<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v2.3 20070202//EN" "journalpublishing.dtd">
<?covid-19-tdm?>
<article xml:lang="EN" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article">
<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Med.</journal-id>
<journal-title>Frontiers in Medicine</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Med.</abbrev-journal-title>
<issn pub-type="epub">2296-858X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fmed.2021.738687</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Medicine</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Unraveling Risk Genes of COVID-19 by Multi-Omics Integrative Analyses</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Baranova</surname> <given-names>Ancha</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/33710/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Cao</surname> <given-names>Hongbao</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/926343/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Zhang</surname> <given-names>Fuquan</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/587031/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>School of Systems Biology, George Mason University</institution>, <addr-line>Manassas, VA</addr-line>, <country>United States</country></aff>
<aff id="aff2"><sup>2</sup><institution>Research Centre for Medical Genetics</institution>, <addr-line>Moscow</addr-line>, <country>Russia</country></aff>
<aff id="aff3"><sup>3</sup><institution>Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country></aff>
<aff id="aff4"><sup>4</sup><institution>Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Victoria Bunik, Lomonosov Moscow State University, Russia</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Guenter Raddatz, German Cancer Research Center (DKFZ), Germany; Andrey A. Mironov, Lomonosov Moscow State University, Russia</p></fn>
<corresp id="c001">&#x0002A;Correspondence: Fuquan Zhang <email>zhangfq&#x00040;njmu.edu.cn</email></corresp>
<fn fn-type="other" id="fn001"><p>This article was submitted to Translational Medicine, a section of the journal Frontiers in Medicine</p></fn></author-notes>
<pub-date pub-type="epub">
<day>07</day>
<month>09</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="collection">
<year>2021</year>
</pub-date>
<volume>8</volume>
<elocation-id>738687</elocation-id>
<history>
<date date-type="received">
<day>09</day>
<month>07</month>
<year>2021</year>
</date>
<date date-type="accepted">
<day>17</day>
<month>08</month>
<year>2021</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2021 Baranova, Cao and Zhang.</copyright-statement>
<copyright-year>2021</copyright-year>
<copyright-holder>Baranova, Cao and Zhang</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract><p><bold>Objectives:</bold> Uncovering the genetic basis of COVID-19 may shed insight into its pathogenesis and help to improve treatment measures. We aimed to investigate the host genetic variants associated with COVID-19.</p>
<p><bold>Methods:</bold> The summary result of a COVID-19 GWAS (9,373 hospitalized COVID-19 cases and 1,197,256 controls) was obtained from the COVID-19 Host Genetic Initiative GWAS meta-analyses. We tested colocalization of the GWAS signals of COVID-19 with expression and methylation quantitative traits loci (eQTL and mQTL, respectively) using the summary data-based Mendelian randomization (SMR) analysis. Four eQTL and two mQTL datasets were utilized in the SMR analysis, including CAGE blood eQTL data (<italic>n</italic> = 2,765), GTEx v7 blood (<italic>n</italic> = 338) and lung (<italic>n</italic> = 278) eQTL data, Geuvadis lymphoblastoid cells eQTL data, LBC-BSGS blood mQTL data (<italic>n</italic> = 1,980), and Hannon blood mQTL summary data (<italic>n</italic> = 1,175). We conducted a transcriptome-wide association study (TWAS) on COVID-19 with precomputed prediction models of GTEx v8 eQTL in lung and blood using S-PrediXcan.</p>
<p><bold>Results:</bold> Our SMR analyses identified seven protein-coding genes (<italic>TYK2, IFNAR2, OAS1, OAS3, XCR1, CCR5</italic>, and <italic>MAPT</italic>) associated with COVID-19, including two novel risk genes, <italic>CCR5</italic> and tau-encoding <italic>MAPT</italic>. The TWAS revealed four genes for COVID-19 (<italic>CXCR6, CCR5, CCR9</italic>, and <italic>PIGN</italic>), including two novel risk genes, <italic>CCR5</italic> and <italic>PIGN</italic>.</p>
<p><bold>Conclusion:</bold> Our study highlighted the functional relevance of some known genome-wide risk genes of COVID-19 and revealed novel genes contributing to differential outcomes of COVID-19 disease.</p></abstract>
<kwd-group>
<kwd>GWAS</kwd>
<kwd>COVID-19</kwd>
<kwd>TWAS</kwd>
<kwd>eQTL</kwd>
<kwd>mQTL</kwd>
</kwd-group>
<counts>
<fig-count count="2"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="39"/>
<page-count count="8"/>
<word-count count="4568"/>
</counts>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>Introduction</title>
<p>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and resultant COVID-19 have created a public health crisis worldwide. The majority of infected persons are either affected mildly or stay asymptomatic. It was reported that &#x0007E;10&#x02013;20% of people with COVID-19 infection need hospitalization (<xref ref-type="bibr" rid="B1">1</xref>). Hypertension, obesity, and diabetes are among the common comorbidities of hospitalized patients (<xref ref-type="bibr" rid="B2">2</xref>). Patients with older age or medical complications tend to have severe symptoms. However, some young and seemingly healthy individuals may also have serious outcomes from the virus infection. As the symptoms, severity, and prognosis of the disease are highly variable, host genetics may influence human&#x00027;s susceptibility to COVID-19, in a similar manner as it was noted for other infectious diseases (<xref ref-type="bibr" rid="B3">3</xref>).</p>
<p>The need in elucidating the genetic drivers of the development of COVID-19 is urgent as it may allow novel insights into its pathogenesis. Host Genetic Initiative (HGI) is one of the global efforts to promote human genetic variance research of COVID-19 by platform building, analytical activities, and data sharing (<xref ref-type="bibr" rid="B4">4</xref>). Genome-wide association studies (GWASs) have been conducted worldwide to characterize gene variants defining the susceptibility and severity of the COVID-19. In particular, Severe Covid-19 GWAS Group has identified two loci associated with SARS-CoV-2 related respiratory failure, including the chr3p21.31 locus with multiple genes encoding chemokine receptors and the chr9q34.2 locus with the blood type gene ABO (<xref ref-type="bibr" rid="B5">5</xref>). Pairo-Castineira et al. also revealed a set of genetic variants enriched in COVID-19 patients admitted to intensive care units [6]. This set highlighted chr3p21.31, chr12q24.13 (<italic>OAS1, OAS2</italic>, and <italic>OAS3</italic>), chr19p13.2 (<italic>TYK2</italic>), chr19p13.3 (<italic>DPP9</italic>), and chr21q22.1 (<italic>IFNAR2</italic>) (<xref ref-type="bibr" rid="B6">6</xref>). These two studies provide valuable evidence for the genetic basis of COVID-19. Both of the studies analyzed the datasets collected by HGI at the early stages of the project.</p>
<p>To get a clear understanding of these GWAS outputs and gain more insight into the SARS-CoV-2 pathophysiology, we performed summary data-based Mendelian randomization (SMR) and transcriptome-wide association analyses. We prioritized the genes co-localized with the COVID-19 GWAS hits and mapped additional involved genes. The resultant list of genes suggests potential therapeutic targets for symptomatic COVID-19.</p>
</sec>
<sec sec-type="methods" id="s2">
<title>Methods</title>
<sec>
<title>The COVID-19 Dataset and the Participants</title>
<p>The summary result of a COVID-19 GWAS (9,373 hospitalized COVID-19 cases and 1,197,256 controls, excluding 23andMe) was obtained from the COVID-19 Host Genetic Initiative (HGI) GWAS meta-analyses round 5 (Release Date: January 18, 2021) (<xref ref-type="bibr" rid="B4">4</xref>). All the participants were of European origins. Ethical approval had been obtained in all original studies. A more detailed description of the datasets is provided in the <xref ref-type="supplementary-material" rid="SM1">Supplementary File</xref>.</p>
</sec>
<sec>
<title>Annotation of the COVID-19 GWAS Dataset</title>
<p>FUMA was used to map SNPs to genes and identify genomic regions independent of linkage disequilibrium (LD) (<xref ref-type="bibr" rid="B7">7</xref>). All genes located closer than 10 kb of each variant were mapped. Independent significant SNPs (IndSigSNPs) were extracted, according to criteria of significance at the genome level (<italic>P</italic> &#x02264; 5.0 &#x000D7; 10<sup>&#x02212;8</sup>) and of independence (<italic>r</italic><sup>2</sup> &#x0003C; 0.6). For each group of IndSigSNPs, lead SNPs were identified when they were in LD with each other at <italic>r</italic><sup>2</sup> &#x0003C; 0.1 within a 500 Kb window. The merging of lead SNPs into genomic risk loci was performed when they were located at a distance of &#x0003C;500 kb from each other. Clumping was carried out according to the European 1,000 Genomes Project phase 3 reference panel, with the entire MHC locus being merged into one region (chr6:25-35Mb).</p>
