| Title: | Scalable and accurate rare variant meta-analysis with Meta-SAIGE |
| Journal: | Nature Genetics |
| Published: | 20 Nov 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41266648/ |
| DOI: | https://doi.org/10.1038/s41588-025-02403-y |
| URL: | https://www.nature.com/articles/s41588-025-02403-y.pdf |
Publication 18870
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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.</p>
8 Authors
- Eunjae Park
- Kisung Nam
- Seokho Jeong
- Karl Keat
- Dokyoon Kim
- Vikas Bansal
- Wei Zhou
- Seunggeun Lee
1 Application
| Application ID | Title |
|---|---|
| 45227 | Scalable and Robust methods for biobank data analysis |
Enabling scientific discoveries that improve human health