Human Molecular Genetics, 2022, Vol. 31, 22, 3789–3806 https://doi.org/10.1093/hmg/ddac132 Advance access publication date 16 June 2022 Original Article Novel genes and sex differences in COVID-19 severity 1,2,3,4,† Raquel Cruz , Silvia Diz-de Almeida4,† , Miguel López de Heredia2 , Inés Quintela1 , Francisco C. Ceballos5 , Guillermo Pita6 , José M. Lorenzo-Salazar7 , Rafaela González-Montelongo7 , Manuela Gago-Domínguez8,3 , Marta Sevilla Porras2,9 , Jair Antonio Tenorio Castaño2,9,10 , Julian Nevado2,9,10 , Jose María Aguado11,12,13,14 , Carlos Aguilar15 , Sergio Aguilera-Albesa16,17 , Virginia Almadana18 , Berta Almoguera19,2 , Nuria Alvarez6 , Álvaro Andreu-Bernabeu20,13 , Eunate Arana-Arri21,22 , Celso Arango20,23,13 , María J. Arranz24 , Maria-Jesus Artiga25 , Raúl C. Baptista-Rosas26,27,28 , María Barreda-Sánchez29,30 , Moncef Belhassen-Garcia31,32 , Joao F. Bezerra33 , Marcos A.C. Bezerra34 , Lucía Boix-Palop35 , María Brion 36,37 , Ramón Brugada38,39,37,40 , Matilde Bustos41 , 42,43,44 Enrique J. Calderón , Cristina Carbonell45,32 , Luis Castano21,46,2,47,48 , Jose E. Castelao49 , Rosa Conde-Vicente50 , M. Lourdes Cordero-Lorenzana51 , Jose L. Cortes-Sanchez52,53 , Marta Corton19,2 , M. Teresa Darnaude54 , Alba De Martino-Rodríguez55,56 , Victor del Campo-Pérez57 , Aranzazu Diaz de Bustamante54 , Elena Domínguez-Garrido58 , Andre D. Luchessi59 , Rocío Eiros60 , Gladys Mercedes Estigarribia Sanabria61 , María Carmen Fariñas62,63,64 , Uxía Fernández-Robelo65 , Amanda Fernández-Rodríguez5,14 , Tania Fernández-Villa66 , Belén Gil-Fournier67 , Javier Gómez-Arrue55,56 , Beatriz González Álvarez55,56 , Fernan Gonzalez Bernaldo de Quirós68 , Javier González-Peñas20,13,23 , Juan F. Gutiérrez-Bautista 69 , 70,71 72 5,14 73,74 75 María José Herrero , Antonio Herrero-Gonzalez , María A. Jimenez-Sousa , María Claudia Lattig , Anabel Liger Borja , 19,2 76,77 75 78,43 Rosario Lopez-Rodriguez , Esther Mancebo , Caridad Martín-López , Vicente Martín , Oscar Martinez-Nieto79,74 , Iciar Martinez-Lopez80,81 , Michel F. Martinez-Resendez52 , Angel Martinez-Perez82 , Juliana F. Mazzeu83,84,85 , Eleuterio Merayo Macías86 , Pablo Minguez19,2 , Victor Moreno Cuerda87,88 , Vivian N. Silbiger59 , Silviene F. Oliveira83,89,90,91,92 , Eva Ortega-Paino25 , Mara Parellada20,23,13 , Estela Paz-Artal76,77,93 , Ney P.C. Santos94 , Patricia Pérez-Matute95 , Patricia Perez96 , M. Elena Pérez-Tomás29 , Teresa Perucho97 , Mel Lina Pinsach-Abuin38,37 , Ericka N. Pompa-Mera98 , Gloria L. Porras-Hurtado99 , Aurora Pujol100,2,101 , Soraya Ramiro León67 , Salvador Resino5,14 , Marianne R. Fernandes94,102 , Emilio Rodríguez-Ruiz103,3 , Fernando Rodriguez-Artalejo104,105,43,106 , José A. Rodriguez-Garcia107 , Francisco Ruiz Cabello69,108,109 , Javier Ruiz-Hornillos110,111,112 , Pablo Ryan113,114,115 , José Manuel Soria82 , Juan Carlos Souto116 , Eduardo Tamayo117,118 , Alvaro Tamayo-Velasco119 , Juan Carlos Taracido-Fernandez72 , Alejandro Teper120 , Lilian Torres-Tobar121 , Miguel Urioste122 , Juan Valencia-Ramos123 , Zuleima Yáñez124 , Ruth Zarate125 , Tomoko Nakanishi 126,127,128,129,130 , Sara Pigazzini131,132 , Frauke Degenhardt 133,134 , 135,128 Guillaume Butler-Laporte , Douglas Maya-Miles136,137 , Luis Bujanda138,137 , Youssef Bouysran139 , Adriana Palom140,141,142 , David Ellinghaus143,133 , Manuel Martínez-Bueno144 , Selina Rolker145 , Sara Amitrano146 , Luisa Roade137,140,147 , Francesca Fava148,149,150 , 151 Christoph D. Spinner , Daniele Prati152 , David Bernardo153,137 , Federico Garcia154,155 , Gilles Darcis156,157 , Israel Fernández-Cadenas158 , Jan Cato Holter159,160 , Jesus M. Banales161,137 , Robert Frithiof162 , Stefano Duga163,164 , Rosanna Asselta163,164 , Alexandre C. Pereira165 , Manuel