-Coray & Hua Tang & Chiara Sabatti & Emmanuel Candès & Mich, 2022. "GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34932-z
    DOI: 10.1038/s41467-022-34932-z
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    References listed on IDEAS

    as
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