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A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response

Nature Genetics volume 53, pages 1504–1516 (2021)Cite this article

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An Author Correction to this article was published on 02 November 2021

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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.

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Fig. 1: A schematic showing the overall study design.
Fig. 2: The multi-ancestry HLA reference panel shows improvement in allele diversity and imputation accuracy.
Fig. 3: Stepwise conditional analysis of the allele and amino acid positions of classical HLA genes to HIV-1 viral load.
Fig. 4: Location and effect of three independently associated amino acid positions in HLA-B.
Fig. 5: Pairwise LD and haplotype structure for eight classical HLA genes in five population groups.

Data availability

All data for generating the figures presented in the manuscript are available at https://github.com/immunogenomics/HLA-TAPAS.

Code availability

HLA-TAPAS, https://github.com/immunogenomics/HLA-TAPAS; GATK version 3.6, https://software.broadinstitute.org/gatk/download/archive; HLA*PRG, https://github.com/AlexanderDilthey/MHC-PRG; HLA*LA, https://github.com/DiltheyLab/HLA-PRG-LA; PLINK version 1.90, https://www.cog-genomics.org/plink2; Beagle version 4.1, https://faculty.washington.edu/browning/beagle/b4_1.html; Hapl-o-Mat version 1.1, https://github.com/DKMS/Hapl-o-Mat/; BIGDAWG version 2.3.6, https://cran.r-project.org/web/packages/BIGDAWG/index.html.

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Acknowledgements

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.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.

Author information

Authors and Affiliations

  1. Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Yang Luo, Saori Sakaue, Maria Gutierrez-Arcelus & Soumya Raychaudhuri

  2. Division of Rheumatology, Immunology, and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Yang Luo, Saori Sakaue, Maria Gutierrez-Arcelus & Soumya Raychaudhuri

  3. Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Yang Luo, Saori Sakaue, Maria Gutierrez-Arcelus & Soumya Raychaudhuri

  4. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Yang Luo, Masahiro Kanai, Saori Sakaue, Maria Gutierrez-Arcelus & Soumya Raychaudhuri

  5. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Yang Luo, Masahiro Kanai, Saori Sakaue, Maria Gutierrez-Arcelus, Sekar Kathiresan, Tonu Esko & Soumya Raychaudhuri

  6. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Masahiro Kanai

  7. Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Masahiro Kanai

  8. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan

    Masahiro Kanai, Kenichi Yamamoto, Kotaro Ogawa & Yukinori Okada

  9. Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea

    Wanson Choi & Buhm Han

  10. Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA

    Xinyi Li

  11. Department of Pediatrics, Osaka University Graduate School of Medicine, Osaka, Japan

    Kenichi Yamamoto

  12. Department of Neurology, Osaka University Graduate School of Medicine, Osaka, Japan

    Kotaro Ogawa

  13. The Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research,North Short LIJ Health System, Manhasset, NY, USA

    Peter K. Gregersen

  14. Department of Dermatology, University of Michigan, Ann Arbor, MI, USA

    Philip E. Stuart & James T. Elder

  15. Ann Arbor Veterans Affairs Hospital, Ann Arbor, MI, USA

    James T. Elder

  16. Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria

    Lukas Forer, Sebastian Schönherr & Christian Fuchsberger

  17. Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA

    Christian Fuchsberger & Albert V. Smith

  18. Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA

    Christian Fuchsberger & Albert V. Smith

  19. Institute for Biomedicine, Eurac Research, Bolzano, Italy

    Christian Fuchsberger

  20. Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

    Jacques Fellay

  21. School of Life Sciences, EPFL, Lausanne, Switzerland

    Jacques Fellay

  22. Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA

    Mary Carrington

  23. Ragon Institute of MGH, MIT and Harvard, Boston, MA, USA

    Mary Carrington

  24. Vanderbilt University Medical Center, Nashville, TN, USA

    David W. Haas

  25. Meharry Medical College, Nashville, TN, USA

    David W. Haas

  26. The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA

    Xiuqing Guo, Yii-Der Ida Chen, Jerome I. Rotter & Kent D. Taylor

  27. Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Nicholette D. Palmer

  28. Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA

    Stephen S. Rich

  29. Medicine, University of Mississippi Medical Center, Jackson, MS, USA

    Adolfo Correa

  30. Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA

    James G. Wilson

  31. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Sekar Kathiresan

  32. Cardiology Division of the Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Sekar Kathiresan

  33. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Michael H. Cho

  34. Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia

    Andres Metspalu & Tonu Esko

  35. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan

    Yukinori Okada

  36. Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea

    Buhm Han

  37. J.C. Wilt Infectious Diseases Research Centre, National Microbiology Laboratories, Public Health Agency of Canada, Winnipeg, Manitoba, Canada

