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<p>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 SPAGxE<sub>CCT</sub>, a scalable and accurate framework for diverse trait types. SPAGxE<sub>CCT</sub> 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 SPAGxE<sub>CCT</sub> to SPAGxEmix<sub>CCT</sub>, which accounts for population stratification and is applicable to multi-ancestry or admixed populations. SPAGxEmix<sub>CCT</sub> can further be extended to SPAGxEmix<sub>CCT-local</sub>, which identifies ancestry-specific G×E effects using local ancestry. Through extensive simulations and real data analyses of UK Biobank data, we demonstrate that SPAGxE<sub>CCT</sub> and SPAGxEmix<sub>CCT</sub> are scalable to analyze large-scale study cohort, control type I error rates effectively, and maintain power.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月29日

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  • Yuzhuo Ma,
  • Yanlong Zhao,
  • Ji-Feng Zhang,
  • Wenjian Bi

摘要

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.