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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Science. Author manuscript; available in PMC Dec 11, 2011.
Published in final edited form as:
PMCID: PMC3235490
NIHMSID: NIHMS331514
The Major Genetic Determinants of HIV-1 Control Affect HLA Class I Peptide Presentation
The International HIV Controllers Study,* Writing team, Florencia Pereyra,1,2 Xiaoming Jia,3 Paul J. McLaren,4,5 Amalio Telenti,6 Paul I.W. de Bakker, co-chair,4,5,7,8 Bruce D. Walker, co-chair,1,9 Analysis team, Xiaoming Jia,3 Paul J. McLaren, project leaders,4,5 Stephan Ripke,4,10 Chanson J. Brumme,1 Sara L. Pulit,4,5 Amalio Telenti,6 Mary Carrington,1,11 Carl M. Kadie,12 Jonathan M. Carlson,13 David Heckerman,13 Paul I.W. de Bakker, chair,4,5,7,8 Study design, Florencia Pereyra,1,2 Paul I.W. de Bakker,4,5,7,8 Robert R. Graham,14 Robert M. Plenge,4,15 Steven G. Deeks,16 Bruce D. Walker, chair,1,9 SNP genotyping, HLA typing, and sample management, Lauren Gianniny,4 Gabriel Crawford,4 Jordan Sullivan,4 Elena Gonzalez,4 Leela Davies,4 Amy Camargo,4 Jamie M. Moore,4 Nicole Beattie,4 Supriya Gupta,4 Andrew Crenshaw,4 Noël P. Burtt,4 Candace Guiducci,4 Namrata Gupta,4 Mary Carrington,1,11 Xiaojiang Gao,11 Ying Qi,11 Yuko Yuki,11 HIV controllers recruitment and sample management, Florencia Pereyra, project leader,1,2 Alicja Piechocka-Trocha,1 Emily Cutrell,1 Rachel Rosenberg,1 Kristin L. Moss,1 Paul Lemay,1 Jessica O’Leary,1 Todd Schaefer,1 Pranshu Verma,1 Ildiko Toth,1 Brian Block,1 Brett Baker,1 Alissa Rothchild,1 Jeffrey Lian,1 Jacqueline Proudfoot,1 Donna Marie L. Alvino,1 Seanna Vine,1 Marylyn M. Addo,1 Todd M. Allen,1 Marcus Altfeld,1 Matthew R. Henn,4 Sylvie Le Gall,1 Hendrik Streeck,1 Bruce D. Walker, chair,1,9 AIDS Clinical Trials Group, David W. Haas,17 Daniel R. Kuritzkes,2 Gregory K. Robbins,18 Robert W. Shafer,19 Roy M. Gulick,20 Cecilia M. Shikuma,21 Richard Haubrich,22 Sharon Riddler,23 Paul E. Sax,2 Eric S. Daar,24 Heather J. Ribaudo,25 HIV controllers referral team, Brian Agan,26 Shanu Agarwal,27 Richard L. Ahern,18 Brady L. Allen,28 Sherly Altidor,29 Eric L. Altschuler,30 Sujata Ambardar,31 Kathryn Anastos,32 Ben Anderson,33 Val Anderson,34 Ushan Andrady,34 Diana Antoniskis,35 David Bangsberg,1,18 Daniel Barbaro,36 William Barrie,37 J. Bartczak,38 Simon Barton,39 Patricia Basden,40 Nesli Basgoz,18 Suzane Bazner,1 Nicholaos C. Bellos,41 Anne M. Benson,40 Judith Berger,42 Nicole F. Bernard,43 Annette M. Bernard,44 Christopher Birch,1 Stanley J. Bodner,45 Robert K. Bolan,46 Emilie T. Boudreaux,47 Meg Bradley,1 James F. Braun,48 Jon E. Brndjar,49 Stephen J. Brown,50 Katherine Brown,51 Sheldon T. Brown,52 Jedidiah Burack,53 Larry M. Bush,54 Virginia Cafaro,55 Omobolaji Campbell,18 John Campbell,56 Robert H. Carlson,57 J. Kevin Carmichael,58 Kathleen K. Casey,59 Chris Cavacuiti,60 Gregory Celestin,61 Steven T. Chambers,62 Nancy Chez,63 Lisa M. Chirch,64 Paul J. Cimoch,65 Daniel Cohen,66 Lillian E. Cohn,67 Brian Conway,68 David A. Cooper,69 Brian Cornelson,60 David T. Cox,70 Michael V. Cristofano,71 George Cuchural, Jr.,72 Julie L. Czartoski,73 Joseph M. Dahman,74 Jennifer S. Daly,75 Benjamin T. Davis,18 Kristine Davis,76 Sheila M. Davod,18 Steven G. Deeks,16 Edwin DeJesus,77 Craig A. Dietz,78 Eleanor Dunham,64 Michael E. Dunn,79 Todd B. Ellerin,80 Joseph J. Eron,81 John J.W. Fangman,82 Claire E. Farel,2 Helen Ferlazzo,83 Sarah Fidler,84 Anita Fleenor-Ford,85 Renee Frankel,86 Kenneth A. Freedberg,18 Neel K. French,87 Jonathan D. Fuchs,88 Jon D. Fuller,89 Jonna Gaberman,90 Joel E. Gallant,91 Rajesh T. Gandhi,18 Efrain Garcia,92 Donald Garmon,93 Joseph C. Gathe, Jr.,94 Cyril R. Gaultier,95 Wondwoosen Gebre,96 Frank D. Gilman,97 Ian Gilson,98 Paul A. Goepfert,99 Michael S. Gottlieb,100 Claudia Goulston,101 Richard K. Groger,102 T. Douglas Gurley,103 Stuart Haber,104 Robin Hardwicke,105 W. David Hardy,24 P. Richard Harrigan,106 Trevor N. Hawkins,107 Sonya Heath,99 Frederick M. Hecht,16 W. Keith Henry,108 Melissa Hladek,109 Robert P. Hoffman,110 James M. Horton,111 Ricky K. Hsu,112 Gregory D. Huhn,113 Peter Hunt,16 Mark J. Hupert,36 Mark L. Illeman,114 Hans Jaeger,115 Robert M. Jellinger,116 Mina John,117 Jennifer A. Johnson,2 Kristin L. Johnson,18 Heather Johnson,36 Kay Johnson,118 Jennifer Joly,64 Wilbert C. Jordan,119 Carol A. Kauffman,120 Homayoon Khanlou,121 Robert K. Killian,122 Arthur Y. Kim,18 David D. Kim,123 Clifford A. Kinder,124 Jeffrey T. Kirchner,125 Laura Kogelman,126 Erna Milunka Kojic,127 P. Todd Korthuis,128 Wayne Kurisu,97 Douglas S. Kwon,1 Melissa LaMar,93 Harry Lampiris,16 Massimiliano Lanzafame,129 Michael M. Lederman,130 David M. Lee,28 Jean M.L. Lee,73 Marah J. Lee,131 Edward T.Y. Lee,132 Janice Lemoine,133 Jay A. Levy,16 Josep M. Llibre,134 Michael A. Liguori,112 Susan J. Little,22 Anne Y. Liu,2 Alvaro J. Lopez,135 Mono R. Loutfy,136 Dawn Loy,137 Debbie Y. Mohammed,30 Alan Man,35 Michael K. Mansour,18 Vincent C. Marconi,138 Martin Markowitz,139 Rui Marques,140 Jeffrey