    Paul J. McLaren

  38. Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada

    Paul J. McLaren

  39. Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, UK

    Soumya Raychaudhuri

  40. New York Genome Center, New York, NY, USA

    Namiko Abe, Karen Bunting, Bo-Juen Chen, Heather Geiger, Soren Germer, Melissa Marton, Catherine Reeves, Nicolas Robine, Alexi Runnels, Tanja Smith, Lara Winterkorn & Michael Zody

  41. University of Michigan, Ann Arbor, MI, USA

    Gonçalo Abecasis, Larry Bielak, Thomas Blackwell, Jeffrey Curtis, Matthew Flickinger, Colin Gross, Hyun Min Kang, Sharon Kardia, Jonathon LeFaive, Patricia Peyser, Jacob Pleiness, Jennifer Smith, Daniel Taliun, Peter VandeHaar, Jiongming Wang, Joshua Weinstock, Cristen Willer, Ketian Yu, Wei Zhao & Sebastian Zoellner

  42. Broad Institute, Cambridge, MA, USA

    Francois Aguet, Kristin Ardlie, Mark Chaffin, Seung Hoan Choi, Clary Clish, Stacey Gabriel, Namrata Gupta, Pradeep Natarajan, Carolina Roselli & Seyedeh Maryam Zekavat

  43. Brigham and Women’s Hospital, Cedars Sinai Boston, Boston, MA, USA

    Christine Albert

  44. Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA

    Laura Almasy

  45. Emory University, Atlanta, GA, USA

    Alvaro Alonso, Rich Johnston, Lawrence S. Phillips, Zhaohui Qin, Vivien Sheehan & Stephanie L. Sherman

  46. University of Maryland, Baltimore, MD, USA

    Seth Ament, Amber Beitelshees, Christy Chang, Coleen Damcott, Scott Devine, Mao Fu, Da-Wei Gong, Yue Guan, Daniel Harris, Elliott Hong, Michael Kessler, Joshua Lewis, Patrick McArdle, Braxton D. Mitchell, May E. Montasser, Jeff O’Connell, Tim O’Connor, James Perry, Toni Pollin, Robert Reed, Kathleen Ryan, Shabnam Salimi, Amol Shetty, Elizabeth Streeten, Simeon Taylor & Huichun Xu

  47. University of Washington, Seattle, WA, USA

    Peter Anderson, Joshua Bis, Jennifer Brody, Jai Broome, Erin Buth, Matthew Conomos, Colleen Davis, Leslie Emery, Chris Frazar, Stephanie M. Fullerton, Stephanie Gogarten, Ben Heavner, Susan Heckbert, Deepti Jain, Craig Johnson, Alyna Khan, Cathy Laurie, Cecelia Laurie, David Levine, Susanne May, Daniel McGoldrick, Caitlin McHugh, Sarah C. Nelson, Deborah Nickerson, Bruce Psaty, Ken Rice, Josh Smith, Nicholas Smith, Nona Sotoodehnia, Adrienne M. Stilp, Adam Szpiro, Timothy A. Thornton, Machiko Threlkeld, David Tirschwell, Catherine Tong, Fei Fei Wang, Bruce Weir, Kayleen Williams & Quenna Wong

  48. University of Mississippi, Jackson, MS, USA

    Pramod Anugu, Lynette Ekunwe, Yan Gao, Michael Hall, Hao Mei & Nancy Min

  49. National Institutes of Health, Bethesda, MD, USA

    Deborah Applebaum-Bowden

  50. Johns Hopkins University, Baltimore, MD, USA

    Dan Arking, Dimitrios Avramopoulos, Emily Barron-Casella, Terri Beaty, Diane Becker, Lewis Becker, James Casella, Kimberly Jones, Barry Make, Rasika Mathias, Rakhi Naik, Wendy Post, Ingo Ruczinski, Steven Salzberg, Margaret Taub, Dhananjay Vaidya & Lisa Yanek

