Detection and interpretation of shared genetic influences on 42 human traits
Identifieur interne : 000096 ( Pmc/Checkpoint ); précédent : 000095; suivant : 000097Detection and interpretation of shared genetic influences on 42 human traits
Auteurs : Joseph K. Pickrell [États-Unis] ; Tomaz Berisa [États-Unis] ; Jimmy Z. Liu [États-Unis] ; Laure Segurel [France] ; Joyce Y. Tung [États-Unis] ; David Hinds [États-Unis]Source :
- Nature genetics [ 1061-4036 ] ; 2016.
Abstract
We performed a scan for genetic variants associated with multiple phenotypes by comparing large genome-wide association studies (GWAS) of 42 traits or diseases. We identified 341 loci (at an FDR of 10%) associated with multiple traits. Several loci are associated with a large number of phenotypes; for example, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of these traits, including risk of schizophrenia (rs13107325: log-odds ratio = 0.15, P = 2 × 10−12) and Parkinson's disease (log-odds ratio = −0.15, P = 1.6 × 10−7), among others. Second, we used these loci to identify traits that share multiple genetic causes in common. For example, variants that increase risk of schizophrenia also tend to increase risk of inflammatory bowel disease. Finally, we developed a method to identify pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased BMI causally increases triglyceride levels.
Url:
DOI: 10.1038/ng.3570
PubMed: 27182965
PubMed Central: 5207801
Affiliations:
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Pickrell</name><affiliation wicri:level="2"><nlm:aff id="A1"> New York Genome Center, New York, NY, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea> New York Genome Center, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation><affiliation wicri:level="4"><nlm:aff id="A2"> Department of Biological Sciences, Columbia University, New York, NY, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea> Department of Biological Sciences, Columbia University, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region><settlement type="city">New York</settlement></placeName><orgName type="university">Université Columbia</orgName></affiliation></author><author><name sortKey="Berisa, Tomaz" sort="Berisa, Tomaz" uniqKey="Berisa T" first="Tomaz" last="Berisa">Tomaz Berisa</name><affiliation wicri:level="2"><nlm:aff id="A1"> New York Genome Center, New York, NY, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea> New York Genome Center, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation></author><author><name sortKey="Liu, Jimmy Z" sort="Liu, Jimmy Z" uniqKey="Liu J" first="Jimmy Z." last="Liu">Jimmy Z. 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Pickrell</name><affiliation wicri:level="2"><nlm:aff id="A1"> New York Genome Center, New York, NY, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea> New York Genome Center, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation><affiliation wicri:level="4"><nlm:aff id="A2"> Department of Biological Sciences, Columbia University, New York, NY, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea> Department of Biological Sciences, Columbia University, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region><settlement type="city">New York</settlement></placeName><orgName type="university">Université Columbia</orgName></affiliation></author><author><name sortKey="Berisa, Tomaz" sort="Berisa, Tomaz" uniqKey="Berisa T" first="Tomaz" last="Berisa">Tomaz Berisa</name><affiliation wicri:level="2"><nlm:aff id="A1"> New York Genome Center, New York, NY, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea> New York Genome Center, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation></author><author><name sortKey="Liu, Jimmy Z" sort="Liu, Jimmy Z" uniqKey="Liu J" first="Jimmy Z." last="Liu">Jimmy Z. Liu</name><affiliation wicri:level="2"><nlm:aff id="A1"> New York Genome Center, New York, NY, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea> New York Genome Center, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation></author><author><name sortKey="Segurel, Laure" sort="Segurel, Laure" uniqKey="Segurel L" first="Laure" last="Segurel">Laure Segurel</name><affiliation wicri:level="3"><nlm:aff id="A3"> UMR7206 Eco-anthropologie et ethnobiologie, CNRS-MNHN-Paris 7, Paris, France</nlm:aff><country xml:lang="fr">France</country><wicri:regionArea> UMR7206 Eco-anthropologie et ethnobiologie, CNRS-MNHN-Paris 7, Paris</wicri:regionArea><placeName><region type="region">Île-de-France</region><region type="old region">Île-de-France</region><settlement