Person:

Li, Zilin

Meta-analysis of whole-genome/exome sequencing (WGS/WES) studies provides an attractive solution to obtain large sample sizes from multiple studies for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to large WGS data. Here we propose MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large WGS/WES data. 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 ~200,000 samples.

Large-scale whole-genome sequencing (WGS) studies have enabled analysis of noncoding rare variant (RV) associations with complex human diseases and traits. Variant set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association-detection framework, STAARpipeline, to automatically annotate a WGS study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed-window and dynamic-window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.