N. Martin,16 Harold L. Martin, Jr.,141 Kenneth Hugh Mayer,66 M. Juliana McElrath,73 Theresa A. McGhee,142 Barbara H. McGovern,126 Katherine McGowan,2 Dawn McIntyre,59 Gavin X. Mcleod,143 Prema Menezes,81 Greg Mesa,144 Craig E. Metroka,29 Dirk Meyer-Olson,145 Andy O. Miller,146 Kate Montgomery,147 Karam C. Mounzer,148 Ellen H. Nagami,1 Iris Nagin,149 Ronald G. Nahass,150 Margret O. Nelson,18 Craig Nielsen,151 David L. Norene,152 David H. O’Connor,153 Bisola O. Ojikutu,18 Jason Okulicz,154 Olakunle O. Oladehin,18 Edward C. Oldfield, III,155 Susan A. Olender,156 Mario Ostrowski,136 William F. Owen, Jr.,157 Eunice Pae,1 Jeffrey Parsonnet,158 Andrew M. Pavlatos,159 Aaron M. Perlmutter,160 Michael N. Pierce,218 Jonathan M. Pincus,161 Leandro Pisani,162 Lawrence Jay Price,163 Laurie Proia,164 Richard C. Prokesch,137 Heather Calderon Pujet,165 Moti Ramgopal,166 Almas Rathod,1 Michael Rausch,167 J. Ravishankar,168 Frank S. Rhame,169 Constance Shamuyarira Richards,170 Douglas D. Richman,22 Gregory K. Robbins,18 Berta Rodes,171 Milagros Rodriguez,162 Richard C. Rose, III,172 Eric S. Rosenberg,18 Daniel Rosenthal,173 Polly E. Ross,174 David S. Rubin,175 Elease Rumbaugh,35 Luis Saenz,162 Michelle R. Salvaggio,176 William C. Sanchez,177 Veeraf M. Sanjana,178 Steven Santiago,162 Wolfgang Schmidt,179 Hanneke Schuitemaker,180 Philip M. Sestak,181 Peter Shalit,182 William Shay,104 Vivian N. Shirvani,183 Vanessa I. Silebi,184 James M. Sizemore, Jr.,185 Paul R. Skolnik,89 Marcia Sokol-Anderson,186 James M. Sosman,153 Paul Stabile,187 Jack T. Stapleton,188 Sheree Starrett,189 Francine Stein,83 Hans-Jurgen Stellbrink,190 F. Lisa Sterman,191 Valerie E. Stone,18 David R. Stone,192 Giuseppe Tambussi,193 Randy A. Taplitz,22 Ellen M. Tedaldi,194 Amalio Telenti,6 William Theisen,2 Richard Torres,195 Lorraine Tosiello,196 Cecile Tremblay,197 Marc A. Tribble,198 Phuong D. Trinh,199 Alice Tsao,1 Peggy Ueda,1 Anthony Vaccaro,200 Emilia Valadas,201 Thanes J. Vanig,202 Isabel Vecino,203 Vilma M. Vega,137 Wenoah Veikley,107 Barbara H. Wade,204 Charles Walworth,65 Chingchai Wanidworanun,205 Douglas J. Ward,206 Daniel A. Warner,207 Robert D. Weber,208 Duncan Webster,209 Steve Weis,203 David A. Wheeler,210 David J. White,211 Ed Wilkins,212 Alan Winston,84 Clifford G. Wlodaver,213 Angelique van’t Wout,180 David P. Wright,214 Otto O. Yang,24 David L. Yurdin,215 Brandon W. Zabukovic,216 Kimon C. Zachary,18 Beth Zeeman,1 and Meng Zhao217
1Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology (MIT) and Harvard, Boston, MA, USA
2Department of Medicine, Division of Infectious Disease, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
3Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA
4Broad Institute of Harvard and MIT, Cambridge, MA, USA
5Department of Medicine, Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
6Institute of Microbiology, University of Lausanne, Lausanne, Switzerland
7Department of Medical Genetics, Division of Biomedical Genetics, University Medical Center Utrecht, Netherlands
8Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Netherlands
9Howard Hughes Medical Institute, Chevy Chase, MD, USA
10Department of Medicine, Center for Human Genetic Research, MGH, Harvard Medical School, Boston, MA, USA
11Cancer and Inflammation Program, Laboratory of Experimental Immunology, SAIC-Frederick, NCI-Frederick, Frederick, MD, USA
12Microsoft Research, Redmond, WA, USA
13Microsoft Research, Los Angeles, CA, USA
14Genentech, South San Francisco, CA, USA
15Department of Medicine, Division of Rheumatology, Immunology and Allergy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
16University of California San Francisco, San Francisco, CA, USA
17Vanderbilt University School of Medicine, Nashville, TN, USA
18MGH, Harvard Medical School, Boston, MA, USA
19Stanford University, Palo Alto, CA, USA
20Weill Medical College of Cornell University, New York, NY, USA
21Hawaii Center for AIDS, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
22University of California San Diego, San Diego, CA, USA
23University of Pittsburgh, Pittsburgh, PA, USA
24University of California Los Angeles, Los Angeles, CA, USA
25Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
26Infectious Disease Clinical Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
27Summa Health System, Akron, OH, USA
28Uptown Physicians Group, Dallas, TX, USA
29St. Luke’s Roosevelt Hospital, New York, NY, USA
30New Jersey Medical School, University Hospital, Newark, NJ, USA
31Infectious Disease Physicians, Annandale, VA, USA
32Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
33St. Leonards Medical Centre, St. Leonards, Australia
34Ysbyty Gwynedd Hospital, Gwynedd, UK
35Kaiser Permanente, Portland, OR, USA
36Tarrant County Infectious Disease Associates, Fort Worth, TX, USA
37Private Practice of William Barrie, Toronto, Canada
38Rowan Tree Medical, Wilton Manors, FL, USA
39Chelsea and Westminster Hospital, St. Stephen’s Centre, London, UK
40Beaver Street Family Practice, Flagstaff, AZ, USA
