Genetic Adaptation and Neandertal Admixture Shaped the Immune System of Human Populations
Identifieur interne : 000353 ( Pmc/Curation ); précédent : 000352; suivant : 000354Genetic Adaptation and Neandertal Admixture Shaped the Immune System of Human Populations
Auteurs : Hélène Quach [France] ; Maxime Rotival [France] ; Julien Pothlichet [France] ; Yong-Hwee Eddie Loh [France] ; Michael Dannemann [Allemagne] ; Nora Zidane [France] ; Guillaume Laval [France] ; Etienne Patin [France] ; Christine Harmant [France] ; Marie Lopez [France] ; Matthieu Deschamps [France] ; Nadia Naffakh [France] ; Darragh Duffy [France] ; Anja Coen [Belgique] ; Geert Leroux-Roels [Belgique] ; Frederic Clément [Belgique] ; Anne Boland [France] ; Jean-François Deleuze [France] ; Janet Kelso [Allemagne] ; Matthew L. Albert [France, États-Unis] ; Lluis Quintana-Murci [France]Source :
- Cell [ 0092-8674 ] ; 2016.
Abstract
Humans differ in the outcome that follows exposure to life-threatening pathogens, yet the extent of population differences in immune responses and their genetic and evolutionary determinants remain undefined. Here, we characterized, using RNA sequencing, the transcriptional response of primary monocytes from Africans and Europeans to bacterial and viral stimuli—ligands activating Toll-like receptor pathways (TLR1/2, TLR4, and TLR7/8) and influenza virus—and mapped expression quantitative trait loci (eQTLs). We identify numerous
Url:
DOI: 10.1016/j.cell.2016.09.024
PubMed: 27768888
PubMed Central: 5075285
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<author><name sortKey="Harmant, Christine" sort="Harmant, Christine" uniqKey="Harmant C" first="Christine" last="Harmant">Christine Harmant</name>
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<author><name sortKey="Lopez, Marie" sort="Lopez, Marie" uniqKey="Lopez M" first="Marie" last="Lopez">Marie Lopez</name>
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<author><name sortKey="Deschamps, Matthieu" sort="Deschamps, Matthieu" uniqKey="Deschamps M" first="Matthieu" last="Deschamps">Matthieu Deschamps</name>
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<author><name sortKey="Naffakh, Nadia" sort="Naffakh, Nadia" uniqKey="Naffakh N" first="Nadia" last="Naffakh">Nadia Naffakh</name>
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<author><name sortKey="Duffy, Darragh" sort="Duffy, Darragh" uniqKey="Duffy D" first="Darragh" last="Duffy">Darragh Duffy</name>
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<author><name sortKey="Coen, Anja" sort="Coen, Anja" uniqKey="Coen A" first="Anja" last="Coen">Anja Coen</name>
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<author><name sortKey="Leroux Roels, Geert" sort="Leroux Roels, Geert" uniqKey="Leroux Roels G" first="Geert" last="Leroux-Roels">Geert Leroux-Roels</name>
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<author><name sortKey="Clement, Frederic" sort="Clement, Frederic" uniqKey="Clement F" first="Frederic" last="Clément">Frederic Clément</name>
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<author><name sortKey="Boland, Anne" sort="Boland, Anne" uniqKey="Boland A" first="Anne" last="Boland">Anne Boland</name>
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<author><name sortKey="Deleuze, Jean Francois" sort="Deleuze, Jean Francois" uniqKey="Deleuze J" first="Jean-François" last="Deleuze">Jean-François Deleuze</name>
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<author><name sortKey="Kelso, Janet" sort="Kelso, Janet" uniqKey="Kelso J" first="Janet" last="Kelso">Janet Kelso</name>
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<author><name sortKey="Albert, Matthew L" sort="Albert, Matthew L" uniqKey="Albert M" first="Matthew L." last="Albert">Matthew L. Albert</name>
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<affiliation wicri:level="1"><nlm:aff id="aff10">Department of Cancer Immunology, Genentech, South San Francisco, CA 94080, USA</nlm:aff>
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<author><name sortKey="Rotival, Maxime" sort="Rotival, Maxime" uniqKey="Rotival M" first="Maxime" last="Rotival">Maxime Rotival</name>
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<author><name sortKey="Pothlichet, Julien" sort="Pothlichet, Julien" uniqKey="Pothlichet J" first="Julien" last="Pothlichet">Julien Pothlichet</name>
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<country xml:lang="fr">France</country>
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<author><name sortKey="Loh, Yong Hwee Eddie" sort="Loh, Yong Hwee Eddie" uniqKey="Loh Y" first="Yong-Hwee Eddie" last="Loh">Yong-Hwee Eddie Loh</name>
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<affiliation wicri:level="1"><nlm:aff id="aff3">Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015, France</nlm:aff>
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<author><name sortKey="Dannemann, Michael" sort="Dannemann, Michael" uniqKey="Dannemann M" first="Michael" last="Dannemann">Michael Dannemann</name>
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</affiliation>
</author>
<author><name sortKey="Zidane, Nora" sort="Zidane, Nora" uniqKey="Zidane N" first="Nora" last="Zidane">Nora Zidane</name>
<affiliation wicri:level="1"><nlm:aff id="aff1">Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff2">CNRS, URA3012, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>CNRS, URA3012, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff3">Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Laval, Guillaume" sort="Laval, Guillaume" uniqKey="Laval G" first="Guillaume" last="Laval">Guillaume Laval</name>
<affiliation wicri:level="1"><nlm:aff id="aff1">Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff2">CNRS, URA3012, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>CNRS, URA3012, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff3">Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Patin, Etienne" sort="Patin, Etienne" uniqKey="Patin E" first="Etienne" last="Patin">Etienne Patin</name>
