STAARpipeline
STAARpipeline performs phenotype–genotype association analyses in R for biobank-scale whole-genome sequencing (WGS) and whole-exome sequencing (WES) data, focusing on detecting noncoding rare-variant associations with complex human traits.
Key Features:
- Noncoding genome analysis: Provides a computational framework specifically tailored to analyze noncoding regions for rare-variant (RV) associations.
- Single-variant and variant-set analysis: Supports both single-variant tests and aggregated variant-set association analyses.
- Flexible association detection framework: Implements gene-centric STAAR grouping of noncoding variants by functional gene categories and non-gene-centric SCANG-STAAR dynamic window-based analyses with varying window sizes.
- Functional annotation integration: Integrates multiple functional annotations to weight or stratify variants during association testing.
- Scalability for biobank-scale data: Engineered to handle large WGS/WES cohorts and demonstrated on Trans-Omics for Precision Medicine (TOPMed) datasets.
Scientific Applications:
- Lipid trait discovery and replication: Identified noncoding RV sets associated with lipid traits in a TOPMed study with 21,015 discovery samples and 9,123 replication samples.
- Analysis of diverse phenotypes: Applied to five additional non-lipid traits within TOPMed and to noncoding RV association studies of complex human diseases and traits.
Methodology:
Annotates WGS/WES data with functional annotations and performs RV association analyses using the STAAR gene-centric method (grouping by gene functional categories) and SCANG-STAAR dynamic window-based analyses, supporting single-variant and variant-set tests.
Topics
Details
- License:
- GPL-3.0
- Cost:
- Free of charge
- Tool Type:
- library
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- R, C++, Shell
- Added:
- 12/8/2022
- Last Updated:
- 11/24/2024
Operations
Publications
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Vaidya D, Van Den Berg D, VandeHaar P, Vrieze S, Walker T, Wallace R, Walts A, Wang FF, Wang H, Wang J, Watson K, Watt J, Weinstock J, Weir B, Weiss ST, Weng L, Wessel J, Williams K, Williams LK, Wilson C, Winterkorn L, Wong Q, Wu J, Xu H, Yang I, Yu K, Zekavat SM, Zhang Y, Zhao SX, Zhu X, Ziv E, Zody M, Zoellner S, Atkinson E, Ballantyne C, Bao W, Bhattacharya R, Bielak L, Bis J, Bodea C, Brody J, Cade B, Calvo S, Carlson J, Chang I, Cho SM, de Vries P, Diallo AF, Do R, Dron J, Elliott A, Finucane H, Floyd C, Ganna A, Gong D, Graham S, Haas M, Haring B, Heemann S, Himes B, Jarvik G, Jiang J, Joehanes R, Joseph PV, Jun G, Kalyani R, Kanai M, Kathiresan S, Khera A, Khetarpal S, Klarin D, Koyama S, Kral B, Lange L, Lemaitre R, Li C, Lu Y, Martin L, Mathias R, Mathur R, McGarvey S, McLenithan J, Miller A, Mootha V, Moran A, Nakao T, O’Connell J, O’Donnell C, Palmer N, Paruchuri K, Patel A, Peloso G, Pettinger M, Peyser P, Pirruccello J, Psaty B, Reiner A, Rich S, Rosenthal S, Rotter J, 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