type="city">Paris</settlement></placeName></affiliation></author><author><name sortKey="Tung, Joyce Y" sort="Tung, Joyce Y" uniqKey="Tung J" first="Joyce Y." last="Tung">Joyce Y. Tung</name><affiliation wicri:level="2"><nlm:aff id="A4"> 23andMe, Inc., Mountain View, CA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea> 23andMe, Inc., Mountain View, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Hinds, David" sort="Hinds, David" uniqKey="Hinds D" first="David" last="Hinds">David Hinds</name><affiliation wicri:level="2"><nlm:aff id="A4"> 23andMe, Inc., Mountain View, CA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea> 23andMe, Inc., Mountain View, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author></analytic><series><title level="j">Nature genetics</title><idno type="ISSN">1061-4036</idno><idno type="eISSN">1546-1718</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">We performed a scan for genetic variants associated with multiple phenotypes by comparing large genome-wide association studies (GWAS) of 42 traits or diseases. We identified 341 loci (at an FDR of 10%) associated with multiple traits. Several loci are associated with a large number of phenotypes; for example, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of these traits, including risk of schizophrenia (rs13107325: log-odds ratio = 0.15, P = 2 × 10<sup>−12</sup>) and Parkinson's disease (log-odds ratio = −0.15, P = 1.6 × 10<sup>−7</sup>), among others. Second, we used these loci to identify traits that share multiple genetic causes in common. For example, variants that increase risk of schizophrenia also tend to increase risk of inflammatory bowel disease. Finally, we developed a method to identify pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased BMI causally increases triglyceride levels.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Stearns, Fw" uniqKey="Stearns F">FW Stearns</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Paaby, Ab" uniqKey="Paaby A">AB Paaby</name></author><author><name sortKey="Rockman, Mv" uniqKey="Rockman M">MV Rockman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Solovieff, N" uniqKey="Solovieff N">N Solovieff</name></author><author><name sortKey="Cotsapas, C" uniqKey="Cotsapas C">C Cotsapas</name></author><author><name sortKey="Lee, Ph" uniqKey="Lee P">PH Lee</name></author><author><name sortKey="Purcell, Sm" uniqKey="Purcell S">SM Purcell</name></author><author><name sortKey="Smoller, Jw" uniqKey="Smoller J">JW Smoller</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chill N, M" uniqKey="Chill 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<front><journal-meta><journal-id journal-id-type="nlm-journal-id">9216904</journal-id><journal-id journal-id-type="pubmed-jr-id">2419</journal-id><journal-id journal-id-type="nlm-ta">Nat Genet</journal-id><journal-id journal-id-type="iso-abbrev">Nat. Genet.</journal-id><journal-title-group><journal-title>Nature genetics</journal-title></journal-title-group><issn pub-type="ppub">1061-4036</issn><issn pub-type="epub">1546-1718</issn></journal-meta><article-meta><article-id pub-id-type="pmid">27182965</article-id><article-id pub-id-type="pmc">5207801</article-id><article-id pub-id-type="doi">10.1038/ng.3570</article-id><article-id pub-id-type="manuscript">NIHMS780506</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Detection and interpretation of shared genetic influences on 42 human traits</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Pickrell</surname><given-names>Joseph K.</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Berisa</surname><given-names>Tomaz</given-names></name><xref ref-type="aff" rid="A1">1</xref></contrib><contrib contrib-type="author"><name><surname>Liu</surname><given-names>Jimmy Z.</given-names></name><xref ref-type="aff" rid="A1">1</xref></contrib><contrib contrib-type="author"><name><surname>Segurel</surname><given-names>Laure</given-names></name><xref ref-type="aff" rid="A3">3</xref></contrib><contrib contrib-type="author"><name><surname>Tung</surname><given-names>Joyce Y.</given-names></name><xref ref-type="aff" rid="A4">4</xref></contrib><contrib contrib-type="author"><name><surname>Hinds</surname><given-names>David</given-names></name><xref ref-type="aff" rid="A4">4</xref></contrib></contrib-group><aff id="A1"><label>1</label> New York Genome Center, New York, NY, USA</aff><aff id="A2"><label>2</label> Department of Biological Sciences, Columbia University, New York, NY, USA</aff><aff id="A3"><label>3</label> UMR7206 Eco-anthropologie et ethnobiologie, CNRS-MNHN-Paris 7, Paris, France</aff><aff id="A4"><label>4</label> 23andMe, Inc., Mountain View, CA, USA</aff><author-notes><corresp id="CR1">Correspondence to: <email>jkpickrell@nygenome.org</email></corresp></author-notes><pub-date pub-type="nihms-submitted"><day>18</day><month>5</month><year>2016</year></pub-date><pub-date pub-type="epub"><day>16</day><month>5</month><year>2016</year></pub-date><pub-date pub-type="ppub"><month>7</month><year>2016</year></pub-date><pub-date pub-type="pmc-release"><day>03</day><month>1</month><year>2017</year></pub-date><volume>48</volume><issue>7</issue><fpage>709</fpage><lpage>717</lpage><pmc-comment>elocation-id from pubmed: 10.1038/ng.3570</pmc-comment>
