Li, ZilinXihao, LiZhou, HufengGaynor, Sheila M.Arapoglou, TheodoreQuick, CorbinDey, RounakXihong, Lin2023-12-042022-10-27Li, Zilin, Li Xihao, Hufeng Zhou, Sheila M. Gaynor, Theodore Arapoglou, Corbin Quick, Rounak Dey et al. "A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies." Nat Methods 19, no. 12 (2022): 1599-1611. DOI: 10.1038/s41592-022-01640-x1548-70911548-7105https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37377456Large-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.en-USCell BiologyMolecular BiologyBiochemistryBiotechnologyJournal Article2023-12-0410.1038/s41592-022-01640-x