</sec>
<sec>
<title>SMR Analyses</title>
<p>Colocalization of GWAS signal with expression and methylation quantitative traits loci (eQTL and mQTL, respectively) was performed in a framework of the SMR v1.03 (<xref ref-type="bibr" rid="B8">8</xref>). In this, the GWAS summary result and eQTL data were used at the gene level to associate its expression level with a trait of interest. We utilized four eQTL and two mQTL datasets, including CAGE blood eQTL data (<italic>n</italic> = 2,765) (<xref ref-type="bibr" rid="B9">9</xref>), GTEx v7 blood (<italic>n</italic> = 338) and lung (<italic>n</italic> = 278) eQTL data (<xref ref-type="bibr" rid="B10">10</xref>), Geuvadis lymphoblastoid cells eQTL data (<italic>n</italic> = 373) (<xref ref-type="bibr" rid="B11">11</xref>), LBC-BSGS blood mQTL data (<italic>n</italic> = 1,980) (<xref ref-type="bibr" rid="B12">12</xref>), and Hannon blood mQTL summary data (<italic>n</italic> = 1,175) (<xref ref-type="bibr" rid="B13">13</xref>). Bonferroni procedure was employed to adjust <italic>P</italic>-values for multiple testing. Pleiotropic effects were sorted from the LD artifacts using the test for non-significant heterogeneity (P<sub>HEIDI</sub> &#x0003E; 0.01) which is embedded in the SMR analysis workflow.</p>
</sec>
<sec>
<title>TWAS Analyses</title>
<p>Putatively causal genes were prioritized by a TWAS procedure, which was conducted for the lung and the whole blood cells. The gene-level association results were calculated from GWAS summary statistics using S-PrediXcan (<xref ref-type="bibr" rid="B14">14</xref>, <xref ref-type="bibr" rid="B15">15</xref>). We used precomputed prediction models of GTEx v8 (<xref ref-type="bibr" rid="B16">16</xref>) eQTL and LD references from <ext-link ext-link-type="uri" xlink:href="http://predictdb.org/">http://predictdb.org/</ext-link>. Bonferroni procedure was employed to adjust <italic>P</italic>-values for multiple testing.</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<sec>
<title>Genomic Loci Identification of the COVID-19 GWAS</title>
<p>A total of six genomic loci were identified in the COVID-19 dataset, respectively (<xref ref-type="table" rid="T1">Table 1</xref>; <xref ref-type="supplementary-material" rid="SM1">Supplementary Figure 1</xref>, <xref ref-type="table" rid="T1">Table 1</xref>). The 3p21.31 locus contains the largest amount of association signals and genes (<xref ref-type="supplementary-material" rid="SM1">Supplementary Figure 2</xref>). A total of 20 genome-wide genes were detected for the COVID-19 GWAS (<xref ref-type="table" rid="T1">Table 1</xref>). These genes included <italic>LIMD1, SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6, XCR1, CCR3, FLT1P1, CCR1, UQCRC2P1, CCR2, LRRC2, VSTM2A, ABO, OAS1, OAS3, OAS2, DPP9</italic>, and <italic>IFNAR2</italic>. Two of these genes, <italic>FLT1P1</italic> and <italic>UQCRC2P1</italic>, are non-coding genes.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p>Genomic loci of the COVID-19 GWAS.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="top" align="left"><bold>CHR</bold></th>
<th valign="top" align="center"><bold>BP</bold></th>
<th valign="top" align="center"><bold>Band</bold></th>
<th valign="top" align="left"><bold>SNP</bold></th>
<th valign="top" align="left"><bold>Alleles</bold></th>
<th valign="top" align="center"><bold>OR [95 CI]</bold></th>
<th valign="top" align="left"><bold>P</bold></th>
<th valign="top" align="left"><bold>Genes</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">3</td>
<td valign="top" align="center">45889921</td>
<td valign="top" align="center">3p21.31</td>
<td valign="top" align="left">rs35081325</td>
<td valign="top" align="left">A/T</td>
<td valign="top" align="center">1.63 [1.53&#x02013;1.73]</td>
<td valign="top" align="left">3.68E-54</td>
<td valign="top" align="left">LIMD1;SLC6A20;LZTFL1;CCR9;FYCO1;CXCR6;XCR1;CCR3;FLT1P1;CCR1;UQCRC2P1;CCR2;LRRC2</td>
</tr>
<tr>
<td valign="top" align="left">7</td>
<td valign="top" align="center">54647894</td>
<td valign="top" align="center">7p11.2</td>
<td valign="top" align="left">rs622568</td>
<td valign="top" align="left">A/C</td>
<td valign="top" align="center">1.17 [1.11&#x02013;1.23]</td>
<td valign="top" align="left">3.64E-09</td>
<td valign="top" align="left">VSTM2A</td>
</tr>
<tr>
<td valign="top" align="left">9</td>
<td valign="top" align="center">136149229</td>
<td valign="top" align="center">9q34.2</td>
<td valign="top" align="left">rs920065566</td>
<td valign="top" align="left">C/T</td>
<td valign="top" align="center">0.89 [0.86&#x02013;0.93]</td>
<td valign="top" align="left">4.42E-09</td>
<td valign="top" align="left">ABO</td>
</tr>
<tr>
<td valign="top" align="left">12</td>
<td valign="top" align="center">113357442</td>
<td valign="top" align="center">12q24.13</td>
<td valign="top" align="left">rs2660</td>
<td valign="top" align="left">G/A</td>
<td valign="top" align="center">1.12 [1.08&#x02013;1.17]</td>
<td valign="top" align="left">2.01E-09</td>
<td valign="top" align="left">OAS1;OAS3;OAS2</td>
</tr>
<tr>
<td valign="top" align="left">19</td>
<td valign="top" align="center">4719443</td>
<td valign="top" align="center">19p13.3</td>
<td valign="top" align="left">rs2109069</td>
<td valign="top" align="left">G/A</td>
<td valign="top" align="center">1.16 [1.12&#x02013;1.21]</td>
<td valign="top" align="left">2.94E-14</td>
<td valign="top" align="left">DPP9</td>
</tr>
<tr>
<td valign="top" align="left">21</td>
<td valign="top" align="center">34615210</td>
<td valign="top" align="center">21q22.11</td>
<td valign="top" align="left">rs13050728</td>
<td valign="top" align="left">T/C</td>
<td valign="top" align="center">0.85 [0.81&#x02013;0.88]</td>
<td valign="top" align="left">7.44E-17</td>
<td valign="top" align="left">IFNAR2</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>CHR, chromosome; BP, base position</italic>.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec>
<title>SMR Analyses of COVID-19</title>
<p>Functionally important genes for COVID-19 were prioritized by the SMR analysis using six eQTL and two mQTL datasets, which identified a total of 25 associations, involving seven protein-coding genes (<italic>TYK2, IFNAR2, OAS1, OAS3, XCR1, CCR5</italic>, and <italic>MAPT</italic>) and four non-coding genes (<italic>LRRC37A4P, IL10RB-AS1, MGC57346</italic>, and <italic>CCR5AS</italic>) (<xref ref-type="table" rid="T2">Table 2</xref>; <xref ref-type="fig" rid="F1">Figure 1</xref>). Several genes were implicated by two or more datasets, including <italic>IFNAR2</italic> (eQTL of CAGE blood, eQTL of Geuvadis lymphoblastoid cell, and mQTL of LBC-BSGS blood), <italic>OAS1</italic> (eQTL of Geuvadis lymphoblastoid cell and mQTL of Hannon blood), and <italic>XCR1</italic> (mQTL of Hannon blood and mQTL of mQTL of Hannon blood). Two protein-coding genes, <italic>CCR5</italic> and <italic>MAPT</italic>, are novel susceptibility genes for COVID-19, which were implicated by mQTL of Hannon blood and mQTL of LBC-BSGS blood, respectively (<xref ref-type="fig" rid="F2">Figure 2</xref>).</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p>The SMR analyses of COVID-19.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="top" align="left"><bold>Type</bold></th>
<th valign="top" align="left"><bold>Data</bold></th>
<th valign="top" align="left"><bold>Tissue</bold></th>
<th valign="top" align="center"><bold>Ch</bold></th>
<th valign="top" align="left"><bold>Gene</bold></th>
<th valign="top" align="left"><bold>Top SNP</bold></th>
<th valign="top" align="center"><bold>P<sub>GWAS</sub></bold></th>
<th valign="top" align="center"><bold>P<sub>eQTL</sub></bold></th>
<th valign="top" align="center"><bold>Beta</bold></th>
<th valign="top" align="center"><bold>P<sub>SMR</sub></bold></th>
<th valign="top" align="center"><bold>P<sub>Bonferroni</sub></bold></th>
<th valign="top" align="center"><bold>P<sub>HEIDI</sub></bold></th>
<th valign="top" align="center"><bold>N<sub>SNP</sub></bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">eQTL</td>
<td valign="top" align="left">CAGE</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">12</td>
<td valign="top" align="left">OAS3</td>
<td valign="top" align="left">rs7955267</td>
<td valign="top" align="center">1.08E-07</td>
<td valign="top" align="center">8.55E-33</td>
<td valign="top" align="center">&#x02212;0.288</td>
<td valign="top" align="center">1.22E-06</td>
<td valign="top" align="center">0.010</td>
<td valign="top" align="center">0.089</td>
<td valign="top" align="center">20</td>
</tr>
<tr>
<td valign="top" align="left">eQTL</td>
<td valign="top" align="left">CAGE</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">17</td>
<td valign="top" align="left">LRRC37A4P</td>
<td valign="top" align="left">rs113661667</td>
<td valign="top" align="center">1.01E-06</td>
<td valign="top" align="center">5.03E-228</td>
<td valign="top" align="center">&#x02212;0.095</td>
<td valign="top" align="center">1.34E-06</td>
<td valign="top" align="center">0.011</td>
<td valign="top" align="center">0.189</td>
<td valign="top" align="center">20</td>
</tr>
<tr>
<td valign="top" align="left">eQTL</td>
<td valign="top" align="left">CAGE</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">17</td>
<td valign="top" align="left">MGC57346</td>
<td valign="top" align="left">rs79600142</td>
<td valign="top" align="center">1.38E-06</td>
<td valign="top" align="center">4.29E-157</td>