  51. University of Kentucky, Lexington, KY, USA

    Donna K. Arnett

  52. Duke University, Durham, NC, USA

    Allison Ashley-Koch, Yongmei Liu & Marilyn Telen

  53. University of Alabama, Birmingham, AL, USA

    Stella Aslibekyan, Bertha Hidalgo, Marguerite Ryan Irvin, Merry-Lynn McDonald & Hemant Tiwari

  54. Stanford University, Stanford, CA, USA

    Tim Assimes, Carlos Bustamante, Chris Gignoux, Yu Liu, Emmanuel Mignot, David T. Paik, Marco Perez, Michael Snyder, Hua Tang & Joseph Wu

  55. University of Wisconsin Milwaukee, Milwaukee, WI, USA

    Paul Auer

  56. Providence Health Care Research Institute, Vancouver, British Columbia, Canada

    Najib Ayas

  57. Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA

    Adithya Balasubramanian, Huyen Dinh, Harsha Doddapaneni, Shannon Dugan-Perez, Jesse Farek, Richard Gibbs, Yi Han, Jianhong Hu, Ziad Khan, Sandra Lee, Vipin Menon, Ginger Metcalf, Zeineen Momin, Donna Muzny, Caitlin Nessner, Osuji Nkechinyere, Geoffrey Okwuonu, Mahitha Rajendran, Sejal Salvi, Jireh Santibanez & Jennifer Watt

  58. Cleveland Clinic, Cleveland, OH, USA

    John Barnard, Gerald Beck, Mina Chung, Suzy Comhair & Serpil Erzurum

  59. University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Kathleen Barnes, Sharon Graw, Luisa Mestroni & Matthew Taylor

  60. Columbia University, New York, NY, USA

    R. Graham Barr & Danish Saleheen

  61. The Emmes Corporation, Rockville, MD, USA

    Lucas Barwick

  62. National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    Rebecca Beer, Weiniu Gan, Cashell Jaquish, Andrew Johnson,  Dan Levy, James Luo, Julie Mikulla, George Papanicolaou & Pankaj Qasba

  63. Boston University, Massachusetts General Hospital, Boston, MA, USA

    Emelia Benjamin

  64. University of Pittsburgh, Pittsburgh, PA, USA

    Takis Benos, Mark Geraci, Mark Gladwin, Ryan L. Minster, Frank Sciurba, Daniel E. Weeks & Yingze Zhang

  65. Fundação de Hematologia e Hemoterapia de Pernambuco – Hemope, Recife, Brazil

    Marcos Bezerra

  66. University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA

    John Blangero, Joanne Curran, Ravi Duggirala, Harald Goring, Michael Mahaney & Juan Manuel Peralta

  67. University of Texas Health at Houston, Houston, TX, USA

    Eric Boerwinkle, Deborah Brown, Paul de Vries, Myriam Fornage & James Hixson

  68. Wake Forest Baptist Health, Winston-Salem, NC, USA

    Donald W. Bowden, David Herrington & Beverly Snively

  69. National Jewish Health, Denver, CO, USA

    Russell Bowler, James Crapo, Tasha Fingerlin, Elizabeth Regan & Snow Xueyan Zhao

  70. Medical College of Wisconsin, Milwaukee, WI, USA

    Ulrich Broeckel

  71. University of California, San Francisco, San Francisco, CA, USA

    Esteban Burchard, Ryan Hernandez & Shannon Kelly

  72. Brigham & Women’s Hospital, Boston, MA, USA

    Brian Cade, Vincent Carey, Juan P. Casas Romero, Peter Castaldi, Daniel Chasman, Dawn DeMeo, Auyon Ghosh, Craig Hersh, Brian Hobbs, Meryl LeBoff, Jiwon Lee, JoAnn Manson, Matt Moll, Dandi Qiao, Susan Redline, Edwin Silverman, Tamar Sofer, Jessica Lasky Su, Jody Sylvia, Scott T. Weiss & Carla Wilson

  73. University of Colorado at Denver, Denver, CO, USA

    Jonathan Cardwell, Sameer Chavan, Michelle Daya, Shanshan Gao, Daniel Grine, John Hokanson, Greg Kinney, Ethan Lange, Leslie Lange, Susan Mathai, Bonnie Neltner, Julia Powers Becker, Meher Preethi Boorgula, Nicholas Rafaels, Pamela Russell, David Schwartz, Aniket Shetty, Garrett Storm, Tarik Walker, Avram Walts & Ivana Yang

