Permalink
Cannot retrieve contributors at this time
Fetching contributors…
Cannot retrieve contributors at this time
| <?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.1d1 20130915//EN" "JATS-archivearticle1.dtd"><article article-type="research-article" dtd-version="1.1d1" xmlns:xlink="http://www.w3.org/1999/xlink"><front><journal-meta><journal-id journal-id-type="nlm-ta">elife</journal-id><journal-id journal-id-type="hwp">eLife</journal-id><journal-id journal-id-type="publisher-id">eLife</journal-id><journal-title-group><journal-title>eLife</journal-title></journal-title-group><issn publication-format="electronic">2050-084X</issn><publisher><publisher-name>eLife Sciences Publications, Ltd</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">01123</article-id><article-id pub-id-type="doi">10.7554/eLife.01123</article-id><article-categories><subj-group subj-group-type="display-channel"><subject>Research article</subject></subj-group><subj-group subj-group-type="heading"><subject>Genomics and evolutionary biology</subject></subj-group><subj-group subj-group-type="heading"><subject>Microbiology and infectious disease</subject></subj-group></article-categories><title-group><article-title>A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control</article-title></title-group><contrib-group><contrib contrib-type="author" id="author-6354"><name><surname>Bartha</surname><given-names>István</given-names></name><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/><xref ref-type="aff" rid="aff3"/><xref ref-type="aff" rid="aff4"/><xref ref-type="other" rid="par-6"/><xref ref-type="fn" rid="con1"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" equal-contrib="yes" id="author-6355"><name><surname>Carlson</surname><given-names>Jonathan M</given-names></name><xref ref-type="aff" rid="aff5"/><xref ref-type="fn" rid="equal-contrib">†</xref><xref ref-type="fn" rid="con2"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" equal-contrib="yes" id="author-6356"><name><surname>Brumme</surname><given-names>Chanson J</given-names></name><xref ref-type="aff" rid="aff6"/><xref ref-type="fn" rid="equal-contrib">†</xref><xref ref-type="fn" rid="con3"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" equal-contrib="yes" id="author-6357"><name><surname>McLaren</surname><given-names>Paul J</given-names></name><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/><xref ref-type="aff" rid="aff4"/><xref ref-type="fn" rid="equal-contrib">†</xref><xref ref-type="fn" rid="con10"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6358"><name><surname>Brumme</surname><given-names>Zabrina L</given-names></name><xref ref-type="aff" rid="aff6"/><xref ref-type="aff" rid="aff7"/><xref ref-type="other" rid="par-4"/><xref ref-type="other" rid="par-5"/><xref ref-type="fn" rid="con13"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6359"><name><surname>John</surname><given-names>Mina</given-names></name><xref ref-type="aff" rid="aff8"/><xref ref-type="fn" rid="con14"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6360"><name><surname>Haas</surname><given-names>David W</given-names></name><xref ref-type="aff" rid="aff9"/><xref ref-type="fn" rid="con15"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6361"><name><surname>Martinez-Picado</surname><given-names>Javier</given-names></name><xref ref-type="aff" rid="aff10"/><xref ref-type="aff" rid="aff11"/><xref ref-type="fn" rid="con16"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6362"><name><surname>Dalmau</surname><given-names>Judith</given-names></name><xref ref-type="aff" rid="aff10"/><xref ref-type="fn" rid="con17"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6382"><name><surname>López-Galíndez</surname><given-names>Cecilio</given-names></name><xref ref-type="aff" rid="aff12"/><xref ref-type="other" rid="par-7"/><xref ref-type="other" rid="par-8"/><xref ref-type="other" rid="par-9"/><xref ref-type="fn" rid="con18"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6364"><name><surname>Casado</surname><given-names>Concepción</given-names></name><xref ref-type="aff" rid="aff12"/><xref ref-type="other" rid="par-7"/><xref ref-type="other" rid="par-8"/><xref ref-type="other" rid="par-9"/><xref ref-type="fn" rid="con19"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6365"><name><surname>Rauch</surname><given-names>Andri</given-names></name><xref ref-type="aff" rid="aff13"/><xref ref-type="fn" rid="con20"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6366"><name><surname>Günthard</surname><given-names>Huldrych F</given-names></name><xref ref-type="aff" rid="aff14"/><xref ref-type="fn" rid="con21"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6367"><name><surname>Bernasconi</surname><given-names>Enos</given-names></name><xref ref-type="aff" rid="aff15"/><xref ref-type="fn" rid="con22"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6368"><name><surname>Vernazza</surname><given-names>Pietro</given-names></name><xref ref-type="aff" rid="aff16"/><xref ref-type="fn" rid="con23"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6369"><name><surname>Klimkait</surname><given-names>Thomas</given-names></name><xref ref-type="aff" rid="aff17"/><xref ref-type="fn" rid="con24"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6370"><name><surname>Yerly</surname><given-names>Sabine</given-names></name><xref ref-type="aff" rid="aff18"/><xref ref-type="fn" rid="con25"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6371"><name><surname>O’Brien</surname><given-names>Stephen J</given-names></name><xref ref-type="aff" rid="aff19"/><xref ref-type="fn" rid="con26"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6372"><name><surname>Listgarten</surname><given-names>Jennifer</given-names></name><xref ref-type="aff" rid="aff5"/><xref ref-type="fn" rid="con4"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6373"><name><surname>Pfeifer</surname><given-names>Nico</given-names></name><xref ref-type="aff" rid="aff5"/><xref ref-type="fn" rid="pa1">‡</xref><xref ref-type="fn" rid="con5"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-1881"><name><surname>Lippert</surname><given-names>Christoph</given-names></name><xref ref-type="aff" rid="aff5"/><xref ref-type="fn" rid="con6"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6375"><name><surname>Fusi</surname><given-names>Nicolo</given-names></name><xref ref-type="aff" rid="aff5"/><xref ref-type="fn" rid="con7"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6376"><name><surname>Kutalik</surname><given-names>Zoltán</given-names></name><xref ref-type="aff" rid="aff4"/><xref ref-type="aff" rid="aff20"/><xref ref-type="fn" rid="con8"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6377"><name><surname>Allen</surname><given-names>Todd M</given-names></name><xref ref-type="aff" rid="aff21"/><xref ref-type="other" rid="par-10"/><xref ref-type="other" rid="par-11"/><xref ref-type="fn" rid="con28"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6378"><name><surname>Müller</surname><given-names>Viktor</given-names></name><xref ref-type="aff" rid="aff3"/><xref ref-type="other" rid="par-3"/><xref ref-type="fn" rid="con9"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6379"><name><surname>Harrigan</surname><given-names>P Richard</given-names></name><xref ref-type="aff" rid="aff6"/><xref ref-type="aff" rid="aff22"/><xref ref-type="fn" rid="con27"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" id="author-6380"><name><surname>Heckerman</surname><given-names>David</given-names></name><xref ref-type="aff" rid="aff5"/><xref ref-type="fn" rid="con29"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" corresp="yes" id="author-6383"><name><surname>Telenti</surname><given-names>Amalio</given-names></name><xref ref-type="aff" rid="aff2"/><xref ref-type="corresp" rid="cor1">*</xref><xref ref-type="other" rid="par-1"/><xref ref-type="other" rid="par-2"/><xref ref-type="fn" rid="con11"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><contrib contrib-type="author" corresp="yes" id="author-4798"><name><surname>Fellay</surname><given-names>Jacques</given-names></name><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/><xref ref-type="aff" rid="aff4"/><xref ref-type="corresp" rid="cor2">*</xref><xref ref-type="other" rid="par-1"/><xref ref-type="other" rid="par-2"/><xref ref-type="other" rid="par-12"/><xref ref-type="fn" rid="con12"/><xref ref-type="fn" rid="conf1"/><xref ref-type="other" rid="dataro1"/><xref ref-type="other" rid="dataro2"/></contrib><on-behalf-of>for the HIV Genome-to-Genome Study and the Swiss HIV Cohort Study</on-behalf-of><aff id="aff1"><institution content-type="dept">School of Life Sciences</institution>, <institution>École Polytechnique Fédérale de Lausanne</institution>, <addr-line><named-content content-type="city">Lausanne</named-content></addr-line>, <country>Switzerland</country></aff><aff id="aff2"><institution content-type="dept">Institute of Microbiology</institution>, <institution>University Hospital and University of Lausanne</institution>, <addr-line><named-content content-type="city">Lausanne</named-content></addr-line>, <country>Switzerland</country></aff><aff id="aff3"><institution content-type="dept">Research Group of Theoretical Biology and Evolutionary Ecology</institution>, <institution>Eötvös Loránd University and the Hungarian Academy of Sciences</institution>, <addr-line><named-content content-type="city">Budapest</named-content></addr-line>, <country>Hungary</country></aff><aff id="aff4"><institution>Swiss Institute of Bioinformatics</institution>, <addr-line><named-content content-type="city">Lausanne</named-content></addr-line>, <country>Switzerland</country></aff><aff id="aff5"><institution content-type="dept">eScience Group</institution>, <institution>Microsoft Research</institution>, <addr-line><named-content content-type="city">Los Angeles</named-content></addr-line>, <country>United States</country></aff><aff id="aff6"><institution>BC Centre for Excellence in HIV/AIDS</institution>, <addr-line><named-content content-type="city">Vancouver</named-content></addr-line>, <country>Canada</country></aff><aff id="aff7"><institution content-type="dept">Faculty of Health Sciences</institution>, <institution>Simon Fraser University</institution>, <addr-line><named-content content-type="city">Burnaby</named-content></addr-line>, <country>Canada</country></aff><aff id="aff8"><institution content-type="dept">Institute of Immunology and Infectious Diseases</institution>, <institution>Murdoch University</institution>, <addr-line><named-content content-type="city">Murdoch</named-content></addr-line>, <country>Australia</country></aff><aff id="aff9"><institution>Vanderbilt University Medical Center</institution>, <addr-line><named-content content-type="city">Nashville</named-content></addr-line>, <country>United States</country></aff><aff id="aff10"><institution content-type="dept">AIDS Research Institute IrsiCaixa, Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol</institution>, <institution>Universitat Autònoma de Barcelona</institution>, <addr-line><named-content content-type="city">Badalona</named-content></addr-line>, <country>Spain</country></aff><aff id="aff11"><institution>Institució Catalana de Recerca i Estudis Avançats (ICREA)</institution>, <addr-line><named-content content-type="city">Barcelona</named-content></addr-line>, <country>Spain</country></aff><aff id="aff12"><institution content-type="dept">Centro Nacional de Microbiología</institution>, <institution>Instituto de Salud Carlos III</institution>, <addr-line><named-content content-type="city">Madrid</named-content></addr-line>, <country>Spain</country></aff><aff id="aff13"><institution content-type="dept">Clinic of Infectious Diseases</institution>, <institution>University of Bern & Inselspital</institution>, <addr-line><named-content content-type="city">Bern</named-content></addr-line>, <country>Switzerland</country></aff><aff id="aff14"><institution content-type="dept">Division of Infectious Diseases and Hospital Epidemiology</institution>, <institution>University Hospital and University of Zürich</institution>, <addr-line><named-content content-type="city">Zürich</named-content></addr-line>, <country>Switzerland</country></aff><aff id="aff15"><institution content-type="dept">Division of Infectious Diseases</institution>, <institution>Regional Hospital of Lugano</institution>, <addr-line><named-content content-type="city">Lugano</named-content></addr-line>, <country>Switzerland</country></aff><aff id="aff16"><institution content-type="dept">Division of Infectious Diseases and Hospital Epidemiology</institution>, <institution>Cantonal Hospital</institution>, <addr-line><named-content content-type="city">St. Gallen</named-content></addr-line>, <country>Switzerland</country></aff><aff id="aff17"><institution content-type="dept">Department of Biomedicine</institution>, <institution>University of Basel</institution>, <addr-line><named-content content-type="city">Basel</named-content></addr-line>, <country>Switzerland</country></aff><aff id="aff18"><institution content-type="dept">Laboratory of Virology</institution>, <institution>Geneva University Hospitals</institution>, <addr-line><named-content content-type="city">Geneva</named-content></addr-line>, <country>Switzerland</country></aff><aff id="aff19"><institution content-type="dept">Theodosius Dobzhansky Center for Genome Bioinformatics</institution>, <institution>St. Petersburg State University</institution>, <addr-line><named-content content-type="city">St. Petersburg</named-content></addr-line>, <country>Russia</country></aff><aff id="aff20"><institution content-type="dept">Institute of Social and Preventive Medicine</institution>, <institution>University Hospital and University of Lausanne</institution>, <addr-line><named-content content-type="city">Lausanne</named-content></addr-line>, <country>Switzerland</country></aff><aff id="aff21"><institution content-type="dept">Ragon Institute of MGH, MIT, and Harvard</institution>, <institution>Massachusetts General Hospital</institution>, <addr-line><named-content content-type="city">Boston</named-content></addr-line>, <country>United States</country></aff><aff id="aff22"><institution content-type="dept">Faculty of Medicine</institution>, <institution>University of British Columbia</institution>, <addr-line><named-content content-type="city">Vancouver</named-content></addr-line>, <country>Canada</country></aff></contrib-group><contrib-group content-type="section"><contrib contrib-type="editor"><name><surname>McVean</surname><given-names>Gil</given-names></name><role>Reviewing editor</role><aff><institution>Oxford University</institution>, <country>United Kingdom</country></aff></contrib></contrib-group><author-notes><corresp id="cor1"><label>*</label>For correspondence: <email>Amalio.Telenti@chuv.ch</email> (AT);</corresp><corresp id="cor2"><label>*</label>For