<affiliation wicri:level="1"><nlm:aff id="aff1">Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff2">CNRS, URA3012, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>CNRS, URA3012, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff3">Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Harmant, Christine" sort="Harmant, Christine" uniqKey="Harmant C" first="Christine" last="Harmant">Christine Harmant</name>
<affiliation wicri:level="1"><nlm:aff id="aff1">Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff2">CNRS, URA3012, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>CNRS, URA3012, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff3">Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Lopez, Marie" sort="Lopez, Marie" uniqKey="Lopez M" first="Marie" last="Lopez">Marie Lopez</name>
<affiliation wicri:level="1"><nlm:aff id="aff1">Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff2">CNRS, URA3012, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>CNRS, URA3012, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff3">Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff5">Université Pierre et Marie Curie, Cellule Pasteur, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Université Pierre et Marie Curie, Cellule Pasteur, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Deschamps, Matthieu" sort="Deschamps, Matthieu" uniqKey="Deschamps M" first="Matthieu" last="Deschamps">Matthieu Deschamps</name>
<affiliation wicri:level="1"><nlm:aff id="aff1">Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff2">CNRS, URA3012, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>CNRS, URA3012, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff3">Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff5">Université Pierre et Marie Curie, Cellule Pasteur, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Université Pierre et Marie Curie, Cellule Pasteur, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Naffakh, Nadia" sort="Naffakh, Nadia" uniqKey="Naffakh N" first="Nadia" last="Naffakh">Nadia Naffakh</name>
<affiliation wicri:level="1"><nlm:aff id="aff6">Molecular Genetics of RNA Viruses Unit, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Molecular Genetics of RNA Viruses Unit, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Duffy, Darragh" sort="Duffy, Darragh" uniqKey="Duffy D" first="Darragh" last="Duffy">Darragh Duffy</name>
<affiliation wicri:level="1"><nlm:aff id="aff7">Dendritic Cell Immunobiology Unit, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Dendritic Cell Immunobiology Unit, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Coen, Anja" sort="Coen, Anja" uniqKey="Coen A" first="Anja" last="Coen">Anja Coen</name>
<affiliation wicri:level="1"><nlm:aff id="aff8">Center for Vaccinology, Ghent University and University Hospital, Ghent 9000, Belgium</nlm:aff>
<country xml:lang="fr">Belgique</country>
<wicri:regionArea>Center for Vaccinology, Ghent University and University Hospital, Ghent 9000</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Leroux Roels, Geert" sort="Leroux Roels, Geert" uniqKey="Leroux Roels G" first="Geert" last="Leroux-Roels">Geert Leroux-Roels</name>
<affiliation wicri:level="1"><nlm:aff id="aff8">Center for Vaccinology, Ghent University and University Hospital, Ghent 9000, Belgium</nlm:aff>
<country xml:lang="fr">Belgique</country>
<wicri:regionArea>Center for Vaccinology, Ghent University and University Hospital, Ghent 9000</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Clement, Frederic" sort="Clement, Frederic" uniqKey="Clement F" first="Frederic" last="Clément">Frederic Clément</name>
<affiliation wicri:level="1"><nlm:aff id="aff8">Center for Vaccinology, Ghent University and University Hospital, Ghent 9000, Belgium</nlm:aff>
<country xml:lang="fr">Belgique</country>
<wicri:regionArea>Center for Vaccinology, Ghent University and University Hospital, Ghent 9000</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Boland, Anne" sort="Boland, Anne" uniqKey="Boland A" first="Anne" last="Boland">Anne Boland</name>
<affiliation wicri:level="1"><nlm:aff id="aff9">Centre National de Génotypage, CEA, Evry 91000, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Centre National de Génotypage, CEA, Evry 91000</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Deleuze, Jean Francois" sort="Deleuze, Jean Francois" uniqKey="Deleuze J" first="Jean-François" last="Deleuze">Jean-François Deleuze</name>
<affiliation wicri:level="1"><nlm:aff id="aff9">Centre National de Génotypage, CEA, Evry 91000, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Centre National de Génotypage, CEA, Evry 91000</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Kelso, Janet" sort="Kelso, Janet" uniqKey="Kelso J" first="Janet" last="Kelso">Janet Kelso</name>
<affiliation wicri:level="1"><nlm:aff id="aff4">Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany</nlm:aff>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Albert, Matthew L" sort="Albert, Matthew L" uniqKey="Albert M" first="Matthew L." last="Albert">Matthew L. Albert</name>
<affiliation wicri:level="1"><nlm:aff id="aff7">Dendritic Cell Immunobiology Unit, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Dendritic Cell Immunobiology Unit, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff10">Department of Cancer Immunology, Genentech, South San Francisco, CA 94080, USA</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Cancer Immunology, Genentech, South San Francisco, CA 94080</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Quintana Murci, Lluis" sort="Quintana Murci, Lluis" uniqKey="Quintana Murci L" first="Lluis" last="Quintana-Murci">Lluis Quintana-Murci</name>
<affiliation wicri:level="1"><nlm:aff id="aff1">Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff2">CNRS, URA3012, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>CNRS, URA3012, Paris 75015</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff3">Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015</wicri:regionArea>
</affiliation>
</author>
</analytic>
<series><title level="j">Cell</title>
<idno type="ISSN">0092-8674</idno>
<idno type="eISSN">1097-4172</idno>