  74. University of Montreal, Montreal, Quebec, Canada

    Julie Carrier

  75. Washington State University, Seattle, WA, USA

    Cara Carty

  76. University of California, Los Angeles, Los Angeles, CA, USA

    Richard Casaburi, Carolyn Crandall & Karol Watson

  77. National Taiwan University, Taipei, Taiwan

    Yi-Cheng Chang & Lee-Ming Chuang

  78. University of Virginia, Charlottesville, VA, USA

    Wei-Min Chen, Charles Farber, Ani Manichaikul, Josyf C. Mychaleckyj & Aakrosh Ratan

  79. National Health Research Institutes, Zhunan, Taiwan

    Ren-Hua Chung & Chao (Agnes) Hsiung

  80. University of Vermont, Burlington, VT, USA

    Elaine Cornell, Jon Peter Durda & Russell Tracy

  81. Boston University, Boston, MA, USA

    L. Adrienne Cupples, Honghuang Lin, Kathryn Lunetta, Gina Peloso & Vasan S. Ramachandran

  82. Vitalant Research Institute, San Francisco, CA, USA

    Brian Custer

  83. University of Illinois at Chicago, Chicago, IL, USA

    Dawood Darbar

  84. University of Chicago, Chicago, IL, USA

    Sean David

  85. Mayo Clinic, Rochester, MN, USA

    Mariza de Andrade

  86. Washington University in St Louis, St Louis, MO, USA

    Lisa de las Fuentes, Susan K. Dutcher, Lucinda Fulton, C. Charles Gu, D. C. Rao, Karen Schwander & Yun Ju Sung

  87. Vanderbilt University, Nashville, TN, USA

    Michael DeBaun,  Dan Roden & M. Benjamin Shoemaker

  88. University of Cincinnati, Cincinnati, OH, USA

    Ranjan Deka

  89. University of North Carolina, Chapel Hill, NC, USA

    Qing Duan, Nora Franceschini, Yun Li, Kari North & Laura Raffield

  90. Brown University, Providence, RI, USA

    Charles Eaton, Simin Liu & Stephen McGarvey

  91. Channing Division of Network Medicine, Harvard University, Boston, MA, USA

    Adel El Boueiz

  92. Massachusetts General Hospital, Boston, MA, USA

    Patrick Ellinor, Steven Lubitz, James Meigs & Lu-Chen Weng

  93. Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Margery Gass, Jeff Haessler, Charles Kooperberg, Ulrike Peters & Lesley Tinker

  94. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Bruce Gelb, Eimear Kenny, Ruth J. F. Loos, Girish Nadkarni & Michael Preuss