<permissions><license><license-p>Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:<uri xlink:type="simple" xlink:href="http://www.nature.com/authors/editorial_policies/license.html#terms">http://www.nature.com/authors/editorial_policies/license.html#terms</uri></license-p></license></permissions><abstract><p id="P1">We performed a scan for genetic variants associated with multiple phenotypes by comparing large genome-wide association studies (GWAS) of 42 traits or diseases. We identified 341 loci (at an FDR of 10%) associated with multiple traits. Several loci are associated with a large number of phenotypes; for example, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of these traits, including risk of schizophrenia (rs13107325: log-odds ratio = 0.15, P = 2 × 10<sup>−12</sup>) and Parkinson's disease (log-odds ratio = −0.15, P = 1.6 × 10<sup>−7</sup>), among others. Second, we used these loci to identify traits that share multiple genetic causes in common. For example, variants that increase risk of schizophrenia also tend to increase risk of inflammatory bowel disease. Finally, we developed a method to identify pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased BMI causally increases triglyceride levels.</p></abstract></article-meta></front><floats-group><fig id="F1" orientation="portrait" position="float"><label>Figure 1</label><caption><title>Schematic of the different models considered for a given genomic region and two GWAS</title><p>We divide the genome into approximately independent blocks (see Methods), and estimate the proportion of blocks that fit into the shown patterns. The null model with no associations is not shown. Each point represents a single genetic variant.</p></caption><graphic xlink:href="nihms-780506-f0001"></graphic></fig><fig id="F2" orientation="portrait" position="float"><label>Figure 2</label><caption><title>Heatmap showing patterns of overlap between traits</title><p>Each square [<italic>i</italic>,<italic>j</italic>] shows the maximum <italic>a posteriori</italic> estimate of the proportion of genetic variants that influence trait <italic>i</italic> that also influence trait <italic>j</italic>, where <italic>i</italic> indexes rows and <italic>j</italic> indexes columns. Note that this is not symmetric. Darker colors represent larger proportions. Colors are shown for all pairs of traits that have at least one region in the set of 341 identified loci; all other pairs are set to white. Phenotypes were clustered by hierarchical clustering in R <sup><xref rid="R74" ref-type="bibr">74</xref></sup>.</p></caption><graphic xlink:href="nihms-780506-f0002"></graphic></fig><fig id="F3" orientation="portrait" position="float"><label>Figure 3</label><caption><title>Multiple associations near the ABO gene. A. Association signals for coronary artery disease and tonsillectomy</title><p>In the top panel, we show the P-values for association with coronary artery disease for variants in the window around the ABO gene. In the bottom panel are the P-values for association with tonsillectomy. In both panels, SNPs that tag functionally-important alleles at ABO are in color. In the middle are the gene models in the region–exons are denoted by blue boxes, and introns with red lines. Note that the ABO gene is transcribed on the negative strand. B. Association effect sizes for rs635634 on all tested traits. Shown are the effect size estimates for rs635634 for all traits. The lines represent 95% confidence intervals. Traits are grouped according to whether they are quantitative traits (in which case the x-axis is in units of standard deviations) or case/control traits (in which case the x-axis is in units of log-odds).