Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/ WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource- efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of similar to 200,000 samples.

Automated summary

In this study, the researchers propose MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR takes into account relatedness and population structure, and improves the power of rare variant tests by incorporating multiple variant functional annotations. The results show that MetaSTAAR performs well in large-scale rare variant meta-analysis and can be applied in biobank-scale cohorts.