School of Medicine Publications and Presentations
Document Type
Article
Publication Date
10-27-2022
Abstract
Large-scale whole-genome sequencing 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 whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based 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 an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.
Recommended Citation
Li, Z., Li, X., Zhou, H. et al. A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies. Nat Methods (2022). https://doi.org/10.1038/s41592-022-01640-x
Publication Title
Nature Methods
DOI
10.1038/s41592-022-01640-x
Academic Level
faculty
Mentor/PI Department
Office of Human Genetics
Comments
PMC Copyright notice
https://rdcu.be/c0c6f