<td valign="top" align="center">0.110</td>
<td valign="top" align="center">2.03E-06</td>
<td valign="top" align="center">0.017</td>
<td valign="top" align="center">0.375</td>
<td valign="top" align="center">20</td>
</tr>
<tr>
<td valign="top" align="left">eQTL</td>
<td valign="top" align="left">CAGE</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">19</td>
<td valign="top" align="left">TYK2</td>
<td valign="top" align="left">rs11085727</td>
<td valign="top" align="center">1.27E-07</td>
<td valign="top" align="center">5.69E-20</td>
<td valign="top" align="center">&#x02212;0.377</td>
<td valign="top" align="center">4.75E-06</td>
<td valign="top" align="center">0.040</td>
<td valign="top" align="center">0.058</td>
<td valign="top" align="center">15</td>
</tr>
<tr>
<td valign="top" align="left">eQTL</td>
<td valign="top" align="left">CAGE</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">21</td>
<td valign="top" align="left">IFNAR2</td>
<td valign="top" align="left">rs2252639</td>
<td valign="top" align="center">1.08E-16</td>
<td valign="top" align="center">5.21E-34</td>
<td valign="top" align="center">0.464</td>
<td valign="top" align="center">7.26E-12</td>
<td valign="top" align="center">6.12E-08</td>
<td valign="top" align="center">0.017</td>
<td valign="top" align="center">9</td>
</tr>
<tr>
<td valign="top" align="left">eQTL</td>
<td valign="top" align="left">Geuvadis</td>
<td valign="top" align="left">Lymphoblastoid Cell</td>
<td valign="top" align="center">12</td>
<td valign="top" align="left">OAS1</td>
<td valign="top" align="left">rs1981555</td>
<td valign="top" align="center">3.10E-08</td>
<td valign="top" align="center">1.39E-14</td>
<td valign="top" align="center">&#x02212;0.189</td>
<td valign="top" align="center">6.99E-06</td>
<td valign="top" align="center">0.013</td>
<td valign="top" align="center">0.297</td>
<td valign="top" align="center">16</td>
</tr>
<tr>
<td valign="top" align="left">eQTL</td>
<td valign="top" align="left">Geuvadis</td>
<td valign="top" align="left">Lymphoblastoid Cell</td>
<td valign="top" align="center">17</td>
<td valign="top" align="left">LRRC37A4P</td>
<td valign="top" align="left">rs62054835</td>
<td valign="top" align="center">1.66E-06</td>
<td valign="top" align="center">8.47E-28</td>
<td valign="top" align="center">&#x02212;0.120</td>
<td valign="top" align="center">1.14E-05</td>
<td valign="top" align="center">0.021</td>
<td valign="top" align="center">0.016</td>
<td valign="top" align="center">20</td>
</tr>
<tr>
<td valign="top" align="left">eQTL</td>
<td valign="top" align="left">Geuvadis</td>
<td valign="top" align="left">Lymphoblastoid Cell</td>
<td valign="top" align="center">21</td>
<td valign="top" align="left">IFNAR2</td>
<td valign="top" align="left">rs2300371</td>
<td valign="top" align="center">5.11E-08</td>
<td valign="top" align="center">3.88E-14</td>
<td valign="top" align="center">&#x02212;0.208</td>
<td valign="top" align="center">9.84E-06</td>
<td valign="top" align="center">0.018</td>
<td valign="top" align="center">0.098</td>
<td valign="top" align="center">12</td>
</tr>
<tr>
<td valign="top" align="left">eQTL</td>
<td valign="top" align="left">Geuvadis</td>
<td valign="top" align="left">Lymphoblastoid Cell</td>
<td valign="top" align="center">21</td>
<td valign="top" align="left">IL10RB-AS1</td>
<td valign="top" align="left">rs2300371</td>
<td valign="top" align="center">5.11E-08</td>
<td valign="top" align="center">4.93E-32</td>
<td valign="top" align="center">0.140</td>
<td valign="top" align="center">7.64E-07</td>
<td valign="top" align="center">1.42E-03</td>
<td valign="top" align="center">0.020</td>
<td valign="top" align="center">18</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">Hannon</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">NA</td>
<td valign="top" align="left">rs34438204</td>
<td valign="top" align="center">4.86E-33</td>
<td valign="top" align="center">1.84E-25</td>
<td valign="top" align="center">&#x02212;12.75</td>
<td valign="top" align="center">3.71E-15</td>
<td valign="top" align="center">4.61E-10</td>
<td valign="top" align="center">0.031</td>
<td valign="top" align="center">20</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">Hannon</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">NA</td>
<td valign="top" align="left">rs13085367</td>
<td valign="top" align="center">6.59E-19</td>
<td valign="top" align="center">6.77E-15</td>
<td valign="top" align="center">&#x02212;16.47</td>
<td valign="top" align="center">4.74E-09</td>
<td valign="top" align="center">5.88E-04</td>
<td valign="top" align="center">0.060</td>
<td valign="top" align="center">15</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">Hannon</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">XCR1</td>
<td valign="top" align="left">rs4443214</td>
<td valign="top" align="center">2.53E-20</td>
<td valign="top" align="center">1.87E-12</td>
<td valign="top" align="center">21.73</td>
<td valign="top" align="center">2.13E-08</td>
<td valign="top" align="center">2.64E-03</td>
<td valign="top" align="center">0.040</td>
<td valign="top" align="center">9</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">Hannon</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">XCR1</td>
<td valign="top" align="left">rs34438204</td>
<td valign="top" align="center">4.86E-33</td>
<td valign="top" align="center">7.29E-20</td>
<td valign="top" align="center">18.64</td>
<td valign="top" align="center">3.96E-13</td>
<td valign="top" align="center">4.91E-08</td>
<td valign="top" align="center">0.411</td>
<td valign="top" align="center">17</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">Hannon</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">NA</td>
<td valign="top" align="left">rs35110864</td>
<td valign="top" align="center">2.78E-19</td>
<td valign="top" align="center">1.06E-64</td>
<td valign="top" align="center">&#x02212;7.46</td>
<td valign="top" align="center">2.08E-15</td>
<td valign="top" align="center">2.57E-10</td>
<td valign="top" align="center">0.020</td>
<td valign="top" align="center">20</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">Hannon</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">NA</td>
<td valign="top" align="left">rs13433997</td>
<td valign="top" align="center">3.26E-34</td>
<td valign="top" align="center">1.49E-13</td>
<td valign="top" align="center">&#x02212;94.85</td>
<td valign="top" align="center">2.63E-10</td>
<td valign="top" align="center">3.26E-05</td>
<td valign="top" align="center">0.024</td>
<td valign="top" align="center">14</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">Hannon</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">NA</td>
<td valign="top" align="left">rs9877748</td>
<td valign="top" align="center">2.05E-31</td>
<td valign="top" align="center">4.33E-94</td>
<td valign="top" align="center">&#x02212;5.12</td>
<td valign="top" align="center">3.51E-24</td>
<td valign="top" align="center">4.36E-19</td>
<td valign="top" align="center">0.018</td>
<td valign="top" align="center">20</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">Hannon</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">NA</td>
<td valign="top" align="left">rs35775079</td>
<td valign="top" align="center">1.10E-13</td>
<td valign="top" align="center">4.06E-14</td>
<td valign="top" align="center">13.07</td>
<td valign="top" align="center">1.17E-07</td>
<td valign="top" align="center">0.014</td>
<td valign="top" align="center">0.145</td>
<td valign="top" align="center">8</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">Hannon</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">NA</td>
<td valign="top" align="left">rs13069742</td>
<td valign="top" align="center">1.81E-31</td>
<td valign="top" align="center">1.10E-21</td>
<td valign="top" align="center">&#x02212;22.65</td>
<td valign="top" align="center">1.38E-13</td>
<td valign="top" align="center">1.71E-08</td>
<td valign="top" align="center">0.229</td>
<td valign="top" align="center">18</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">Hannon</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">CCR5</td>
<td valign="top" align="left">rs7642320</td>
<td valign="top" align="center">6.66E-28</td>
<td valign="top" align="center">3.25E-14</td>
<td valign="top" align="center">&#x02212;12.70</td>
<td valign="top" align="center">4.46E-10</td>
<td valign="top" align="center">5.54E-05</td>
<td valign="top" align="center">0.101</td>
<td valign="top" align="center">13</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">Hannon</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">CCR5AS</td>
<td valign="top" align="left">rs7642320</td>
<td valign="top" align="center">6.66E-28</td>
<td valign="top" align="center">1.67E-11</td>
<td valign="top" align="center">&#x02212;18.20</td>
<td valign="top" align="center">9.76E-09</td>
<td valign="top" align="center">1.21E-03</td>
<td valign="top" align="center">0.073</td>
<td valign="top" align="center">7</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">Hannon</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">12</td>
<td valign="top" align="left">OAS1</td>
<td valign="top" align="left">rs10850097</td>
<td valign="top" align="center">1.01E-08</td>
<td valign="top" align="center">1.15E-30</td>
<td valign="top" align="center">5.63</td>
<td valign="top" align="center">2.92E-07</td>
<td valign="top" align="center">0.036</td>