<imprint><date when="2016">2016</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"><title>Summary</title>
<p>Humans differ in the outcome that follows exposure to life-threatening pathogens, yet the extent of population differences in immune responses and their genetic and evolutionary determinants remain undefined. Here, we characterized, using RNA sequencing, the transcriptional response of primary monocytes from Africans and Europeans to bacterial and viral stimuli—ligands activating Toll-like receptor pathways (TLR1/2, TLR4, and TLR7/8) and influenza virus—and mapped expression quantitative trait loci (eQTLs). We identify numerous <italic>cis</italic>
-eQTLs that contribute to the marked differences in immune responses detected within and between populations and a strong <italic>trans</italic>
-eQTL hotspot at <italic>TLR1</italic>
that decreases expression of pro-inflammatory genes in Europeans only. We find that immune-responsive regulatory variants are enriched in population-specific signals of natural selection and show that admixture with Neandertals introduced regulatory variants into European genomes, affecting preferentially responses to viral challenges. Together, our study uncovers evolutionarily important determinants of differences in host immune responsiveness between human populations.</p>
</div>
</front>
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</author>
<author><name sortKey="Akey, J M" uniqKey="Akey J">J.M. Akey</name>
</author>
</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Vernot, B" uniqKey="Vernot B">B. Vernot</name>
</author>
<author><name sortKey="Akey, J M" uniqKey="Akey J">J.M. Akey</name>
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</div1>
</back>
</TEI>
<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Cell</journal-id>
<journal-id journal-id-type="iso-abbrev">Cell</journal-id>
<journal-title-group><journal-title>Cell</journal-title>
</journal-title-group>
<issn pub-type="ppub">0092-8674</issn>
<issn pub-type="epub">1097-4172</issn>
<publisher><publisher-name>Cell Press</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">27768888</article-id>
<article-id pub-id-type="pmc">5075285</article-id>
<article-id pub-id-type="publisher-id">S0092-8674(16)31306-X</article-id>
<article-id pub-id-type="doi">10.1016/j.cell.2016.09.024</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Genetic Adaptation and Neandertal Admixture Shaped the Immune System of Human Populations</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Quach</surname>
<given-names>Hélène</given-names>
</name>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
<xref rid="fn1" ref-type="fn">11</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Rotival</surname>
<given-names>Maxime</given-names>
</name>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
<xref rid="fn1" ref-type="fn">11</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Pothlichet</surname>
<given-names>Julien</given-names>
</name>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
<xref rid="fn1" ref-type="fn">11</xref>
<xref rid="fn2" ref-type="fn">12</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Loh</surname>
<given-names>Yong-Hwee Eddie</given-names>
</name>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
<xref rid="fn1" ref-type="fn">11</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Dannemann</surname>
<given-names>Michael</given-names>
</name>
<xref rid="aff4" ref-type="aff">4</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Zidane</surname>
<given-names>Nora</given-names>
</name>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Laval</surname>
<given-names>Guillaume</given-names>
</name>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Patin</surname>
<given-names>Etienne</given-names>
</name>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Harmant</surname>
<given-names>Christine</given-names>
</name>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Lopez</surname>
<given-names>Marie</given-names>
</name>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
<xref rid="aff5" ref-type="aff">5</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Deschamps</surname>
<given-names>Matthieu</given-names>
</name>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
<xref rid="aff5" ref-type="aff">5</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Naffakh</surname>
<given-names>Nadia</given-names>
</name>
<xref rid="aff6" ref-type="aff">6</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Duffy</surname>
<given-names>Darragh</given-names>
</name>
<xref rid="aff7" ref-type="aff">7</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Coen</surname>
<given-names>Anja</given-names>
</name>
<xref rid="aff8" ref-type="aff">8</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Leroux-Roels</surname>
<given-names>Geert</given-names>
</name>
<xref rid="aff8" ref-type="aff">8</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Clément</surname>
<given-names>Frederic</given-names>
</name>
<xref rid="aff8" ref-type="aff">8</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Boland</surname>
<given-names>Anne</given-names>
</name>
<xref rid="aff9" ref-type="aff">9</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Deleuze</surname>
<given-names>Jean-François</given-names>
</name>
<xref rid="aff9" ref-type="aff">9</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Kelso</surname>
<given-names>Janet</given-names>
</name>
<xref rid="aff4" ref-type="aff">4</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Albert</surname>
<given-names>Matthew L.</given-names>
</name>
<xref rid="aff7" ref-type="aff">7</xref>
<xref rid="aff10" ref-type="aff">10</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Quintana-Murci</surname>
<given-names>Lluis</given-names>
</name>
<email>quintana@pasteur.fr</email>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
<xref rid="fn3" ref-type="fn">13</xref>
<xref rid="cor1" ref-type="corresp">∗</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
Human Evolutionary Genetics Unit, Institut Pasteur, Paris 75015, France</aff>
<aff id="aff2"><label>2</label>
