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Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.
Monday,2023-01-30 11:50 America/Los_Angeles — ecterry
| Title | Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies. |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Authors | Li, X, Quick, C, Zhou, H, Gaynor, SM, Liu, Y, Chen, H, Selvaraj, MSunitha, Sun, R, Dey, R, Arnett, DK, Bielak, LF, Bis, JC, Blangero, J, Boerwinkle, E, Bowden, DW, Brody, JA, Cade, BE, Correa, A, Cupples, AL, Curran, JE, de Vries, PS, Duggirala, R, Freedman, BI, Göring, HHH, Guo, X, Haessler, J, Kalyani, RR, Kooperberg, C, Kral, BG, Lange, LA, Manichaikul, A, Martin, LW, McGarvey, ST, 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, Sitlani, CM, Smith, JA, Taylor, KD, Vasan, RS, Willer, CJ, Wilson, JG, Yanek, LR, Zhao, W, Rotter, JI, Natarajan, P, Peloso, GM, Li, Z, Lin, X |
| Corporate/Institutional Authors | NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group |
| Journal | Nat Genet |
| Volume | 55 |
| Issue | 1 |
| Pagination | 154-164 |
| Date Published | 2023 Jan |
| ISSN | 1546-1718 |
| Keywords | Exome Sequencing, Genome-Wide Association Study, Lipids, Phenotype, Whole Genome Sequencing |
| Abstract | <p>Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.</p> |
| DOI | 10.1038/s41588-022-01225-6 |
| Alternate Journal | Nat Genet |
| PubMed ID | 36564505 |
| Grant List | HHSN268201800015I / HB / NHLBI NIH HHS / United States HHSN268201600002C / HL / NHLBI NIH HHS / United States HHSN268201500001I / HL / NHLBI NIH HHS / United States HHSN268201800012I / HB / NHLBI NIH HHS / United States 75N92019D00031 / HL / NHLBI NIH HHS / United States HHSN268201600004C / HL / NHLBI NIH HHS / United States HHSN268201700005I / HL / NHLBI NIH HHS / United States HHSN268201500003I / HL / NHLBI NIH HHS / United States HHSN268201700004I / HL / NHLBI NIH HHS / United States HHSN268201800011I / HB / NHLBI NIH HHS / United States HHSN268201700003I / HL / NHLBI NIH HHS / United States HHSN268201800010I / HB / NHLBI NIH HHS / United States HHSN268201700001I / HL / NHLBI NIH HHS / United States HHSN268201600018C / HL / NHLBI NIH HHS / United States HHSN268201700002I / HL / NHLBI NIH HHS / United States HHSN268201600001C / HL / NHLBI NIH HHS / United States HHSN268201600003C / HL / NHLBI NIH HHS / United States HHSN268201800013I / MD / NIMHD NIH HHS / United States R03 OD030608 / OD / NIH HHS / United States HHSN268201800014I / HB / NHLBI NIH HHS / United States HHSN268201700001I / HL / NHLBI NIH HHS / United States HHSN268201700002I / HL / NHLBI NIH HHS / United States HHSN268201700003I / HL / NHLBI NIH HHS / United States HHSN268201700005I / HL / NHLBI NIH HHS / United States HHSN268201700004I / HL / NHLBI NIH HHS / United States HHSN268201800010I / HB / NHLBI NIH HHS / United States HHSN268201800011I / HB / NHLBI NIH HHS / United States HHSN268201800012I / HB / NHLBI NIH HHS / United States HHSN268201800013I / MD / NIMHD NIH HHS / United States HHSN268201800014I / HB / NHLBI NIH HHS / United States HHSN268201800015I / HB / NHLBI NIH HHS / United States HHSN268201500001I / HL / NHLBI NIH HHS / United States HHSN268201500003I / HL / NHLBI NIH HHS / United States HHSN268201600018C / HL / NHLBI NIH HHS / United States HHSN268201600001C / HL / NHLBI NIH HHS / United States HHSN268201600002C / HL / NHLBI NIH HHS / United States HHSN268201600003C / HL / NHLBI NIH HHS / United States HHSN268201600004C / HL / NHLBI NIH HHS / United States HHSN268201500001I / HL / NHLBI NIH HHS / United States 75N92019D00031 / HL / NHLBI NIH HHS / United States HHSN268201500003I / HL / NHLBI NIH HHS / United States 75N92019D00031 / HL / NHLBI NIH HHS / United States |
ePub date:
23/01