<td valign="top" align="center">0.266</td>
<td valign="top" align="center">9</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">LBC-BSGS</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">17</td>
<td valign="top" align="left">MAPT</td>
<td valign="top" align="left">rs112572874</td>
<td valign="top" align="center">3.29E-07</td>
<td valign="top" align="center">0</td>
<td valign="top" align="center">&#x02212;0.081</td>
<td valign="top" align="center">3.75E-07</td>
<td valign="top" align="center">0.034</td>
<td valign="top" align="center">0.070</td>
<td valign="top" align="center">20</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">LBC-BSGS</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">21</td>
<td valign="top" align="left">IFNAR2</td>
<td valign="top" align="left">rs2300370</td>
<td valign="top" align="center">7.27E-15</td>
<td valign="top" align="center">1.06E-35</td>
<td valign="top" align="center">&#x02212;0.380</td>
<td valign="top" align="center">4.09E-11</td>
<td valign="top" align="center">3.76E-06</td>
<td valign="top" align="center">0.013</td>
<td valign="top" align="center">11</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">LBC-BSGS</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">XCR1</td>
<td valign="top" align="left">rs13069742</td>
<td valign="top" align="center">1.81E-31</td>
<td valign="top" align="center">8.71E-12</td>
<td valign="top" align="center">&#x02212;0.842</td>
<td valign="top" align="center">3.81E-09</td>
<td valign="top" align="center">3.50E-04</td>
<td valign="top" align="center">0.413</td>
<td valign="top" align="center">12</td>
</tr>
<tr>
<td valign="top" align="left">mQTL</td>
<td valign="top" align="left">LBC-BSGS</td>
<td valign="top" align="left">blood</td>
<td valign="top" align="center">3</td>
<td valign="top" align="left">NA</td>
<td valign="top" align="left">rs71325091</td>
<td valign="top" align="center">1.55E-21</td>
<td valign="top" align="center">1.72E-10</td>
<td valign="top" align="center">&#x02212;0.888</td>
<td valign="top" align="center">1.13E-07</td>
<td valign="top" align="center">0.010</td>
<td valign="top" align="center">0.055</td>
<td valign="top" align="center">19</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>Chr, chromosome; N<sub>SNP</sub>, number of SNPs</italic>.</p>
</table-wrap-foot>
</table-wrap>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p>Summary data-based Mendelian randomization (SMR) analysis of COVID-19. Each horizontal dashed line denotes a genome-wide significance level adjusted by Bonferroni.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmed-08-738687-g0001.tif"/>
</fig>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption><p>Two loci from the SMR analysis. <bold>(A)</bold>: Top plot, gray dots represent the <italic>P</italic>-values for SNPs from the COVID-19 GWAS. Bottom plots, the mQTL <italic>P</italic>-values of SNPs from the Hannon study for the cg14546840 and cg17786516 probes tagging CCR5 and CCR5AS, respectively. Highlighted in red are the genes (CCR5 and CCR5AS) that passed the SMR and HEIDI tests. <bold>(B)</bold>: Top plot, gray dots represent the <italic>P</italic>-values for SNPs from the COVID-19 GWAS. The bottom plot, the mQTL <italic>P</italic>-values of SNPs from the LBC-BSGS study for the cg02228913 probe tagging MAPT. Highlighted in red is the gene (MAPT) that passed the SMR and HEIDI tests.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmed-08-738687-g0002.tif"/>
</fig>
</sec>
<sec>
<title>TWAS of COVID-19</title>
<p>To connect GWAS signals to tissue-specific gene expression values, the TWAS framework was used (<xref ref-type="bibr" rid="B14">14</xref>, <xref ref-type="bibr" rid="B15">15</xref>). Inferences were made for known genetic variants in lung and whole blood tissues from the GTEx v8 expression dataset. We discovered two genes associated with the lung eQTL dataset (<italic>CXCR6</italic> and <italic>CCR5</italic>) and two genes associated with the blood eQTL dataset (<italic>CCR9</italic> and <italic>PIGN</italic>) (<xref ref-type="table" rid="T3">Table 3</xref>). Among these genes, <italic>CCR5</italic> and <italic>PIGN</italic> were novel susceptibility genes for COVID-19.</p>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p>Transcriptome-wide association study of the COVID-19 outcomes.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="top" align="left"><bold>Tissue</bold></th>
<th valign="top" align="left"><bold>Gene</bold></th>
<th valign="top" align="center"><bold>Z</bold></th>
<th valign="top" align="center"><bold>P</bold></th>
<th valign="top" align="center"><bold>P<sub>Bonferroni</sub></bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Lung</td>
<td valign="top" align="left"><italic>CXCR6</italic></td>
<td valign="top" align="center">9.81</td>
<td valign="top" align="center">1.07E-22</td>
<td valign="top" align="center">1.46E-18</td>
</tr>
<tr>
<td valign="top" align="left">Lung</td>
<td valign="top" align="left"><italic>CCR5</italic></td>
<td valign="top" align="center">6.89</td>
<td valign="top" align="center">5.56E-12</td>
<td valign="top" align="center">7.56E-08</td>
</tr>
<tr>
<td valign="top" align="left">Blood</td>
<td valign="top" align="left"><italic>CCR9</italic></td>
<td valign="top" align="center">&#x02212;11.33</td>
<td valign="top" align="center">9.09E-30</td>
<td valign="top" align="center">1.04E-25</td>
</tr>
<tr>
<td valign="top" align="left">Blood</td>
<td valign="top" align="left"><italic>PIGN</italic></td>
<td valign="top" align="center">4.78</td>
<td valign="top" align="center">1.78E-06</td>
<td valign="top" align="center">0.02</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Together, our SMR analysis and TWAS of COVID-19 identified a total of 14 genes associated with COVID-19, comprising seven genes implicated by the input or previous GWASs and seven novel genes (including three protein-coding genes, <italic>CCR5, MAPT</italic>, and <italic>PIGN</italic>).</p>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>Exploration of the host genetic factors contributing COVID-19 has being started as early as the first datasets became available, including ones collected in HGI. Here we present the result of our exploration of the differential susceptibility to COVID-19 in the latest HGI dataset, which we dissected using both SMR and transcriptome-wide analyses.</p>
<p>Genomic loci, as well as risk genes associated with the disease, were described. Notably, chromosome 3p21.31 with its chemokine receptor genes was highlighted as the peak for associations, along with chromosome 12q24.13 with the oligoadenylate synthase protein family gene cluster <italic>OAS1, OAS2</italic>, and <italic>OAS3</italic>. These enzymes activate RNAse L and degrade viral nucleic acids. The <italic>IFNAR2</italic> gene (21q22.11 locus) encodes a subunit for interferons alpha and beta binding receptors. Notably, <italic>IFNAR2</italic> is capable of producing soluble receptors, which binds and regulates endogenous production of type I IFNs (<xref ref-type="bibr" rid="B17">17</xref>, <xref ref-type="bibr" rid="B18">18</xref>). This soluble IFNAR2 possesses both anti-proliferative and antiviral functions as well as therapeutic properties (<xref ref-type="bibr" rid="B19">19</xref>). In particular, in COVID-19, a protective role of <italic>IFNAR2</italic> has been suggested (<xref ref-type="bibr" rid="B6">6</xref>, <xref ref-type="bibr" rid="B20">20</xref>). Another region previously associated with COVID-19 was the blood group <italic>ABO</italic> locus at 9q34.2 [5].</p>
<p>Out of 18 protein-coding genes associated with COVID-19, twelve have been reported previously (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B6">6</xref>), including <italic>ABO, CCR9, CXCR6, DPP9, FYCO1, IFNAR2, LZTFL1, OAS1, OAS2, OAS3, SLC6A20</italic>, and <italic>XCR1</italic>. The present analysis uncovered six novel risk genes contributing to severe COVID-19, including <italic>LIMD1, CCR3, CCR1, CCR2, LRRC2</italic>, and <italic>VSTM2A</italic>. A majority of these six genes are located in the 3p21.31 locus (<italic>CCR1, CCR2, CCR3, CCRL2</italic>, and <italic>LRRC2</italic>), while another gene <italic>VSTM2A</italic> maps to newly identified loci in the 7q31.1.</p>
<p>Our SMR analysis identified seven protein-coding genes significantly associated with predicted expression levels in the lung or the blood. Among these genes, two are novel, including <italic>CCR5</italic> and <italic>MAPT</italic>. Our TWAS analysis identified four genes significantly associated with predicted expression levels in the lung or the blood. Two of these genes, <italic>CCR9</italic> and <italic>CXCR6</italic>, were also found within the set of COVID-19 associated genes, while <italic>CCR5</italic> and <italic>PIGN</italic> genes were novel. Together, our SMR and TWAS analyses identified three novel protein-coding genes for COVID-19, namely, <italic>CCR5, MAPT</italic>, and <italic>PIGN</italic>.</p>
