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

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

SUBMITTER: Joseph TA 

PROVIDER: S-EPMC12695653 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature