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Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks

Author

Listed:
  • Yuzhuo Ma

    (Peking University)

  • Yanlong Zhao

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Ji-Feng Zhang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Wenjian Bi

    (Peking University
    Peking University
    Peking University
    Peking University)

Abstract

Gene-environment interaction (G×E) analysis elucidates the interplay between genetic and environmental factors. Genome-wide association studies (GWAS) have expanded to encompass complex traits like time-to-event and ordinal traits, which provide richer phenotypic information. However, most existing scalable approaches focus only on quantitative or binary traits. Here we propose SPAGxECCT, a scalable and accurate framework for diverse trait types. SPAGxECCT fits a genotype-independent model and employs a hybrid strategy including saddlepoint approximation (SPA) for accurate p value calculation, especially for low-frequency variants and unbalanced phenotypic distributions. We extend SPAGxECCT to SPAGxEmixCCT, which accounts for population stratification and is applicable to multi-ancestry or admixed populations. SPAGxEmixCCT can further be extended to SPAGxEmixCCT-local, which identifies ancestry-specific G×E effects using local ancestry. Through extensive simulations and real data analyses of UK Biobank data, we demonstrate that SPAGxECCT and SPAGxEmixCCT are scalable to analyze large-scale study cohort, control type I error rates effectively, and maintain power.

Suggested Citation

  • Yuzhuo Ma & Yanlong Zhao & Ji-Feng Zhang & Wenjian Bi, 2025. "Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks," Nature Communications, Nature, vol. 16(1), pages 1-21, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57887-3
    DOI: 10.1038/s41467-025-57887-3
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    References listed on IDEAS

    as
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    3. Jae Hoon Sul & Michael Bilow & Wen-Yun Yang & Emrah Kostem & Nick Furlotte & Dan He & Eleazar Eskin, 2016. "Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models," PLOS Genetics, Public Library of Science, vol. 12(3), pages 1-19, March.
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