| Title: | Computationally efficient meta-analysis of gene-based tests using summary statistics in large-scale genetic studies |
| Journal: | Nature Genetics |
| Published: | 12 Nov 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41225158/ |
| DOI: | https://doi.org/10.1038/s41588-025-02390-0 |
| URL: | https://www.nature.com/articles/s41588-025-02390-0.pdf |
Publication 18948
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Abstract
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>
15 Authors
- 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
1 Application
| Application ID | Title |
|---|---|
| 26041 | Large-Scale Sequencing in the UK Biobank to Facilitate Gene Discovery, Genome Sciences, and Precision Medicine |
Enabling scientific discoveries that improve human health