correspondence: <email>jacques.fellay@epfl.ch</email> (JF)</corresp><fn fn-type="con" id="equal-contrib"><label>†</label><p>These authors contributed equally to this work</p></fn><fn fn-type="present-address" id="pa1"><label>‡</label><p>Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany</p></fn></author-notes><pub-date date-type="pub" publication-format="electronic"><day>29</day><month>10</month><year>2013</year></pub-date><pub-date pub-type="collection"><year>2013</year></pub-date><volume>2</volume><elocation-id>e01123</elocation-id><history><date date-type="received"><day>01</day><month>07</month><year>2013</year></date><date date-type="accepted"><day>26</day><month>09</month><year>2013</year></date></history><permissions><copyright-statement>© 2013, Bartha et al</copyright-statement><copyright-year>2013</copyright-year><copyright-holder>Bartha et al</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/3.0/"><license-p>This article is distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use and redistribution provided that the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="elife01123.pdf"/><abstract><object-id pub-id-type="doi">10.7554/eLife.01123.001</object-id><p>HIV-1 sequence diversity is affected by selection pressures arising from host genomic factors. Using paired human and viral data from 1071 individuals, we ran >3000 genome-wide scans, testing for associations between host DNA polymorphisms, HIV-1 sequence variation and plasma viral load (VL), while considering human and viral population structure. We observed significant human SNP associations to a total of 48 HIV-1 amino acid variants (p<2.4 × 10<sup>−12</sup>). All associated SNPs mapped to the HLA class I region. Clinical relevance of host and pathogen variation was assessed using VL results. We identified two critical advantages to the use of viral variation for identifying host factors: (1) association signals are much stronger for HIV-1 sequence variants than VL, reflecting the ‘intermediate phenotype’ nature of viral variation; (2) association testing can be run without any clinical data. The proposed genome-to-genome approach highlights sites of genomic conflict and is a strategy generally applicable to studies of host–pathogen interaction.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.01123.001">http://dx.doi.org/10.7554/eLife.01123.001</ext-link></p></abstract><abstract abstract-type="executive-summary"><object-id pub-id-type="doi">10.7554/eLife.01123.002</object-id><title>eLife digest</title><p>Developing treatments or vaccines for HIV is challenging because the genetic makeup of the virus is constantly changing in an effort to outwit the human immune system. Moreover, the immune system is highly variable as a result of the long-standing co-evolution of humans and microbes. Each individual will try to oppose the invading virus in a unique way, forcing the virus to acquire specific mutations that can be interpreted as the genetic signature of this one-against-one battle.</p><p>To explore the influence of co-evolution on HIV, Bartha et al. took samples of both human and viral genomes from 1071 individuals infected with HIV, the AIDS virus, and used genotyping and sequencing technology to obtain a comprehensive description of the genetic variation in both. Computational techniques were then used to search for links between variants in the human DNA sequences and variants in the viral sequences.</p><p>The most common type of genetic variation found in the human genome is a single nucleotide polymorphism, or SNP for short: a SNP is produced when a single nucleotide – an A, C, G or T – is replaced by a different nucleotide. Bartha et al. found that SNPs within the human DNA sequences in their study were linked to variations in 48 amino acids in HIV. Moreover, all these SNPs were found within a group of genes known as the HLA (human leukocyte antigen) system, which encodes for proteins that play a vital role in the immune response. This work identified the areas of the human genome that put pressure on the AIDS virus, and the regions of the virus that serve to escape human control.</p><p>The approach developed by Bartha et al. allows the interactions between a microbe and a human host to be studied by looking at the genome of the microbe and the genome of the infected person. It also differentiates host-induced mutations that limit the capacity of the virus to do harm from those that are tolerated by the pathogen. A similar strategy could be used to study other infectious diseases.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.01123.002">http://dx.doi.org/10.7554/eLife.01123.002</ext-link></p></abstract><kwd-group kwd-group-type="author-keywords"><title>Author keywords</title><kwd>human genomics</kwd><kwd>HIV</kwd><kwd>viral mutations</kwd></kwd-group><kwd-group kwd-group-type="research-organism"><title>Research organism</title><kwd>Human</kwd></kwd-group><funding-group><award-group id="par-1"><funding-source><institution-wrap><institution>Swiss National Science Foundation</institution></institution-wrap></funding-source><award-id>33CS30_134277/Swiss HIV Cohort Study, 31003A_132863/1, PP00P3_133703/1</award-id><principal-award-recipient><name><surname>Telenti</surname><given-names>Amalio</given-names></name><name><surname>Fellay</surname><given-names>Jacques</given-names></name></principal-award-recipient></award-group><award-group id="par-2"><funding-source><institution-wrap><institution>Santos Suarez Foundation, Lausanne</institution></institution-wrap></funding-source><principal-award-recipient><name><surname>Telenti</surname><given-names>Amalio</given-names></name><name><surname>Fellay</surname><given-names>Jacques</given-names></name></principal-award-recipient></award-group><award-group id="par-3"><funding-source><institution-wrap><institution>Hungarian Academy of Sciences</institution></institution-wrap></funding-source><award-id>Bolyai János Research Fellowship</award-id><principal-award-recipient><name><surname>Müller</surname><given-names>Viktor</given-names></name></principal-award-recipient></award-group><award-group id="par-4"><funding-source><institution-wrap><institution>Michael Smith Foundation for Health Research</institution></institution-wrap></funding-source><principal-award-recipient><name><surname>Brumme</surname><given-names>Zabrina L</given-names></name></principal-award-recipient></award-group><award-group id="par-5"><funding-source><institution-wrap><institution>Canadian Institutes of Health Research</institution></institution-wrap></funding-source><principal-award-recipient><name><surname>Brumme</surname><given-names>Zabrina L</given-names></name></principal-award-recipient></award-group><award-group id="par-6"><funding-source><institution-wrap><institution>Sciex-NMS Program</institution></institution-wrap></funding-source><award-id>10.267</award-id><principal-award-recipient><name><surname>Bartha</surname><given-names>István</given-names></name></principal-award-recipient></award-group><award-group id="par-7"><funding-source><institution-wrap><institution>Spanish Ministry of Science and Innovation</institution></institution-wrap></funding-source><award-id>SAF 2007-61036, 2010-17226 and 2010-18917</award-id><principal-award-recipient><name><surname>López-Galíndez</surname><given-names>Cecilio</given-names></name><name><surname>Casado</surname><given-names>Concepción</given-names></name></principal-award-recipient></award-group><award-group id="par-8"><funding-source><institution-wrap><institution>Fundacion para la investigacion y prevencion del SIDA en Espana</institution></institution-wrap></funding-source><award-id>36558/06, 36641/07, 36779/08, 360766/09</award-id><principal-award-recipient><name><surname>López-Galíndez</surname><given-names>Cecilio</given-names></name><name><surname>Casado</surname><given-names>Concepción</given-names></name></principal-award-recipient></award-group><award-group id="par-9"><funding-source><institution-wrap><institution>RETIC de Investigacion en SIDA</institution></institution-wrap></funding-source><award-id>RD06/006/0036</award-id><principal-award-recipient><name><surname>López-Galíndez</surname><given-names>Cecilio</given-names></name><name><surname>Casado</surname><given-names>Concepción</given-names></name></principal-award-recipient></award-group><award-group id="par-10"><funding-source><institution-wrap><institution>National Institute of Allergy and Infectious Diseases (NIAID)</institution></institution-wrap></funding-source><award-id>P01-AI074415</award-id><principal-award-recipient><name><surname>Allen</surname><given-names>Todd M</given-names></name></principal-award-recipient></award-group><award-group id="par-11"><funding-source><institution-wrap><institution>Bill and Melinda Gates Foundation</institution></institution-wrap></funding-source><principal-award-recipient><name><surname>Allen</surname><given-names>Todd M</given-names></name></principal-award-recipient></award-group><award-group id="par-12"><funding-source><institution-wrap><institution>SNF Professorship</institution></institution-wrap></funding-source><award-id>PP00P3_133703/1</award-id><principal-award-recipient><name><surname>Fellay</surname><given-names>Jacques</given-names></name></principal-award-recipient></award-group><funding-statement>The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.</funding-statement></funding-group><custom-meta-group><custom-meta><meta-name>elife-xml-version</meta-name><meta-value>2</meta-value></custom-meta><custom-meta specific-use="meta-only"><meta-name>Author impact statement</meta-name><meta-value>An innovative strategy in genome analysis has generated a detailed description of how pathogens mutate when facing human genetic diversity.</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="s1" sec-type="intro"><title>Introduction</title><p>Through multiple rounds of selection and escape, host and pathogen genomes are imprinted with signatures of co-evolution that are governed by Darwinian forces. On the host side, well-characterized anti-retroviral restriction factors, such as <italic>TRIM5α</italic>, <italic>APOBEC3G</italic> and <italic>BST2</italic>, harbor strong signals of selection in primate genomes, clear examples of retroviral pressure (<xref ref-type="bibr" rid="bib27">Ortiz et al., 2009</xref>). On the virus side, obvious signs of selection are observable in the HIV-1 genome: escape mutations and reversions have been described in epitopes restricted by human leukocyte antigen (HLA) class I molecules and targeted by cytotoxic T lymphocyte (CTL) responses (<xref ref-type="bibr" rid="bib16">Goulder et al., 2001</xref>; <xref ref-type="bibr" rid="bib21">Kawashima et al., 2009</xref>). Sequence polymorphisms have also been reported recently in regions targeted by killer immunoglobulin-like receptors (KIR), suggesting evasion from immune pressure by natural killer (NK) cells (<xref ref-type="bibr" rid="bib3">Alter et al., 2011</xref>). Evidence for the remodeling of retroviral genomes by host genetic pressure also comes from simian immunodeficiency virus (SIV) infection studies in rhesus macaques, where escape from restrictive <italic>TRIM5α</italic> alleles has been observed in the viral capsid upon cross-species transmission of SIVsm (<xref ref-type="bibr" rid="bib22">Kirmaier et al., 2010</xref>). In contrast, human alleles of <italic>TRIM5α</italic> do not result in escape mutations, likely because of adaptation of the pathogen to the host (<xref ref-type="bibr" rid="bib32">Rahm et al., 2013</xref>). Sequence adaptation is also a known feature of cross-species transmission. For example, a methionine in the matrix protein (Gag-30) in SIV<sub>cpzPtt</sub> changed to arginine in lineages leading to HIV-1 and reverted to methionine when HIV-1 was passaged through chimpanzees (<xref ref-type="bibr" rid="bib41">Wain et al., 2007</xref>).</p><p>To date, combined analyses of human and HIV-1 genetic data have addressed the association of HLA and KIR genes with variants in the retroviral genome (<xref ref-type="bibr" rid="bib26">Moore et al., 2002</xref>; <xref ref-type="bibr" rid="bib7">Brumme et al., 2007</xref>; <xref ref-type="bibr" rid="bib4">Bhattacharya et al., 2007</xref>; <xref ref-type="bibr" rid="bib21">Kawashima et al., 2009</xref>; <xref ref-type="bibr" rid="bib3">Alter et al., 2011</xref>; <xref ref-type="bibr" rid="bib8">Carlson et al., 2012</xref>; <xref ref-type="bibr" rid="bib42">Wright et al., 2012</xref>). Additionally, genome-wide association studies (GWAS) performed in the host have focused on various HIV-related clinical phenotypes (<xref ref-type="bibr" rid="bib15">Fellay et al., 2007</xref>; <xref ref-type="bibr" rid="bib14">Fellay et al., 2009</xref>; <xref ref-type="bibr" rid="bib28">Pereyra et al., 2010</xref>). In parallel, large amounts of HIV-1 sequence data have been generated for phylogenetic studies, which shed new light on viral transmission and evolution (<xref ref-type="bibr" rid="bib23">Kouyos et al., 2010</xref>; <xref ref-type="bibr" rid="bib1">Alizon et al., 2010</xref>; <xref ref-type="bibr" rid="bib39">Von Wyl et al., 2011</xref>), or allow clinically driven analyses of viral genes targeted by antiretroviral drugs (resistance testing) (<xref ref-type="bibr" rid="bib40">Von Wyl et al., 2009</xref>).</p><p>Building on the unprecedented possibility to acquire and combine paired human and viral genomic information from the same infected individuals; we employ an innovative strategy for global genome-to-genome host–pathogen analysis. By simultaneously testing for associations between genome-wide human variation, HIV-1 sequence diversity, and plasma viral load (VL), our approach allows the mapping of all sites of host–pathogen genomic interaction, the correction for both host and viral population stratification, and the assessment of the respective impact of human and HIV-1 variation on a clinical outcome (<xref ref-type="fig" rid="fig1">Figure 1</xref>).<fig id="fig1" position="float"><object-id pub-id-type="doi">10.7554/eLife.01123.003</object-id><label>Figure 1.</label><caption><title>A triangle of association testing.</title><p>The following association analyses were performed: [Study A] human SNPs vs plasma viral load (1 GWAS); [Study B] human SNPs vs variable HIV-1 amino acids (3007 GWAS); and [Study C] variable HIV-1 amino acids vs plasma viral load (1 proteome-wide association study).