  95. Beth Israel Deaconess Medical Center, Boston, MA, USA

    Robert Gerszten & James Wilson

  96. Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    David Glahn

  97. Mass General Brigham, Boston, MA, USA

    Kathryn J. Gray

  98. Indiana University, Indianapolis, IN, USA

    David M. Haas & Jennifer Wessel

  99. University of Calgary, Calgary, Alberta, Canada

    Patrick Hanly

  100. Yale University, New Haven, CT, USA

    Nicola L. Hawley

  101. Tulane University, New Orleans, LA, USA

    Jiang He

  102. University of Iowa, Iowa City, IA, USA

    Karin Hoth & Robert Wallace

  103. Tri-Service General Hospital National Defense Medical Center, Taipei, Taiwan

    Yi-Jen Hung

  104. Bloodworks Northwest, Seattle, WA, USA

    Haley Huston, Jill Johnsen, Barbara Konkle & Sarah Ruuska

  105. Taichung Veterans General Hospital Taiwan, Taichung, Taiwan

    Chii Min Hwu, Wen-Jane Lee & Wayne Hui-Heng Sheu

  106. Oklahoma State University Medical Center, Tulsa, OK, USA

    Rebecca Jackson

  107. Albert Einstein College of Medicine, Bronx, NY, USA

    Robert Kaplan & Sylvia Smoller

  108. Harvard University, Cambridge, MA, USA

    Wonji Kim & Sean McFarland

  109. McGill University, Montreal, Quebec, Canada

    John Kimoff

  110. Loyola University, Chicago, IL, USA

    Holly Kramer

  111. Harvard School of Public Health, Boston, MA, USA

    Christoph Lange & Xihong Lin

  112. Lundquist Institute, Torrence, CA, USA

    Xiaohui Li, Henry Lin & Kevin Sandow

  113. Ohio State University, Columbus, OH, USA

    Ulysses Magalang

  114. Broad Institute, Harvard University, Massachusetts General Hospital, Cambridge, MA, USA

    Alisa Manning

  115. George Washington University, Washington DC, USA

    Lisa Martin

  116. RTI International, Durham, NC, USA

    Becky McNeil & Cora Parker

  117. University of Arizona, Tucson, AZ, USA

    Deborah A. Meyers

  118. National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA

    Mollie Minear

  119. Oklahoma Medical Research Foundation, Oklahoma City, OK, USA

    Courtney Montgomery

  120. Ministry of Health, Government of Samoa, Apia, Samoa

    Take Naseri

  121. Howard University, Washington DC, USA

    Sergei Nekhai

  122. University at Buffalo, Buffalo, NY, USA

    Heather Ochs-Balcom

  123. University of Pennsylvania, Philadelphia, PA, USA

    Allan Pack & Sarah Tishkoff

  124. University of Minnesota, Minneapolis, MN, USA

    James Pankow, Michael Tsai & Scott Vrieze

  125. Northwestern University, Chicago, IL, USA

    Laura Rasmussen-Torvik

  126. Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA, USA

    Alex Reiner

  127. Lutia I Puava Ae Mapu I Fagalele, Apia, Samoa

    Muagututi’a Sefuiva Reupena

  128. University of Ottawa, Ottawa, Ontario, Canada

    Rebecca Robillard

  129. Universidade de São Paulo, São Paulo, Brazil

    Ester Cerdeira Sabino

  130. Division of Hematology/Oncology, Broad Institute, Harvard University, Boston, MA, USA

    Vijay G. Sankaran

  131. Harvard Medical School, Boston, MA, USA

    Christine Seidman & Jonathan Seidman

  132. Université Laval, Quebec City, Quebec, Canada

    Frédéric Sériès

  133. University of Massachusetts Memorial Medical Center, Worcester, MA, USA

    Brian Silver

  134. University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    Robert Skomro

  135. University of Southern California, Los Angeles, CA, USA

    David Van Den Berg

  136. Brigham and Women’s Hospital, Partners, Boston, MA, USA

    Heming Wang

  137. Henry Ford Health System, Detroit, MI, USA

    L. Keoki Williams

  138. Case Western Reserve University, Cleveland, OH, USA

    Xiaofeng Zhu

Authors
  1. Yang Luo
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  2. Masahiro Kanai
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  3. Wanson Choi
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  4. Xinyi Li
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  5. Saori Sakaue
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  6. Kenichi Yamamoto
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  7. Kotaro Ogawa
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  8. Maria Gutierrez-Arcelus
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  9. Peter K. Gregersen
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  10. Philip E. Stuart
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  11. James T. Elder
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  12. Lukas Forer
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  13. Sebastian Schönherr
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  14. Christian Fuchsberger
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  15. Albert V. Smith
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  16. Jacques Fellay
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  17. Mary Carrington
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  18. David W. Haas
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  19. Xiuqing Guo
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  20. Nicholette D. Palmer
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  21. Yii-Der Ida Chen
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  22. Jerome I. Rotter
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  23. Kent D. Taylor
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  24. Stephen S. Rich
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  25. Adolfo Correa
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  26. James G. Wilson
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  27. Sekar Kathiresan
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  28. Michael H. Cho
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  29. Andres Metspalu
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  30. Tonu Esko
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  31. Yukinori Okada
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  32. Buhm Han
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  33. Paul J. McLaren
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  34. Soumya Raychaudhuri
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Consortia

NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium

  • Namiko Abe
  • , Gonçalo Abecasis
  • , Francois Aguet
  • , Christine Albert
  • , Laura Almasy
  • , Alvaro Alonso
  • , Seth Ament
  • , Peter Anderson
  • , Pramod Anugu
  • , Deborah Applebaum-Bowden
  • , Kristin Ardlie
  • ,  Dan Arking
  • , Donna K. Arnett
  • , Allison Ashley-Koch
  • , Stella Aslibekyan
  • , Tim Assimes
  • , Paul Auer
  • , Dimitrios Avramopoulos
  • , Najib Ayas
  • , Adithya Balasubramanian
  • , John Barnard
  • , Kathleen Barnes
  • , R. Graham Barr
  • , Emily Barron-Casella
  • , Lucas Barwick
  • , Terri Beaty
  • , Gerald Beck
  • , Diane Becker
  • , Lewis Becker
  • , Rebecca Beer
  • , Amber Beitelshees
  • , Emelia Benjamin
  • , Takis Benos
  • , Marcos Bezerra
  • , Larry Bielak
  • , Joshua Bis
  • , Thomas Blackwell
  • , John Blangero
  • , Eric Boerwinkle
  • , Donald W. Bowden
  • , Russell Bowler
  • , Jennifer Brody
  • , Ulrich Broeckel
  • , Jai Broome
  • , Deborah Brown
  • , Karen Bunting
  • , Esteban Burchard
  • , Carlos Bustamante
  • , Erin Buth
  • , Brian Cade
  • , Jonathan Cardwell
  • , Vincent Carey
  • , Julie Carrier
  • , Cara Carty
  • , Richard Casaburi
  • , Juan P. Casas Romero
  • , James Casella
  • , Peter Castaldi
  • , Mark Chaffin
  • , Christy Chang
  • , Yi-Cheng Chang
  • , Daniel Chasman
  • , Sameer Chavan
  • , Bo-Juen Chen
  • , Wei-Min Chen
  • , Yii-Der Ida Chen
  • , Michael H. Cho
  • , Seung Hoan Choi
  • , Lee-Ming Chuang
  • , Mina Chung
  • , Ren-Hua Chung
  • , Clary Clish
  • , Suzy Comhair
  • , Matthew Conomos
  • , Elaine Cornell
  • , Adolfo Correa
  • , Carolyn Crandall
  • , James Crapo
  • , L. Adrienne Cupples
  • , Joanne Curran
  • , Jeffrey Curtis
  • , Brian Custer
  • , Coleen Damcott
  • , Dawood Darbar
  • , Sean David
  • , Colleen Davis
  • , Michelle Daya
  • , Mariza de Andrade
  • , Lisa de las Fuentes
  • , Paul de Vries
  • , Michael DeBaun
  • , Ranjan Deka
  • , Dawn DeMeo
  • , Scott Devine
  • , Huyen Dinh
  • , Harsha Doddapaneni
  • , Qing Duan
  • , Shannon Dugan-Perez
  • , Ravi Duggirala
  • , Jon Peter Durda
  • , Susan K. Dutcher
  • , Charles Eaton
  • , Lynette Ekunwe
  • , Adel El Boueiz
  • , Patrick Ellinor
  • , Leslie Emery
  • , Serpil Erzurum
  • , Charles Farber
  • , Jesse Farek
  • , Tasha Fingerlin
  • , Matthew Flickinger
  • , Myriam Fornage
  • , Nora Franceschini
  • , Chris Frazar
  • , Mao Fu
  • , Stephanie M. Fullerton
  • , Lucinda Fulton
  • , Stacey Gabriel
  • , Weiniu Gan
  • , Shanshan Gao
  • , Yan Gao
  • , Margery Gass
  • , Heather Geiger
  • , Bruce Gelb
  • , Mark Geraci
  • , Soren Germer
  • , Robert Gerszten
  • , Auyon Ghosh
  • , Richard Gibbs
  • , Chris Gignoux
  • , Mark Gladwin
  • , David Glahn
  • , Stephanie Gogarten
  • , Da-Wei Gong
  • , Harald Goring
  • , Sharon Graw
  • , Kathryn J. Gray
  • , Daniel Grine
  • , Colin Gross
  • , C. Charles Gu
  • , Yue Guan
  • , Xiuqing Guo
  • , Namrata Gupta
  • , David M. Haas
  • , Jeff Haessler
  • , Michael Hall
  • , Yi Han
  • , Patrick Hanly
  • , Daniel Harris
  • , Nicola L. Hawley
  • , Jiang He
  • , Ben Heavner
  • , Susan Heckbert
  • , Ryan Hernandez
  • , David Herrington
  • , Craig Hersh
  • , Bertha Hidalgo
  • , James Hixson
  • , Brian Hobbs
  • , John Hokanson
  • , Elliott Hong
  • , Karin Hoth
  • , Chao (Agnes) Hsiung
  • , Jianhong Hu
  • , Yi-Jen Hung
  • , Haley Huston
  • , Chii Min Hwu
  • , Marguerite Ryan Irvin
  • , Rebecca Jackson
  • , Deepti Jain
  • , Cashell Jaquish
  • , Jill Johnsen
  • , Andrew Johnson
  • , Craig Johnson
  • , Rich Johnston
  • , Kimberly Jones
  • , Hyun Min Kang
  • , Robert Kaplan
  • , Sharon Kardia
  • , Shannon Kelly
  • , Eimear Kenny
  • , Michael Kessler
  • , Alyna Khan
  • , Ziad Khan
  • , Wonji Kim
  • , John Kimoff
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Contributions