41Southwest Infectious Disease Associates, Dallas, TX, USA
42St. Barnabas Hospital, Bronx, NY, USA
43Research Institute, McGill University Health Centre, Montreal General Hospital, Montreal, Canada
44Thacker, Thompson and Bernard, Atlanta, GA, USA
45Vanderbilt University School of Medicine, Hermitage, TN, USA
46LA Gay and Lesbian Center, Los Angeles, CA, USA
47Louisiana State University Health Sciences Center, University Medical Center East Clinic, Lafayatte, LA, USA
48Physicians’ Research Network, Callen-Lorde Community Health Center, New York, NY, USA
49Brndjar Medical Associates, Allentown, PA, USA
50AIDS Research Alliance, Los Angeles, CA, USA
51David Powell Community Health Center, Austin, TX, USA
52James J. Peters VA Medical Center, Bronx, NY, USA
53Sunrise Medical Group, Brooklyn, NY, USA
54University of Miami-Miller School of Medicine, Lake Worth, FL, USA
55WellSpring Medical Group, San Francisco, CA, USA
56Moses Cone Health System, Greensboro, NC, USA
57HealthPartners Infectious Disease, St Paul, MN, USA
58El Rio Special Immunology Associates, Tuscon, AZ, USA
59Jersey Shore University Medical Center, Neptune, NJ, USA
60St. Michaels Hospital, Toronto, Canada
61The Brooklyn Hospital Center, PATH Center, Brooklyn, NY, USA
62University of Otago, Christchurch, New Zealand
63H.E.L.P./Project Samaritan, Bronx, NY, USA
64David E. Rogers Center for HIV/AIDS Care, Southampton, NY, USA
65Center for Special Immunology, Fountain Valley, CA, USA
66Fenway Community Health, Boston, MA, USA
679th Street Internal Medicine Associates, Philadelphia, PA, USA
68University of British Columbia, Vancouver, Canada
69National Centre in HIV Epidemiology and Clinical Research, Sydney, Australia
70Metro Infectious Disease Consultants, Indianapolis, IN, USA
71John H. Stroger Hospital of Cook County, Chicago, IL, USA
72New England Quality Care Alliance, Braintree, MA, USA
73Fred Hutchinson Cancer Research Center, Seattle, WA, USA
74Desert AIDS Project, Palm Springs, CA, USA
75University of Massachusetts Memorial Medical Center, Worcester, MA, USA
76University of Iowa Hospitals and Clinics, Iowa City, IA, USA
77Orlando Immunology Center, Orlando, FL, USA
78The Kansas City Free Health Clinic, Kansas City, MO, USA
79Private Practice of Michael E. Dunn, M.D., Tampa, FL, USA
80South Shore Hospital, Weymouth, MA, USA
81University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
82AIDS Resource Center of Wisconsin, Milwaukee, WI, USA
83Visiting Nurse Association of Central New Jersey, Community Health Center, Asbury Park, NJ, USA
84Imperial College, London, UK
85Heartland Clinic, Paducah, KY, USA
86Morristown Memorial Hospital, Morristown, NJ, USA
87Private Practice of Neel K. French, M.D., Chicago, IL, USA
88San Francisco Department of Public Health, San Francisco, CA, USA
89Boston University Medical Center, Boston, MA, USA
90Baystate Medical Center, Springfield, MA, USA
91Johns Hopkins University School of Medicine, Baltimore, MD, USA
92Private Practice of Efrain Garcia, M.D., Miami, FL, USA
93The Rockefeller University, New York, NY, USA
94Private Practice of Joseph C. Gathe Jr., M.D., Houston, TX, USA
95Tower Infectious Disease, Los Angeles, CA, USA
96Nassau University Medical Center, East Meadow, NY, USA
97Sharp Rees Stealy Medical Center, San Diego, CA, USA
98Medical College of Wisconsin, Milwaukee, WI, USA
99University of Alabama, Birmingham, Birmingham, AL, USA
100Synergy Hematology and Oncology, Los Angeles, CA, USA
101University of Utah, Salt Lake City, UT, USA
102South Dayton Acute Care Consultants, Dayton, OH, USA
103T. Douglas Gurley, M.D., Atlanta, GA, USA
104St. Vincent’s Hospital, New York, NY, USA
105University of Texas Health Science Center, Houston, TX, USA
106British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada
107Southwest C.A.R.E. Center, Santa Fe, NM, USA
108Hennepin County Medical Center, Minneapolis, MN, USA
109The Catholic University of America, School of Nursing, Washington, DC, USA
110Mercy Medical Center, Springfield, MA, USA
111CMC Myers Park Medical Center, Charlotte, NC, USA
112New York University Medical Center, New York, NY, USA
113The Ruth M. Rothshon Care Center, Chicago, IL, USA
114Feldman Medical Group, San Francisco, CA, USA
115HIV Research and Clinical Care Centre, Munich, Germany
116Albany Medical College, Albany, NY, USA
117Murdoch University, Murdoch, Australia
118University of Cincinnati, Cincinnati, OH, USA
119OASIS Clinic, Los Angeles, CA, USA
120VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
121AIDS Healthcare Foundation, Los Angeles, CA, USA
122Capitol Hill Medical, Seattle, WA, USA
123Astor Medical Group, New York, NY, USA
124The Kinder Medical Group, Miami, FL, USA
125Lancaster General Hospital, Lancaster, PA, USA
126Tufts Medical Center, Boston, MA, USA
127Alpert Medical School of Brown University, Providence, RI, USA
128Oregon Health and Science University, Portland, OR, USA
129G.B. Rossi Hospital, Verona, Italy
130Case Western Reserve University, Cleveland, OH, USA