</p></caption><graphic xlink:href="nihms-780506-f0003"></graphic></fig><fig id="F4" orientation="portrait" position="float"><label>Figure 4</label><caption><title>Heatmap showing patterns of correlated effect sizes of variants across pairs of traits</title><p>For each pair of traits [<italic>i</italic>,<italic>j</italic>], we extracted the set of variants that influence trait <italic>i</italic> and their effect sizes on both <italic>i</italic> and <italic>j</italic>. We then calculated Spearman's rank correlation between the effect sizes on <italic>i</italic> and the effect sizes on <italic>j</italic>, and tested whether this correlation was significantly different from zero. Shown in color are all pairs where this test had a P-value less than 0.01. Darker colors correspond to smaller P-values, and the color corresponds to the direction of the correlation (in red are positive correlations and in blue are negative correlations). The phenotypes are in the same order as in <xref ref-type="fig" rid="F2">Figure 2</xref>. For a comparison to genome-wide genetic correlations, see <xref ref-type="supplementary-material" rid="SD1">Supplementary Figure 13</xref>.</p></caption><graphic xlink:href="nihms-780506-f0004"></graphic></fig><fig id="F5" orientation="portrait" position="float"><label>Figure 5</label><caption><title>Putative causal relationships between pairs of traits</title><p>For each pair of traits identified as candidates to be related in a causal manner (see Methods), we show the effect sizes of genetic variants on the two traits (at genetic variants successfully genotyped or imputed in both studies). Lines represent one standard error. <bold>A. and B. BMI and triglycerides.</bold> The effect sizes of genetic variants on BMI and triglyceride levels for variants identified in the GWAS for BMI (<bold>A.</bold>) or triglycerides (<bold>B.</bold>). <bold>C. and D. LDL and coronary artery disease.</bold> The effect sizes of genetic variants on LDL levels and coronary artery disease for variants identified in the GWAS for LDL (<bold>C.</bold>) or coronary artery disease (<bold>D.</bold>). <bold>E. and F. BMI and type 2 diabetes.</bold> The effect sizes of genetic variants on BMI and type 2 diabetes for variants identified in the GWAS for BMI (<bold>E.</bold>) or type 2 diabetes (<bold>F.</bold>). <bold>G. and H. Hypothyroidism and height.</bold> The effect sizes of genetic variants on hypothyroidism and height for variants identified in the GWAS for hypothyroidism (<bold>G.</bold>) or height (<bold>H.</bold>).</p></caption><graphic xlink:href="nihms-780506-f0005"></graphic></fig><table-wrap id="T1" position="float" orientation="portrait"><label>Table 1</label><caption><title>Phenotypes used in this study</title><p>For each study, we show the name of the phenotype, the abbreviation that will be used throughout this paper, the data source, the number of independent autosomal loci identified at a false discovery rate of 10%, and the number of participants in the study. For studies where the data source is 23andMe, a complete description of the GWAS is presented in the <xref ref-type="supplementary-material" rid="SD1">Supplementary Material</xref>.</p></caption><table frame="box" rules="cols"><thead><tr><th align="center" valign="top" rowspan="1" colspan="1">Phenotype</th><th align="center" valign="top" rowspan="1" colspan="1">Abbreviation</th><th align="center" valign="top" rowspan="1" colspan="1">Data source</th><th align="center" valign="top" rowspan="1" colspan="1">Approx # of loci</th><th align="center" valign="top" rowspan="1" colspan="1">Approx # of participants, in thousands (cases/controls, if applicable)</th></tr><tr><th colspan="5" align="center" valign="top" rowspan="1"><hr></hr></th></tr></thead><tbody><tr><td colspan="5" align="center" valign="top" rowspan="1">Neurological phenotypes<hr></hr></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Alzheimer's disease</td><td align="center" valign="top" rowspan="1" colspan="1">AD</td><td align="center" valign="top" rowspan="1" colspan="1">75</td><td align="center" valign="top" rowspan="1" colspan="1">11</td><td align="center" valign="top" rowspan="1" colspan="1">17 / 37</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Migraine</td><td align="center" valign="top" rowspan="1" colspan="1">MIGR</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">37</td><td align="center" valign="top" rowspan="1" colspan="1">53 / 231</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Parkinson's disease</td><td align="center" valign="top" rowspan="1" colspan="1">PD</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">43</td><td align="center" valign="top" rowspan="1" colspan="1">10 / 325</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Photic sneeze reflex</td><td align="center" valign="top" rowspan="1" colspan="1">PS</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">66</td><td