CNRS, URA3012, Paris 75015, France</aff>
<aff id="aff3"><label>3</label>
Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015, France</aff>
<aff id="aff4"><label>4</label>
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany</aff>
<aff id="aff5"><label>5</label>
Université Pierre et Marie Curie, Cellule Pasteur, Institut Pasteur, Paris 75015, France</aff>
<aff id="aff6"><label>6</label>
Molecular Genetics of RNA Viruses Unit, Institut Pasteur, Paris 75015, France</aff>
<aff id="aff7"><label>7</label>
Dendritic Cell Immunobiology Unit, Institut Pasteur, Paris 75015, France</aff>
<aff id="aff8"><label>8</label>
Center for Vaccinology, Ghent University and University Hospital, Ghent 9000, Belgium</aff>
<aff id="aff9"><label>9</label>
Centre National de Génotypage, CEA, Evry 91000, France</aff>
<aff id="aff10"><label>10</label>
Department of Cancer Immunology, Genentech, South San Francisco, CA 94080, USA</aff>
<author-notes><corresp id="cor1"><label>∗</label>
Corresponding author <email>quintana@pasteur.fr</email>
</corresp>
<fn id="fn1"><label>11</label>
<p id="ntpara0010">Co-first author</p>
</fn>
<fn id="fn2"><label>12</label>
<p id="ntpara0015">Present address: DIACCURATE, Institut Pasteur, PARIS 75015, France</p>
</fn>
<fn id="fn3"><label>13</label>
<p id="ntpara0020">Lead Contact</p>
</fn>
</author-notes>
<pub-date pub-type="pmc-release"><day>20</day>
<month>10</month>
<year>2016</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="ppub"><day>20</day>
<month>10</month>
<year>2016</year>
</pub-date>
<volume>167</volume>
<issue>3</issue>
<fpage>643</fpage>
<lpage>656.e17</lpage>
<history><date date-type="received"><day>12</day>
<month>4</month>
<year>2016</year>
</date>
<date date-type="rev-recd"><day>14</day>
<month>7</month>
<year>2016</year>
</date>
<date date-type="accepted"><day>15</day>
<month>9</month>
<year>2016</year>
</date>
</history>
<permissions><copyright-statement>© 2016 The Author(s)</copyright-statement>
<copyright-year>2016</copyright-year>
<license license-type="CC BY-NC-ND" xlink:href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><license-p>This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).</license-p>
</license>
</permissions>
<related-article related-article-type="article-reference" id="d32e12" ext-link-type="doi" xlink:href="10.1016/j.cell.2016.09.025"></related-article>
<abstract id="abs0010"><title>Summary</title>
<p>Humans differ in the outcome that follows exposure to life-threatening pathogens, yet the extent of population differences in immune responses and their genetic and evolutionary determinants remain undefined. Here, we characterized, using RNA sequencing, the transcriptional response of primary monocytes from Africans and Europeans to bacterial and viral stimuli—ligands activating Toll-like receptor pathways (TLR1/2, TLR4, and TLR7/8) and influenza virus—and mapped expression quantitative trait loci (eQTLs). We identify numerous <italic>cis</italic>
-eQTLs that contribute to the marked differences in immune responses detected within and between populations and a strong <italic>trans</italic>
-eQTL hotspot at <italic>TLR1</italic>
that decreases expression of pro-inflammatory genes in Europeans only. We find that immune-responsive regulatory variants are enriched in population-specific signals of natural selection and show that admixture with Neandertals introduced regulatory variants into European genomes, affecting preferentially responses to viral challenges. Together, our study uncovers evolutionarily important determinants of differences in host immune responsiveness between human populations.</p>
</abstract>
<abstract abstract-type="graphical" id="abs0015"><title>Graphical Abstract</title>
<fig id="undfig1" position="anchor"><graphic xlink:href="fx1"></graphic>
</fig>
</abstract>
<abstract abstract-type="author-highlights" id="abs0020"><title>Highlights</title>
<p><list list-type="simple"><list-item id="u0010"><label>•</label>
<p>Human populations differ in their transcriptional responses to immune challenges</p>
</list-item>
<list-item id="u0015"><label>•</label>
<p><italic>Cis</italic>
- and <italic>trans</italic>
-eQTLs contribute to population differences in immune responses</p>
</list-item>
<list-item id="u0020"><label>•</label>
<p>Immune-responsive regulatory variants have participated in human adaptation</p>
</list-item>
<list-item id="u0025"><label>•</label>
<p>Neandertals introduced variants affecting immune responses into European genomes</p>
</list-item>
</list>
</p>
</abstract>
<abstract abstract-type="teaser" id="abs0025"><p>Genetic variants enriched in population-specific signals of natural selection and, among Europeans, of Neandertal ancestry play a major role in the differences in transcriptional responses to inflammatory and infectious challenges observed between human populations.</p>
</abstract>
<kwd-group id="kwrds0010"><title>Keywords</title>
<kwd>population genetics</kwd>
<kwd>immune response</kwd>
<kwd>eQTL mapping</kwd>
<kwd>transcriptional responses</kwd>
<kwd>monocytes</kwd>
<kwd>evolution</kwd>
<kwd>natural selection</kwd>
<kwd>Neandertal admixture</kwd>
</kwd-group>
</article-meta>
<notes><p id="misc0010">Published: October 20, 2016</p>
</notes>
</front>
<floats-group><fig id="fig1"><label>Figure 1</label>
<caption><p>Transcriptional Response of Primary Monocytes to TLR Activation and Influenza A Virus Infection</p>
<p>(A) PC analysis of adjusted RNA-sequencing expression profiles in the five conditions tested in Africans (AFB) and Europeans (EUB).</p>
<p>(B) Weighted correlation network analysis. Relative size of the modules (left), expression patterns of genes in modules that are upregulated after stimulation (1–4) with boxplots representing relative expression based on PC1 (middle), and most associated transcription factor binding motifs in genes within modules (right).</p>
<p>(C) Most significant GO biological process enrichments of genes in modules 1–4.</p>
<p>See also <xref rid="figs4" ref-type="fig">Figure S4</xref>
and <xref rid="mmc1" ref-type="supplementary-material">Table S1</xref>
.</p>
</caption>