131LifeWay, Fort Lauderdale, FL, USA
132Saint Claire Medical Associate, Toronto, Canada
133Greater Lawrence Family Health Center, Lawrence, MA, USA
134Hospital Universitari Germans Trias i Pujol, Barcelona, Spain
135Infectious Disease Consultants, Tucker, GA, USA
136University of Toronto, Toronto, Canada
137Infectious Disease Associates, Sarasota, FL, USA
138Emory University, School of Medicine, Atlanta, GA, USA
139Aaron Diamond AIDS Research Center, Rockefeller University, New York, NY, USA
140Deruico Doencas Infecciosas, Porto, Portugal
141Park Nicollet Clinic, St. Louis, MN, USA
142Absolute Care, Atlanta, GA, USA
143College of Physicians and Surgeons, Columbia University, New York, NY, USA
144Highland Medical Associates, Hendersonville, NC, USA
145Medizinische Hochschule, Abteilung Klinische Immunologie, Hannover, Germany
146Hospital for Special Surgery, New York, NY, USA
147Family Practice Specialists,Phoenix,AZ,USA
148Philadelphia FIGHT, Philadelphia, PA, USA
149Lower East Side Service Center, New York, NY, USA
150Infectious Diseases Care, Hillsborough, NJ, USA
151University of Colorado, Denver, Aurora, CO, USA
152Sutter Medical Group, Sacramento, CA, USA
153University of Wisconsin in Madison, Madison, WI, USA
154Brooke Army Medical Center, San Antonio, TX, USA
155Eastern Virginia Medical School, Norfolk, VA, USA
156Columbia University Medical Center, New York, NY, USA
157CA Pacific Medical Center, San Francisco, CA, USA
158Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
159St. Joseph Hospital, Chicago, IL, USA
160Aaron M. Perlmutter, M.D., Beverly Hills, CA, USA
161Codman Square Health Center, Dorchester, MA, USA
162CARE Resource, Miami, FL, USA
163Castro-Mission Health Center, San Francisco, CA, USA
164Rush Medical College, Chicago, IL, USA
165Boulder Community Hospital, Boulder, CO, USA
166Midway Immunology and Research Center, Fort Pierce, FL, USA
167Aerztezentrum Nollendorfplatz, Berlin, Germany
168State University of New York Downstate Medical Center, Brooklyn, NY, USA
169Clinic 42, Minneapolis, MN, USA
170King Edward Memorial Hospital, Paget, Bermuda
171Fundacion para la Investigacion Biomedica del Hospital Carlos III, Madrid, Spain
172Summit Medical Group, Knoxville, TN, USA
173Medical Consultants of South Florida, Coral Springs, FL, USA
174Western North Carolina Community Health Services, Asheville, NC, USA
175New York Hospital Medical Center of Queens, Flushing, NY, USA
176University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
177Georgetown University Medical Center, Washington, DC, USA
178Village Care Health Center, New York, NY, USA
179Aerzteforum Seestrasse, Berlin, Germany
180Academic Medical Center Amsterdam, Amsterdam, Netherlands
181St. Paul’s Hospital, Vancouver, Canada
182Swedish Medical Center, Seattle, WA, USA
183Cedars-Sinai Medical Center, Los Angeles, CA, USA
184Mercy Hospital, Miami, FL, USA
185University of Tennessee, Chattanooga, TN, USA
186St. Louis University, St Louis, MO, USA
187William F. Ryan Community Health Center, New York, NY, USA
188The University of Iowa, Iowa City, IA, USA
189Rivington House and Village Care, New York, NY, USA
190Infektionsmedizinisches Centrum Hamburg, Hamburg, Germany
191California Pacific Medical Center, San Francisco, CA, USA
192Lemuel Shattuck Hospital, Boston, MA, USA
193Fondazione San Raffaele Del Monte Tabor, Milan, Italy
194Temple University School of Medicine, Philadelphia, PA, USA
195Yale University School of Medicine, Bridgeport, CT, USA
196Jersey City Medical Center, Jersey City, NJ, USA
197University of Montreal, Montreal, Canada
198Baylor University Medical Center, Dallas, TX, USA
199Montgomery Infectious Disease Associates, Silver Spring, MD, USA
200Northwestern University, Chicago, IL, USA
201Hospital de Santa Maria, Faculdade de Medicina de Lisboa, Lisbon, Portugal
202Spectrum Medical Group, Phoenix, AZ, USA
203University of North Texas Health Science Center, Fort Worth, TX, USA
204Infectious Diseases Associates of Northwest Florida, Pensacola, FL, USA
205Private Practice of Chingchai Wanidworanun, M.D., Arlington, VA, USA
206Dupont Circle Physician’s Group, Washington, DC, USA
207Consultive Medicine, Daytona Beach, FL, USA
208Infectious Disease Specialists, Colorado Springs, CO, USA
209Saint John Regional Hospital, Saint John, Canada
210Clinical Alliance for Research and Education-Infectious Diseases, Annandale, VA, USA
211Hawthorn House, Birmingham Heartlands Hospital, Birmingham, UK
212North Manchester General Hospital, Manchester, UK
213Private Practice of Clifford Wlodaver, Midwest City, OK, USA
214Central Texas Clinical Research, Austin, TX, USA
215Primary Health Care, Des Moines, IA, USA
216Memorial Neighborhood Health Center Central Clinic, South Bend, IN, USA
217United Health Services Hospitals, Binghamton, NY, USA
218All Med and Rehabilitation of New York, Bronx, NY, USA
To whom correspondence should be addressed. bwalker/at/partners.org (B.D.W.); pdebakker/at/rics.bwh.harvard.edu (P.I.W.d.B.)