</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.01123.003">http://dx.doi.org/10.7554/eLife.01123.003</ext-link></p></caption><graphic xlink:href="elife01123f001"/></fig></p></sec><sec id="s2" sec-type="results"><title>Results</title><sec id="s2-1"><title>Study participants, host genotypes, and HIV-1 sequence variation</title><p>Full-length HIV-1 genome sequence and human genome-wide SNP data were obtained from seven studies or institutions on a total of 1071 antiretroviral naive patients of Western European ancestry, infected with HIV-1 subtype B. The homogeneity of the study population was confirmed by principal component analysis of the genotype matrix: together, the first five principal components explained 1% of total genotypic variation. After quality control of the human genotype data, imputation and filtering, ∼7 million SNPs were available for association testing. The full-length HIV-1 sequence is approximately 9.5 Kb long, corresponding to over 3000 encoded amino acids. Not all sequences were complete; on an average, viral residues were covered in 85% of the study population (range: 75% in Tat to 95% in Gag). Due to its hypervariable nature, the portion of the HIV-1 envelope gene that encodes the gp120 protein was not sequenced in most study samples and was therefore excluded. Overall 1126 residues of the HIV-1 proteome were found to be variable in at least 10 samples, for a total of 3381 different viral amino acids that could be represented by 3007 distinct binary variables.</p></sec><sec id="s2-2"><title>Host VL GWAS</title><p>We first performed a classical GWAS of host determinants of HIV-1 VL (<xref ref-type="fig" rid="fig2">Figure 2A</xref>, <underline>Study A</underline>) using data from 698 patients (65% of the study population) for whom a VL phenotype could be reliably estimated. The top associations were observed in the HLA class I region on chromosome 6 and were highly consistent with results observed previously (<xref ref-type="bibr" rid="bib14">Fellay et al., 2009</xref>; <xref ref-type="bibr" rid="bib28">Pereyra et al., 2010</xref>). The strongest associated SNP, rs9267454 (p = 1.5 × 10<sup>−8</sup>), is in partial linkage disequilibrium (LD) with HLA-B*57:01 (r<sup>2</sup> = 0.47, D′ = 0.92), HLA-B*14:01 (r<sup>2</sup> = 0.12, D′ = 1.0), HLA-B*27:05 (r<sup>2</sup> = 0.01, D′ = 0.99), and the HLA-C -35 rs9264942 SNP (r<sup>2</sup> = 0.07, D′ = 0.77), and thus reflects these well-known associations with HIV-1 control. These results confirm the quality of the study population for the purpose of genome analysis of determinants of HIV-1-related outcomes.<fig id="fig2" position="float"><object-id pub-id-type="doi">10.7554/eLife.01123.004</object-id><label>Figure 2.</label><caption><title>Results of the genome-wide association analyses.</title><p>(<bold>A</bold>) Associations between human SNPs and HIV-1 plasma viral load. The dotted line shows the Bonferroni-corrected significance threshold (p-value < 7.25 × 10<sup>−9</sup>). (<bold>B</bold>) Associations between human SNPs and HIV-1 amino acid variants, with 3007 GWAS collapsed in a single Manhattan plot. The dotted line shows the Bonferroni-corrected significance threshold (p-value < 2.4 × 10<sup>−12</sup>). (<bold>C</bold>) Schematic representation of the HLA class I genes and of the SNPs associated with HIV-1 amino acid variants in the region. (<bold>D</bold>) Same association results as in panel <bold>B</bold>, projected on the HIV-1 proteome. Only the strongest association is shown for each amino acid. Significant associations are indicated by a blue dot. The gp120 part of the HIV-1 proteome was not tested. The colored bar below the plot area shows the positions of the optimally defined CD8+ T cell epitopes. An interactive version of this figure can be found at <ext-link ext-link-type="uri" xlink:href="http://g2g.labtelenti.org/">http://g2g.labtelenti.org</ext-link> (which is also available to download from Zenodo, <ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.5281/zenodo.7138">http://dx.doi.org/10.5281/zenodo.7138</ext-link>).</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.01123.004">http://dx.doi.org/10.7554/eLife.01123.004</ext-link></p></caption><graphic xlink:href="elife01123f002"/></fig></p></sec><sec id="s2-3"><title>Genome-to-genome analyses</title><p>3007 genome-wide analyses of associations between human SNPs and HIV-1 amino acid variants were performed in the full sample of 1071 individuals (<xref ref-type="fig" rid="fig2">Figure 2B</xref>, <underline>Study B</underline>) using logistic regression corrected for viral phylogeny (<xref ref-type="bibr" rid="bib9">Carlson et al., 2008</xref>; <xref ref-type="bibr" rid="bib8">Carlson et al., 2012</xref>). Highly significant associations were observed between SNPs in the major histocompatibility complex (MHC) region and multiple amino acids throughout the HIV-1 proteome (except in Vpu, Rev and the RNaseH subunit of RT) (<xref ref-type="fig" rid="fig2">Figure 2C</xref>), with Gag and Nef having a significantly higher density of associated variable sites than the rest of the proteome (Gag: 6.8% vs 2.6% p=0.001; Nef: 11% vs 2.6% p = 1.2 × 10<sup>−5</sup>, binomial tests). Using Bonferroni correction for multiple testing (threshold p = 2.4 × 10<sup>−12</sup>), significant human SNP associations were observed with 48 viral amino acids (<xref ref-type="fig" rid="fig2">Figure 2</xref> and <xref ref-type="table" rid="tbl1">Table 1</xref>). None of these 48 amino acids mapped to known sites of major antiretroviral drug resistance mutations (<xref ref-type="bibr" rid="bib17">Hirsch et al., 2008</xref>). The strongest association found was between rs72845950 and Nef position 135 (p = 2.7 × 10<sup>−66</sup>). Associations were much stronger between human SNPs and HIV-1 amino acids than with VL. For example, the SNP rs2395029, a proxy for HLA-B*57:01 (r<sup>2</sup> = 0.93), has a p-value of 1.21 × 10<sup>−6</sup> for association with VL, while it reaches a p-value of 4 × 10<sup>−59</sup> for association with amino acid variation in Gag at position 242 (a well known position of escape from HLA-B*57:01). No significant signals were identified outside the MHC. A link to the complete set of association results can be found at <ext-link ext-link-type="uri" xlink:href="http://g2g.labtelenti.org/">http://g2g.labtelenti.org</ext-link> (which is also available to download from Zenodo, <ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.5281/zenodo.7139">http://dx.doi.org/10.5281/zenodo.7139</ext-link>). These results demonstrate the feasibility and improved power of performing association testing using viral genetic variation as outcome, independent of clinical phenotype.<table-wrap id="tbl1" position="float"><object-id pub-id-type="doi">10.7554/eLife.01123.005</object-id><label>Table 1.</label><caption><p>Associations between HIV-1 amino acid variants and human polymorphisms</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.01123.005">http://dx.doi.org/10.7554/eLife.01123.005</ext-link></p></caption><table frame="hsides" rules="groups"><thead><tr><th>HIV gene</th><th>HIV position</th><th>SNP</th><th>CTL epitope (codons)</th><th>Tagging HLA (D’/r<sup>2</sup>)</th><th>SNP vs aa (p)</th><th>SNP vs VL (p)</th><th>aa vs VL (p)</th></tr></thead><tbody><tr><td>GAG</td><td align="char" char=".">12</td><td>chr6:31285512</td><td>–</td><td>B*49:01 (1.00/1.00)</td><td>2.20E-13</td><td>6.70E-01</td><td>5.60E-01</td></tr><tr><td>GAG</td><td align="char" char=".">26</td><td>rs12524487</td><td>–</td><td>B*15:01 (1.00/0.82)</td><td>6.10E-19</td><td>2.10E-01</td><td>1.40E-01</td></tr><tr><td>GAG</td><td align="char" char=".">28</td><td>rs1655912</td><td>RLRPGGKKK (20–28)</td><td>A*03:01 (1.00/0.81)</td><td>2.70E-55</td><td>5.60E-01</td><td>2.00E-02</td></tr><tr><td>GAG</td><td align="char" char=".">79</td><td>chr6:31267544</td><td>LYNTVATL (78-85)</td><td>C*14:02 (1.00/0.96)</td><td>2.40E-12</td><td>3.50E-01</td><td>2.80E-01</td></tr><tr><td>GAG</td><td align="char" char=".">147</td><td>rs1055821</td><td>–</td><td>C*06:02 (0.95/0.71)</td><td>3.10E-17</td><td>3.30E-07</td><td>2.90E-05</td></tr><tr><td>GAG</td><td align="char" char=".">242</td><td>rs73392116</td><td>TSTLQEQIGW (240–249)</td><td>B*57:01 (1.00/0.98)</td><td>2.40E-62</td><td>1.90E-06</td><td>1.70E-05</td></tr><tr><td>GAG</td><td align="char" char=".">248</td><td>rs41557213</td><td>TSTLQEQIGW (240-249)</td><td>B*57:01 (1.00/0.97)</td><td>4.80E-15</td><td>2.00E-06</td><td>5.30E-03</td></tr><tr><td>GAG</td><td align="char" char=".">264</td><td>chr6:31376564</td><td>KRWIILGLNK (263–272)</td><td>B*27:05 (1.00/0.92)</td><td>2.30E-13</td><td>5.50E-02</td><td>3.50E-01</td></tr><tr><td>GAG</td><td align="char" char=".">268</td><td>rs2249935</td><td>GEIYKRWIIL (259–268)</td><td>B*08:01 (1.00/0.43)</td><td>2.20E-14</td><td>5.10E-01</td><td>1.90E-01</td></tr><tr><td rowspan="2">GAG</td><td align="char" char="." rowspan="2">340</td><td rowspan="2">rs11966319</td><td>–</td><td>B*15:01 (0.94/0.42)</td><td rowspan="2">6.70E-14</td><td rowspan="2">4.60E-01</td><td rowspan="2">7.70E-01</td></tr><tr><td>–</td><td>C*03:04 (0.99/0.59)</td></tr><tr><td rowspan="2">GAG</td><td align="char" char="." rowspan="2">357</td><td rowspan="2">rs2523612</td><td>GPGHKARVL (355-363)</td><td>B*07:02 (0.99/0.95)</td><td rowspan="2">2.70E-19</td><td rowspan="2">2.20E-01</td><td rowspan="2">1.20E-01</td></tr><tr><td>–</td><td>C*07:02 (0.99/0.84)</td></tr><tr><td>GAG</td><td align="char" char=".">397</td><td>rs61754472</td><td>–</td><td>A*31:01 (0.97/0.83)</td><td>8.80E-21</td><td>3.50E-01</td><td>8.30E-01</td></tr><tr><td>GAG</td><td align="char" char=".">403</td><td>rs28896571</td><td>–</td><td>–</td><td>8.90E-21</td><td>7.90E-01</td><td>8.60E-01</td></tr><tr><td>GAG</td><td align="char" char=".">437</td><td>rs34268928</td><td>RQANFLGKI (429-437)</td><td>B*13:02 (1.00/0.96)</td><td>8.70E-14</td><td>1.80E-02</td><td>6.80E-02</td></tr><tr><td>GP41</td><td align="char" char=".">206</td><td>rs17881210</td><td>–</td><td>B*15:01 (1.00/0.88)</td><td>6.10E-17</td><td>6.10E-01</td><td>3.00E-01</td></tr><tr><td>GP41</td><td align="char" char=".">267</td><td>rs9278477</td><td>RLRDLLLIVTR (259–269)</td><td>A*03:01 (1.00/0.01)</td><td>1.00E-12</td><td>7.80E-01</td><td>2.60E-01</td></tr><tr><td>INT</td><td align="char" char=".">11</td><td>rs2596477</td><td>–</td><td>B*44:02 (1.00/0.64)</td><td>5.10E-33</td><td>1.50E-01</td><td>1.80E-01</td></tr><tr><td>INT</td><td align="char" char=".">32</td><td>rs1050502</td><td>–</td><td>B*51:01 (0.97/0.92)</td><td>4.80E-18</td><td>7.20E-01</td><td>4.00E-01</td></tr><tr><td>INT</td><td align="char" char=".">119</td><td>rs9264954</td><td>–</td><td>C*05:01 (1.00/1.00)</td><td>1.30E-24</td><td>7.90E-01</td><td>1.10E-01</td></tr><tr><td>INT</td><td align="char" char=".">122</td><td>rs9264419</td><td>–</td><td>C*05:01 (1.00/0.95)</td><td>4.50E-22</td><td>8.30E-01</td><td>7.80E-01</td></tr><tr><td>INT</td><td align="char" char=".">124</td><td>chr6:31345421</td><td>STTVKAACWW (123–132)</td><td>B*57:01 (1.00/1.00)</td><td>3.00E-13</td><td>1.10E-06</td><td>9.70E-03</td></tr><tr><td rowspan="2">NEF</td><td align="char" char="." rowspan="2">71</td><td rowspan="2">rs2596488</td><td>FPVTPQVPLR (68–77)</td><td>B*07:02 (1.00/0.98)</td><td rowspan="2">3.80E-55</td><td rowspan="2">2.50E-01</td><td rowspan="2">8.10E-02</td></tr><tr><td>–</td><td>C*07:02 (0.95/0.83)</td></tr><tr><td rowspan="2">NEF</td><td align="char" char="." rowspan="2">81</td><td rowspan="2">rs9295987</td><td>RPMTYKAAL (77–85)</td><td>B*07:02 (1.00/0.01)</td><td rowspan="2">4.80E-36</td><td rowspan="2">2.50E-01</td><td rowspan="2">9.50E-02</td></tr><tr><td>–</td><td>C*04:01 (0.90/0.63)</td></tr><tr><td rowspan="2">NEF</td><td align="char" char="." rowspan="2">83</td><td rowspan="2">rs34768512</td><td>–</td><td>B*15:01 (1.00/0.47)</td><td rowspan="2">2.20E-17</td><td rowspan="2">2.80E-01</td><td rowspan="2">1.50E-02</td></tr><tr><td>–</td><td>C*03:04 (0.96/0.54)</td></tr><tr><td rowspan="3">NEF</td><td align="char" char="." rowspan="3">85</td><td rowspan="3">rs2395475</td><td>RPMTYKAAL (77–85)</td><td>B*07:02 (1.00/0.29)</td><td rowspan="3">1.90E-24</td><td rowspan="3">8.10E-01</td><td rowspan="3">1.30E-03</td></tr><tr><td>–</td><td>B*08:01 (1.00/0.22)</td></tr><tr><td>–</td><td>C*07:02 (0.97/0.30)</td></tr><tr><td>NEF</td><td align="char" char=".">92</td><td>rs16896166</td><td>AVDLSHFLK (84–92)</td><td>A*11:01 (1.00/0.99)</td><td>1.00E-27</td><td>5.30E-01</td><td>1.50E-01</td></tr><tr><td>NEF</td><td align="char" char=".">94</td><td>rs9265972</td><td>FLKEKGGL (90–97)</td><td>B*08:01 (1.00/0.97)</td><td>9.60E-35</td><td>9.80E-01</td><td>1.20E-01</td></tr><tr><td>NEF</td><td align="char" char=".">102</td><td>rs2524277</td><td>–</td><td>B*44:03 (0.98/0.96)</td><td>1.10E-13</td><td>4.40E-01</td><td>2.40E-01</td></tr><tr><td>NEF</td><td align="char" char=".">105</td><td>rs1049709</td><td>–</td><td>C*07:01 (1.00/0.98)</td><td>1.10E-35</td><td>9.00E-01</td><td>2.70E-01</td></tr><tr><td>NEF</td><td align="char" char=".">116</td><td>chr6:31402358</td><td>HTQGYFPDW (116–124)</td><td>B*57:01 (1.00/1.00)</td><td>3.00E-22</td><td>1.90E-06</td><td>3.30E-01</td></tr><tr><td>NEF</td><td align="char" char=".">120</td><td>chr6:31236168</td><td>-</td><td>C*14:02 (1.00/1.00)</td><td>4.40E-16</td><td>3.60E-01</td><td>1.20E-02</td></tr><tr><td>NEF</td><td align="char" char=".">126</td><td>chr6:31102273</td><td>–</td><td>B*51:01 (1.00/0.18)</td><td>1.10E-12</td><td>1.80E-01</td><td>4.90E-02</td></tr><tr><td>NEF</td><td align="char" char=".">133</td><td>chr6:31397689</td><td>–</td><td>B*35:01 (0.95/0.89)</td><td>2.80E-19</td><td>2.50E-01</td><td>3.40E-01</td></tr><tr><td>NEF</td><td align="char" char=".">135</td><td>rs72845950</td><td>RYPLTFGW (134–141)</td><td>A*24:02 (1.00/0.88)</td><td>2.70E-66</td><td>9.10E-02</td><td>5.50E-03</td></tr><tr><td>PR</td><td align="char" char=".">35</td><td>rs2523577</td><td>EEMNLPGRW (34-42)</td><td>B*44:02 (1.00/0.64)</td><td>1.70E-18</td><td>1.60E-01</td><td>5.70E-01</td></tr><tr><td>PR</td><td align="char" char=".">93</td><td>rs2263323</td><td>–</td><td>B*15:01 (0.98/0.92)</td><td>5.60E-30</td><td>4.70E-01</td><td>9.50E-01</td></tr><tr><td>RNASE</td><td align="char" char=".">28</td><td>rs2428481</td><td>–</td><td>B*08:01 (1.00/1.00)</td><td>1.80E-12</td><td>8.10E-01</td><td>6.20E-01</td></tr><tr><td>RT</td><td align="char" char=".">135</td><td>rs1050502</td><td>TAFTIPSI (128–135)</td><td>B*51:01 (0.97/0.92)</td><td>6.70E-45</td><td>7.20E-01</td><td>3.00E-01</td></tr><tr><td>RT</td><td align="char" char=".">245</td><td>chr6:31411714</td><td>IVLPEKDSW (244–252)</td><td>B*57:01 (1.00/0.98)</td><td>2.90E-21</td><td>1.20E-06</td><td>5.40E-02</td></tr><tr><td>RT</td><td align="char" char=".">277</td><td>rs3128902</td><td>QIYPGIKVR (269–277)</td><td>A*03:01 (1.00/0.99)</td><td>1.20E-65</td><td>8.20E-01</td><td>2.70E-01</td></tr><tr><td>RT</td><td align="char" char=".">369</td><td>rs17190134</td><td>–</td><td>B*13:02 (0.93/0.86)</td><td>3.50E-20</td><td>6.40E-02</td><td>1.40E-01</td></tr><tr><td>RT</td><td align="char" char=".">395</td><td>rs17194293</td><td>–</td><td>-</td><td>1.50E-12</td><td>1.20E-01</td><td>7.70E-02</td></tr><tr><td>TAT</td><td align="char" char=".">29</td><td>rs9260615</td><td>–</td><td>A*32:01 (0.98/0.95)</td><td>4.40E-14</td><td>3.90E-01</td><td>1.40E-01</td></tr><tr><td>TAT</td><td align="char" char=".">32</td><td>rs16899214</td><td>CCFHCQVC (30–37)</td><td>C*12:03 (0.98/0.96)</td><td>6.40E-21</td><td>3.40E-01</td><td>4.90E-01</td></tr><tr><td>VIF</td><td align="char" char=".">33</td><td>chr6:31430060</td><td>ISKKAKGWF (31–39)</td><td>B*57:01 (1.00/0.98)</td><td>1.50E-13</td><td>9.90E-07</td><td>9.30E-03</td></tr><tr><td>VIF</td><td align="char" char=".">51</td><td>rs7767850</td><td>–</td><td>B*49:01 (1.00/1.00)</td><td>1.40E-12</td><td>5.20E-01</td><td>2.10E-01</td></tr><tr><td>VIF</td><td align="char" char=".">74</td><td>rs2395029</td><td>–</td><td>B*57:01 (1.00/0.98)</td><td>5.40E-13</td><td>9.70E-07</td><td>2.80E-01</td></tr><tr><td>VPR</td><td align="char" char=".">32</td><td>chr6:31362941</td><td>VRHFPRIWL (31–39)</td><td>B*27:05 (1.00/0.94)</td><td>3.10E-13</td><td>5.40E-02</td><td>6.50E-01</td></tr></tbody></table><table-wrap-foot><fn><p>Significant associations (p < 2.4 × 10<sup>-12</sup>) were observed for 48 HIV-1 amino acid variants. The table shows the major amino acid variants present at each specific HIV-1 position, the strongest associated SNP and its linked HLA class I allele(s), if applicable. The column ‘CTL Epitope (codons)’ lists published, optimally described CTL epitopes (available at <ext-link ext-link-type="uri" xlink:href="http://www.hiv.lanl.gov/content/immunology/tables/optimal_ctl_summary.html">http://www.hiv.lanl.gov/content/immunology/tables/optimal_ctl_summary.html</ext-link> and in [<xref ref-type="bibr" rid="bib8">Carlson et al., 2012</xref>]) restricted by the tagged HLA class I allele(s) specified, and their positions within the protein. Where multiple overlapping epitopes restricted by the same HLA class I allele have been described, only one is shown. Associations where no relevant CTL epitope has been described are indicated with a dash. The last three columns give association p-values for comparisons between human SNPs and viral amino acids, human SNPs and plasma VL and viral amino acids and plasma VL, respectively. For tests involving viral amino acids accommodating more than 1 alternate allele, the smallest association p-value observed at that position is reported.