<graphic xlink:href="gr1"></graphic>
</fig>
<fig id="fig2"><label>Figure 2</label>
<caption><p>Genetic Determinants of Population Differences in Immune Response</p>
<p>(A) Number of genes harboring reQTLs in single conditions or combinations of stimulations (<sup>∗∗∗</sup>
p < 0.001, significance of overlap between stimulation conditions).</p>
<p>(B) Enrichment of (r)eQTLs in transcription factor (TF) binding sites. The five TFs presenting the strongest enrichments are shown for basal state eQTLs and reQTLs in different sets of stimulated conditions. Dots show the estimated odds ratio for the presence of binding sites of the TFs under consideration among (r)eQTLs, and horizontal lines show the 95% confidence interval of the odds ratio.</p>
<p>(C) Proportion (in percentage) of popDRGs harboring a local reQTL. Within each condition, popDRGs of different strengths (light color, |log<sub>2</sub>
FC<sub>pop</sub>
| > 0.2; dark color, |log<sub>2</sub>
FC<sub>pop</sub>
| > 0.5), as well as the proportion of reQTLs expected at the genome-wide level (in gray), are represented (<sup>∗∗∗</sup>
p < 0.001, significance of enrichment).</p>
<p>(D) Fraction of population differences attributable to (r)eQTLs among popDEGs and popDRGs. (r)eQTLs are sorted by increasing effect size and divided into quintiles. Dark color bars indicate the fraction of |log<sub>2</sub>
FC<sub>pop</sub>
| of popDRGs attributable to reQTLs for each stimulation, and the light color bars represent the fraction that is not explained by reQTLs. For the non-stimulated (NS) condition, the fraction of |log<sub>2</sub>
FC<sub>pop</sub>
| of popDEGs attributable to eQTLs is reported.</p>
<p>(E) TLR-induced reQTL at <italic>NCF2</italic>
in both Africans and Europeans (left), and mean population expression of <italic>NCF2</italic>
(right).</p>
<p>(F) European-specific reQTL at <italic>ST3GAL6</italic>
induced by R848 (left), and mean population expression of <italic>ST3GAL6</italic>
(right).</p>
<p>See also <xref rid="figs6" ref-type="fig">Figure S6</xref>
and <xref rid="mmc2" ref-type="supplementary-material">Table S2</xref>
.</p>
</caption>
<graphic xlink:href="gr2"></graphic>
</fig>
<fig id="fig3"><label>Figure 3</label>
<caption><p>Allele-Specific Expression upon Immune Stimulation</p>
<p>(A) Strongest allele-specific response QTLs (asrQTLs) for each stimulation condition and population.</p>
<p>(B) asrQTL of <italic>ARL5B</italic>
. Individual allelic ratios, in non-stimulated and IAV-infected conditions, are grouped by reQTL rs2130531 genotype and phase with exonic variants, with color-coding for population (dark and light colors for Africans and Europeans, respectively). Vertical bars show the 95% binomial confidence interval of the allelic ratio.</p>
<p>(C) Distribution of allelic ratios across genes harboring aseQTLs. Ratios are grouped by eQTL genotype (HMZ, homozygous; HTZ, heterozygous) for basal state or stimulated conditions.</p>
<p>(D) Percentage of ASE events observed for different categories of rare exonic variants. Vertical bars indicate 95% confidence intervals for the estimated percentage (<sup>∗∗∗</sup>
p < 0.001).</p>
<p>See also <xref rid="figs6" ref-type="fig">Figure S6</xref>
and <xref rid="mmc2" ref-type="supplementary-material">Table S2</xref>
.</p>
</caption>
<graphic xlink:href="gr3"></graphic>
</fig>
<fig id="fig4"><label>Figure 4</label>
<caption><p>Identification of Master Regulatory Loci of Immune Responses</p>
<p>(A) Genome-wide distribution of <italic>trans</italic>
-eQTLs. For each locus, the number of associated genes identified at a genome-wide FDR of 5% or using an SNP-based Bonferroni correction is represented by black and gray bars, respectively.</p>
<p>(B) Enrichment of popDRGs in genes regulated by <italic>trans</italic>
-eQTLs. Within each condition of stimulation, popDRGs of different strengths (light color, |log<sub>2</sub>
FC<sub>pop</sub>
| > 0.2; dark color, |log<sub>2</sub>
FC<sub>pop</sub>
| > 0.5) are represented (<sup>∗</sup>
p < 0.05, <sup>∗∗∗</sup>
p < 0.001).</p>
<p>(C) Fine mapping of the Pam<sub>3</sub>
CSK<sub>4</sub>
-induced <italic>trans</italic>
-eQTL at <italic>TLR1</italic>
. The significance of SNP associations with the expression patterns (PC1) of the 432 <italic>trans</italic>
-regulated genes is shown for basal and Pam<sub>3</sub>
CSK<sub>4</sub>
conditions (gray and green dots, respectively). Only the gene overlapping the strongest <italic>trans</italic>
-eQTL signal is represented.</p>
<p>(D) <italic>TLR1 trans</italic>
-associated genes at p<sub>Bonferroni</sub>
< 0.05. The size of the circles reflects the proportion of the variance of gene expression explained by rs5743618, and colors indicate the direction of the change in expression associated with the derived allele. Only the 100 most significant genes are shown.</p>
<p>(E) Fraction of population differences in gene expression (|log<sub>2</sub>
FC<sub>pop</sub>
|) attributable to rs5743618 among popDRGs regulated by the <italic>TLR1</italic>
locus (in dark green). Only the tail distribution of popDRGs with the largest population differences is represented. Genes involved in the GO biological process “response to molecule of bacterial origin” are reported.</p>
<p>(F) Impact of the derived allele of <italic>TLR1</italic>
rs5743618 (C allele) on the expression patterns (PC1) of the 81 inflammatory genes from module 1.</p>
<p>See also <xref rid="mmc3" ref-type="supplementary-material">Table S3</xref>
.</p>
</caption>
<graphic xlink:href="gr4"></graphic>
</fig>
<fig id="fig5"><label>Figure 5</label>
<caption><p>Natural Selection Driving Population Differences of Immune Response</p>
<p>(A) Distribution of neutrality statistics among local (r)eQTLs. For each locus, the maximum <italic>F</italic>
<sub>ST</sub>
or |iHS| across all SNPs in high LD (r<sup>2</sup>
> 0.8) is considered, focusing on selection signals targeting the derived allele. The genome-wide distribution of these statistics (after pruning for LD, to avoid overweighting long haplotypes) is provided as a reference (all). Significance was assessed by resampling random SNPs from the genome, matched for MAF and LD (<sup>∗</sup>
