A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies

Abstract

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.