These authors contributed equally to this work.
*All authors with their contributions and affiliations appear at the end of this paper.
Infectious and inflammatory diseases have repeatedly shown strong genetic associations within the major histocompatibility complex (MHC); however, the basis for these associations remains elusive. To define host genetic effects on the outcome of a chronic viral infection, we performed genome-wide association analysis in a multiethnic cohort of HIV-1 controllers and progressors, and we analyzed the effects of individual amino acids within the classical human leukocyte antigen (HLA) proteins. We identified >300 genome-wide significant single-nucleotide polymorphisms (SNPs) within the MHC and none elsewhere. Specific amino acids in the HLA-B peptide binding groove, as well as an independent HLA-C effect, explain the SNP associations and reconcile both protective and risk HLA alleles. These results implicate the nature of the HLA–viral peptide interaction as the major factor modulating durable control of HIV infection.
Hiv infection is characterized by acute viremia, often in excess of 5 million viral particles per milliliter of plasma, followed by an average 100-fold or greater decline to a relatively stable plasma virus load set point (1). In the absence of antiretroviral therapy, the level of viremia is associated with the rate of CD4+ T cell decline and progression to AIDS. There is substantial interperson variability in the virus load set point, with most individuals having stable levels exceeding 10,000 RNA copies/ml. Yet a small number of people demonstrate sustained ability to control HIV replication without therapy. Such individuals, referred to as HIV controllers, typically maintain stable CD4+ cell counts, do not develop clinical disease, and are less likely to transmit HIV to others (2).
To determine the genetic basis for this rare phenomenon, we established a multinational consortium (www.hivcontrollers.org) to recruit HIV-1 controllers, who are defined by at least three measurements of plasma virus load (VL) < 2000 RNA copies/ml over at least a 12-month period in the absence of antiviral therapy. We performed a genome-wide association study (GWAS) in the HIV controllers (median VL, CD4 count, and disease duration of 241 copies/ml, 699 cells/mm3, and 10 years, respectively) and treatment-naïve chronically infected individuals with advanced disease (median VL and CD4 count of 61,698 copies/ml and 224 cells/mm3, respectively) enrolled in antiviral treatment studies led by the AIDS Clinical Trials Group. After quality control and imputation on the basis of HapMap Phase 3 (3), we obtained data on 1,384,048 single-nucleotide polymorphisms (SNPs) in 974 controllers (cases) and 2648 progressors (controls) from multiple populations (table S1).
After stratification into European, African American, and Hispanic ethnic groups (fig. S1), we tested each SNP for association using logistic regression, including the major principal components as covariates to correct for population substructure (4). In the largest group, comprising 1712 individuals of European ancestry, we identified 313 SNPs with genome-wide significance, defined by P < 5 × 10−8 due to correction for multiple comparisons (table S2). All SNPs that reached genome-wide significance were located in the major histocompatibility complex (MHC) region on chromosome 6 (Fig. 1A). We obtained similar results for the other two ethnic groups and in a meta-analysis of all participants (fig. S2). We also performed a genome-wide analysis to test the influence of local chromosomal ancestry in the African American sample (4), but we detected no signal outside the MHC (figs. S3 and S4). The impact of the MHC was further underscored when we specifically tested published associations related to HIV disease progression outside the MHC. Only variants in the CCR5-CCR2 locus—namely, CCR5Δ32 deletion polymorphism (5), C927T in CCR5 (6), and Val64→Ile64 in CCR2 (7)—replicate with nominal statistical significance in our study (Fig. 1B and table S3).
Fig. 1
Fig. 1
Genome-wide association results in the European sample. (A) Manhattan plot of 1.3 million autosomal SNPs. Only SNPs in the MHC on chromosome 6 reach genome-wide significance, indicated by the horizontal dotted line (P < 5 × 10−8 (more ...)