p < 0.05, <sup>∗∗</sup>
p < 0.01, <sup>∗∗∗</sup>
p < 0.001). Top: Africans. Bottom: Europeans.</p>
<p>(B) Fine mapping of the selection signal detected at the <italic>CCR1</italic>
reQTL in Africans. Top: SNP associations with <italic>CCR1</italic>
expression (-log<sub>10</sub>
(p<sub>eQTL</sub>
)) are represented for the basal (gray) and Pam<sub>3</sub>
CSK<sub>4</sub>
(green) conditions. SNPs located in the <italic>CCR1</italic>
reQTL peak region are in high LD (r<sup>2</sup>
> 0.8) with the reQTL peak-SNP. Middle and bottom: <italic>F</italic>
<sub>ST</sub>
and |iHS| values for SNPs with a DAF ≥ 0.2, respectively. For each SNP, the size of the dots is proportional to the association with <italic>CCR1</italic>
expression, with blue and red indicating selection on derived and ancestral alleles, respectively. The blue line represents the recombination rate at the locus. Only the gene for which the eQTL was detected is represented.</p>
<p>See also <xref rid="mmc4" ref-type="supplementary-material">Table S4</xref>
.</p>
</caption>
<graphic xlink:href="gr5"></graphic>
</fig>
<fig id="fig6"><label>Figure 6</label>
<caption><p>Neandertal Introgression of Immune Regulatory Variants in Europeans</p>
<p>(A) Enrichment of (r)eQTLs in archaic SNPs. The observed number of archaic eQTLs is presented for each condition (colored dots) in Europeans with respect to the expected distribution of archaic eQTLs under the assumption of independence (gray bars) (see <xref rid="sec4" ref-type="sec">STAR Methods</xref>
) (<sup>∗</sup>
p < 0.05, <sup>∗∗</sup>
p < 0.01, <sup>∗∗∗</sup>
p < 0.001).</p>
<p>(B) Fine mapping of the archaic reQTL at <italic>PNMA1</italic>
in Europeans. SNP associations with <italic>PNMA1</italic>
expression (−log<sub>10</sub>
(p<sub>eQTL</sub>
)) for the basal (gray) and stimulated (in colors) conditions (top). European individuals, and their corresponding archaic and modern haplotypes at the <italic>PNMA1</italic>
locus, are sorted by increasing levels of <italic>PNMA1</italic>
expression. Red dots represent archaic SNPs, and red lines represent the largest consecutive stretch of archaic alleles associated with <italic>PNMA1</italic>
expression (middle). Frequency distribution of archaic SNPs at the locus is shown (bottom). Only the gene for which the eQTL was observed is represented.</p>
<p>See also <xref rid="figs7" ref-type="fig">Figure S7</xref>
and <xref rid="mmc5" ref-type="supplementary-material">Table S5</xref>
.</p>
</caption>
<graphic xlink:href="gr6"></graphic>
</fig>
<fig id="figs1"><label>Figure S1</label>
<caption><p>Overview of the Experimental Setting, Related to <xref rid="sec4" ref-type="sec">STAR Methods</xref>
</p>
<p>The transcriptional response of primary monocytes from 200 healthy donors of European and African descent to various immune stimulations was dissected to pinpoint molecular phenotypes differing between populations and under genetic control.</p>
</caption>
<graphic xlink:href="figs1"></graphic>
</fig>
<fig id="figs2"><label>Figure S2</label>
<caption><p>Processing of RNA-Sequencing Data, Related to <xref rid="sec4" ref-type="sec">STAR Methods</xref>
</p>
<p>(A) Read counts across the 970 RNA samples (in millions of reads). (B) Alignment of reads with various genomic features. Reads aligning with splice junctions are counted as many times as the number of genomic features they overlap. TSS: transcription start site, UTR: untranslated region, CDS: coding sequence, TES: transcription end site, up: upstream, down: downstream. (C) Distribution of GC content across samples. (D) Correlations between gene expression and global GC content. The expression of a large proportion of high-GC content genes was positively correlated with GC content (3<sup>rd</sup>
quartile of GC content, in red), whereas the expression of low-GC content genes tended to be negatively correlated to GC content (1<sup>st</sup>
quartile of GC content, in blue). The correlation distribution for total genes is shown in gray. Expectations were calculated by permutation (black). (E) Illustration of the effect of global GC content on gene expression. <italic>UFM1</italic>
(GC content: 33.9%) expression is plotted for all RNA samples, ordered by global GC content, for each condition and population. (F) Effect of various technical batches and confounding factors on gene expression. The proportion of genes whose expression levels are associated, for different levels of significance, with each factor is presented before and after correction of the data (up and bottom panels, respectively). The association of each gene with each cofactor was assessed by determining the proportion of the variance of gene expression explained by the cofactor under consideration, after adjustment for all other cofactors. (G) Boxplots of coefficient of variation (<italic>CV</italic>
) in technical and biological replicates across conditions. <italic>CV</italic>
distributions of technical replicates are smaller in magnitude and less variable compared to distributions of pairwise biological replicates (Wilcoxon Rank-Sum Test, <sup>∗∗∗</sup>
p < 0.001). (H) Boxplots of correlation coefficient (<italic>r</italic>
) between technical and biological replicates. <italic>r</italic>
calculated between technical replicates (red circles) are significant outliers to the <italic>r</italic>
distributions of pairwise biological replicates (boxplots).</p>
</caption>
<graphic xlink:href="figs2"></graphic>
</fig>
<fig id="figs3"><label>Figure S3</label>
<caption><p>Overview of the Pipeline Used for RNA-Seq Data Pre-processing, Related to <xref rid="sec4" ref-type="sec">STAR Methods</xref>
</p>
<p>We filtered out samples with irregular gene body coverage and used Cufflinks/CuffDiff to estimate transcript level FPKM. We removed all transcripts for which the total gene FPKM was less than 1 in all conditions. We then log-transformed the data and performed adjustments for GC content, 3′/5′ bias, date of experiment and library preparation batch. We carried out kNN imputation before ComBat, to handle missing values. Batch covariates were treated sequentially, as ComBat can only handle one batch variable at a time. The corrected FPKM values were then transformed back to the normal scale and forced to positive values to calculate gene-level FPKM. Gene expression was considered on the log scale for all subsequent analyses.</p>