</p></fn></table-wrap-foot></table-wrap></p></sec><sec id="s2-4"><title>SNPs, HLA alleles and CTL epitopes</title><p>We next assessed whether the top SNPs associated with HIV-1 amino acids represent indirect markers of HLA class I alleles known to exert evolutionary pressure on HIV-1 (<xref ref-type="table" rid="tbl1">Table 1</xref>). We tested pairwise correlations between significant MHC SNPs and HLA class I alleles. The analysis confirmed the existence of high LD between SNPs and HLA alleles targeting corresponding epitopes. For example, the strongest association (p = 2.7 × 10<sup>−66</sup>) was observed between residue 135 in Nef, located in an optimally defined A*24:02 epitope, and rs72845950, which strongly tags HLA-A*24:02 (r<sup>2</sup> = 0.89). Furthermore, we observed that a substantial fraction of the identified viral amino acids (24/48, 50%) were located within an optimally defined CTL epitope restricted by one or more HLA alleles tagged by the associated SNP (<ext-link ext-link-type="uri" xlink:href="http://www.hiv.lanl.gov/content/immunology/tables/optimal_ctl_summary.html">http://www.hiv.lanl.gov/content/immunology/tables/optimal_ctl_summary.html</ext-link>, supplemented with a recently updated list of epitopes [<xref ref-type="bibr" rid="bib8">Carlson et al., 2012</xref>]). However, in seven cases, the classical HLA allele implicated through LD with a tagging SNP did not match previously reported restriction patterns (<xref ref-type="table" rid="tbl1">Table 1</xref>). These data demonstrate that this approach can reconstruct a map of targets of HLA pressure across the viral proteome and identify sites outside classical epitopes that could represent additional escape variants or compensatory mutations. That a substantial proportion of associated viral amino acids lay outside known CTL epitopes also highlights this approach as a tool to guide novel epitope discovery (<xref ref-type="bibr" rid="bib4">Bhattacharya et al., 2007</xref>; <xref ref-type="bibr" rid="bib2">Almeida et al., 2011</xref>).</p><p>Analysis of polymorphic amino acids within the HLA genes has been shown to improve power for detection of association with clinical outcome and has demonstrated the biological relevance of key residues in the HLA-B binding groove (<xref ref-type="bibr" rid="bib28">Pereyra et al., 2010</xref>). Therefore, we used the genome-to-genome framework to characterize the evolutionary pressure of HLA class I amino acids on the viral genome. The top associations in all classical class I genes mapped to discrete residues in the binding grooves of the HLA molecule: HLA-A position 62 (p = 3.3 × 10<sup>−76</sup> with HIV Nef 135), HLA-B position 70 (p = 7.1 × 10<sup>−57</sup> with HIV Gag 242), HLA-C position 99 (p = 5.4 × 10<sup>−63</sup> with HIV Nef 70). These data indicate that all class I HLA genes can exert strong pressure on the viral proteome through a shared mechanism. The association results for HLA amino acids can also be found at <ext-link ext-link-type="uri" xlink:href="http://g2g.labtelenti.org/">http://g2g.labtelenti.org</ext-link> (which is also available to download from Zenodo).</p></sec><sec id="s2-5"><title>HIV-1 amino acids vs plasma viral load</title><p>To address whether there was an observable impact of viral mutation on a clinical outcome in this sample, we tested for associations between all HIV-1 amino acid variant and VL (Study C). After correction for multiple testing (p threshold = 1.6 × 10<sup>−5</sup> based on 3125 viral amino acids), we did not observe any significant associations. We then focused on estimating the changes in VL associated with the 48 HIV-1 amino acid variants that were identified as significantly associated with host SNPs (<xref ref-type="fig" rid="fig2">Figure 2D</xref>). The effects of amino acid variation at these sites on VL ranged from –0.16 to +0.07 log<sub>10</sub> copies/ml (<xref ref-type="fig" rid="fig3">Figure 3A</xref>). We also explored the combined fitness effect of multiple HIV-1 viral amino acid variants targeted by a single host marker using the well-understood model of HLA-B*57:01. We evaluated the effect on VL of 23 viral residues that associated with host variant rs2395029 (r<sup>2</sup> = 0.93 with HLA-B*57:01) in the genome-to-genome analysis (selection cutoff: p<0.001). The marker rs2395029 was associated with a 0.38 log decrease in viral RNA copies/ml. The univariate effect on VL for each of the 23 viral amino acids targeted by this allele ranged from –0.16 to +0.12 (<xref ref-type="fig" rid="fig3">Figure 3B</xref>). These results suggest that the genome-to-genome approach can be linked to clinical/laboratory phenotypes, allowing for detailed understanding of the distribution and relative contribution of sites of host–pathogen interaction to disease outcome.<fig id="fig3" position="float"><object-id pub-id-type="doi">10.7554/eLife.01123.006</object-id><label>Figure 3.</label><caption><title>Association of HIV-1 amino acid variants with plasma viral load.</title><p>(A) Changes in VL (slope coefficients from the univariate regression model and standard error, log<sub>10</sub> copies/ml) for the 48 HIV-1 amino acids that are associated with host SNPs in the genome-to-genome analysis. (<bold>B</bold>) rs2395029, a marker of HLA-B*57:01 is associated with a 0.38 log<sub>10</sub> copies/ml lower VL (black bar) in comparison to the population mean. Gray bars represent changes in VL for amino acid variants associated with rs2395029 (p<0.001). In case of multiallelic positions, the change in VL is shown for all minor amino acids combined vs the major amino acid (e.g., GAG147 not I).</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.01123.006">http://dx.doi.org/10.7554/eLife.01123.006</ext-link></p></caption><graphic xlink:href="elife01123f003"/></fig></p></sec></sec><sec id="s3" sec-type="discussion"><title>Discussion</title><p>HIV-1 host genomic studies performed so far have focused on clinically defined outcomes (resistance to infection, clinical presentation, disease progression or death) or on pathogen-related laboratory results (such as CD4+ T cell counts and VL set point). While useful, these phenotypes have significant drawbacks. First, consistency of phenotypic determination can be hard to achieve, and such inconsistency can adversely affect power in large-scale genetic studies performed across multiple centers (<xref ref-type="bibr" rid="bib13">Evangelou et al., 2011</xref>). Second, a relatively long follow-up in the absence of antiretroviral treatment is necessary to obtain informative data about the natural history of infection. However, international guidelines now propose an early start of antiretroviral therapy in most HIV-1 infected individuals (<xref ref-type="bibr" rid="bib38">Thompson et al., 2012</xref>), making the collection of large numbers of long-term untreated patients not only unrealistic but also ethically questionable.</p><p>To overcome these limitations, we developed a novel approach for host genetic studies of infectious diseases, built on the unprecedented possibility to obtain paired genome-wide information from hosts and pathogens. We combined human polymorphism and HIV-1 sequence diversity in the same analytical framework to search for sites of human-virus genomic conflict, effectively using variation in HIV-1 amino acids as an ‘intermediate phenotype’ for association studies. Intermediate phenotypes have recently been shown to be useful in uncovering association signals that are not detectable using more complex clinical endpoints: illustrative examples include metabolomic biomarkers in cardiovascular research (<xref ref-type="bibr" rid="bib36">Suhre et al., 2011</xref>), serum IgE concentration in the study of asthma (<xref ref-type="bibr" rid="bib25">Moffatt et al., 2010</xref>), or neuroimaging-based phenotypes in psychiatry genetics (<xref ref-type="bibr" rid="bib33">Rasetti and Weinberger, 2011</xref>). Variation in the pathogen sequence is an as-yet-untapped intermediate phenotype, specific by nature to genomic research in infectious diseases. Importantly, it depends on sequencing the pathogen, which could prove in many cases easier and more standardized than obtaining detailed clinical phenotypes.</p><p>Our approach allowed the mapping of host genetic pressure on the HIV-1 genome. The strongest association signals genome-wide were observed between human SNPs tagging HLA class I alleles and viral mutations in their corresponding CTL epitopes. Additional association signals were observed outside of optimally defined CTL epitopes, which could indicate novel epitopes, or represent secondary (compensatory) mutations. In a single experiment, these results recapitulate extensive epidemiological and immunogenetic research and represent a proof-of-concept that biologically meaningful association signals are identifiable using a hypothesis-free strategy. Indeed, host factors leading to viral adaptation can be uncovered by searching for associated imprints in the viral genome. Of note, the International HIV Controllers Study demonstrated the importance of specific amino acid positions in the HLA-B binding groove on a clinical outcome (elite control) (<xref ref-type="bibr" rid="bib28">Pereyra et al., 2010</xref>)<italic>.</italic> We here extend this observation to the HLA-A and C grooves, emphasizing the similarity in mechanism of host pressure on the viral proteome that is not necessarily translated into observable clinical outcomes.</p><p>We found a higher density of amino acid positions under selection in Gag and Nef compared with the rest of the HIV proteome. This is consistent with earlier findings that indicate the importance of Gag p24-specific CTL responses in slower progression to AIDS (<xref ref-type="bibr" rid="bib5">Borghans et al., 2007</xref>; <xref ref-type="bibr" rid="bib6">Brennan et al., 2012</xref>) or controller status (<xref ref-type="bibr" rid="bib12">Dyer et al., 2008</xref>). Moreover, this further demonstrates that mapping host pressure on the pathogen proteome can reveal biologically relevant effects.</p><p>Analyses were performed using samples from clinically well-characterized patients, most of them with repeated and reliable HIV-1 VL measurements in the absence of antiretroviral therapy. We were thus able to compare the results of GWAS assessing human genetic determinants of mean VL, a standard clinical correlate of HIV-1 control, and genome-to-genome GWAS on amino acid variants in the viral proteome. The use of HIV-1 variation as outcome resulted in a considerable gain in power to detect host factors: the lowest p-values were observed for SNPs mapping to the HLA class I region in both approaches, but associations were much stronger with HIV-1 amino acid variation than for HIV-1 VL (2.7 × 10<sup>−66</sup> vs 1 × 10<sup>−08</sup>), even when accounting for the increased number of multiple tests.</p><p>In addition to identifying sites of interaction between the host and the pathogen, the study design allowed the scoring of biological consequences of such interaction, by assessing associations between host-driven escape at viral sites and an in vivo phenotype (VL). For example, we decomposed the effect of rs2395029 (a marker of HLA-B*57:01) on VL to the effects of the multiple viral amino acid variants that are associated with that SNP. While some HIV-1 amino acid changes individually associate with decrease in VL, the compound image that emerges is one of a multiplicity of modest effects distributed across many residues. Correlations between host-associated variants and VL are difficult to interpret, because they may reflect fitness costs or compensation, the existence of strong (<xref ref-type="bibr" rid="bib18">Iversen et al., 2006</xref>; <xref ref-type="bibr" rid="bib8">Carlson et al.,, 2012</xref>) or novel (<xref ref-type="bibr" rid="bib2">Almeida et al., 2011</xref>) immune responses, or the indirect impact of specific HLA class I alleles. Nevertheless, the observation that the majority of host-associated HIV-1 mutations do not correlate with any detectable change in VL confirms HIV’s remarkable capacity to adapt and compensate to immune pressure, often without measurable fitness cost.</p><p>A significant confounder in both human and viral genomic analyses is the existence of population stratification, where shared ancestry between infected individuals, stratification by ethnic groups, non-random distribution of HIV-1 subtypes, or clusters of viral transmission can all have an influence on the population frequencies of specific mutations, and thus create spurious associations if not carefully controlled for. Previous studies usually controlled for viral population substructure but were limited in the control of human population stratification (<xref ref-type="bibr" rid="bib26">Moore et al., 2002</xref>; <xref ref-type="bibr" rid="bib4">Bhattacharya et al., 2007</xref>). Our approach offers the opportunity to correct for both factors, thanks to the availability of extensive host and viral genomic information.