<p><italic>CCR5</italic> is located in 3p21.31 and encodes chemokine receptors expressed in macrophages and T cells. In macrophages, CCR5 protein serves as a gateway for many viruses including HIV (<xref ref-type="bibr" rid="B21">21</xref>). Notably, some people lack functional CCR5 allele due to 31-bp deletion within its open reading frame, and resultant loss-of-the function. In both homo- and heterozygous individuals this deletion known as rs333 is a major determinant of the resistance to HIV. It is of interest that anti-CCR5 antibody leronlimab has been tried as a post-COVID-19 therapeutic molecule and shown to downregulate both inflammatory cytokine profile and the copy number of SARS-CoV2 RNA (<xref ref-type="bibr" rid="B22">22</xref>). In a recent study, the CCR5-&#x00394;32 variant was found to be significantly less frequent in hospitalized COVID-19 than in healthy controls (<italic>P</italic> = 0.01, OR = 0.66, 95% CI = 0.49&#x02013;0.88), with no homozygotes found among the patients, compared to 1% of the controls (<xref ref-type="bibr" rid="B23">23</xref>). In addition, <italic>CCR5</italic> transcript was expressed among the patients at significantly higher levels than in the healthy non-deletion carriers (<italic>P</italic> = 0.01). Independent identification of <italic>CCR5</italic> as overexpressed in hospitalized COVID-19 patients supports the validity of our TWAS analysis.</p>
<p>The <italic>MAPT</italic> gene encodes the microtubule-associated protein tau. Genetic variation within <italic>MAPT</italic> was reported to be associated with multiple neurodegenerative disorders, including Parkinson&#x00027;s disease and Alzheimer&#x00027;s disease (<xref ref-type="bibr" rid="B24">24</xref>&#x02013;<xref ref-type="bibr" rid="B26">26</xref>). It was also a genome-wide risk gene for autoimmune diseases and some cardiometabolic traits, including body mass index, blood cell count, osteoarthritis (<xref ref-type="bibr" rid="B25">25</xref>, <xref ref-type="bibr" rid="B27">27</xref>&#x02013;<xref ref-type="bibr" rid="B29">29</xref>). In addition, the gene has been implicated in interstitial lung disease (<xref ref-type="bibr" rid="B30">30</xref>) and lung function (<xref ref-type="bibr" rid="B31">31</xref>). Recently, it was shown that the tau protein binds to SARS-CoV-2 S1 receptor-binding domain (RBD) with the implication that the heparin-binding site on the S1 protein participates in the aggregation of amyloid-like proteins and promotes neurodegeneration (<xref ref-type="bibr" rid="B32">32</xref>). In another study, amounts of tau protein in the neuronal-enriched extracellular vesicles of patients recovering from COVID-19 were larger than in historic controls (<xref ref-type="bibr" rid="B33">33</xref>). In 3D human brain organoids, SARS-CoV-2 preferably targets the neurons, where it changes the distribution of Tau from axons to soma, its hyperphosphorylation, the neurotoxic death (<xref ref-type="bibr" rid="B34">34</xref>). Taken together, these observations point that functional variation within the tau locus may indeed be relevant to COVID-19 and especially to its neurological sequelae.</p>
<p>Mutations in gene <italic>PIGN</italic> lead to well-characterized defects in the biosynthesis of glycosylphosphatidylinositol (GPI), an anchor that tether proteins to the extracellular face of eukaryotic plasma membranes (<xref ref-type="bibr" rid="B35">35</xref>). Notably, genome-scale CRISPR knockout screen of cells seeded with SARS-CoV-2 and three seasonal coronaviruses (HCoV-OC43, HCoV-NL63, and HCoV-229E) highlighted glycosylphosphatidylinositol biosynthesis as one of the key dependencies for these infectious agents in two independent studies (<xref ref-type="bibr" rid="B36">36</xref>, <xref ref-type="bibr" rid="B37">37</xref>). In the mammalian plasma membranes, GPI-anchored proteins interact with glycosphingolipids, forming dynamic microdomains. When the transfer of synthesized GPI to proteins is defective, for the lack of a particular glycosphingolipid, a lactosylceramide, the GPI biosynthesis in the endoplasmic reticulum (ER) is severely suppressed (<xref ref-type="bibr" rid="B38">38</xref>). The metabolism of ceramides, and lactosylceramide, in particular, is severely disturbed in SARS-Cov-2 infection, with glucosylceramide synthase inhibitors displaying marked anti-SARS-CoV-2 effects (<xref ref-type="bibr" rid="B39">39</xref>). It seems that GPI-glucosylceramide equilibrium may be profoundly altered by SARS-CoV-2 replication and that these changes may contribute to COVID sequelae.</p>
<p>The strengths of this study include the use of the largest COVID-19 dataset available. Furthermore, we have diminished the potential population heterogeneity by limiting our analysis to individuals of European ancestry. Among the noticeable study limitations are that TWAS associations are considered to be noisy and that we have limited ourselves to testing only the genetic factors associated with COVID-19 risk, rather than taking into account the social and the environmental variables as well. As the findings from our study may be relevant to the European population only, uncovered associations certainly require further validation and detailed investigation.</p>
</sec>
<sec sec-type="conclusions" id="s5">
<title>Conclusions</title>
<p>In summary, our study highlighted some known and revealed some novel genes contributing to differential outcomes of COVID-19 disease.</p>
</sec>
<sec sec-type="data-availability" id="s6">
<title>Data Availability Statement</title>
<p>Publicly available datasets were analyzed in this study. This data can be found here: <ext-link ext-link-type="uri" xlink:href="https://www.covid19hg.org/results/">https://www.covid19hg.org/results/</ext-link>.</p>
</sec>
<sec id="s7">
<title>Author Contributions</title>
<p>FZ designed the study and performed the statistical analyses. FZ and AB contributed to data interpretation and wrote the manuscript. HC contributed to the data preparation. All the authors approved the final manuscript for submission and publication and agreed to be accountable for all aspects of the work.</p>
</sec>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s8">
<title>Publisher&#x00027;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
</body>
<back>
<ack><p>The authors thank all investigators and participants from the COVID-19 Host Genetics Initiative for sharing these data.</p>
</ack>
<sec sec-type="supplementary-material" id="s9">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fmed.2021.738687/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fmed.2021.738687/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.doc" id="SM1" mimetype="application/msword" xmlns:xlink="http://www.w3.org/1999/xlink"/></sec>
<ref-list>
<title>References</title>
<ref id="B1">
<label>1.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Richardson</surname> <given-names>S</given-names></name> <name><surname>Hirsch</surname> <given-names>JS</given-names></name> <name><surname>Narasimhan</surname> <given-names>M</given-names></name></person-group>. <article-title>Clarification of mortality rate and data in abstract, results, and Table 2</article-title>. <source>JAMA.</source> (<year>2020</year>) <volume>323</volume>:<fpage>2098</fpage>. <pub-id pub-id-type="doi">10.1001/jama.2020.7681</pub-id><pub-id pub-id-type="pmid">32330939</pub-id></citation></ref>
<ref id="B2">
<label>2.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Richardson</surname> <given-names>S</given-names></name> <name><surname>Hirsch</surname> <given-names>JS</given-names></name> <name><surname>Narasimhan</surname> <given-names>M</given-names></name> <name><surname>Crawford</surname> <given-names>JM</given-names></name> <name><surname>McGinn</surname> <given-names>T</given-names></name> <name><surname>Davidson</surname> <given-names>KW</given-names></name> <etal/></person-group>. <article-title>Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area</article-title>. <source>JAMA.</source> (<year>2020</year>) <volume>323</volume>:<fpage>2052</fpage>&#x02013;<lpage>9</lpage>. <pub-id pub-id-type="doi">10.1001/jama.2020.6775</pub-id><pub-id pub-id-type="pmid">32320003</pub-id></citation></ref>
<ref id="B3">
<label>3.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Chapman</surname> <given-names>SJ</given-names></name> <name><surname>Hill</surname> <given-names>AV</given-names></name></person-group>. <article-title>Human genetic susceptibility to infectious disease</article-title>. <source>Nat Rev Genet.</source> (<year>2012</year>) <volume>13</volume>:<fpage>175</fpage>&#x02013;<lpage>88</lpage>. <pub-id pub-id-type="doi">10.1038/nrg3114</pub-id><pub-id pub-id-type="pmid">22310894</pub-id></citation></ref>
<ref id="B4">
<label>4.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Initiative</surname> <given-names>C-HG</given-names></name></person-group>. <article-title>The COVID-19 host genetics initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic</article-title>. <source>Eur J Hum Genet.</source> (<year>2020</year>) <volume>28</volume>:<fpage>715</fpage>&#x02013;<lpage>8</lpage>. <pub-id pub-id-type="doi">10.1038/s41431-020-0636-6</pub-id><pub-id pub-id-type="pmid">32404885</pub-id></citation></ref>
<ref id="B5">
<label>5.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Severe Covid</surname> <given-names>GG</given-names></name> <name><surname>Ellinghaus</surname> <given-names>D</given-names></name> <name><surname>Degenhardt</surname> <given-names>F</given-names></name> <name><surname>Bujanda</surname> <given-names>L</given-names></name> <name><surname>Buti</surname> <given-names>M</given-names></name> <name><surname>Albillos</surname> <given-names>A</given-names></name> <etal/></person-group>. <article-title>Genomewide association study of severe Covid-19 with respiratory failure</article-title>. <source>N Engl J Med.</source> (<year>2020</year>) <volume>383</volume>:<fpage>1522</fpage>&#x02013;<lpage>34</lpage>. <pub-id pub-id-type="doi">10.1056/NEJMoa2020283</pub-id><pub-id pub-id-type="pmid">32558485</pub-id></citation></ref>