</caption>
<graphic xlink:href="figs3"></graphic>
</fig>
<fig id="figs4"><label>Figure S4</label>
<caption><p>Weighted Correlation Network Analysis, Related to <xref rid="fig1" ref-type="fig">Figure 1</xref>
</p>
<p>The relative expression of each gene module is based on the first principal component for the expression of genes present in the corresponding module.</p>
</caption>
<graphic xlink:href="figs4"></graphic>
</fig>
<fig id="figs5"><label>Figure S5</label>
<caption><p>Genetic Structure of the Population Samples, Related to <xref rid="sec4" ref-type="sec">STAR Methods</xref>
</p>
<p>(A) Genetic ancestry of African-descent Belgians (AFB) and European-descent Belgians (AFB), estimated by ADMIXTURE. Each vertical line represents an individual genome, which is partitioned into <italic>K</italic>
different genetic clusters. This analysis was performed on 229,320 independent SNPs and 789 individuals from 22 populations, including EUB and AFB, together with a selection of representative populations from sub-Saharan Africa, North Africa, the Near East and Europe (<xref rid="bib10" ref-type="bibr">Behar et al., 2010</xref>
, <xref rid="bib57" ref-type="bibr">Patin et al., 2014</xref>
). We made <italic>K</italic>
vary from 2 to 10, and ran five iterations with different random seeds for each <italic>K</italic>
value. The run with the lowest cross-validation error rate for each <italic>K</italic>
value is shown for <italic>K</italic>
= 2 to 5. (B) Cross-validation (CV) error rates of ADMIXTURE results for 5 different iterations and <italic>K</italic>
prior values. Minimum CV values for each <italic>K</italic>
are in red. CV values start increasing at <italic>K</italic>
= 6. (C) Local genetic sub-structure in the AFB population sample, estimated by principal component analysis (PCA). This analysis was performed on 341,593 independent SNPs and 511 individuals from 7 western and central African populations (<xref rid="bib57" ref-type="bibr">Patin et al., 2014</xref>
). (D) Local genetic sub-structure in the EUB population, estimated by PCA. This analysis was performed on 182,572 independent SNPs and 220 individuals from 13 European populations (<xref rid="bib10" ref-type="bibr">Behar et al., 2010</xref>
). (C-D) PC1 and PC2 are shown, together with the proportion of variance explained.</p>
</caption>
<graphic xlink:href="figs5"></graphic>
</fig>
<fig id="figs6"><label>Figure S6</label>
<caption><p>eQTL and aseQTL Mapping, Related to <xref rid="fig2" ref-type="fig">Figures 2</xref>
and <xref rid="fig3" ref-type="fig">3</xref>
</p>
<p>(A) Number of genes associated with an eQTL across conditions. (B) Enrichment of (r)eQTLs in regulatory elements. CD14<sup>+</sup>
cell regulatory elements from Ensembl Regulatory Build were considered for the analysis. Significance was assessed relative to the genome-wide distribution of SNPs overlapping regulatory elements within 1Mb of the gene transcription start site. <sup>∗</sup>
p < 0.05, <sup>∗∗</sup>
p < 0.01, <sup>∗∗∗</sup>
p < 0.001. (C) General rationale of allele-specific eQTL mapping. We identified eQTLs leading to allelic imbalance (<italic>i.e</italic>
., aseQTLs) by determining the ratio of reads from the two alleles of a heterozygous exonic SNP, according to their phase with the genotypes of the regulatory SNP that affects a transcription factor binding site. (D) Allele-specific expression in the context of cell stimulation. The aseQTL is detectable only after stimulation (TLR/IAV) by the presence of a transcription factor that is not expressed at the basal state (NS). (E) Power to detect aseQTLs as a function of eQTL effect size, number of observations and number of reads per exonic SNP. (F-G) Effect of regulatory variants on allele-specific expression, with (F) distribution of aseQTLs among local eQTLs and (G) distribution of asrQTLs among local reQTLs.</p>
</caption>
<graphic xlink:href="figs6"></graphic>
</fig>
<fig id="figs7"><label>Figure S7</label>
<caption><p>Introgression of Regulatory Variants from Neandertals, Related to <xref rid="fig6" ref-type="fig">Figure 6</xref>
</p>
<p>(A) Models of incomplete lineage sorting and introgression from Neandertals. Incomplete lineage sorting (ILS) scenario (left panel). An ancient variant predating the split between humans and Neandertals was retained in both lineages, but lost from the African population. Haplotypes carrying this ancient allele in Europeans are expected to be short because the time window allowed multiple recombination events to occur. Scenario of introgression from Neandertals (right panel). An archaic allele from Neandertals has been introgressed in Europeans and haplotypes containing this allele are expected to be longer than those resulting from ILS, due to the more recent occurrence of the admixture event. (B) Frequency of the archaic haplotype of <italic>PNMA1</italic>
, tagged by SNP rs12436322, in different world-wide populations. Pie size is proportional to the number of individuals. The non-archaic allele is reported in violet (1000 Genomes data) and in dark blue (Simons Genome Diversity Project Dataset), and the archaic allele is presented in orange. (C) SNP associations, [-log10(p<sub>eQTL</sub>
)], with <italic>PNMA1</italic>
expression in Africans and Europeans are shown (upper panel). Archaic haplotypes at the <italic>PNMA1</italic>
locus in Europeans (lower panel) are ordered by increasing levels of <italic>PNMA1</italic>
expression, and archaic SNPs are represented in red for each haplotype. Red lines indicate the core archaic haplotype, defined as the haplotype within the eQTL carrying the largest number of archaic alleles. The eQTL identified in Europeans spans a region of 102 kb surrounding the gene, and overlaps an eQTL present in Africans. (D) Dissection of the <italic>PNMA1</italic>
core haplotype. Haplotypes at the <italic>PNMA1</italic>