</p><p>The present sample size provided approximately 80% power to detect a common human variant (minor allele frequency of 10%) with an odds ratio of 4.2 in the genome-to-genome analysis (Study B) and a viral amino acid explaining approximately 4% of the variation in plasma viral load (Study C) at the respective significance thresholds (<xref ref-type="bibr" rid="bib30">Purcell et al., 2003</xref>). Consistent with most studies performed in HIV-1 host genetics over the past few years (reviewed in <xref ref-type="bibr" rid="bib37">Telenti and Johnson (2012)</xref>), we did not identify previously unknown host genetic loci involved in host-viral interaction and HIV-1 restriction. The proposed approach can only detect polymorphic host factors that leave an imprint on the virus, which may exclude mediators of immunopathogenesis or genes involved in the establishment of tolerance (<xref ref-type="bibr" rid="bib24">Medzhitov et al., 2012</xref>). An additional limitation is the incomplete nature of genomic information available both on the host side (common genotypes from GWAS) and on the viral side (near full-length consensus sequence; gp120 was not included in the analyses). Finally, the multiple hypothesis burden of a genome-to-genome scan is extremely high. It is conceivable that larger studies, or studies that focus on a subgroup of predefined host genes, would have power to detect novel associations. A comprehensive, but computationally challenging description of host–pathogen genomic interactions would require human genome sequencing, coupled with deep sequencing of intra-host retroviral subpopulations.</p><p>In summary, we used a genome-to-genome, hypothesis-free approach to identify associations between host polymorphisms and HIV-1 genomic variation. This strategy allows a global assessment of host–pathogen interactions at the genome level and reveals sites of genomic conflict. Comparable approaches are immediately applicable to explore other important infectious diseases, as long as polymorphic host factors exert sufficient selective pressure to trigger escape mutations in the pathogen. The observation that pathogen sequence variation, used as an intermediate phenotype, is more powerful than clinical and laboratory outcomes to identify some host factors allows smaller-scale studies and encourages analyses of less prevalent infectious diseases. Researchers involved in pathogen genome studies and host genetic studies should strongly consider the gathering of paired host–pathogen data.</p></sec><sec id="s4" sec-type="materials|methods"><title>Materials and methods</title><sec id="s4-1"><title>Ethics statement</title><p>Participating centers provided local Institutional Review Board approval for genetic analysis. Study participants provided informed consent for genetic testing, with the exception of a subset where a procedure approved by the relevant Research Ethics Board allowed the use of anonymized historical specimens in the absence of a specific informed consent.</p></sec><sec id="s4-2"><title>Participants</title><p>Study participants are treatment-naïve individuals followed in one of the following cohorts or institutions: the Swiss HIV Cohort Study (SHCS, <ext-link ext-link-type="uri" xlink:href="http://www.shcs.ch">www.shcs.ch</ext-link>, [<xref ref-type="bibr" rid="bib35">Schoeni-Affolter et al., 2010</xref>]); the HAART Observational Medical Evaluation and Research (HOMER) study in Vancouver, Canada (<ext-link ext-link-type="uri" xlink:href="http://www.cfenet.ubc.ca/our-work/initiatives/homer">www.cfenet.ubc.ca/our-work/initiatives/homer</ext-link>); the AIDS Clinical Trials Group (ACTG) Network in the USA (<ext-link ext-link-type="uri" xlink:href="http://actgnetwork.org">actgnetwork.org</ext-link>); the International HIV Controllers Study in Boston, USA (IHCS, <ext-link ext-link-type="uri" xlink:href="http://www.hivcontrollers.org">www.hivcontrollers.org</ext-link>); Western Australian HIV Cohort Study, Perth, Australia; the AIDS Research Institute IrsiCaixa in Badalona, Spain; and the Instituto de Salud Carlos III in Madrid, Spain. To reduce noise due to host and viral diversity, we only included individuals of recent Western European ancestry (confirmed by clustering with HapMap CEU individuals in principal component analysis of the genotype data [<xref ref-type="bibr" rid="bib29">Price et al., 2006</xref>]), and infected with HIV-1 subtype B (as assessed by the REGA Subtyping Tool [<xref ref-type="bibr" rid="bib11">De Oliveira et al., 2005</xref>]). Plasma VL determinations in the absence of antiretroviral therapy were available from patients from the SHCS and the HOMER study. The VL phenotype was defined as the average of the log<sub>10</sub>-transformed numbers of HIV-1 RNA copies per ml of plasma, excluding measurements obtained in the first 6 months after seroconversion and during advanced immunosuppression (i.e., with <100 CD4+ T cells per ml of blood). Consequently, 698 study participants were eligible for VL analysis.</p></sec><sec id="s4-3"><title>Human genotype data</title><p>DNA samples were genotyped in the context of previous GWAS (<xref ref-type="bibr" rid="bib14">Fellay et al., 2009</xref>; <xref ref-type="bibr" rid="bib28">Pereyra et al., 2010</xref>) or for the current study on various platforms, including the HumanHap550, Human 660W-Quad, Human1M and HumanOmniExpress BeadChips (Illumina Inc., San Diego, CA, USA), as well as the Genome-Wide Human SNP Array 6.0 (Affymetrix Inc., Santa Clara, CA, USA) (<xref ref-type="table" rid="tbl2">Table 2</xref>). Study participants were filtered on the basis of genotyping quality, a sex check, and cryptic relatedness. SNP quality control was performed separately for each dataset: SNPs were filtered on the basis of missingness (excluded if called in <99% of participants), minor allele frequency (excluded if <0.01), and marked deviation from Hardy-Weinberg equilibrium (excluded if p<0.00005). Missing genotype imputation was performed with the Mach software per genotyping platform (in separate batches for Illumina 1M, OmniExpress, 550K and Affymetrix data) using 1000 Genomes Phase I CEU population data as reference haplotypes. Imputed markers were filtered on minor allele frequency (excluded if <0.01) and imputation quality using Mach’s reported r-squared measure (excluded if <0.3). SNPs with a deviation in the allele frequencies between platforms were excluded. High-resolution HLA class I typing (4 digits; HLA-A, HLA-B, and HLA-C) was obtained using sequence-based methods, or imputed from the SNP genotyping data as described elsewhere (<xref ref-type="bibr" rid="bib19">Jia et al., 2013</xref>).<table-wrap id="tbl2" position="float"><object-id pub-id-type="doi">10.7554/eLife.01123.007</object-id><label>Table 2.</label><caption><p>Distribution of samples across genotyping platforms and cohorts</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.01123.007">http://dx.doi.org/10.7554/eLife.01123.007</ext-link></p></caption><table frame="hsides" rules="groups"><thead><tr><th>N</th><th>Genotyping platform</th><th>Cohort</th></tr></thead><tbody><tr><td align="char" char=".">140</td><td>Illumina 1M</td><td>ACTG</td></tr><tr><td align="char" char=".">6</td><td>Illumina OmniExpress 12v1H</td><td>CARLOS III</td></tr><tr><td align="char" char=".">518</td><td>Affymetrix 6.0</td><td>HOMER</td></tr><tr><td align="char" char=".">136</td><td>Illumina OmniExpress12v1H</td><td>HOMER</td></tr><tr><td align="char" char=".">47</td><td>Illumina 650k</td><td>IHCS</td></tr><tr><td align="char" char=".">6</td><td>Illumina 660W-Quad</td><td>IRSICAIXA</td></tr><tr><td align="char" char=".">2</td><td>Illumina 1M</td><td>SHCS</td></tr><tr><td align="char" char=".">79</td><td>Illumina 550k</td><td>SHCS</td></tr><tr><td align="char" char=".">122</td><td>Illumina OmniExpress12v1H</td><td>SHCS</td></tr><tr><td align="char" char=".">15</td><td>Illumina 550k</td><td>WAHCS</td></tr></tbody></table><table-wrap-foot><fn><p>ACTG = AIDS Clinical Trials Group Network; CARLOS III = Instituto de Salud Carlos III; HOMER = HAART Observational Medical Evaluation and Research Study; IHCS = International HIV Controllers Study; IRSICAIXA = AIDS Research Institute IrsiCaixa; SHCS = Swiss HIV Cohort Study; WAHCS = Western Australian HIV Cohort Study.</p></fn></table-wrap-foot></table-wrap></p></sec><sec id="s4-4"><title>HIV-1 sequence data</title><p>Near full-length retroviral sequence data were obtained by bulk sequencing of viral RNA present in pretreatment-stored plasma, and in 11 cases, of proviral DNA isolated from peripheral blood mononuclear cells, as previously described (<xref ref-type="bibr" rid="bib34">Sandonís et al., 2009</xref>; <xref ref-type="bibr" rid="bib20">John et al., 2010</xref>). We defined an amino acid residue as variable if at least 10 study samples presented an alternative allele. Per position, separate binary variables were generated for each alternate amino acid, indicating the presence or absence of that allele in a given sample.</p></sec><sec id="s4-5"><title>Association analyses</title><p>To globally assess the association between human genomic variation (SNPs), HIV-1 proteomic variation (amino acids) and clinical outcome (VL), we performed three series of analyses (<xref ref-type="fig" rid="fig1">Figure 1</xref>): [A] human SNPs vs VL; [B] human SNPs vs HIV-1 amino acids; and [C] HIV-1 amino acids vs VL. To test for association between human SNPs and HIV-1 amino acids, we used phylogenetically corrected logistic regression (<xref ref-type="bibr" rid="bib9">Carlson et al., 2008</xref>; <xref ref-type="bibr" rid="bib10">Carlson et al., 2012</xref>). For association testing between polymorphic amino acids in human HLA genes and HIV sequence variation, we used standard logistic regression (for a binary HLA amino acid) or a multivariate omnibus test (when more than one alternate allele was present) including sex, cohort, and the coordinates of the first two principal component axes as covariates. We used linear regression models in PLINK to test for association between human SNPs and VL, and between HIV-1 amino acids and VL (<xref ref-type="bibr" rid="bib31">Purcell et al., 2007</xref>), including sex, cohort, and the coordinates of the first two principal component axes as covariates (<xref ref-type="bibr" rid="bib29">Price et al., 2006</xref>). An additive genetic model was used for all analyses involving human SNPs. Significance was assessed using Bonferroni correction (significance thresholds of 7.25 × 10<sup>−9</sup>, 2.4 × 10<sup>−12</sup>, and 1.6 × 10<sup>−5</sup> for analyses A, B, and C, respectively, <xref ref-type="fig" rid="fig1">Figure 1</xref>).</p></sec></sec></body><back><ack id="ack"><title>Acknowledgements</title><p>We would like to thank all the patients participating in these genetic studies, the many study nurses, physicians, data managers and laboratories involved in all the cohorts; Tanja Stadler and Sebastian Bonhoeffer (at ETH Zürich, Switzerland) and Samuel Alizon (at MIGEVEC, Montpellier, France) for helpful discussions; and Jennifer Troyer (at the Laboratory for Genomic Diversity, NCI) for her work on the HOMER genotyping data.</p></ack><sec sec-type="additional-information"><title>Additional information</title><fn-group content-type="competing-interest"><title>Competing interests</title><fn fn-type="conflict" id="conf1"><p>The authors declare that no competing interests exist.</p></fn></fn-group><fn-group content-type="author-contribution"><title>Author contributions</title><fn fn-type="con" id="con1"><p>IB, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con2"><p>JMC, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con3"><p>CJB, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con4"><p>JL, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con5"><p>NP, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con6"><p>CL, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con7"><p>NF, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con8"><p>ZK, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con9"><p>VM, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con10"><p>PJM, Conception and design, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con11"><p>AT, Conception and design, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con12"><p>JF, Conception and design, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con13"><p>ZLB, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents</p></fn><fn fn-type="con" id="con14"><p>MJ, Acquisition of data, Drafting or revising the article, Contributed unpublished essential data or reagents</p></fn><fn fn-type="con" id="con15"><p>DWH, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con16"><p>JM-P, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con17"><p>JD, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con18"><p>CL-G, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con19"><p>CC, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con20"><p>AR, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con21"><p>HFG, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con22"><p>EB, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con23"><p>PV, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con24"><p>TK, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con25"><p>SY, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con26"><p>SJO’B, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con27"><p>PRH, Acquisition of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con28"><p>TMA, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con29"><p>DH, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents</p></fn></fn-group><fn-group content-type="ethics-information"><title>Ethics</title><fn fn-type="other"><p>Human subjects: Participating centers provided local Institutional Review Board approval for genetic analysis. Study participants provided informed consent for genetic testing, with the exception of a subset where a procedure approved by the relevant Research Ethics Board allowed the use of anonymized historical specimens in the absence of a specific informed consent.