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

Tuesday,2023-01-31 13:57 America/Los_Angeles — ecterry
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, Cupples, AL, 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/Institutional 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<p>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.</p>
DOI10.1038/s41592-022-01640-x
Alternate JournalNat Methods
PubMed ID36303018
PubMed Central ID2831613
Grant ListU01 HG007417 / HG / NHGRI NIH HHS / United States
KL2 TR001100 / TR / NCATS NIH HHS / United States
R01 HL112064 / HL / NHLBI NIH HHS / United States
R35 HG010692 / HG / NHGRI NIH HHS / United States
F30 HL149180 / HL / NHLBI NIH HHS / United States
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R01 HL132947 / HL / NHLBI NIH HHS / United States
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P30 DK040561 / DK / NIDDK NIH HHS / United States
U01 HL137183 / HL / NHLBI NIH HHS / United States
P30 CA016672 / CA / NCI NIH HHS / United States
T32 HL144442 / HL / NHLBI NIH HHS / United States
DP5 OD029586 / OD / NIH HHS / United States
R01 HL123915 / HL / NHLBI NIH HHS / United States
R01 HL120393 / HL / NHLBI NIH HHS / United States
R01 HL046380 / HL / NHLBI NIH HHS / United States
U54 HG003067 / HG / NHGRI NIH HHS / United States
75N92020D00003 / HL / NHLBI NIH HHS / United States
K01 AG059898 / AG / NIA NIH HHS / United States
R01 HL155742 / HL / NHLBI NIH HHS / United States
U01 DK085524 / DK / NIDDK NIH HHS / United States
KL2 TR002542 / TR / NCATS NIH HHS / United States
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HHSN268201500001I / HL / NHLBI NIH HHS / United States
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R01 HL163972 / HL / NHLBI NIH HHS / United States
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HHSN268201800011I / HB / NHLBI NIH HHS / United States
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HHSN268201700003I / HL / NHLBI NIH HHS / United States
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HHSN268201800010I / HB / NHLBI NIH HHS / United States
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HHSN268201700001I / HL / NHLBI NIH HHS / United States
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R01 HL143221 / HL / NHLBI NIH HHS / United States
R01 DA037904 / DA / NIDA NIH HHS / United States
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UH2 AR067677 / AR / NIAMS NIH HHS / United States
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75N92020D00006 / HL / NHLBI NIH HHS / United States
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HHSN268201700002I / HL / NHLBI NIH HHS / United States
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F32 HL150992 / HL / NHLBI NIH HHS / United States
R01 MH101244 / MH / NIMH NIH HHS / United States
U01 CA182913 / CA / NCI NIH HHS / United States
T32 CA154274 / CA / NCI NIH HHS / United States
U01 HG009088 / HG / NHGRI NIH HHS / United States
P42 ES016454 / ES / NIEHS NIH HHS / United States
UM1 DK078616 / DK / NIDDK NIH HHS / United States
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P20 GM121334 / GM / NIGMS NIH HHS / United States
R01 HL131565 / HL / NHLBI NIH HHS / United States
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P01 HL132825 / HL / NHLBI NIH HHS / United States
K01 HL146980 / HL / NHLBI NIH HHS / United States
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K23 DK127073 / DK / NIDDK NIH HHS / United States
HHSN268201800013I / MD / NIMHD NIH HHS / United States
U54 GM115428 / GM / NIGMS NIH HHS / United States
R01 HL149683 / HL / NHLBI NIH HHS / United States
P30 DK020595 / DK / NIDDK NIH HHS / United States
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K08 HL145095 / HL / NHLBI NIH HHS / United States
K01 HL135405 / HL / NHLBI NIH HHS / United States
R03 OD030608 / OD / NIH HHS / United States
HHSN268201800014I / HB / NHLBI NIH HHS / United States
UM1 AI068636 / AI / NIAID NIH HHS / United States
R01 AG057381 / AG / NIA NIH HHS / United States
ePub date: 
22/12

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