Closer examination of the significant SNPs within the MHC showed that they are located within a 3-Mb region concentrated around class I human leukocyte antigen (HLA) genes (fig. S5), but extensive linkage disequilibrium (LD) makes precise assignment of causal variants challenging (8). Therefore, we used stepwise regression to define independent markers associated with host control. From the initial set of 313 SNPs that reached genome-wide significance in the European sample, for which the greatest numbers of participants were available, we found only four independent markers of association (Table 1). rs9264942, located 35 kb upstream of HLA-C and a putative variant associated with HLA-C expression levels [odds ratio (OR) = 2.9, P = 2.8 × 10−35, where an OR > 1 indicates a protective effect], and rs2395029, a proxy for HLA-B*57:01 (OR = 5.3, P = 9.7 × 10−26), had been previously reported to be associated with virus load set point after acute infection (9). We also defined rs4418214, a noncoding SNP near MICA (OR = 4.4, P = 1.4 × 10−34), and rs3131018 in PSORS1C3, a gene implicated in psoriasis (OR = 2.1, P = 4.2 × 10−16). These four SNPs explain 19% of the observed variance of host control in the European sample; together with those in CCR5, these SNPs explain 23%, using Nagelkerke’s approximation (Fig. 1C) (10).
Table 1
Table 1
Association results for the independent SNPs in the MHC identified with stepwise regression in the European and African American samples. The odds ratio and frequency is given for the A1 allele, where OR > 1 indicates a protective effect. Odds (more ...)
In the smaller African American sample, we observed 33 SNPs with genome-wide significance, four of which were identified as independent markers, but all differed from those in the European sample (Table 1). This suggests that shared causal variants are tagged by different SNPs in these two populations or that the mechanism of control differs with ethnicity. Only rs2523608 was previously identified, in a recent study of virus load set point in African Americans (11). Despite no evidence for historical recombination (D’ = 1), this SNP is only weakly correlated (r2 < 0.1) with HLA-B*57:03, the class I allele most strongly associated with durable control of HIV in populations of African ancestry (11-13). In the Hispanic sample, which was much smaller, the most significant SNP was rs2523590, 2 kb upstream of HLA-B, also identified in the African American sample described here.
Given the localization of significant SNPs entirely to the HLA class I region, as well as previous studies showing HLA alleles to affect disease progression (13-20), we next sought to evaluate whether these SNP and HLA associations might be due to specific amino acids within HLA. Because HLA types were available for only a portion of the entire cohort, we developed a method to impute classical HLA alleles and their corresponding amino acid sequences (4) on the basis of haplotype patterns in an independent data set collected by the Type 1 Diabetes Genetics Consortium (T1DGC) (21). This data set contains genotype data for 639 SNPs in the MHC that overlap with genotyped SNPs in our GWAS and classical HLA types for class I and II loci at four-digit resolution in 2767 unrelated individuals of European descent.
We imputed HLA types in the European sample of our study and validated the imputations by comparing to empirical four-digit HLA typing data collected for class I loci in a subset (n = 371) of the HIV controllers. The quality of the imputations was such that the imputed and true frequencies for all HLA alleles in this subset were in near-perfect agreement (Fig. 2A) (r2 = 0.99). Furthermore, the positive predictive value was 95.2% and the sensitivity was 95.2% at two-digit resolution (92.7 and 95.6%, respectively, at four-digit resolution) for HLA alleles with frequency >2% (Fig. 2B). This indicates that the performance of the imputation was generally excellent for common alleles, consistent with previous work (22). We used HLA allele imputations in all participants (even those with HLA types defined by sequencing) for association analyses to avoid systematic bias between cases and controls. Lower imputation quality would only decrease power, not increase the false-positive rate, because cases and controls would be equally affected.
Fig. 2
Fig. 2
Imputation quality of classical HLA alleles in the European sample. (A) Concordance between imputed (y-axis) and observed (x-axis) frequencies of classical HLA types in 371 HIV-1 controllers with four-digit HLA types obtained through Sanger sequencing. (more ...)
We tested all HLA alleles for association via logistic regression, adjusting for the same covariates used in SNP analysis (tables S4 and S5). The most significant HLA association is B*57:01 (OR = 5.5, P = 1.4 × 10−26), which explains the proxy association of rs2395029 in HCP5. With the use of stepwise regression modeling in the European sample of controllers and progressors, we were able to implicate B*57:01, B*27:05, B*14/Cw*08:02, B*52, and A*25 as protective alleles and B*35 and Cw*07 as risk alleles. These associations are consistent with earlier studies that highlighted a role for HLA class I loci (13-20), and particularly HLA-B alleles in control of HIV, which indicated that the imputations are robust. Collectively explaining 19% of the variance of host control, these HLA allele associations are consistent with the effects of the four independent SNPs.
Virus-infected cells are recognized by CD8+ T cells after presentation of short viral peptides within the binding groove of HLA class I, and HIV-specific CD8+ T cells are strongly associated with control (23). We thus evaluated whether the SNP associations identified in the GWAS, and the HLA associations derived from imputation, might be due to specific amino acid positions within the HLA molecules, particularly those involved in the interaction between the viral peptide and the HLA class I molecule. Using the official DNA sequences defined for known HLA alleles (24), we encoded all variable amino acid positions within the coding regions of the HLA genes in each of the previously HLA-typed 2767 individuals in the T1DGC reference panel, and we used this data set to impute the amino acids in the cases and controls (4). Among a total of 372 polymorphic amino acid positions in class I and II HLA proteins, 286 are biallelic like a typical nonsynonymous coding SNP. The remaining 86 positions accommodate more than two amino acids; position 97 is the most diverse in HLA-B with six possible amino acids observed in European populations.