</p></fn></fn-group></sec><sec sec-type="supplementary-material"><title>Additional files</title><sec sec-type="datasets"><title>Major dataset</title><p>The following datasets were generated:</p><p><related-object content-type="generated-dataset" document-id="Dataset ID and/or url" document-id-type="dataset" document-type="data" id="dataro1"><name><surname>Bartha</surname><given-names>I</given-names></name>, <name><surname>Carlson</surname><given-names>JM</given-names></name>, <name><surname>Brumme</surname><given-names>CJ</given-names></name>, <name><surname>McLaren</surname><given-names>PJ</given-names></name>, <name><surname>Brumme</surname><given-names>ZL</given-names></name>, <name><surname>John</surname><given-names>M</given-names></name>, <etal/>, <year>2013</year><x>, </x><source>Interactive HIV-Host Genome-to-Genome Map</source><x>, </x><ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.5281/zenodo.7138">http://dx.doi.org/10.5281/zenodo.7138</ext-link><x>, </x><comment>Publicly available at Zenodo (<ext-link ext-link-type="uri" xlink:href="http://https//zenodo.org">https://zenodo.org</ext-link>).</comment></related-object></p><p><related-object content-type="generated-dataset" document-id="Dataset ID and/or url" document-id-type="dataset" document-type="data" id="dataro2"><name><surname>Bartha</surname><given-names>I</given-names></name>, <name><surname>Carlson</surname><given-names>JM</given-names></name>, <name><surname>Brumme</surname><given-names>CJ</given-names></name>, <name><surname>McLaren</surname><given-names>PJ</given-names></name>, <name><surname>Brumme</surname><given-names>ZL</given-names></name>, <name><surname>John</surname><given-names>M</given-names></name>, <etal/>, <year>2013</year><x>, </x><source>Online Supplementary Dataset of the HIV Genome-to-Genome Study</source><x>, </x><ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.5281/zenodo.7139">http://dx.doi.org/10.5281/zenodo.7139</ext-link><x>, </x><comment>Publicly available at Zenodo (<ext-link ext-link-type="uri" xlink:href="http://https//zenodo.org">https://zenodo.org</ext-link>).</comment></related-object></p></sec></sec><ref-list><title>References</title><ref id="bib1"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Alizon</surname><given-names>S</given-names></name><name><surname>von Wyl</surname><given-names>V</given-names></name><name><surname>Stadler</surname><given-names>T</given-names></name><name><surname>Kouyos</surname><given-names>RD</given-names></name><name><surname>Yerly</surname><given-names>S</given-names></name><name><surname>Hirschel</surname><given-names>B</given-names></name><name><surname>Böni</surname><given-names>J</given-names></name><etal/></person-group><year>2010</year><article-title>Phylogenetic approach reveals that virus genotype largely determines HIV set-point viral load</article-title><source>PLOS Pathogens</source><volume>6</volume><fpage>e1001123</fpage><pub-id pub-id-type="doi">10.1371/journal.ppat.1001123</pub-id></element-citation></ref><ref id="bib2"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Almeida</surname><given-names>CA</given-names></name><name><surname>Bronke</surname><given-names>C</given-names></name><name><surname>Roberts</surname><given-names>SG</given-names></name><name><surname>McKinnon</surname><given-names>E</given-names></name><name><surname>Keane</surname><given-names>NM</given-names></name><name><surname>Chopra</surname><given-names>A</given-names></name><name><surname>Kadie</surname><given-names>C</given-names></name><etal/></person-group><year>2011</year><article-title>Translation of HLA-HIV associations to the cellular level: HIV adapts to inflate CD8 T cell responses against Nef and HLA-adapted variant epitopes</article-title><source>J Immunol</source><volume>187</volume><fpage>2502</fpage><lpage>13</lpage><pub-id pub-id-type="doi">10.4049/jimmunol.1100691</pub-id></element-citation></ref><ref id="bib3"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Alter</surname><given-names>G</given-names></name><name><surname>Heckerman</surname><given-names>D</given-names></name><name><surname>Schneidewind</surname><given-names>A</given-names></name><name><surname>Fadda</surname><given-names>L</given-names></name><name><surname>Kadie</surname><given-names>CM</given-names></name><name><surname>Carlson</surname><given-names>JM</given-names></name><name><surname>Oniangue-Ndza</surname><given-names>C</given-names></name><etal/></person-group><year>2011</year><article-title>HIV-1 adaptation to NK-cell-mediated immune pressure</article-title><source>Nature</source><volume>476</volume><fpage>96</fpage><lpage>100</lpage><pub-id pub-id-type="doi">10.1038/nature10237</pub-id></element-citation></ref><ref id="bib4"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Bhattacharya</surname><given-names>T</given-names></name><name><surname>Daniels</surname><given-names>M</given-names></name><name><surname>Heckerman</surname><given-names>D</given-names></name><name><surname>Foley</surname><given-names>B</given-names></name><name><surname>Frahm</surname><given-names>N</given-names></name><name><surname>Kadie</surname><given-names>C</given-names></name><name><surname>Carlson</surname><given-names>J</given-names></name><etal/></person-group><year>2007</year><article-title>Founder effects in the assessment of HIV polymorphisms and HLA allele associations</article-title><source>Science</source><volume>315</volume><fpage>1583</fpage><lpage>6</lpage><pub-id pub-id-type="doi">10.1126/science.1131528</pub-id></element-citation></ref><ref id="bib5"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Borghans</surname><given-names>JA</given-names></name><name><surname>Mølgaard</surname><given-names>A</given-names></name><name><surname>de Boer</surname><given-names>RJ</given-names></name><name><surname>Keşmir</surname><given-names>C</given-names></name></person-group><year>2007</year><article-title>HLA alleles associated with slow progression to AIDS truly prefer to present HIV-1 P24</article-title><source>PLOS ONE</source><volume>2</volume><fpage>e920</fpage><pub-id pub-id-type="doi">10.1371/journal.pone.0000920</pub-id></element-citation></ref><ref id="bib6"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Brennan</surname><given-names>CA</given-names></name><name><surname>Ibarrondo</surname><given-names>FJ</given-names></name><name><surname>Sugar</surname><given-names>CA</given-names></name><name><surname>Hausner</surname><given-names>MA</given-names></name><name><surname>Shih</surname><given-names>R</given-names></name><name><surname>Ng</surname><given-names>HL</given-names></name><name><surname>Detels</surname><given-names>R</given-names></name><etal/></person-group><year>2012</year><article-title>Early HLA-B*57-restricted CD8+ T lymphocyte responses predict HIV-1 disease progression</article-title><source>J Virol</source><volume>86</volume><fpage>10505</fpage><lpage>16</lpage><pub-id pub-id-type="doi">10.1128/JVI.00102-12</pub-id></element-citation></ref><ref id="bib7"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Brumme</surname><given-names>ZL</given-names></name><name><surname>Brumme</surname><given-names>CJ</given-names></name><name><surname>Heckerman</surname><given-names>D</given-names></name><name><surname>Korber</surname><given-names>BT</given-names></name><name><surname>Daniels</surname><given-names>M</given-names></name><name><surname>Carlson</surname><given-names>J</given-names></name><name><surname>Kadie</surname><given-names>C</given-names></name><etal/></person-group><year>2007</year><article-title>Evidence of differential HLA class i-mediated viral evolution in functional and accessory/regulatory genes of HIV-1</article-title><source>PLOS Pathogens</source><volume>3</volume><fpage>e94</fpage><pub-id pub-id-type="doi">10.1371/journal.ppat.0030094</pub-id></element-citation></ref><ref id="bib8"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Carlson</surname><given-names>JM</given-names></name><name><surname>Brumme</surname><given-names>CJ</given-names></name><name><surname>Martin</surname><given-names>E</given-names></name><name><surname>Listgarten</surname><given-names>J</given-names></name><name><surname>Brockman</surname><given-names>MA</given-names></name><name><surname>Le</surname><given-names>AQ</given-names></name><name><surname>Chui</surname><given-names>CK</given-names></name><etal/></person-group><year>2012</year><article-title>Correlates of protective cellular immunity revealed by analysis of population-level immune escape pathways in HIV-1</article-title><source>J Virol</source><volume>86</volume><fpage>13202</fpage><lpage>16</lpage><pub-id pub-id-type="doi">10.1128/JVI.01998-12</pub-id></element-citation></ref><ref id="bib9"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Carlson</surname><given-names>JM</given-names></name><name><surname>Brumme</surname><given-names>ZL</given-names></name><name><surname>Rousseau</surname><given-names>CM</given-names></name><name><surname>Brumme</surname><given-names>CJ</given-names></name><name><surname>Matthews</surname><given-names>P</given-names></name><name><surname>Kadie</surname><given-names>C</given-names></name><name><surname>Mullins</surname><given-names>JI</given-names></name><etal/></person-group><year>2008</year><article-title>Phylogenetic dependency networks: inferring patterns of CTL escape and codon covariation in HIV-1 Gag</article-title><comment>Edited by Rob J De Boer</comment><source>PLOS Comput Biol</source><volume>4</volume><fpage>23</fpage><pub-id pub-id-type="doi">10.1371/journal.pcbi.1000225</pub-id></element-citation></ref><ref id="bib10"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Carlson</surname><given-names>JM</given-names></name><name><surname>Listgarten</surname><given-names>J</given-names></name><name><surname>Pfeifer</surname><given-names>N</given-names></name><name><surname>Tan</surname><given-names>V</given-names></name><name><surname>Kadie</surname><given-names>C</given-names></name><name><surname>Walker</surname><given-names>BD</given-names></name><name><surname>Ndung’u</surname><given-names>T</given-names></name><etal/></person-group><year>2012</year><article-title>Widespread impact of HLA restriction on immune control and escape pathways of HIV-1</article-title><source>J Virol</source><volume>86</volume><fpage>5230</fpage><lpage>43</lpage><pub-id pub-id-type="doi">10.1128/JVI.06728-11</pub-id></element-citation></ref><ref id="bib11"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>De Oliveira</surname><given-names>T</given-names></name><name><surname>Deforche</surname><given-names>K</given-names></name><name><surname>Cassol</surname><given-names>S</given-names></name><name><surname>Salminen</surname><given-names>M</given-names></name><name><surname>Paraskevis</surname><given-names>D</given-names></name><name><surname>Seebregts</surname><given-names>C</given-names></name><name><surname>Snoeck</surname><given-names>J</given-names></name><etal/></person-group><year>2005</year><article-title>An automated genotyping system for analysis of HIV-1 and other microbial sequences</article-title><source>Bioinformatics</source><volume>21</volume><fpage>3797</fpage><lpage>800</lpage><pub-id pub-id-type="doi">10.1093/bioinformatics/bti607</pub-id></element-citation></ref><ref id="bib12"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Dyer</surname><given-names>WB</given-names></name><name><surname>Zaunders</surname><given-names>JJ</given-names></name><name><surname>Yuan</surname><given-names>FF</given-names></name><name><surname>Wang</surname><given-names>B</given-names></name><name><surname>Learmont</surname><given-names>JC</given-names></name><name><surname>Geczy</surname><given-names>AF</given-names></name><name><surname>Saksena</surname><given-names>NK</given-names></name><name><surname>McPhee</surname><given-names>DA</given-names></name><name><surname>Gorry</surname><given-names>PR</given-names></name><name><surname>Sullivan</surname><given-names>JS</given-names></name></person-group><year>2008</year><article-title>Mechanisms of HIV non-progression; robust and sustained CD4+ T-cell proliferative responses to P24 antigen correlate with control of viraemia and lack of disease progression after long-term transfusion-acquired HIV-1 infection</article-title><source>Retrovirology</source><volume>5</volume><fpage>112</fpage><pub-id pub-id-type="doi">10.1186/1742-4690-5-112</pub-id></element-citation></ref><ref id="bib13"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Evangelou</surname><given-names>E</given-names></name><name><surname>Fellay</surname><given-names>J</given-names></name><name><surname>Colombo</surname><given-names>S</given-names></name><name><surname>Martinez-Picado</surname><given-names>J</given-names></name><name><surname>Obel</surname><given-names>N</given-names></name><name><surname>Goldstein</surname><given-names>DB</given-names></name><name><surname>Telenti</surname><given-names>A</given-names></name><name><surname>Ioannidis</surname><given-names>JPA</given-names></name></person-group><year>2011</year><article-title>Impact of phenotype definition on genome-wide association signals: empirical evaluation in human immunodeficiency virus type 1 infection</article-title><source>Am J Epidemiol</source><volume>173</volume><fpage>1336</fpage><lpage>42</lpage><pub-id pub-id-type="doi">10.1093/aje/kwr024</pub-id></element-citation></ref><ref id="bib14"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Fellay</surname><given-names>J</given-names></name><name><surname>Ge</surname><given-names>D</given-names></name><name><surname>Shianna</surname><given-names>KV</given-names></name><name><surname>Colombo</surname><given-names>S</given-names></name><name><surname>Ledergerber</surname><given-names>B</given-names></name><name><surname>Cirulli</surname><given-names>ET</given-names></name><name><surname>Urban</surname><given-names>TJ</given-names></name><etal/></person-group><year>2009</year><article-title>Common genetic variation and the control of HIV-1 in humans</article-title><source>PLOS Genet</source><volume>5</volume><fpage>e1000791</fpage><pub-id pub-id-type="doi">10.1371/journal.pgen.1000791</pub-id></element-citation></ref><ref id="bib15"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Fellay</surname><given-names>J</given-names></name><name><surname>Shianna</surname><given-names>KV</given-names></name><name><surname>Ge</surname><given-names>D</given-names></name><name><surname>Colombo</surname><given-names>S</given-names></name><name><surname>Ledergerber</surname><given-names>B</given-names></name><name><surname>Weale</surname><given-names>M</given-names></name><name><surname>Zhang</surname><given-names>K</given-names></name><etal/></person-group><year>2007</year><article-title>A Whole-genome association study of major determinants for host control of HIV-1</article-title><source>Science</source><volume>317</volume><fpage>944</fpage><lpage>7</lpage><pub-id pub-id-type="doi">10.1126/science.1143767</pub-id></element-citation></ref><ref id="bib16"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Goulder</surname><given-names>PJ</given-names></name><name><surname>Brander</surname><given-names>C</given-names></name><name><surname>Tang</surname><given-names>Y</given-names></name><name><surname>Tremblay</surname><given-names>C</given-names></name><name><surname>Colbert</surname><given-names>RA</given-names></name><name><surname>Addo</surname><given-names>MM</given-names></name><name><surname>Rosenberg</surname><given-names>ES</given-names></name><etal/></person-group><year>2001</year><article-title>Evolution