<ref id="B6">
<label>6.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Pairo-Castineira</surname> <given-names>E</given-names></name> <name><surname>Clohisey</surname> <given-names>S</given-names></name> <name><surname>Klaric</surname> <given-names>L</given-names></name> <name><surname>Bretherick</surname> <given-names>AD</given-names></name> <name><surname>Rawlik</surname> <given-names>K</given-names></name> <name><surname>Pasko</surname> <given-names>D</given-names></name> <etal/></person-group>. <article-title>Genetic mechanisms of critical illness in COVID-19</article-title>. <source>Nature.</source> (<year>2021</year>) <volume>591</volume>:<fpage>92</fpage>&#x02013;<lpage>8</lpage>. <pub-id pub-id-type="doi">10.1038/s41586-020-03065-y</pub-id><pub-id pub-id-type="pmid">33307546</pub-id></citation></ref>
<ref id="B7">
<label>7.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Watanabe</surname> <given-names>K</given-names></name> <name><surname>Taskesen</surname> <given-names>E</given-names></name> <name><surname>van Bochoven</surname> <given-names>A</given-names></name> <name><surname>Posthuma</surname> <given-names>D</given-names></name></person-group>. <article-title>Functional mapping and annotation of genetic associations with FUMA</article-title>. <source>Nat Commun.</source> (<year>2017</year>) <volume>8</volume>:<fpage>1826</fpage>. <pub-id pub-id-type="doi">10.1038/s41467-017-01261-5</pub-id><pub-id pub-id-type="pmid">29184056</pub-id></citation></ref>
<ref id="B8">
<label>8.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Zhu</surname> <given-names>Z</given-names></name> <name><surname>Zhang</surname> <given-names>F</given-names></name> <name><surname>Hu</surname> <given-names>H</given-names></name> <name><surname>Bakshi</surname> <given-names>A</given-names></name> <name><surname>Robinson</surname> <given-names>MR</given-names></name> <name><surname>Powell</surname> <given-names>JE</given-names></name> <etal/></person-group>. <article-title>Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets</article-title>. <source>Nat Genet.</source> (<year>2016</year>) <volume>48</volume>:<fpage>481</fpage>&#x02013;<lpage>7</lpage>. <pub-id pub-id-type="doi">10.1038/ng.3538</pub-id><pub-id pub-id-type="pmid">27019110</pub-id></citation></ref>
<ref id="B9">
<label>9.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Lloyd-Jones</surname> <given-names>LR</given-names></name> <name><surname>Holloway</surname> <given-names>A</given-names></name> <name><surname>McRae</surname> <given-names>A</given-names></name> <name><surname>Yang</surname> <given-names>J</given-names></name> <name><surname>Small</surname> <given-names>K</given-names></name> <name><surname>Zhao</surname> <given-names>J</given-names></name> <etal/></person-group>. <article-title>The genetic architecture of gene expression in peripheral blood</article-title>. <source>Am J Hum Genet.</source> (<year>2017</year>) <volume>100</volume>:<fpage>228</fpage>&#x02013;<lpage>37</lpage>. <pub-id pub-id-type="doi">10.1016/j.ajhg.2016.12.008</pub-id><pub-id pub-id-type="pmid">28157541</pub-id></citation></ref>
<ref id="B10">
<label>10.</label>
<citation citation-type="journal"><person-group person-group-type="author"><collab>Consortium GT, Laboratory DA, Coordinating Center -Analysis Working G, Statistical Methods groups-Analysis Working G, Enhancing Gg, Fund NIHC</collab><etal/></person-group>. <article-title>Genetic effects on gene expression across human tissues</article-title>. <source>Nature.</source> (<year>2017</year>) <volume>550</volume>:<fpage>204</fpage>&#x02013;<lpage>13</lpage>. <pub-id pub-id-type="doi">10.1038/nature24277</pub-id><pub-id pub-id-type="pmid">29022597</pub-id></citation></ref>
<ref id="B11">
<label>11.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Lappalainen</surname> <given-names>T</given-names></name> <name><surname>Sammeth</surname> <given-names>M</given-names></name> <name><surname>Friedlander</surname> <given-names>MR</given-names></name> <name><surname>t Hoen</surname> <given-names>PA</given-names></name> <name><surname>Monlong</surname> <given-names>J</given-names></name> <name><surname>Rivas</surname> <given-names>MA</given-names></name> <etal/></person-group>. <article-title>Transcriptome and genome sequencing uncovers functional variation in humans</article-title>. <source>Nature.</source> (<year>2013</year>) <volume>501</volume>:<fpage>506</fpage>&#x02013;<lpage>11</lpage>. <pub-id pub-id-type="doi">10.1038/nature12531</pub-id><pub-id pub-id-type="pmid">24037378</pub-id></citation></ref>
<ref id="B12">
<label>12.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Wu</surname> <given-names>Y</given-names></name> <name><surname>Zeng</surname> <given-names>J</given-names></name> <name><surname>Zhang</surname> <given-names>F</given-names></name> <name><surname>Zhu</surname> <given-names>Z</given-names></name> <name><surname>Qi</surname> <given-names>T</given-names></name> <name><surname>Zheng</surname> <given-names>Z</given-names></name> <etal/></person-group>. <article-title>Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits</article-title>. <source>Nat Commun.</source> (<year>2018</year>) <volume>9</volume>:<fpage>918</fpage>. <pub-id pub-id-type="doi">10.1038/s41467-018-03371-0</pub-id><pub-id pub-id-type="pmid">29500431</pub-id></citation></ref>
<ref id="B13">
<label>13.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Hannon</surname> <given-names>E</given-names></name> <name><surname>Gorrie-Stone</surname> <given-names>TJ</given-names></name> <name><surname>Smart</surname> <given-names>MC</given-names></name> <name><surname>Burrage</surname> <given-names>J</given-names></name> <name><surname>Hughes</surname> <given-names>A</given-names></name> <name><surname>Bao</surname> <given-names>Y</given-names></name> <etal/></person-group>. <article-title>Leveraging DNA-methylation quantitative-trait loci to characterize the relationship between methylomic variation, gene expression, and complex traits</article-title>. <source>Am J Hum Genet.</source> (<year>2018</year>) <volume>103</volume>:<fpage>654</fpage>&#x02013;<lpage>65</lpage>. <pub-id pub-id-type="doi">10.1016/j.ajhg.2018.09.007</pub-id><pub-id pub-id-type="pmid">30401456</pub-id></citation></ref>
<ref id="B14">
<label>14.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Gamazon</surname> <given-names>ER</given-names></name> <name><surname>Wheeler</surname> <given-names>HE</given-names></name> <name><surname>Shah</surname> <given-names>KP</given-names></name> <name><surname>Mozaffari</surname> <given-names>SV</given-names></name> <name><surname>Aquino-Michaels</surname> <given-names>K</given-names></name> <name><surname>Carroll</surname> <given-names>RJ</given-names></name> <etal/></person-group>. <article-title>A gene-based association method for mapping traits using reference transcriptome data</article-title>. <source>Nat Genet.</source> (<year>2015</year>) <volume>47</volume>:<fpage>1091</fpage>&#x02013;<lpage>8</lpage>. <pub-id pub-id-type="doi">10.1038/ng.3367</pub-id><pub-id pub-id-type="pmid">27199780</pub-id></citation></ref>
<ref id="B15">
<label>15.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Barbeira</surname> <given-names>AN</given-names></name> <name><surname>Dickinson</surname> <given-names>SP</given-names></name> <name><surname>Bonazzola</surname> <given-names>R</given-names></name> <name><surname>Zheng</surname> <given-names>J</given-names></name> <name><surname>Wheeler</surname> <given-names>HE</given-names></name> <name><surname>Torres</surname> <given-names>JM</given-names></name> <etal/></person-group>. <article-title>Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics</article-title>. <source>Nat Commun.</source> (<year>2018</year>) <volume>9</volume>:<fpage>1825</fpage>. <pub-id pub-id-type="doi">10.1038/s41467-018-03621-1</pub-id><pub-id pub-id-type="pmid">29739930</pub-id></citation></ref>
<ref id="B16">
<label>16.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Consortium</surname> <given-names>GT</given-names></name></person-group>. <article-title>The GTEx consortium atlas of genetic regulatory effects across human tissues</article-title>. <source>Science.</source> (<year>2020</year>) <volume>369</volume>:<fpage>1318</fpage>&#x02013;<lpage>30</lpage>. <pub-id pub-id-type="doi">10.1126/science.aaz1776</pub-id><pub-id pub-id-type="pmid">32913098</pub-id></citation></ref>
<ref id="B17">
<label>17.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Shepardson</surname> <given-names>KM</given-names></name> <name><surname>Larson</surname> <given-names>K</given-names></name> <name><surname>Johns</surname> <given-names>LL</given-names></name> <name><surname>Stanek</surname> <given-names>K</given-names></name> <name><surname>Cho</surname> <given-names>H</given-names></name> <name><surname>Wellham</surname> <given-names>J</given-names></name> <etal/></person-group>. <article-title>IFNAR2 is required for anti-influenza immunity and alters susceptibility to post-influenza bacterial superinfections</article-title>. <source>Front Immunol.</source> (<year>2018</year>) <volume>9</volume>:<fpage>2589</fpage>. <pub-id pub-id-type="doi">10.3389/fimmu.2018.02589</pub-id><pub-id pub-id-type="pmid">30473701</pub-id></citation></ref>