align="center" valign="top" rowspan="1" colspan="1">32 / 67</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Schizophrenia</td><td align="center" valign="top" rowspan="1" colspan="1">SCZ</td><td align="center" valign="top" rowspan="1" colspan="1">59</td><td align="center" valign="top" rowspan="1" colspan="1">222</td><td align="center" valign="top" rowspan="1" colspan="1">34 / 46</td></tr><tr><td colspan="5" align="center" valign="top" rowspan="1"><hr></hr>Anthropometric/social traits<hr></hr></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Beighton hypermobility</td><td align="center" valign="top" rowspan="1" colspan="1">BHM</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">18</td><td align="center" valign="top" rowspan="1" colspan="1">64</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Breast size</td><td align="center" valign="top" rowspan="1" colspan="1">CUP</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">14</td><td align="center" valign="top" rowspan="1" colspan="1">34</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Body mass index</td><td align="center" valign="top" rowspan="1" colspan="1">BMI</td><td align="center" valign="top" rowspan="1" colspan="1">72</td><td align="center" valign="top" rowspan="1" colspan="1">30</td><td align="center" valign="top" rowspan="1" colspan="1">240</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Bone mineral density (femoral neck)</td><td align="center" valign="top" rowspan="1" colspan="1">FNBMD</td><td align="center" valign="top" rowspan="1" colspan="1">17</td><td align="center" valign="top" rowspan="1" colspan="1">19</td><td align="center" valign="top" rowspan="1" colspan="1">33</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Bone mineral density (lumbar spine)</td><td align="center" valign="top" rowspan="1" colspan="1">LSBMD</td><td align="center" valign="top" rowspan="1" colspan="1">17</td><td align="center" valign="top" rowspan="1" colspan="1">21</td><td align="center" valign="top" rowspan="1" colspan="1">32</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Chin dimples</td><td align="center" valign="top" rowspan="1" colspan="1">DIMP</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">57</td><td align="center" valign="top" rowspan="1" colspan="1">58 / 13</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Educational attainment</td><td align="center" valign="top" rowspan="1" colspan="1">EDU</td><td align="center" valign="top" rowspan="1" colspan="1">76</td><td align="center" valign="top" rowspan="1" colspan="1">93</td><td align="center" valign="top" rowspan="1" colspan="1">294</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Height</td><td align="center" valign="top" rowspan="1" colspan="1">HEIGHT</td><td align="center" valign="top" rowspan="1" colspan="1">71</td><td align="center" valign="top" rowspan="1" colspan="1">584</td><td align="center" valign="top" rowspan="1" colspan="1">253</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Male pattern baldness</td><td align="center" valign="top" rowspan="1" colspan="1">MPB</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">49</td><td align="center" valign="top" rowspan="1" colspan="1">9 / 8</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Nearsightedness</td><td align="center" valign="top" rowspan="1" colspan="1">NST</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">183</td><td align="center" valign="top" rowspan="1" colspan="1">106 / 86</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Nose size</td><td align="center" valign="top" rowspan="1" colspan="1">NOSE</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">13</td><td align="center" valign="top" rowspan="1" colspan="1">67</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Waist-hip ratio</td><td align="center" valign="top" rowspan="1" colspan="1">WHR</td><td align="center" valign="top" rowspan="1" colspan="1">77</td><td align="center" valign="top" rowspan="1" colspan="1">13</td><td align="center" valign="top" rowspan="1" colspan="1">143</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Unibrow</td><td align="center" valign="top" rowspan="1" colspan="1">UB</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">61</td><td align="center" valign="top" rowspan="1" colspan="1">69</td></tr><tr><td colspan="5" align="center" valign="top" rowspan="1"><hr></hr>Immune-related traits<hr></hr></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Any allergies</td><td align="center" valign="top" rowspan="1" colspan="1">ALL</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">43</td><td