and transmission of stable CTL escape mutations in HIV infection</article-title><source>Nature</source><volume>412</volume><fpage>334</fpage><lpage>8</lpage><pub-id pub-id-type="doi">10.1038/35085576</pub-id></element-citation></ref><ref id="bib17"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hirsch</surname><given-names>MS</given-names></name><name><surname>Günthard</surname><given-names>HF</given-names></name><name><surname>Schapiro</surname><given-names>JM</given-names></name><name><surname>Brun-Vézinet</surname><given-names>F</given-names></name><name><surname>Clotet</surname><given-names>B</given-names></name><name><surname>Hammer</surname><given-names>SM</given-names></name><name><surname>Johnson</surname><given-names>VA</given-names></name><etal/></person-group><year>2008</year><article-title>Antiretroviral drug resistance testing in adult HIV-1 infection: 2008 recommendations of an international AIDS Society-USA panel</article-title><source>Clin Infect Dis</source><volume>47</volume><fpage>266</fpage><lpage>85</lpage><pub-id pub-id-type="doi">10.1086/589297</pub-id></element-citation></ref><ref id="bib18"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Iversen</surname><given-names>AKN</given-names></name><name><surname>Stewart-Jones</surname><given-names>G</given-names></name><name><surname>Learn</surname><given-names>GH</given-names></name><name><surname>Christie</surname><given-names>N</given-names></name><name><surname>Sylvester-Hviid</surname><given-names>C</given-names></name><name><surname>Armitage</surname><given-names>AE</given-names></name><name><surname>Kaul</surname><given-names>R</given-names></name><etal/></person-group><year>2006</year><article-title>Conflicting selective forces affect T cell receptor contacts in an immunodominant human immunodeficiency virus epitope</article-title><source>Nat Immunol</source><volume>7</volume><fpage>179</fpage><lpage>89</lpage><pub-id pub-id-type="doi">10.1038/ni1298</pub-id></element-citation></ref><ref id="bib19"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Jia</surname><given-names>X</given-names></name><name><surname>Han</surname><given-names>B</given-names></name><name><surname>Onengut-Gumuscu</surname><given-names>S</given-names></name><name><surname>Chen</surname><given-names>WM</given-names></name><name><surname>Concannon</surname><given-names>PJ</given-names></name><name><surname>Rich</surname><given-names>SS</given-names></name><name><surname>Raychaudhuri</surname><given-names>S</given-names></name><name><surname>de Bakker</surname><given-names>PIW</given-names></name></person-group><year>2013</year><article-title>Imputing amino acid polymorphisms in human leukocyte antigens</article-title><source>PLOS ONE</source><volume>8</volume><fpage>e64683</fpage><pub-id pub-id-type="doi">10.1371/journal.pone.0064683</pub-id></element-citation></ref><ref id="bib20"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>John</surname><given-names>M</given-names></name><name><surname>Heckerman</surname><given-names>D</given-names></name><name><surname>James</surname><given-names>I</given-names></name><name><surname>Park</surname><given-names>LP</given-names></name><name><surname>Carlson</surname><given-names>JM</given-names></name><name><surname>Chopra</surname><given-names>A</given-names></name><name><surname>Gaudieri</surname><given-names>S</given-names></name><etal/></person-group><year>2010</year><article-title>Adaptive interactions between HLA and HIV-1: highly divergent selection imposed by HLA class I molecules with common supertype motifs</article-title><source>J Immunol</source><volume>184</volume><fpage>4368</fpage><lpage>77</lpage><pub-id pub-id-type="doi">10.4049/jimmunol.0903745</pub-id></element-citation></ref><ref id="bib21"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kawashima</surname><given-names>Y</given-names></name><name><surname>Pfafferott</surname><given-names>K</given-names></name><name><surname>Frater</surname><given-names>J</given-names></name><name><surname>Matthews</surname><given-names>P</given-names></name><name><surname>Payne</surname><given-names>R</given-names></name><name><surname>Addo</surname><given-names>M</given-names></name><name><surname>Gatanaga</surname><given-names>H</given-names></name><etal/></person-group><year>2009</year><article-title>Adaptation of HIV-1 to human leukocyte antigen class I</article-title><source>Nature</source><volume>458</volume><fpage>641</fpage><lpage>5</lpage><pub-id pub-id-type="doi">10.1038/nature07746</pub-id></element-citation></ref><ref id="bib22"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kirmaier</surname><given-names>A</given-names></name><name><surname>Wu</surname><given-names>F</given-names></name><name><surname>Newman</surname><given-names>RM</given-names></name><name><surname>Hall</surname><given-names>LR</given-names></name><name><surname>Morgan</surname><given-names>JS</given-names></name><name><surname>O’Connor</surname><given-names>S</given-names></name><name><surname>Marx</surname><given-names>PA</given-names></name><etal/></person-group><year>2010</year><article-title>TRIM5 suppresses cross-species transmission of a primate immunodeficiency virus and selects for emergence of resistant variants in the new species</article-title><source>PLOS Biol</source><volume>8</volume><fpage>e1000462</fpage><pub-id pub-id-type="doi">10.1371/journal.pbio.1000462</pub-id></element-citation></ref><ref id="bib23"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kouyos</surname><given-names>RD</given-names></name><name><surname>von Wyl</surname><given-names>V</given-names></name><name><surname>Yerly</surname><given-names>S</given-names></name><name><surname>Böni</surname><given-names>J</given-names></name><name><surname>Taffé</surname><given-names>P</given-names></name><name><surname>Shah</surname><given-names>C</given-names></name><name><surname>Bürgisser</surname><given-names>P</given-names></name><etal/></person-group><year>2010</year><article-title>Molecular epidemiology reveals long-term changes in hiv type 1 subtype B transmission in Switzerland</article-title><source>J Infect Dis</source><volume>201</volume><fpage>1488</fpage><lpage>97</lpage><pub-id pub-id-type="doi">10.1086/651951</pub-id></element-citation></ref><ref id="bib24"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Medzhitov</surname><given-names>R</given-names></name><name><surname>Schneider</surname><given-names>DS</given-names></name><name><surname>Soares</surname><given-names>MP</given-names></name></person-group><year>2012</year><article-title>Disease tolerance as a defense strategy</article-title><source>Science</source><volume>335</volume><fpage>936</fpage><lpage>41</lpage><pub-id pub-id-type="doi">10.1126/science.1214935</pub-id></element-citation></ref><ref id="bib25"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Moffatt</surname><given-names>MF</given-names></name><name><surname>Gut</surname><given-names>IG</given-names></name><name><surname>Demenais</surname><given-names>F</given-names></name><name><surname>Strachan</surname><given-names>DP</given-names></name><name><surname>Bouzigon</surname><given-names>E</given-names></name><name><surname>Heath</surname><given-names>S</given-names></name><name><surname>von Mutius</surname><given-names>E</given-names></name><name><surname>Farrall</surname><given-names>M</given-names></name><name><surname>Lathrop</surname><given-names>M</given-names></name><name><surname>Cookson</surname><given-names>WOCM</given-names></name></person-group><year>2010</year><article-title>A large-scale, consortium-based genomewide association study of asthma</article-title><source>N Engl J Med</source><volume>363</volume><fpage>1211</fpage><lpage>21</lpage><pub-id pub-id-type="doi">10.1056/NEJMoa0906312</pub-id></element-citation></ref><ref id="bib26"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Moore</surname><given-names>CB</given-names></name><name><surname>John</surname><given-names>M</given-names></name><name><surname>James</surname><given-names>IR</given-names></name><name><surname>Christiansen</surname><given-names>FT</given-names></name><name><surname>Witt</surname><given-names>CS</given-names></name><name><surname>Mallal</surname><given-names>SA</given-names></name></person-group><year>2002</year><article-title>Evidence of HIV-1 adaptation to HLA-restricted immune responses at a population level</article-title><source>Science</source><volume>296</volume><fpage>1439</fpage><lpage>43</lpage><pub-id pub-id-type="doi">10.1126/science.1069660</pub-id></element-citation></ref><ref id="bib27"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ortiz</surname><given-names>M</given-names></name><name><surname>Guex</surname><given-names>N</given-names></name><name><surname>Patin</surname><given-names>E</given-names></name><name><surname>Martin</surname><given-names>O</given-names></name><name><surname>Xenarios</surname><given-names>I</given-names></name><name><surname>Ciuffi</surname><given-names>A</given-names></name><name><surname>Quintana-Murci</surname><given-names>L</given-names></name><name><surname>Telenti</surname><given-names>A</given-names></name></person-group><year>2009</year><article-title>Evolutionary trajectories of primate genes involved in HIV pathogenesis</article-title><source>Mol Biol Evol</source><volume>26</volume><fpage>2865</fpage><lpage>75</lpage><pub-id pub-id-type="doi">10.1093/molbev/msp197</pub-id></element-citation></ref><ref id="bib28"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Pereyra</surname><given-names>F</given-names></name><name><surname>Jia</surname><given-names>X</given-names></name><name><surname>McLaren</surname><given-names>PJ</given-names></name></person-group><year>2010</year><article-title>The major genetic determinants of HIV-1 control affect HLA class I peptide presentation</article-title><source>Science</source><volume>330</volume><fpage>1551</fpage><lpage>7</lpage><pub-id pub-id-type="doi">10.1126/science.1195271</pub-id></element-citation></ref><ref id="bib29"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Price</surname><given-names>AL</given-names></name><name><surname>Patterson</surname><given-names>NJ</given-names></name><name><surname>Plenge</surname><given-names>RM</given-names></name><name><surname>Weinblatt</surname><given-names>ME</given-names></name><name><surname>Shadick</surname><given-names>NA</given-names></name><name><surname>Reich</surname><given-names>D</given-names></name></person-group><year>2006</year><article-title>Principal components analysis corrects for stratification in genome-wide association studies</article-title><source>Nat Genet</source><volume>38</volume><fpage>904</fpage><lpage>9</lpage><pub-id pub-id-type="doi">10.1038/ng1847</pub-id></element-citation></ref><ref id="bib30"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Purcell</surname><given-names>S</given-names></name><name><surname>Cherny</surname><given-names>SS</given-names></name><name><surname>Sham</surname><given-names>PC</given-names></name></person-group><year>2003</year><article-title>Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits</article-title><source>Bioinformatics</source><volume>19</volume><fpage>149</fpage><lpage>50</lpage><pub-id pub-id-type="doi">10.1093/bioinformatics/19.1.149</pub-id></element-citation></ref><ref id="bib31"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Purcell</surname><given-names>S</given-names></name><name><surname>Neale</surname><given-names>B</given-names></name><name><surname>Todd-Brown</surname><given-names>K</given-names></name><name><surname>Thomas</surname><given-names>L</given-names></name><name><surname>Ferreira</surname><given-names>MAR</given-names></name><name><surname>Bender</surname><given-names>D</given-names></name><name><surname>Maller</surname><given-names>J</given-names></name><etal/></person-group><year>2007</year><article-title>PLINK: a tool set for whole-genome association and population-based linkage analyses</article-title><source>Am J Human Genet</source><volume>81</volume><fpage>559</fpage><lpage>75</lpage><pub-id pub-id-type="doi">10.1086/519795</pub-id></element-citation></ref><ref id="bib32"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Rahm</surname><given-names>N</given-names></name><name><surname>Gfeller</surname><given-names>D</given-names></name><name><surname>Snoeck</surname><given-names>J</given-names></name><name><surname>Martinez</surname><given-names>R</given-names></name><name><surname>McLaren</surname><given-names>PJ</given-names></name><name><surname>Ortiz</surname><given-names>M</given-names></name><name><surname>Ciuffi</surname><given-names>A</given-names></name><name><surname>Telenti</surname><given-names>A</given-names></name></person-group><year>2013</year><article-title>Susceptibility and adaptation to human TRIM5α alleles at positive selected sites in HIV-1 capsid</article-title><source>Virology</source><fpage>1</fpage><lpage>9</lpage><pub-id pub-id-type="doi">10.1016/j.virol.2013.03.021</pub-id></element-citation></ref><ref id="bib33"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Rasetti</surname><given-names>R</given-names></name><name><surname>Weinberger</surname><given-names>DR</given-names></name></person-group><year>2011</year><article-title>Intermediate phenotypes in psychiatric disorders</article-title><source>Curr Opin Genet Dev</source><volume>21</volume><fpage>340</fpage><lpage>8</lpage><pub-id pub-id-type="doi">10.1016/j.gde.2011.02.003</pub-id></element-citation></ref><ref id="bib34"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Sandonís</surname><given-names>V</given-names></name><name><surname>Casado</surname><given-names>C</given-names></name><name><surname>Alvaro</surname><given-names>T</given-names></name><name><surname>Pernas</surname><given-names>M</given-names></name><name><surname>Olivares</surname><given-names>I</given-names></name><name><surname>García</surname><given-names>S</given-names></name><name><surname>Rodríguez</surname><given-names>C</given-names></name><name><surname>del Romero</surname><given-names>J</given-names></name><name><surname>López-Galíndez</surname><given-names>C</given-names></name></person-group><year>2009</year><article-title>A combination of defective DNA and protective host factors are found in a set of HIV-1 ancestral LTNPs</article-title><source>Virology</source><volume>391</volume><fpage>73</fpage><lpage>82</lpage><pub-id pub-id-type="doi">10.1016/j.virol.2009.05.022</pub-id></element-citation></ref><ref