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<p>Meta-analysis enhances the power of rare variant association tests by combining summary statistics across several cohorts. However, existing methods often fail to control type I error for low-prevalence binary traits and are computationally intensive. Here we introduce Meta-SAIGE—a scalable method for rare variant meta-analysis that accurately estimates the null distribution to control type I error and reuses the linkage disequilibrium matrix across phenotypes to boost computational efficiency in phenome-wide analyses. Simulations using UK Biobank whole-exome sequencing data show that Meta-SAIGE effectively controls type I error and achieves power comparable to pooled individual-level analysis with SAIGE-GENE+. Applying Meta-SAIGE to 83 low-prevalence phenotypes in UK Biobank and All of Us whole-exome sequencing data identified 237 gene–trait associations. Notably, 80 of these associations were not significant in either dataset alone, underscoring the power of our meta-analysis.</p>

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Scalable and accurate rare variant meta-analysis with Meta-SAIGE

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月20日

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  • Eunjae Park,
  • Kisung Nam,
  • Seokho Jeong,
  • Karl Keat,
  • Dokyoon Kim,
  • Vikas Bansal,
  • Wei Zhou,
  • Seunggeun Lee

摘要

Meta-analysis enhances the power of rare variant association tests by combining summary statistics across several cohorts. However, existing methods often fail to control type I error for low-prevalence binary traits and are computationally intensive. Here we introduce Meta-SAIGE—a scalable method for rare variant meta-analysis that accurately estimates the null distribution to control type I error and reuses the linkage disequilibrium matrix across phenotypes to boost computational efficiency in phenome-wide analyses. Simulations using UK Biobank whole-exome sequencing data show that Meta-SAIGE effectively controls type I error and achieves power comparable to pooled individual-level analysis with SAIGE-GENE+. Applying Meta-SAIGE to 83 low-prevalence phenotypes in UK Biobank and All of Us whole-exome sequencing data identified 237 gene–trait associations. Notably, 80 of these associations were not significant in either dataset alone, underscoring the power of our meta-analysis.