Y. Luo and S. Raychaudhuri conceived, designed and performed analyses, wrote the manuscript and supervised the research. M. Kanai implemented the omnibus test for the HIV-1 fine-mapping study. Y. Luo, W.C., M. Kanai, P.E.S., J.T.E. and B. Han contributed to the development of the HLA-TAPAS pipeline. X. Li performed the selection analysis. S. Sakaue performed imputation comparison between Beagle v.4 and Minimac4. L. Forer, S. Schoenherr, C. Fuchsberger and A.V.S. hosted the HLA imputation server. J.T.E., M.G.-A. and P.K.G. helped with the GaP data acquisition. K. Yamamoto, K.O., D.W.H., X.G., N.D.P., Y.-D.I.C., J.I.R., K.D.T., S.S.R., A.C., J.G.W., S. Kathiresan, M.H.C., A. Metspalu, T.E. and Y.O. contributed to the WGS data acquisition. J. Fellay, M. Carrington and P.J.M. contributed to the HIV-1 data acquisition. All authors contributed to the writing of the manuscript.

Corresponding authors

Correspondence to Yang Luo or Soumya Raychaudhuri.

Ethics declarations

Competing interests

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.

Additional information

Peer review information Nature Genetics thanks Pierre-Antoine Gourraud and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 HLA nomenclature.

Description of a classical HLA allele using current standard nomenclature. The first field corresponds to the serological antigen. The second field distinguishes HLA alleles that differ by one or more missense variants. The third field distinguishes HLA alleles that differ by one or more synonymous variants. The G-group distinguishes HLA alleles that differ by one or more synonymous variants within the exons that encode the peptide binding groove regions (exon 2 and 3 for HLA class I genes and exon 2 for HLA class II genes).

Extended Data Fig. 2 Correlation between imputed and typed dosage (dosage r2) of classical HLA alleles in 1,067 Admixed African HIV-1 individuals.

The x-axis shows the minor allele frequency observed in the SBT dataset. Blue points show G-group HLA alleles. Red points show one-field HLA alleles.

Extended Data Fig. 3 Association tests within the MHC to HIV-1 viral load.

The x-axis shows the genomic positions of chromosome 6 (build 37), and the y-axis is the -log10 (P-value) obtained from two-sided regression analyses for SNPs (gray), classical HLA alleles (blue) and amino acids (red). The dashed black line indicates the genome-wide significance threshold (P = 5 × 10−8). For biallelic markers, results were calculated by a linear regression model including sex, cohort-specific principal components and ancestry indicator as covariates (circle). Association at amino acid positions with more than two residues was calculated using a multi-degree-of-freedom omnibus test (one-sided F-test) including the same covariates (diamond). The top associated amino acid, classical HLA allele and SNPs are annotated in the figure. a, Of all variants tested, the top hit maps to amino acid position 97 in HLA-B. b, Subsequent conditional analysis controlling for all residues at position 97 in HLA-B revealed an independent association at position 67 in HLA-B. c, Results conditioned on position 97 and 67 in HLA-B showed a third signal at position 156 in HLA-B. d, Results conditioned on position 97, 67 and 156 in HLA-B showed position 77 in HLA-A has the strongest association signal outside HLA-B among all amino acid positions. e, Results conditioned on all amino acid positions in HLA-B. Notably, amino acid positions were more significant than any single SNP or classical HLA allele in each conditional analysis for the three amino acid positions in HLA-B.

Extended Data Fig. 4 Effect on set point viral load of individual residues at position 97 in HLA-B.

Mean set point viral load (spVL, RNA copies per milliliter) and its standard error of all six residues at position 97 in HLA-B in three populations independently. Data are presented as mean values ± standard errors. Residues are ranked from the most protective to the riskiest in the overall population. There are 3,901 AA, 7,455 EUR, and 677 LAT independent individuals included in the analysis.

Extended Data Fig. 5 Global diversity of the MHC region.