id="bib35"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Schoeni-Affolter</surname><given-names>F</given-names></name><name><surname>Ledergerber</surname><given-names>B</given-names></name><name><surname>Rickenbach</surname><given-names>M</given-names></name><name><surname>Rudin</surname><given-names>C</given-names></name><name><surname>Günthard</surname><given-names>HF</given-names></name><name><surname>Telenti</surname><given-names>A</given-names></name><name><surname>Furrer</surname><given-names>H</given-names></name><name><surname>Yerly</surname><given-names>S</given-names></name><name><surname>Francioli</surname><given-names>P</given-names></name></person-group><year>2010</year><article-title>Cohort profile: the Swiss HIV cohort study</article-title><source>Int J Epidemiol</source><volume>39</volume><fpage>1179</fpage><lpage>89</lpage><pub-id pub-id-type="doi">10.1093/ije/dyp321</pub-id></element-citation></ref><ref id="bib36"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Suhre</surname><given-names>K</given-names></name><name><surname>Shin</surname><given-names>SY</given-names></name><name><surname>Petersen</surname><given-names>AK</given-names></name><name><surname>Mohney</surname><given-names>RP</given-names></name><name><surname>Meredith</surname><given-names>D</given-names></name><name><surname>Wägele</surname><given-names>B</given-names></name><name><surname>Altmaier</surname><given-names>E</given-names></name><etal/></person-group><year>2011</year><article-title>Human metabolic individuality in biomedical and pharmaceutical research</article-title><source>Nature</source><volume>477</volume><fpage>54</fpage><lpage>60</lpage><pub-id pub-id-type="doi">10.1038/nature10354</pub-id></element-citation></ref><ref id="bib37"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Telenti</surname><given-names>A</given-names></name><name><surname>Johnson</surname><given-names>WE</given-names></name></person-group><year>2012</year><article-title>Host genes important to HIV replication and evolution</article-title><source>Cold Spring Harbor Perspectives in Medicine</source><volume>2</volume><fpage>a007203</fpage><pub-id pub-id-type="doi">10.1101/cshperspect.a007203</pub-id></element-citation></ref><ref id="bib38"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Thompson</surname><given-names>MA</given-names></name><name><surname>Aberg</surname><given-names>JA</given-names></name><name><surname>Hoy</surname><given-names>JF</given-names></name><name><surname>Telenti</surname><given-names>A</given-names></name><name><surname>Benson</surname><given-names>C</given-names></name><name><surname>Cahn</surname><given-names>P</given-names></name><name><surname>Eron</surname><given-names>JJ</given-names></name><etal/></person-group><year>2012</year><article-title>Antiretroviral treatment of adult HIV infection: 2012 recommendations of the International Antiviral Society-USA panel</article-title><source>J Am Med Assoc</source><volume>308</volume><fpage>387</fpage><lpage>402</lpage><pub-id pub-id-type="doi">10.1001/jama.2012.7961</pub-id></element-citation></ref><ref id="bib39"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Von Wyl</surname><given-names>V</given-names></name><name><surname>Kouyos</surname><given-names>RD</given-names></name><name><surname>Yerly</surname><given-names>S</given-names></name><name><surname>Böni</surname><given-names>J</given-names></name><name><surname>Shah</surname><given-names>C</given-names></name><name><surname>Bürgisser</surname><given-names>P</given-names></name><name><surname>Klimkait</surname><given-names>T</given-names></name><etal/></person-group><year>2011</year><article-title>The Role of migration and domestic transmission in the spread of HIV-1 non-B subtypes in Switzerland</article-title><source>J Infect Dis</source><volume>204</volume><fpage>1095</fpage><lpage>103</lpage><pub-id pub-id-type="doi">10.1093/infdis/jir491</pub-id></element-citation></ref><ref id="bib40"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Von Wyl</surname><given-names>V</given-names></name><name><surname>Yerly</surname><given-names>S</given-names></name><name><surname>Bürgisser</surname><given-names>P</given-names></name><name><surname>Klimkait</surname><given-names>T</given-names></name><name><surname>Battegay</surname><given-names>M</given-names></name><name><surname>Bernasconi</surname><given-names>E</given-names></name><name><surname>Cavassini</surname><given-names>M</given-names></name><etal/></person-group><year>2009</year><article-title>Long-term trends of HIV type 1 drug resistance prevalence among antiretroviral treatment-experienced patients in Switzerland</article-title><source>Clin Infect Dis</source><volume>48</volume><fpage>979</fpage><lpage>87</lpage><pub-id pub-id-type="doi">10.1086/597352</pub-id></element-citation></ref><ref id="bib41"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wain</surname><given-names>LV</given-names></name><name><surname>Bailes</surname><given-names>E</given-names></name><name><surname>Bibollet-Ruche</surname><given-names>F</given-names></name><name><surname>Decker</surname><given-names>JM</given-names></name><name><surname>Keele</surname><given-names>BF</given-names></name><name><surname>Van Heuverswyn</surname><given-names>F</given-names></name><name><surname>Li</surname><given-names>Y</given-names></name><etal/></person-group><year>2007</year><article-title>Adaptation of HIV-1 to its human host</article-title><source>Mol Biol Evol</source><volume>24</volume><fpage>1853</fpage><lpage>60</lpage><pub-id pub-id-type="doi">10.1093/molbev/msm110</pub-id></element-citation></ref><ref id="bib42"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wright</surname><given-names>JK</given-names></name><name><surname>Brumme</surname><given-names>ZL</given-names></name><name><surname>Julg</surname><given-names>B</given-names></name><name><surname>van der Stok</surname><given-names>M</given-names></name><name><surname>Mncube</surname><given-names>Z</given-names></name><name><surname>Gao</surname><given-names>X</given-names></name><name><surname>Carlson</surname><given-names>JM</given-names></name><etal/></person-group><year>2012</year><article-title>Lack of association between HLA class II alleles and in vitro replication capacities of recombinant viruses encoding HIV-1 subtype C Gag-protease from chronically infected individuals</article-title><source>J Virol</source><volume>86</volume><fpage>1273</fpage><lpage>6</lpage><pub-id pub-id-type="doi">10.1128/JVI.06533-11</pub-id></element-citation></ref></ref-list></back><sub-article article-type="article-commentary" id="SA1"><front-stub><article-id pub-id-type="doi">10.7554/eLife.01123.008</article-id><title-group><article-title>Decision letter</article-title></title-group><contrib-group content-type="section"><contrib contrib-type="editor"><name><surname>McVean</surname><given-names>Gil</given-names></name><role>Reviewing editor</role><aff><institution>Oxford University</institution>, <country>United Kingdom</country></aff></contrib></contrib-group></front-stub><body><boxed-text><p>eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see <ext-link ext-link-type="uri" xlink:href="http://elife.elifesciences.org/review-process">review process</ext-link>). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.</p></boxed-text><p>Thank you for sending your work entitled “A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control” for consideration at <italic>eLife</italic>. Your article has been favorably evaluated by a Senior editor, a Reviewing editor, and 3 reviewers.</p><p>The Reviewing editor and the reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.</p><p>The study represents an important and novel direction in the analysis of host-pathogen interactions, namely the joint analysis of both host and pathogen genomes. With additional information on pathogen phenotype (viral load) this enables the authors to provide a detailed dissection of how genetic variation within the players influences outcome.</p><p>Overall, we felt the study was well designed and analysed. Although there are perhaps no great surprises in terms of findings, the finding of many associated viral variants outside epitopes and the stronger association between human variation and HIV variation, compared to viral load, are both interesting results. When revising, we would like you to focus on the following issues:</p><p>1) The authors wish to include a clinical correlate in the study, and we understand the use of viral load as it is easy to measure and is associated with progression. However, viral load is not only the only marker of disease progression – CD4 count, rate of CD4 decline, immune activation, time to starting therapy from seroconversion etc, so it is clear that clinical progression is multifactorial. (It is also widely reported that CD8 immune responses, especially ELISpots, do not associate with viral load). With this in mind, the authors can only conclude that when using viral load as the surrogate their method produces stronger P values, but it cannot be stated that their ‘intermediate phenotype’ can replace possible other clinical correlates.</p><p>2) The authors have used sequences from proviral DNA and plasma DNA in the same analysis, without mentioning this apart from in the Methods. Whereas proviral DNA may represent a record of HLA-imposed selection pressure it may not represent the circulating virus, and therefore associations with viral load etc. may be misleading. One would expect to see some justification of this approach.</p><p>3) It would help frame the findings better if the power of the association studies was given. How big an effect size were the studies powered to detect? Given the finding of Alizon et al. cited here and other related papers, are we to be surprised by the lack of associations in the viral proteome to VL study? Does this place an upper bound for effect size of associations? Similarly for the lack of associations outside of MHC, which seem quite definitive especially in the case of the host genome to viral genome study.</p><p>4) The GWAS to viral sequence variation is probably the most interesting finding of this study, with 48 viral amino acids showing significant associations with host SNPs in the MHC. The strongest association is observed for position 135/Nef within an A*24:04 restricted epitope and for a SNP which is known to tag A*24:02. Assuming that this effect has a structural basis (i.e., the molecular interaction between peptide and MHC), why only show amino acids in the viral genome? Why not make it more “symmetric” and also look at the individual amino acids in the HLA molecules? Because the authors have imputed amino acid polymorphisms of the HLA proteins (Jia et al.), this should be relatively easy and potentially interesting.</p></body></sub-article><sub-article article-type="reply" id="SA2"><front-stub><article-id pub-id-type="doi">10.7554/eLife.01123.009</article-id><title-group><article-title>Author response</article-title></title-group></front-stub><body><p><italic>1) The authors wish to include a clinical correlate in the study, and we understand the use of viral load as it is easy to measure and is associated with progression. However, viral load is not only the only marker of disease progression – CD4 count, rate of CD4 decline, immune activation, time to starting therapy from seroconversion etc., so it is clear that clinical progression is multifactorial. (It is also widely reported that CD8 immune responses, especially ELISpots, do not associate with viral load). With this in mind, the authors can only conclude that when using viral load as the surrogate their method produces stronger P values, but it cannot be stated that their ‘intermediate phenotype’ can replace possible other clinical correlates</italic>.</p><p>We fully agree that plasma viral load is one clinical variable associated with progression, but that it does not capture all aspects of clinical disease. We have modified the Abstract and the main text accordingly to correctly convey the message that the improvement in power refers to our greater capacity to detect host factors relevant to viral biology.</p><p><italic>2) The authors have used sequences from proviral DNA and plasma DNA in the same analysis, without mentioning this apart from in the Methods. Whereas proviral DNA may represent a record of HLA-imposed selection pressure it may not represent the circulating virus, and therefore associations with viral load etc. may be misleading. One would expect to see some justification of this approach</italic>.</p><p>HIV-1 sequences were generated from proviral DNA in a very small number of study participants (N=11). In addition, there is no clear evidence that proviral DNA is less likely to reflect intra-host host pressure. While it has been shown that escape mutations in proviral DNA can lag a few months vs the circulating virus, in this cross-sectional study of chronically infected subjects most escape mutations should already have occurred and any minor delays in escape rates would not be expected to effect the results given the stability of viral loads during chronic infection.</p><p><italic>3) It would help frame the findings better if the power of the association studies was given. How big an effect size were the studies powered to detect? Given the finding of Alizon et al. cited here and other related papers, are we to be surprised by the lack of associations in the viral proteome to VL study? Does this place an upper bound for effect size of associations? Similarly for the lack of associations outside of MHC, which seem quite definitive especially in the case of the host genome to viral genome study</italic>.</p><p>We agree with the reviewers that power calculations are important to frame our findings in their appropriate context and we have included these in the text of the manuscript.</p><p><italic>4) The GWAS to viral sequence variation is probably the most interesting finding of this study, with 48 viral amino acids showing significant associations with host SNPs in the MHC. The strongest association is observed for position 135/Nef within an A*24:04 restricted epitope and for a SNP which is known to tag A*24:02. Assuming that this effect has a structural basis (i.e., the molecular interaction between peptide and MHC), why only show amino acids in the viral genome? Why not make it more “symmetric” and also look at the individual amino acids in the HLA molecules? Because the authors have imputed amino acid polymorphisms of the HLA proteins (Jia et al.), this should be relatively easy and potentially interesting</italic>.</p><p>We now include the analysis proposed by the reviewers. The amino acid association testing is largely consistent with the analysis of classical HLA alleles. It also adds a mechanistic dimension. We have added this analysis to the text and provide full association results online (<ext-link ext-link-type="uri" xlink:href="http://g2g.labtelenti.org/">http://g2g.labtelenti.org</ext-link>).</p></body></sub-article></article> |