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. In this work in Nature Genetics by CGM Investigator Pradeep Natarajan and colleagues, a new, powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies is developed, which they name MetaSTAAR. The group puts MetaSTAAR to work by ananlyzing four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, proving that it performs comparable to using pooled data, and in the process identifying several conditionally significant rare variant associations with lipid traits. MetaSTAAR is scalable to biobank-scale cohorts and lays the groundwork for very large (100K+) sample size analyses through TOPMed WGS data, UK Biobank WES data, and additional resources as they come online.