A meta-analysis is a practical approach to increasing the power of rare variant tests by combining summary statistics from multiple cohorts. However, existing methods for rare variant meta-analysis often fail to correctly control type I error rates when analyzing low-prevalence binary traits and are computationally intensive when analyzing many phenotypes. This paper introduces Meta-SAIGE, a novel approach for rare variant meta-analyses that addresses these challenges. Meta-SAIGE reduces type I error inflation through precise estimation of the distribution of test statistics and allows the reuse of the linkage disequilibrium (LD) matrix across phenotypes, significantly improving computational efficiency for phenome-wide analyses. Simulation studies using UK Biobank whole-exome sequencing (WES) genotypes demonstrate that Meta-SAIGE effectively controls type I error rates and yields power similar to that of pooled individual-level data through SAIGE-GENE+. A meta-analysis of UK Biobank and All of Us WES data for 83 low prevalence disease phenotypes identified 237 associations. Notably, 80 of these associations were not significant in either dataset alone, underscoring the power of our meta-analysis.

The authors have declared no competing interest.

This research was supported by the Brain Pool Plus (BP+, Brain Pool+) Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT(2020H1D3A2A03100666).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

UKBiobank and All of Us Whole Exome Sequencing data was used

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Meta-SAIGE is implemented as an open-source R package available at https://github.com/leelabsg/META_SAIGE. Single variant summary statistics and LD matrix can be generated from SAIGE available at https://github.com/saigegit/SAIGE. UKB and All of US European meta-analysis results are available at https://meta-saige.leelabsg.org/.