After imputing these amino acids in the European sample, we used logistic regression to test all positions for association with host control (fig. S6 and table S6). Notably, position 97 in HLA-B was more significant (omnibus P = 4 × 10−45) than any single SNP in the GWAS, and three amino acid positions (67, 70, and 97), all in HLA-B, showed much stronger associations than any single classical HLA allele, including B*57:01 (Fig. 3A). Moreover, allelic variants at these positions were associated with substantial frequency differences between cases and controls (Fig. 3B). These results indicate that the effect of HLA-B on disease outcome could be mediated, at least in part, by these positions. These three amino acid positions are located in the peptide binding groove, which suggests that conformational differences in peptide presentation at these sites contribute to the protective or susceptible nature of the various HLA-B allotypes. Although both innate and adaptive mechanisms could be at play, the hypothesis that HLA affects peptide presentation and subsequent T cell functionality is supported by experimental data showing substantial functional differences between CTL targeting identical epitopes but restricted by different HLA alleles (25).
Fig. 3
Fig. 3
Associations at amino acids in HLA-B in the European sample. (A) Association results for all variable amino acid positions, as calculated by the omnibus test. Colors denote conventional pocket positions. P values for significant classical HLA-B alleles (more ...)
We next performed stepwise regression modeling and identified six residues as independent markers associated with durable control of HIV. These include Arg97, Cys67, Gly62, and Glu63, all in HLA-B; Ser77 in HLA-A; and Met304 in HLA-C, which collectively explain 20% of the observed variance (similar to the variance explained by the seven classical HLA alleles described above). With the exception of Met304 in the transmembrane domain of HLA-C, these residues are all located in the MHC class I peptide binding groove, again suggesting that the binding pocket—and, by inference, the conformational presentation of class I-restricted epitopes—plays a key role in host control.
Having identified these amino acid positions as strong candidates to account for the SNP and HLA association signals in this study, we next investigated their effects on protection or risk, revealing allelic variants at these positions linked to both extremes (Table 2). HLA-B position 97 (omnibus P = 4 × 10−45), located at the base of the C pocket, has important conformational properties for peptide binding (26). Position 97 has six allelic variants: Protective haplotypes B*57:01, B*27:05, and B*14 are uniquely defined by Val97 (3% frequency in controls), Asn97 (4%) and Trp97 (3%), respectively; the other amino acids at this position (Ser, Thr, Arg) segregate on a diverse set of haplotypes. Ser97 (27% frequency) lies on risk haplotypes Cw*07, B*07, and others, where-as Thr97 (11%) lies on protective B*52 (and others). Arg97 is the most common amino acid (51%) and is carried by risk allele B*35, among others. The importance of this amino acid position to host control is underscored by conditional analyses revealing significance when we adjust incrementally for Val97 (omnibus test for position 97, P = 3 × 10−20), Asn97 (P = 2 × 10−9), and Trp97 (P = 7 × 10−5). Thus, at a single position within the peptide binding groove (position 97, C-pocket), discrete amino acids are associated with opposite disease outcomes, even after controlling for B*57 and B*27, alleles associated with host control.
Table 2
Table 2
Haplotypes defined by the four independent SNPs, classical HLA alleles, and amino acids associated with host control in the European sample. Haplotypes are ordered by the estimated odds ratio, where the most common haplotype was taken as reference (OR (more ...)
We also found similar discordant associations for alleles at positions 67, 63, and 62 (Table 2), all of which line the α1 helix along the peptide binding groove and help shape the B-pocket (Fig. 4). At position 67 (omnibus P = 2 × 10−42), risk haplotypes B*35 and B*07 carry aromatic residues Phe67 and Tyr67, respectively, whereas protective B*57:01, B*27:05, and B*14 alleles carry sulfur-containing residues Met67 or Cys67. Position 62 (P = 5 × 10−27) is biallelic (Arg/Gly) with the Gly62 allele segregating with protective alleles B*57:01 and B*58 (<1% frequency, OR = 1.7, P = 0.2). Adjacent position 63 (P = 9 × 10−16) is also biallelic (Glu/Asn) with Glu63 appearing in complete LD (D’ = 1) with B*57:01, B*27:05, and B*52. In contrast, at this position the risk alleles B*07 (14% frequency, OR = 0.5, P = 1 × 10−7) and B*35 both carry Asn63. Position 70 (omnibus P = 3 × 10−39) accommodates four alleles that are tightly coupled with positions 67 and 97: Ser70 appears exclusively with Met67 (which defines B*57 and B*58), Gln70 with Tyr67, and Lys70 with Asn97 (B*27). Hence, these data create a consistent and parsimonious model that can explain the associations of classical HLA-B alleles by specific amino acids lining the binding groove (and residues tightly coupled to them), which are expected to have an impact on the three-dimensional structure of the peptide-MHC complex.
Fig. 4
Fig. 4
Three-dimensional ribbon representation of the HLA-B protein based on Protein Data Bank entry 2bvp (30), highlighting amino acid positions 62, 63, 67, 70, and 97 lining the peptide binding pocket. The peptide backbone of the epitope is also displayed. (more ...)
To further investigate the role of individual amino acid positions in HLA-B, we implemented a permutation procedure to assess how consistent the above observations are with a null model in which there is no relation between amino acids at a particular position and host control (4). The results of this procedure provided evidence that multiple amino acid positions in the peptide binding groove are indeed associated with host control (table S7), including positions 62, 63, 67, 70, and 97, thus providing a structural basis for the effect of HLA-B on host control (Fig. 4).
Within HLA-A position 77, which lies on the α helix contributing to the F-pocket, we identified a weaker but still significant association (omnibus P = 3 × 10−6). Ser77 (6% frequency, OR = 2.0, P = 2 × 10−6) is carried by only two HLA-A alleles (joint r2 = 1): A*25 (2.4% frequency, OR = 2.6, P = 1 × 10−5) and A*32 (3.2%, OR = 1.6, P = 0.02). Given its location and earlier association evidence for the A10 supertype (27), HLA-A could play a role in host control, although the evidence is not as strong as for HLA-B.