align="center" valign="top" rowspan="1" colspan="1">67 / 114</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Asthma</td><td align="center" valign="top" rowspan="1" colspan="1">ATH</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">35</td><td align="center" valign="top" rowspan="1" colspan="1">28 / 129</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Childhood ear infections</td><td align="center" valign="top" rowspan="1" colspan="1">CEI</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">15</td><td align="center" valign="top" rowspan="1" colspan="1">47 / 75</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Crohn's disease</td><td align="center" valign="top" rowspan="1" colspan="1">CD</td><td align="center" valign="top" rowspan="1" colspan="1">78</td><td align="center" valign="top" rowspan="1" colspan="1">61</td><td align="center" valign="top" rowspan="1" colspan="1">6 / 15</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Hypothyroidism</td><td align="center" valign="top" rowspan="1" colspan="1">HTHY</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">30</td><td align="center" valign="top" rowspan="1" colspan="1">18 / 117</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Rheumatoid arthritis</td><td align="center" valign="top" rowspan="1" colspan="1">RA</td><td align="center" valign="top" rowspan="1" colspan="1">79</td><td align="center" valign="top" rowspan="1" colspan="1">74</td><td align="center" valign="top" rowspan="1" colspan="1">14 / 44</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Tonsillectomy</td><td align="center" valign="top" rowspan="1" colspan="1">TS</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">48</td><td align="center" valign="top" rowspan="1" colspan="1">60 / 113</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Ulcerative colitis</td><td align="center" valign="top" rowspan="1" colspan="1">UC</td><td align="center" valign="top" rowspan="1" colspan="1">78</td><td align="center" valign="top" rowspan="1" colspan="1">42</td><td align="center" valign="top" rowspan="1" colspan="1">7 / 21</td></tr><tr><td colspan="5" align="center" valign="top" rowspan="1"><hr></hr>Metabolic phenotypes<hr></hr></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Age at menarche</td><td align="center" valign="top" rowspan="1" colspan="1">AAM</td><td align="center" valign="top" rowspan="1" colspan="1">43</td><td align="center" valign="top" rowspan="1" colspan="1">70</td><td align="center" valign="top" rowspan="1" colspan="1">133</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Age at menarche (23andMe)</td><td align="center" valign="top" rowspan="1" colspan="1">AAM (23)</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">55</td><td align="center" valign="top" rowspan="1" colspan="1">77</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Age at voice drop</td><td align="center" valign="top" rowspan="1" colspan="1">AVD</td><td align="center" valign="top" rowspan="1" colspan="1">23andMe</td><td align="center" valign="top" rowspan="1" colspan="1">5</td><td align="center" valign="top" rowspan="1" colspan="1">56</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Coronary artery disease</td><td align="center" valign="top" rowspan="1" colspan="1">CAD</td><td align="center" valign="top" rowspan="1" colspan="1">45</td><td align="center" valign="top" rowspan="1" colspan="1">11</td><td align="center" valign="top" rowspan="1" colspan="1">22 / 65</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Type 2 diabetes</td><td align="center" valign="top" rowspan="1" colspan="1">T2D</td><td align="center" valign="top" rowspan="1" colspan="1">80</td><td align="center" valign="top" rowspan="1" colspan="1">11</td><td align="center" valign="top" rowspan="1" colspan="1">12 / 57</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Fasting glucose</td><td align="center" valign="top" rowspan="1" colspan="1">FG</td><td align="center" valign="top" rowspan="1" colspan="1">81</td><td align="center" valign="top" rowspan="1" colspan="1">15</td><td align="center" valign="top" rowspan="1" colspan="1">58</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Low-density lipoproteins</td><td align="center" valign="top" rowspan="1" colspan="1">LDL</td><td align="center" valign="top" rowspan="1" colspan="1">82</td><td align="center" valign="top" rowspan="1" colspan="1">41</td><td align="center" valign="top" rowspan="1" colspan="1">85</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">High-density lipoproteins</td><td align="center" valign="top" rowspan="1" colspan="1">HDL</td><td align="center" valign="top" rowspan="1" colspan="1">82</td><td