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

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.

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Publication Details

Journal
Nature Genetics
Published
2025-11-20
DOI
https://doi.org/10.1038/s41588-025-02403-y
Citations
1
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
1.82

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article

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

Kisung Nam, Karl Keat, Seunggeun Lee, V. Bansal et al.
1 citations
Nature Genetics
Genetic Associations and Epidemiology
1.82
article

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

Kisung Nam, Karl Keat, Seunggeun Lee, V. Bansal, Dokyoon Kim, Seokho Jeong, Wei Zhou, Eunjae Park
article en
1 citations

Abstract

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.

Nature GeneticsVol. 57(12)
Broad Institute (US), University of Helsinki (FI), Seoul National University (KR), University of California San Diego (US), NeuroDevelopment Center (US), Finland University (FI), National University College (PR), Institute for Molecular Medicine Finland (FI), University of Pennsylvania (US)
National Research Foundation, Ministry of Food and Drug Safety, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, National Institutes of Health, National Human Genome Research Institute
Openalex Percentile: Top 11%
Genetic Associations and Epidemiology
1.82
DOIOpen Access PDFOpenAlex
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