The signals within HLA-C are less straight-forward to interpret. Position 304 is a biallelic variant (Val/Met) located in the transmembrane domain (Met304, 28% frequency, OR = 2.3, P = 7 × 10−23). Met304 is in moderate LD (r2 = 0.5) with rs9264942, which is known to be associated with HLA-C expression levels (28). Addition of this SNP to a multivariate model of all six amino acids is marginally significant (P = 0.013) but eliminates the effect of Met304 (P = 0.06). Similarly, addition of rs9264942 to a multivariate model of all seven independent classical HLA alleles is also significant (P = 2 × 10−4) but eliminates the effect of Cw*07 (P = 0.08). These observations make it difficult to determine the extent to which epitope presentation in the HLA-C peptide binding pocket is important for host control. Thus, rs9264942 could be a proxy for not only many protective and risk HLA alleles (predominantly at HLA-B), but also for an independent effect on HLA-C gene expression, differentially affecting the response to HIV (29).
We next evaluated associations for the SNPs in the MHC, classical HLA alleles, and amino acids in a second independent cohort of untreated HIV-infected persons from Switzerland (fig. S7 and tables S8 and S9) (4), in whom virus load set point was measured as a quantitative trait. Allelic variants at positions 67, 70, and 97 were also associated with highly significant differences in virus load set point in this second cohort (Fig. 3C). The effect estimates of all variable amino acids in HLA-B (r2 > 0.9) and, to a lesser degree, those in HLA-C (r2 > 0.8) in that cohort are in excellent agreement (figs. S8 and S9). As before, position 97 in HLA-B is the most significant association (omnibus P = 1 × 10−13). The HLA-A associations (A*25 or Ser77) did not replicate, which reduces the likelihood that HLA-A plays a major role in host control.
In the African American sample (fig. S10), the most significant HLA allele association was observed for two-digit B*57 (OR = 5.1, P = 1.7 × 10−21) and four-digit B*57:03 (OR = 5.1, P = 2.8 × 10−17; tables S10 and S11), consistent with previous studies (11-13). Position 97 in HLA-B (omnibus P = 2 × 10−25) is again the most significant amino acid (table S12). The consistency of these results demonstrates that imputation and association testing at amino acid resolution in multiple ethnicities can resolve disparate SNP associations in the MHC and help with fine-mapping of classical HLA associations.
Altogether, these results link the major genetic impact of host control of HIV-1 to specific amino acids involved in the presentation of viral peptides on infected cells. Moreover, they reconcile previously reported SNP and HLA associations with host control and lack of control to specific amino acid positions within the MHC class I peptide binding groove. Although variation in the entire HLA protein is involved in the differential response to HIV across HLA allotypes, the major genetic effects are condensed to the positions highlighted in this study, indicating a structural basis for the HLA association with disease progression that is probably mediated by the conformation of the peptide within the class I binding groove. The most significant residue, position 97 in the floor of the peptide binding groove of HLA-B, is associated with the extremes of viral load, depending on the expressed amino acid. This residue has been shown to have important conformational properties that affect epitope-contacting residues within the binding groove (26, 30) and has also been implicated in HLA protein folding and cell-surface expression (31).
Although the main focus of this study was on common sequence variation, it remains an open question as to the role of variants outside the MHC and the contribution of epistatic effects and epigenetic regulation. Additional factors also contribute to immune control of HIV, including fitness-altering mutations, immuno-regulatory networks, T cell help, thymic selection, and innate effector mechanisms such as killer cell immunoglobulin-like receptor recognition (23), some of which are influenced by the peptide-HLA class I complex. However, the combination and location of the significant amino acids defined here are most consistent with the genetic associations observed being modulated by HLA class I restricted CD8+ T cells. These results implicate the nature of the HLA-viral peptide interaction as the major genetic factor modulating durable control of HIV infection and provide the basis for future studies of the impact of HLA-peptide conformation on immune cell induction and function.
Supplementary Material
Supplementary data
Acknowledgments
This work was made possible through a generous donation from the Mark and Lisa Schwartz Foundation and a subsequent award from the Collaboration for AIDS Vaccine Discovery of the Bill and Melinda Gates Foundation. This work was also supported in part by the Harvard University Center for AIDS Research (grant P-30-AI060354); University of California San Francisco (UCSF) Center for AIDS Research (grant P-30 AI27763); UCSF Clinical and Translational Science Institute (grant UL1 RR024131); Center for AIDS Research Network of Integrated Clinical Systems (grant R24 AI067039); and NIH grants AI28568 and AI030914 (B.D.W.); AI087145 and K24AI069994 (S.G.D.); AI069513, AI34835, AI069432, AI069423, AI069477, AI069501, AI069474, AI069428, AI69467, AI069415, Al32782, AI27661, AI25859, AI28568, AI30914, AI069495, AI069471, AI069532, AI069452, AI069450, AI069556, AI069484, AI069472, AI34853, AI069465, AI069511, AI38844, AI069424, AI069434, AI46370, AI68634, AI069502, AI069419, AI068636, and RR024975 (AIDS Clinical Trials Group); and AI077505 and MH071205 (D.W.H.). The Swiss HIV Cohort Study is supported by the Swiss National Science Foundation (SNF grants 33CSC0-108787 and 310000-110012). S. Ripke acknowledges support from NIH/National Institute of Mental Health (grant MH085520). This project has been funded in whole or in part with funds from National Cancer Institute/NIH (grant HHSN261200800001E to M. Carrington). The content of this publication does not necessarily reflect the views or policies of the U.S. Department of Health and Human Services, nor does the mention of trade names, commercial products, or organizations imply endorsement by the U.S. government. This research was supported in part by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.
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33. Single-letter abbreviations for the amino acid residues referred to in Table 2 are as follows: Glu E, Phe F, Gly G, Lys K, Met M, Asn N, Gln Q, Arg R, Ser S, Val V, Trp W, Tyr Y