align="center" valign="top" rowspan="1" colspan="1">46</td><td align="center" valign="top" rowspan="1" colspan="1">89</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Triglycerides</td><td align="center" valign="top" rowspan="1" colspan="1">TG</td><td align="center" valign="top" rowspan="1" colspan="1">82</td><td align="center" valign="top" rowspan="1" colspan="1">31</td><td align="center" valign="top" rowspan="1" colspan="1">86</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Total cholesterol</td><td align="center" valign="top" rowspan="1" colspan="1">TC</td><td align="center" valign="top" rowspan="1" colspan="1">82</td><td align="center" valign="top" rowspan="1" colspan="1">53</td><td align="center" valign="top" rowspan="1" colspan="1">89</td></tr><tr><td colspan="5" align="center" valign="top" rowspan="1"><hr></hr>Hematopoeitic traits<hr></hr></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Hemoglobin</td><td align="center" valign="top" rowspan="1" colspan="1">HB</td><td align="center" valign="top" rowspan="1" colspan="1">83</td><td align="center" valign="top" rowspan="1" colspan="1">16</td><td align="center" valign="top" rowspan="1" colspan="1">51</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Mean cell hemoglobin concentration</td><td align="center" valign="top" rowspan="1" colspan="1">MCHC</td><td align="center" valign="top" rowspan="1" colspan="1">83</td><td align="center" valign="top" rowspan="1" colspan="1">15</td><td align="center" valign="top" rowspan="1" colspan="1">46</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Mean red cell volume</td><td align="center" valign="top" rowspan="1" colspan="1">MCV</td><td align="center" valign="top" rowspan="1" colspan="1">83</td><td align="center" valign="top" rowspan="1" colspan="1">42</td><td align="center" valign="top" rowspan="1" colspan="1">48</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Packed red cell volume</td><td align="center" valign="top" rowspan="1" colspan="1">PCV</td><td align="center" valign="top" rowspan="1" colspan="1">83</td><td align="center" valign="top" rowspan="1" colspan="1">13</td><td align="center" valign="top" rowspan="1" colspan="1">44</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Red blood cell count</td><td align="center" valign="top" rowspan="1" colspan="1">RBC</td><td align="center" valign="top" rowspan="1" colspan="1">83</td><td align="center" valign="top" rowspan="1" colspan="1">25</td><td align="center" valign="top" rowspan="1" colspan="1">45</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Platelet count</td><td align="center" valign="top" rowspan="1" colspan="1">PLT</td><td align="center" valign="top" rowspan="1" colspan="1">84</td><td align="center" valign="top" rowspan="1" colspan="1">50</td><td align="center" valign="top" rowspan="1" colspan="1">44</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Mean platelet volume</td><td align="center" valign="top" rowspan="1" colspan="1">MPV</td><td align="center" valign="top" rowspan="1" colspan="1">84</td><td align="center" valign="top" rowspan="1" colspan="1">29</td><td align="center" valign="top" rowspan="1" colspan="1">17</td></tr></tbody></table></table-wrap></floats-group></pmc><affiliations><list><country><li>France</li><li>États-Unis</li></country><region><li>Californie</li><li>État de New York</li><li>Île-de-France</li></region><settlement><li>New York</li><li>Paris</li></settlement><orgName><li>Université Columbia</li></orgName></list><tree><country name="États-Unis"><region name="État de New York"><name sortKey="Pickrell, Joseph K" sort="Pickrell, Joseph K" uniqKey="Pickrell J" first="Joseph K." last="Pickrell">Joseph K. Pickrell</name></region><name sortKey="Berisa, Tomaz" sort="Berisa, Tomaz" uniqKey="Berisa T" first="Tomaz" last="Berisa">Tomaz Berisa</name><name sortKey="Hinds, David" sort="Hinds, David" uniqKey="Hinds D" first="David" last="Hinds">David Hinds</name><name sortKey="Liu, Jimmy Z" sort="Liu, Jimmy Z" uniqKey="Liu J" first="Jimmy Z." last="Liu">Jimmy Z. Liu</name><name sortKey="Pickrell, Joseph K" sort="Pickrell, Joseph K" uniqKey="Pickrell J" first="Joseph K." last="Pickrell">Joseph K. Pickrell</name><name sortKey="Tung, Joyce Y" sort="Tung, Joyce Y" uniqKey="Tung J" first="Joyce Y." last="Tung">Joyce Y. Tung</name></country><country name="France"><region name="Île-de-France"><name sortKey="Segurel, Laure" sort="Segurel, Laure" uniqKey="Segurel L" first="Laure" last="Segurel">Laure Segurel</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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