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<p>Meta-analysis of gene-based tests using single-variant summary statistics is a powerful strategy for genetic association studies. However, current approaches require sharing the covariance matrix between variants for each study and trait of interest. For large-scale studies with many phenotypes, these matrices can be cumbersome to calculate, store and share. Here, to address this challenge, we present REMETA—an efficient tool for meta-analysis of gene-based tests. REMETA uses a single sparse covariance reference file per study that is rescaled for each phenotype using single-variant summary statistics. We develop new methods for binary traits with case–control imbalance, and to estimate allele frequencies, genotype counts and effect sizes of burden tests. We demonstrate the performance and advantages of our approach through meta-analysis of five traits in 469,376 samples in UK Biobank. The open-source REMETA software will facilitate meta-analysis across large-scale exome sequencing studies from diverse studies that cannot easily be combined.</p>

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Computationally efficient meta-analysis of gene-based tests using summary statistics in large-scale genetic studies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月12日

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  • Tyler A. Joseph,
  • Joelle Mbatchou,
  • Arkopravo Ghosh,
  • Anthony Marcketta,
  • Christopher E. Gillies,
  • Jing Tang,
  • Priyanka Nakka,
  • Xinyuan Zhang,
  • Jack A. Kosmicki,
  • Carlo Sidore,
  • Lauren Gurski,
  • Maya Ghoussaini,
  • Manuel A. R. Ferreira,
  • Gonçalo Abecasis,
  • Jonathan Marchini

摘要

Meta-analysis of gene-based tests using single-variant summary statistics is a powerful strategy for genetic association studies. However, current approaches require sharing the covariance matrix between variants for each study and trait of interest. For large-scale studies with many phenotypes, these matrices can be cumbersome to calculate, store and share. Here, to address this challenge, we present REMETA—an efficient tool for meta-analysis of gene-based tests. REMETA uses a single sparse covariance reference file per study that is rescaled for each phenotype using single-variant summary statistics. We develop new methods for binary traits with case–control imbalance, and to estimate allele frequencies, genotype counts and effect sizes of burden tests. We demonstrate the performance and advantages of our approach through meta-analysis of five traits in 469,376 samples in UK Biobank. The open-source REMETA software will facilitate meta-analysis across large-scale exome sequencing studies from diverse studies that cannot easily be combined.