Principal component analysis of the pairwise IBD distance between 21,546 individuals using MHC region markers. The first two principal components show separation of continental groups.

Extended Data Fig. 6 Diversity of eight classical HLA genes in the constructed multi-ancestry MHC reference panel.

Each gene is stratified by six populations (AA, Admixed African; EAS, East Asian; EUR, European; LAT, Latino; SAS, South Asian). The top two most common alleles within each classical gene of each population are plotted across all panels. Alleles that have frequencies greater than 1% are also labelled in the bar plots. a, Class I genes. b, Class II genes.

Extended Data Fig. 7 Allele diversity of eight classical HLA genes in global populations.

For each gene, the top five most frequent alleles across all populations are shown (light blue, most frequent; dark blue, second frequent; light green, third frequent; dark green, fourth frequent; red, fifth frequent; gray, all other alleles).

Extended Data Fig. 8 Pairwise normalized entropy (ε) among all population groups.

The normalized entropy (ε) measures the difference of the haplotype frequency distribution for linkage disequilibrium and linkage equilibrium, and takes values between 0 (no LD) to 1 (perfect LD).

Extended Data Fig. 9 Deviation from average genome-wide ancestry in Admixed African and Latino populations.

a,b, The x-axis is the genomic position of chromosome 6. The y-axis shows the local African ancestry deviation measure inferred at a given position for Admixed Africans (a) and Latinos (b). The MHC region (chr6:28Mb-34Mb) is highlighted in red shading. Local ancestries were estimated using RFMix (red) and ELAI (blue). The ancestry deviation measure is the difference between African ancestry at a given genomic position with respect to the genome-wide average estimated by ADMIXTURE with K = 3, normalized by the standard deviation of the ancestry estimate. The dashed line indicates the genome-wide significance threshold at ±4.42 standard deviation of the ancestry estimate deviated from the genome-wide average.

Extended Data Fig. 10 Conditional analysis of other previously reported independently associated amino acid positions.

a,b, Manhattan plots of amino acid positions in the six classical HLA genes. Each point shows a single amino acid position and its omnibus P-value after controlling for independent positions that are associated with spVL in this study (position 97, 67 and 156 in HLA-B) (a) and independent positions that are only reported in previous studies14,18 and not in the presented work (position 45, 63 and 116 in HLA-B and position 77, 95 in HLA-A) (b). Independently associated amino acid positions that are only reported in the European population14 are shown in blue. Independently associated amino acid positions that are only reported in the African American population18 are shown in purple. Independently associated amino acid positions identified in this study are shown in red.

Supplementary information

Supplementary Information

Supplementary Figs. 1–18, Tables 1–23 and Note.

Reporting Summary

Supplementary Data 1

Summary of inferred HLA alleles at G-group resolution. For each allele observed in the reference panel, we listed its overall frequency (Freq); P value (Pval) for difference in frequencies across populations (a two-sided chi-square test with four degrees of freedom); frequency within each continental population (European, EUR; AA, admixed African; LAT, Latino; SAS, South Asian; EAS, East Asian) and its accuracy in two validation cohorts (JPN (n = 288 independent individuals) and 1KG (n = 955 independent individuals)).

Supplementary Data 2

Imputation dosage r2 of each allele. Each row shows each imputed classical HLA allele and its imputation accuracy (measure in dosage r2) in three cohorts with validation data (G1K (n = 955), GaP Registry (GAP, n = 75) and HIV (n = 1,067)). We also report the allelic frequency using the gold standard and the inferred alleles.

Supplementary Data 3

Association study within the MHC to HIV-1 viral load. For each row, we list the results of association testing for each of the binary markers that we imputed across the extended MHC region. For each marker, its unique identifier (ID), genome base pair position in Grch37 (BP), reference allele (REF), alternative allele (ALT), minor allele frequency (MAF), effect size (BETA) and standard error (SE) in each conditional analysis are listed.

Supplementary Data 4

HLA haplotype frequency based on eight classical HLA alleles inferred at G-group resolution. All haplotypes with count > 1 in each and overall population are listed. AA represents individuals of admixed African ancestry; EAS represents individuals of East Asian ancestry; EUR represents individuals of European ancestry; LAT represents individuals of Native American ancestry; SAS represents individuals of South Asian ancestry.

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Luo, Y., Kanai, M., Choi, W. et al. A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response. Nat Genet 53, 1504–1516 (2021). https://doi.org/10.1038/s41588-021-00935-7

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