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

TitleA framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.
Publication TypeJournal Article
Year of Publication2022
AuthorsLi, Z, Li, X, Zhou, H, Gaynor, SM, Selvaraj, MSunitha, Arapoglou, T, Quick, C, Liu, Y, Chen, H, Sun, R, Dey, R, Arnett, DK, Auer, PL, Bielak, LF, Bis, JC, Blackwell, TW, Blangero, J, Boerwinkle, E, Bowden, DW, Brody, JA, Cade, BE, Conomos, MP, Correa, A, L Cupples, A, Curran, JE, de Vries, PS, Duggirala, R, Franceschini, N, Freedman, BI, Göring, HHH, Guo, X, Kalyani, RR, Kooperberg, C, Kral, BG, Lange, LA, Lin, BM, Manichaikul, A, Manning, AK, Martin, LW, Mathias, RA, Meigs, JB, Mitchell, BD, Montasser, ME, Morrison, AC, Naseri, T, O'Connell, JR, Palmer, ND, Peyser, PA, Psaty, BM, Raffield, LM, Redline, S, Reiner, AP, Reupena, M'aSefuiva, Rice, KM, Rich, SS, Smith, JA, Taylor, KD, Taub, MA, Vasan, RS, Weeks, DE, Wilson, JG, Yanek, LR, Zhao, W, Rotter, JI, Willer, CJ, Natarajan, P, Peloso, GM, Lin, X
Corporate AuthorsNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group
JournalNat Methods
Volume19
Issue12
Pagination1599-1611
Date Published2022 Dec
ISSN1548-7105
KeywordsGenetic Variation, Genome, Genome-Wide Association Study, Humans, Phenotype, Whole Genome Sequencing
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.

DOI10.1038/s41592-022-01640-x
Alternate JournalNat Methods
PubMed ID36303018
PubMed Central IDPMC10008172
Grant List75N92020D00005 / HL / NHLBI NIH HHS / United States
R01 DK078616 / DK / NIDDK NIH HHS / United States
U01 HG007417 / HG / NHGRI NIH HHS / United States
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