Abstract
Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance.
Original language | English (US) |
---|---|
Pages (from-to) | 1504-1516 |
Number of pages | 13 |
Journal | Nature Genetics |
Volume | 53 |
Issue number | 10 |
DOIs | |
State | Published - Oct 2021 |
ASJC Scopus subject areas
- Genetics
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In: Nature Genetics, Vol. 53, No. 10, 10.2021, p. 1504-1516.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response
AU - NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
AU - Luo, Yang
AU - Kanai, Masahiro
AU - Choi, Wanson
AU - Li, Xinyi
AU - Sakaue, Saori
AU - Yamamoto, Kenichi
AU - Ogawa, Kotaro
AU - Gutierrez-Arcelus, Maria
AU - Gregersen, Peter K.
AU - Stuart, Philip E.
AU - Elder, James T.
AU - Forer, Lukas
AU - Schönherr, Sebastian
AU - Fuchsberger, Christian
AU - Smith, Albert V.
AU - Fellay, Jacques
AU - Carrington, Mary
AU - Haas, David W.
AU - Guo, Xiuqing
AU - Palmer, Nicholette D.
AU - Chen, Yii Der Ida
AU - Rotter, Jerome I.
AU - Taylor, Kent D.
AU - Rich, Stephen S.
AU - Correa, Adolfo
AU - Wilson, James G.
AU - Kathiresan, Sekar
AU - Cho, Michael H.
AU - Metspalu, Andres
AU - Esko, Tonu
AU - Okada, Yukinori
AU - Han, Buhm
AU - Abe, Namiko
AU - Abecasis, Gonçalo
AU - Aguet, Francois
AU - Albert, Christine
AU - Almasy, Laura
AU - Alonso, Alvaro
AU - Ament, Seth
AU - Anderson, Peter
AU - Anugu, Pramod
AU - Applebaum-Bowden, Deborah
AU - Ardlie, Kristin
AU - Dan Arking, Arking
AU - Arnett, Donna K.
AU - Ashley-Koch, Allison
AU - Aslibekyan, Stella
AU - Assimes, Tim
AU - Auer, Paul
AU - Rasmussen-Torvik, Laura
N1 - Funding Information: org/robert-s-boas-center-for-genomics-and-human-genetics/gap-registry/). For some HIV cohort participants, DNA and data collection was supported by NIH/ NIAID AIDS Clinical Trial Group (ACTG) grants UM1 AI068634, UM1 AI068636 and UM1 AI106701, and ACTG clinical research site grants A1069412, A1069423, A1069424, A1069503, AI025859, AI025868, AI027658, AI027661, AI027666, AI027675, AI032782, AI034853, AI038858, AI045008, AI046370, AI046376, AI050409, AI050410, AI050410, AI058740, AI060354, AI068636, AI069412, AI069415, AI069418, AI069419, AI069423, AI069424, AI069428, AI069432, AI069432, AI069434, AI069439, AI069447, AI069450, AI069452, AI069465, AI069467, AI069470, AI069471, AI069472, AI069474, AI069477, AI069481, AI069484, AI069494, AI069495, AI069496, AI069501, AI069501, AI069502, AI069503, AI069511, AI069513, AI069532, AI069534, AI069556, AI072626, AI073961, RR000046, RR000425, RR023561, RR024156, RR024160, RR024996, RR025008, RR025747, RR025777, RR025780, TR000004, TR000058, TR000124, TR000170, TR000439, TR000445, TR000457, TR001079, TR001082, TR001111 and TR024160. Molecular data for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the NHLBI. See the TOPMed omics support table (Supplementary Table 23) for study-specific omics support information. Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering, was 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. The COPDGene project was supported by Award Number U01 HL089897 and Award Number U01 HL089856 from the NHLBI. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NHLBI or the National Institutes of Health. The COPDGene project is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Novartis, Pfizer, Siemens and Sunovion. A full listing of COPDGene investigators can be found at http://www.copdgene.org/directory. The JHS is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), Mississippi State Department of Health (HHSN268201800015I) and University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the NHLBI and the National Institute on Minority Health and Health Disparities. We also thank the staff and participants of the JHS. MESA and the MESA SHARe project are conducted and supported by the NHLBI in collaboration with MESA investigators. Support for MESA is provided by contracts 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 and UL1-TR-001420. MESA Family is conducted and supported by the NHLBI in collaboration with MESA investigators. Support is provided by grants and contracts R01HL071051, R01HL071205, R01HL071250, R01HL071251, R01HL071258 and R01HL071259, and by the National Center for Research Resources, grant UL1RR033176. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. This project has been funded in whole or in part with federal funds from the Frederick National Laboratory for Cancer Research, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government. This research was supported in part by the Intramural Research Program of the NIH, Frederick National Laboratory, Center for Cancer Research. The Diabetes Heart Study was supported by R01 HL92301, R01 HL67348, R01 NS058700, R01 AR48797, R01 DK071891, R01 AG058921, the General Clinical Research Center of the Wake Forest University School of Medicine (M01 RR07122, F32 HL085989), the American Diabetes Association and a pilot grant from the Claude Pepper Older Americans Independence Center of Wake Forest University Health Sciences (P60 AG10484). A. Metspalu is supported by Gentransmed grant 2014-2020.4.01.15-0012. D.W.H. is supported by NIH grants AI110527, AI077505, TR000445, AI069439 and AI110527. J.T.E. and P.E.S. were supported by NIH/NIAMS R01 AR042742, R01 AR050511 and R01 AR063611. Y.O. was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (19H01021, 20K21834), AMED (JP20km0405211, JP20ek0109413, JP20ek0410075, JP20gm4010006 and JP20km0405217), Takeda Science Foundation, JST Moonshot R&D (JPMJMS2021, JPMJMS2024) and the Bioinformatics Initiative of Osaka University Graduate School of Medicine, Osaka University. Funding Information: M.H.C. has received consulting or speaking fees from Illumina and AstraZeneca, and grant support from GSK and Bayer. The remaining authors declare no competing interests. Funding Information: The study was supported by the National Institutes of Health (NIH) TB Research Unit Network, Grant U19 AI111224-01. We thank C. Willer, B. Vanderwerff and B. Klunder from the University of Michigan for help facilitating getting the constructed reference panel on the Michigan Imputation Server. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute (NHLBI); the National Institutes of Health; or the US Department of Health and Human Services. The GaP Registry at The Feinstein Institute for Medical Research provided fresh, de-identified human plasma; blood was collected from control participants under an institutional review board–approved protocol (IRB No. 09-081) and processed to isolate plasma. The GaP is a sub-protocol of the Tissue Donation Program at Northwell Health and a national resource for genotype–phenotype studies (https://www.feinsteininstitute. Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2021/10
Y1 - 2021/10
N2 - Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance.
AB - Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance.
UR - http://www.scopus.com/inward/record.url?scp=85116750590&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85116750590&partnerID=8YFLogxK
U2 - 10.1038/s41588-021-00935-7
DO - 10.1038/s41588-021-00935-7
M3 - Article
C2 - 34611364
AN - SCOPUS:85116750590
SN - 1061-4036
VL - 53
SP - 1504
EP - 1516
JO - Nature Genetics
JF - Nature Genetics
IS - 10
ER -