<ref id="B18">
<label>18.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Hardy</surname> <given-names>MP</given-names></name> <name><surname>Owczarek</surname> <given-names>CM</given-names></name> <name><surname>Trajanovska</surname> <given-names>S</given-names></name> <name><surname>Liu</surname> <given-names>X</given-names></name> <name><surname>Kola</surname> <given-names>I</given-names></name> <name><surname>Hertzog</surname> <given-names>PJ</given-names></name></person-group>. <article-title>The soluble murine type I interferon receptor Ifnar-2 is present in serum, is independently regulated, and has both agonistic and antagonistic properties</article-title>. <source>Blood.</source> (<year>2001</year>) <volume>97</volume>:<fpage>473</fpage>&#x02013;<lpage>82</lpage>. <pub-id pub-id-type="doi">10.1182/blood.V97.2.473</pub-id><pub-id pub-id-type="pmid">11154225</pub-id></citation></ref>
<ref id="B19">
<label>19.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Hurtado-Guerrero</surname> <given-names>I</given-names></name> <name><surname>Hernaez</surname> <given-names>B</given-names></name> <name><surname>Pinto-Medel</surname> <given-names>MJ</given-names></name> <name><surname>Calonge</surname> <given-names>E</given-names></name> <name><surname>Rodriguez-Bada</surname> <given-names>JL</given-names></name> <name><surname>Urbaneja</surname> <given-names>P</given-names></name> <etal/></person-group>. <article-title>Antiviral, immunomodulatory and antiproliferative activities of recombinant soluble IFNAR2 without IFN-ss mediation</article-title>. <source>J Clin Med.</source> (<year>2020</year>) <volume>9</volume>:<fpage>959</fpage>. <pub-id pub-id-type="doi">10.3390/jcm9040959</pub-id><pub-id pub-id-type="pmid">32244308</pub-id></citation></ref>
<ref id="B20">
<label>20.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Liu</surname> <given-names>D</given-names></name> <name><surname>Yang</surname> <given-names>J</given-names></name> <name><surname>Feng</surname> <given-names>B</given-names></name> <name><surname>Lu</surname> <given-names>W</given-names></name> <name><surname>Zhao</surname> <given-names>C</given-names></name> <name><surname>Li</surname> <given-names>L</given-names></name></person-group>. <article-title>Mendelian randomization analysis identified genes pleiotropically associated with the risk and prognosis of COVID-19</article-title>. <source>J Infect.</source> (<year>2021</year>) <volume>82</volume>:<fpage>126</fpage>&#x02013;<lpage>32</lpage>. <pub-id pub-id-type="doi">10.1016/j.jinf.2020.11.031</pub-id><pub-id pub-id-type="pmid">33259846</pub-id></citation></ref>
<ref id="B21">
<label>21.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Alkhatib</surname> <given-names>G</given-names></name> <name><surname>Combadiere</surname> <given-names>C</given-names></name> <name><surname>Broder</surname> <given-names>CC</given-names></name> <name><surname>Feng</surname> <given-names>Y</given-names></name> <name><surname>Kennedy</surname> <given-names>PE</given-names></name> <name><surname>Murphy</surname> <given-names>PM</given-names></name> <etal/></person-group>. <article-title>CC CKR5: a RANTES, MIP-1alpha, MIP-1beta receptor as a fusion cofactor for macrophage-tropic HIV-1</article-title>. <source>Science.</source> (<year>1996</year>) <volume>272</volume>:<fpage>1955</fpage>&#x02013;<lpage>8</lpage>. <pub-id pub-id-type="doi">10.1126/science.272.5270.1955</pub-id><pub-id pub-id-type="pmid">8658171</pub-id></citation></ref>
<ref id="B22">
<label>22.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Patterson</surname> <given-names>BK</given-names></name> <name><surname>Seethamraju</surname> <given-names>H</given-names></name> <name><surname>Dhody</surname> <given-names>K</given-names></name> <name><surname>Corley</surname> <given-names>MJ</given-names></name> <name><surname>Kazempour</surname> <given-names>K</given-names></name> <name><surname>Lalezari</surname> <given-names>J</given-names></name> <etal/></person-group>. <article-title>CCR5 inhibition in critical COVID-19 patients decreases inflammatory cytokines, increases CD8 T-cells, and decreases SARS-CoV2 RNA in plasma by day 14</article-title>. <source>Int J Infect Dis.</source> (<year>2020</year>) <volume>103</volume>:<fpage>25</fpage>&#x02013;<lpage>32</lpage>. <pub-id pub-id-type="doi">10.1016/j.ijid.2020.10.101</pub-id><pub-id pub-id-type="pmid">33186704</pub-id></citation></ref>
<ref id="B23">
<label>23.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Cuesta-Llavona</surname> <given-names>E</given-names></name> <name><surname>Gomez</surname> <given-names>J</given-names></name> <name><surname>Albaiceta</surname> <given-names>GM</given-names></name> <name><surname>Amado-Rodriguez</surname> <given-names>L</given-names></name> <name><surname>Garcia-Clemente</surname> <given-names>M</given-names></name> <name><surname>Gutierrez-Rodriguez</surname> <given-names>J</given-names></name> <etal/></person-group>. <article-title>Variant-genetic and transcript-expression analysis showed a role for the chemokine-receptor CCR5 in COVID-19 severity</article-title>. <source>Int Immunopharmacol.</source> (<year>2021</year>) <volume>98</volume>:<fpage>107825</fpage>. <pub-id pub-id-type="doi">10.1016/j.intimp.2021.107825</pub-id><pub-id pub-id-type="pmid">34116286</pub-id></citation></ref>
<ref id="B24">
<label>24.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Chang</surname> <given-names>D</given-names></name> <name><surname>Nalls</surname> <given-names>MA</given-names></name> <name><surname>Hallgrimsdottir</surname> <given-names>IB</given-names></name> <name><surname>Hunkapiller</surname> <given-names>J</given-names></name> <name><surname>van der Brug</surname> <given-names>M</given-names></name> <name><surname>Cai</surname> <given-names>F</given-names></name> <etal/></person-group>. <article-title>A meta-analysis of genome-wide association studies identifies 17 new Parkinson&#x00027;s disease risk loci</article-title>. <source>Nat Genet.</source> (<year>2017</year>) <volume>49</volume>:<fpage>1511</fpage>&#x02013;<lpage>6</lpage>. <pub-id pub-id-type="doi">10.1038/ng.3955</pub-id><pub-id pub-id-type="pmid">28892059</pub-id></citation></ref>
<ref id="B25">
<label>25.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Witoelar</surname> <given-names>A</given-names></name> <name><surname>Jansen</surname> <given-names>IE</given-names></name> <name><surname>Wang</surname> <given-names>Y</given-names></name> <name><surname>Desikan</surname> <given-names>RS</given-names></name> <name><surname>Gibbs</surname> <given-names>JR</given-names></name> <name><surname>Blauwendraat</surname> <given-names>C</given-names></name> <etal/></person-group>. <article-title>Genome-wide pleiotropy between parkinson disease and autoimmune diseases</article-title>. <source>JAMA Neurol.</source> (<year>2017</year>) <volume>74</volume>:<fpage>780</fpage>&#x02013;<lpage>92</lpage>. <pub-id pub-id-type="doi">10.1001/jamaneurol.2017.0469</pub-id><pub-id pub-id-type="pmid">28586827</pub-id></citation></ref>
<ref id="B26">
<label>26.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Zhang</surname> <given-names>CC</given-names></name> <name><surname>Zhu</surname> <given-names>JX</given-names></name> <name><surname>Wan</surname> <given-names>Y</given-names></name> <name><surname>Tan</surname> <given-names>L</given-names></name> <name><surname>Wang</surname> <given-names>HF</given-names></name> <name><surname>Yu</surname> <given-names>JT</given-names></name> <etal/></person-group>. <article-title>Meta-analysis of the association between variants in MAPT and neurodegenerative diseases</article-title>. <source>Oncotarget.</source> (<year>2017</year>) <volume>8</volume>:<fpage>44994</fpage>&#x02013;<lpage>5007</lpage>. <pub-id pub-id-type="doi">10.18632/oncotarget.16690</pub-id><pub-id pub-id-type="pmid">28402959</pub-id></citation></ref>
<ref id="B27">
<label>27.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Zhu</surname> <given-names>Z</given-names></name> <name><surname>Guo</surname> <given-names>Y</given-names></name> <name><surname>Shi</surname> <given-names>H</given-names></name> <name><surname>Liu</surname> <given-names>CL</given-names></name> <name><surname>Panganiban</surname> <given-names>RA</given-names></name> <name><surname>Chung</surname> <given-names>W</given-names></name> <etal/></person-group>. <article-title>Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank</article-title>. <source>J Allergy Clin Immunol.</source> (<year>2020</year>) <volume>145</volume>:<fpage>537</fpage>&#x02013;<lpage>49</lpage>. <pub-id pub-id-type="doi">10.1016/j.jaci.2019.09.035</pub-id><pub-id pub-id-type="pmid">31669095</pub-id></citation></ref>
<ref id="B28">
<label>28.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Tachmazidou</surname> <given-names>I</given-names></name> <name><surname>Hatzikotoulas</surname> <given-names>K</given-names></name> <name><surname>Southam</surname> <given-names>L</given-names></name> <name><surname>Esparza-Gordillo</surname> <given-names>J</given-names></name> <name><surname>Haberland</surname> <given-names>V</given-names></name> <name><surname>Zheng</surname> <given-names>J</given-names></name> <etal/></person-group>. <article-title>Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data</article-title>. <source>Nat Genet.</source> (<year>2019</year>) <volume>51</volume>:<fpage>230</fpage>&#x02013;<lpage>6</lpage>. <pub-id pub-id-type="doi">10.1038/s41588-018-0327-1</pub-id><pub-id pub-id-type="pmid">30664745</pub-id></citation></ref>