locus are represented by horizontal lines showing, for SNPs with a MAF ≥ 5% in either population, the ancestral allele in white and the derived allele in color. The black horizontal line separates European haplotypes (top) from African haplotypes (bottom). Within each population, SNPs associated with <italic>PNMA1</italic>
expression are highlighted in blue for those specific to the Neandertal lineage (<italic>i.e</italic>
. archaic SNPs) and in red for the others. In total, 12 archaic alleles at the locus tag the archaic core haplotype associated with an upregulation of <italic>PNMA1</italic>
expression, either in the basal condition or in response to R848 and IAV. This haplotype is longer than expected under the ILS scenario, suggesting that introgression occurred in Europeans through admixture with Neandertals. This archaic haplotype, tagged by the aSNP rs12436322, has re-introduced the ancestral allele of rs6574138 in Europeans (<italic>i.e</italic>
. all individuals not carrying the archaic haplotypes have the derived allele), which is also present in Africans and associated with <italic>PNMA1</italic>
expression. SNP rs6574138 overlaps with ENCODE binding sites, consistent with its putative functional role in the regulation of <italic>PNMA1</italic>
expression in both Europeans and Africans. (E) Inferred history of the ancestral and derived alleles at rs6574138 in modern human populations, up to their most recent common ancestor. Our data suggest that rs6574138 predates the split between Neandertals and modern humans and that the derived allele of this variant was fixed in early Europeans. The ancestral allele of rs6574138 was then reintroduced in Europeans by introgression of the Neandertal haplotype tagged by rs12436322, and is responsible for the variability in <italic>PNMA1</italic>
expression in the contemporary European population.</p>
</caption>
<graphic xlink:href="figs7"></graphic>
</fig>
<table-wrap id="tbl1" position="float"><label>Table 1</label>
<caption><p>Genes Displaying the Highest Degree of Differential Expression or Differential Immune-Induced Responses between Africans and Europeans</p>
</caption>
<table frame="hsides" rules="groups"><thead><tr><th>Condition</th>
<th>Africans</th>
<th>Europeans</th>
</tr>
</thead>
<tbody><tr><td>Resting cells (NS)</td>
<td><italic>CCL3L1, CCL3L3, <underline>CX3CR1</underline>
, LPL, TMEM14C, TREML4, VNN1</italic>
</td>
<td><italic><underline>HTRA3</underline>
, MARCO, <underline>MT1X</underline>
, PADI4, RP11-105C19.1, RP11-645C24.5, S100P, TMEM176A, TMEM176B, USP32P1</italic>
</td>
</tr>
<tr><td>TLR4 (LPS)</td>
<td><italic><underline>AC131056.3</underline>
, CEP128, LPL, RP11-1143G9.4, <underline>RP11-7F17.7</underline>
, TREML3P, TREML4, VNN1</italic>
</td>
<td><italic><underline>AC004988.1</underline>
, <underline>APOBEC3A</underline>
, <underline>BATF2</underline>
, <underline>CCL13</underline>
, <underline>CCL15</underline>
, <underline>CCL23</underline>
, <underline>CCL8</underline>
, <underline>CMPK2</underline>
, <underline>CXCL10</underline>
, <underline>DHX58</underline>
, <underline>DNAAF1</underline>
, <underline>ETV7</underline>
, <underline>GBP4</underline>
, <underline>HERC5</underline>
, <underline>IFIT1</underline>
, <underline>IFIT2</underline>
, <underline>IFIT3</underline>
, MARCO, <underline>NCOA7</underline>
, PLXNA3, RP11-105C19.1, RP11-645C24.5, <underline>RSAD2</underline>
, <underline>SIGLEC1,</underline>
TMEM176A, TMEM176B, <underline>TNFSF10</underline>
, U1, <underline>USP18</underline>
, USP32P1</italic>
</td>
</tr>
<tr><td>TLR1/2 (Pam<sub>3</sub>
CSK<sub>4</sub>
)</td>
<td><italic><underline>AC131056.3</underline>
, <underline>C2CD4B</underline>
, CCL3L1, CCL3L3, CEP128, <underline>CPXM1</underline>
, <underline>CSF3</underline>
, <underline>GBA3</underline>
, <underline>IL12B</underline>
, <underline>IRG1</underline>
, LPL, <underline>NKX3-1</underline>
, <underline>SLC25A37</underline>
, <underline>SNORD3B-1</underline>
, <underline>SUCNR1</underline>
, TREML4, VNN1</italic>
</td>
<td><italic><underline>CCL15</underline>
, HMOX1, <underline>IFIT1</underline>
, <underline>IFIT2</underline>
, <underline>IFIT3</underline>
, <underline>PLXNA3</underline>
, RP11-105C19.1, RP11-645C24.5, <underline>RSAD2</underline>
, TMEM176A, TMEM176B, U1, USP32P1</italic>
</td>
</tr>
<tr><td>TLR7/8 (R848)</td>
<td><italic><underline>AC131056.3</underline>
, LPL, <underline>RP11-7F17.7</underline>
, <underline>SUCNR1</underline>
, TREML3P, TREML4</italic>
</td>
<td><italic><underline>PAM</underline>
, <underline>PLXNA3</underline>
, RP11-105C19.1, <underline>RP11-128M1.1</underline>
, RP11-645C24.5, <underline>TMEM176A</underline>
, TMEM176B, <underline>U1</underline>
</italic>
</td>
</tr>
<tr><td>Influenza A virus (IAV)</td>
<td><italic>CCL3L1, CCL3L3, CTSC, HS3ST3B1, <underline>IL6</underline>
, <underline>LGALS17A</underline>
, <underline>NUPR1</underline>
, RP11-1143G9.4, <underline>SLC25A37</underline>
, TREML4</italic>
</td>
<td><italic><underline>J01415.23</underline>
, MARCO, <underline>MDGA1</underline>
, PADI4, <underline>PAM</underline>
, <underline>RASD2</underline>
, RP11-105C19.1, RP11-105C19.2, RP11-645C24.5, S100P, SNHG5, TMEM176A, TMEM176B, <underline>U1</underline>
</italic>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot><fn><p>The genes listed are divided according to the population where they present the highest expression. All genes reported are differentially expressed between populations in the various cellular conditions (popDEGs, |log<sub>2</sub>
(FC<sub>pop</sub>
)| > 1), while those presented as underlined are further characterized by their stronger population differences in response to treatment, with respect to the non-stimulated condition (popDRGs). Underlined genes in the non-stimulated (NS) condition correspond to those that are differentially expressed between populations only in that condition. Genes presenting a |log<sub>2</sub>
(FC<sub>pop</sub>
)| > 1 at FDR of 5% are presented.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</floats-group>
</pmc>
</record>
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