Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 154-164 |
| Number of pages | 11 |
| Journal | Nature Genetics |
| Volume | 55 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2023 |
Bibliographical note
Funding Information:S.M.G. is now an employee of Regeneron Genetics Center. For B.D.M., The Amish Research Program receives partial support from Regeneron Pharmaceuticals. M.E.M. reports grant from Regeneron Pharmaceutical unrelated to the present work. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. L.M.R. is a consultant for the TOPMed Administrative Coordinating Center (through Westat). For S.R., Jazz Pharma, Eli Lilly, Apnimed, unrelated to the present work. The spouse of C.J.W. works at Regeneron Pharmaceuticals. P.N. reports investigator-initiated grants from Amgen, Apple, AstraZeneca, Boston Scientific and Novartis, personal fees from Apple, AstraZeneca, Blackstone Life Sciences, Foresite Labs, Novartis, Roche/Genentech, is a cofounder of TenSixteen Bio, is a shareholder of geneXwell and TenSixteen Bio, and spousal employment at Vertex, all unrelated to the present work. X. Lin is a consultant of AbbVie Pharmaceuticals and Verily Life Sciences. The remaining authors declare no competing interests.
Funding Information:
This work was supported by grants R35-CA197449, U19-CA203654, R01-HL113338, U01-HG012064 and U01-HG009088 (X. Lin), NHLBI BioData Catalyst Fellowship (Z.L.), R01-HL142711 and R01-HL127564 (P.N. and G.M.P.), 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1-TR001881, DK063491, R01-HL071051, R01-HL071205, R01-HL071250, R01-HL071251, R01-HL071258, R01-HL071259 and UL1-RR033176 (J.I.R. and X.G.), R35-HL135824 (C.J.W.), U01-HL72518, HL087698, HL49762, HL59684, HL58625, HL071025, HL112064, NR0224103 and M01-RR000052 (to the Johns Hopkins General Clinical Research Center), NO1-HC-25195, HHSN268201500001I, 75N92019D00031 and R01-HL092577-06S1 (R.S.V. and L.A.C.), the Evans Medical Foundation and the Jay and Louis Coffman Endowment from the Department of Medicine, Boston University School of Medicine (R.S.V.), HHSN268201800001I and U01-HL137162 (K.M.R.), R01-HL093093 and R01-HL133040 (S.T.M.), R35-HL135818, R01-HL113338 and HL436801 (S.R.), KL2TR002490 (L.M.R.), R01-HL92301, R01-HL67348, R01-NS058700, R01-AR48797 and R01-AG058921 (N.D.P. and D.W.B.), R01-DK071891 (N.D.P., B.I.F. and D.W.B.), M01-RR07122 and F32-HL085989 (to the General Clinical Research Center of the Wake Forest University School of Medicine), the American Diabetes Association, P60-AG10484 (to the Claude Pepper Older Americans Independence Center of Wake Forest University Health Sciences), U01-HL137181 (J.R.O.), HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C and HHSN268201600004C (C.K.), R01-HL113323, U01-DK085524, R01-HL045522, R01-MH078143, R01-MH078111 and R01-MH083824 (H.H.H.G., R.D., J.E.C. and J.B.), 18CDA34110116 from American Heart Association (P.S.d.V.), HHSN268201800010I, HHSN268201800011I, HHSN268201800012I, HHSN268201800013I, HHSN268201800014I and HHSN268201800015I (A.C.), R01-HL153805, R03-HL154284 (B.E.C.), HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I and HHSN268201700004I (E.B.), U01-HL072524, R01-HL104135-04S1, U01-HL054472, U01-HL054473, U01-HL054495, U01-HL054509 and R01-HL055673-18S1 (D.K.A.). Molecular data for the Trans Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity QC and general program coordination was provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed and UK Biobank. The full study-specific acknowledgements and NHLBI BioData Catalyst acknowledgement are detailed in the Supplementary Note.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.