<ref id="B29">
<label>29.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Chen</surname> <given-names>MH</given-names></name> <name><surname>Raffield</surname> <given-names>LM</given-names></name> <name><surname>Mousas</surname> <given-names>A</given-names></name> <name><surname>Sakaue</surname> <given-names>S</given-names></name> <name><surname>Huffman</surname> <given-names>JE</given-names></name> <name><surname>Moscati</surname> <given-names>A</given-names></name> <etal/></person-group>. <article-title>Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations</article-title>. <source>Cell.</source> (<year>2020</year>) <volume>182</volume>:<fpage>1198</fpage>&#x02013;<lpage>213</lpage> e1114. <pub-id pub-id-type="doi">10.1016/j.cell.2020.06.045</pub-id><pub-id pub-id-type="pmid">32888493</pub-id></citation></ref>
<ref id="B30">
<label>30.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Fingerlin</surname> <given-names>TE</given-names></name> <name><surname>Murphy</surname> <given-names>E</given-names></name> <name><surname>Zhang</surname> <given-names>W</given-names></name> <name><surname>Peljto</surname> <given-names>AL</given-names></name> <name><surname>Brown</surname> <given-names>KK</given-names></name> <name><surname>Steele</surname> <given-names>MP</given-names></name> <etal/></person-group>. <article-title>Genome-wide association study identifies multiple susceptibility loci for pulmonary fibrosis</article-title>. <source>Nat Genet.</source> (<year>2013</year>) <volume>45</volume>:<fpage>613</fpage>&#x02013;<lpage>20</lpage>. <pub-id pub-id-type="doi">10.1038/ng.2609</pub-id><pub-id pub-id-type="pmid">23583980</pub-id></citation></ref>
<ref id="B31">
<label>31.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Kichaev</surname> <given-names>G</given-names></name> <name><surname>Bhatia</surname> <given-names>G</given-names></name> <name><surname>Loh</surname> <given-names>PR</given-names></name> <name><surname>Gazal</surname> <given-names>S</given-names></name> <name><surname>Burch</surname> <given-names>K</given-names></name> <name><surname>Freund</surname> <given-names>MK</given-names></name> <etal/></person-group>. <article-title>Leveraging polygenic functional enrichment to improve GWAS power</article-title>. <source>Am J Hum Genet.</source> (<year>2019</year>) <volume>104</volume>:<fpage>65</fpage>&#x02013;<lpage>75</lpage>. <pub-id pub-id-type="doi">10.1016/j.ajhg.2018.11.008</pub-id><pub-id pub-id-type="pmid">30595370</pub-id></citation></ref>
<ref id="B32">
<label>32.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Idrees</surname> <given-names>D</given-names></name> <name><surname>Kumar</surname> <given-names>V</given-names></name></person-group>. <article-title>SARS-CoV-2 spike protein interactions with amyloidogenic proteins: potential clues to neurodegeneration</article-title>. <source>Biochem Biophys Res Commun.</source> (<year>2021</year>) <volume>554</volume>:<fpage>94</fpage>&#x02013;<lpage>8</lpage>. <pub-id pub-id-type="doi">10.1016/j.bbrc.2021.03.100</pub-id><pub-id pub-id-type="pmid">33789211</pub-id></citation></ref>
<ref id="B33">
<label>33.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Sun</surname> <given-names>B</given-names></name> <name><surname>Tang</surname> <given-names>N</given-names></name> <name><surname>Peluso</surname> <given-names>MJ</given-names></name> <name><surname>Iyer</surname> <given-names>NS</given-names></name> <name><surname>Torres</surname> <given-names>L</given-names></name> <name><surname>Donatelli</surname> <given-names>JL</given-names></name> <etal/></person-group>. <article-title>Characterization and biomarker analyses of post-COVID-19 complications and neurological manifestations</article-title>. <source>Cells.</source> (<year>2021</year>) <volume>10</volume>:<fpage>386</fpage>. <pub-id pub-id-type="doi">10.3390/cells10020386</pub-id><pub-id pub-id-type="pmid">33668514</pub-id></citation></ref>
<ref id="B34">
<label>34.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Ramani</surname> <given-names>A</given-names></name> <name><surname>Muller</surname> <given-names>L</given-names></name> <name><surname>Ostermann</surname> <given-names>PN</given-names></name> <name><surname>Gabriel</surname> <given-names>E</given-names></name> <name><surname>Abida-Islam</surname> <given-names>P</given-names></name> <name><surname>Muller-Schiffmann</surname> <given-names>A</given-names></name> <etal/></person-group>. <article-title>SARS-CoV-2 targets neurons of 3D human brain organoids</article-title>. <source>EMBO J.</source> (<year>2020</year>) <volume>39</volume>:<fpage>e106230</fpage>. <pub-id pub-id-type="doi">10.15252/embj.2020106230</pub-id><pub-id pub-id-type="pmid">32876341</pub-id></citation></ref>
<ref id="B35">
<label>35.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Nakagawa</surname> <given-names>T</given-names></name> <name><surname>Taniguchi-Ikeda</surname> <given-names>M</given-names></name> <name><surname>Murakami</surname> <given-names>Y</given-names></name> <name><surname>Nakamura</surname> <given-names>S</given-names></name> <name><surname>Motooka</surname> <given-names>D</given-names></name> <name><surname>Emoto</surname> <given-names>T</given-names></name> <etal/></person-group>. <article-title>A novel PIGN mutation and prenatal diagnosis of inherited glycosylphosphatidylinositol deficiency</article-title>. <source>Am J Med Genet A.</source> (<year>2016</year>) <volume>170A</volume>:<fpage>183</fpage>&#x02013;<lpage>88</lpage>. <pub-id pub-id-type="doi">10.1002/ajmg.a.37397</pub-id><pub-id pub-id-type="pmid">26419326</pub-id></citation></ref>
<ref id="B36">
<label>36.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Schneider</surname> <given-names>WM</given-names></name> <name><surname>Luna</surname> <given-names>JM</given-names></name> <name><surname>Hoffmann</surname> <given-names>HH</given-names></name> <name><surname>Sanchez-Rivera</surname> <given-names>FJ</given-names></name> <name><surname>Leal</surname> <given-names>AA</given-names></name> <name><surname>Ashbrook</surname> <given-names>AW</given-names></name> <etal/></person-group>. <article-title>Genome-scale identification of SARS-CoV-2 and pan-coronavirus host factor networks</article-title>. <source>Cell.</source> (<year>2021</year>) <volume>184</volume>:<fpage>120</fpage>&#x02013;<lpage>32</lpage> e114. <pub-id pub-id-type="doi">10.1016/j.cell.2020.12.006</pub-id><pub-id pub-id-type="pmid">33382968</pub-id></citation></ref>
<ref id="B37">
<label>37.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Hoffmann</surname> <given-names>HH</given-names></name> <name><surname>Sanchez-Rivera</surname> <given-names>FJ</given-names></name> <name><surname>Schneider</surname> <given-names>WM</given-names></name> <name><surname>Luna</surname> <given-names>JM</given-names></name> <name><surname>Soto-Feliciano</surname> <given-names>YM</given-names></name> <name><surname>Ashbrook</surname> <given-names>AW</given-names></name> <etal/></person-group>. <article-title>Functional interrogation of a SARS-CoV-2 host protein interactome identifies unique and shared coronavirus host factors</article-title>. <source>Cell Host Microbe.</source> (<year>2020</year>) <volume>29</volume>:<fpage>267</fpage>&#x02013;<lpage>280</lpage>.e5. <pub-id pub-id-type="doi">10.1101/2020.09.11.291716</pub-id><pub-id pub-id-type="pmid">33357464</pub-id></citation></ref>
<ref id="B38">
<label>38.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Wang</surname> <given-names>Y</given-names></name> <name><surname>Maeda</surname> <given-names>Y</given-names></name> <name><surname>Liu</surname> <given-names>YS</given-names></name> <name><surname>Takada</surname> <given-names>Y</given-names></name> <name><surname>Ninomiya</surname> <given-names>A</given-names></name> <name><surname>Hirata</surname> <given-names>T</given-names></name> <etal/></person-group>. <article-title>Cross-talks of glycosylphosphatidylinositol biosynthesis with glycosphingolipid biosynthesis and ER-associated degradation</article-title>. <source>Nat Commun.</source> (<year>2020</year>) <volume>11</volume>:<fpage>860</fpage>. <pub-id pub-id-type="doi">10.1038/s41467-020-14678-2</pub-id><pub-id pub-id-type="pmid">32054864</pub-id></citation></ref>
<ref id="B39">
<label>39.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Vitner</surname> <given-names>EB</given-names></name> <name><surname>Achdout</surname> <given-names>H</given-names></name> <name><surname>Avraham</surname> <given-names>R</given-names></name> <name><surname>Politi</surname> <given-names>B</given-names></name> <name><surname>Cherry</surname> <given-names>L</given-names></name> <name><surname>Tamir</surname> <given-names>H</given-names></name> <etal/></person-group>. <article-title>Glucosylceramide synthase inhibitors prevent replication of SARS-CoV-2 and influenza virus</article-title>. <source>J Biol Chem.</source> (<year>2021</year>) <volume>296</volume>:<fpage>100470</fpage>. <pub-id pub-id-type="doi">10.1016/j.jbc.2021.100470</pub-id><pub-id pub-id-type="pmid">33639165</pub-id></citation></ref>
</ref-list>
</back>
</article>