Romero-Gómez136,137 , Beatriz Nafría-Jiménez166 , Johannes R. Hov167,160,168 , Isabelle Migeotte169,139 , Alessandra Renieri 148,149,150 , Anna M. Planas170,171 , Kerstin U. Ludwig 145 , Maria Buti137,140,147 , Souad Rahmouni156 , Marta E. Alarcón-Riquelme144,172 , Eva C. Schulte173,174,175 , Andre Franke133,134 , Tom H. Karlsen167,160,176 , Luca Valenti177,178 , Hugo Zeberg179,180 , Brent Richards181,128,182 , Andrea Ganna132,183 , Mercè Boada184,185 , Itziar de Rojas184,185 , Agustín Ruiz184,185 , Pascual Sánchez-Juan186 , Luis Miguel Real187 , SCOURGE Cohort Group*, HOSTAGE Cohort Group*, GRA@CE Cohort Group*, Encarna Guillen-Navarro29,188,189,190 , Carmen Ayuso19,2 , Anna González-Neira6 , José A. Riancho62,63,64 , 191 Augusto Rojas-Martinez , Carlos Flores7,192,193,194,‡ , Pablo Lapunzina2,9,10,‡ and Angel Carracedo1,2,3,4,8,‡ 1 Centro Nacional de Genotipado (CEGEN), Universidade de Santiago de Compostela, 15706 Santiago de Compostela, Spain 2 Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, 28029 Madrid, Spain 3 Instituto de Investigación Sanitaria de Santiago (IDIS), 15706 Santiago de Compostela, Spain Received: February 15, 2022. Revised: May 16, 2022. Accepted: June 1, 2022 © The Author(s) 2022. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. 3790 | Human Molecular Genetics, 2022, Vol. 31, No. 22 4 Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain 5 Unidad de Infección Viral e Inmunidad, Centro Nacional de Microbiología (CNM), Instituto de Salud Carlos III (ISCIII), 28220 Madrid, Spain 6 Spanish National Cancer Research Centre, Human Genotyping-CEGEN Unit, 28029 Madrid, Spain 7 Genomics Division, Instituto Tecnológico y de Energías Renovables, 38600 Santa Cruz de Tenerife, Spain 8 Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS), 15706 Santiago de Compostela, Spain 9 Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz-IDIPAZ, 28046 Madrid, Spain 10 ERN-ITHACA-European Reference Network 11 Unit of Infectious Diseases, Hospital Universitario 12 de Octubre, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain 12 Spanish Network for Research in Infectious Diseases (REIPI RD16/0016/0002), Instituto de Salud Carlos III, 28029 Madrid, Spain 13 School of Medicine, Universidad Complutense, 28040 Madrid, Spain 14 Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, 28029 Madrid, Spain 15 Hospital General Santa Bárbara de Soria, 42005 Soria, Spain 16 Pediatric Neurology Unit, Department of Pediatrics, Navarra Health Service Hospital, 31008 Pamplona, Spain 17 Navarra Health Service, NavarraBioMed Research Group, 31008 Pamplona, Spain 18 Hospital Universitario Virgen Macarena, Neumología, 41009 Seville, Spain 19 Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), 28040 Madrid, Spain 20 Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), 28007 Madrid, Spain 21 Biocruces Bizkai HRI, 48903 Barakaldo, Bizkaia, Spain 22 Cruces University Hospital, Osakidetza, 48903 Barakaldo, Bizkaia, Spain 23 Centre for Biomedical Network Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Spain 24 Fundació Docència I Recerca Mutua Terrassa, 08221 Terrassa, Spain 25 Spanish National Cancer Research Center, CNIO Biobank, 28029 Madrid, Spain 26 Hospital General de Occidente, 45170 Zapopan, Jalisco, Mexico 27 Centro Universitario de Tonalá, Universidad de Guadalajara, 45425 Tonalá, Jalisco, Mexico 28 Centro de Investigación Multidisciplinario en Salud, Universidad de Guadalajara, 45425 Tonalá, Jalisco, Mexico 29 Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), 30120 Murcia, Spain 30 Universidad Católica San Antonio de Murcia (UCAM), 30120 Murcia, Spain 31 Hospital Universitario de Salamanca-IBSAL, Servicio de Medicina Interna-Unidad de Enfermedades Infecciosas, 37007 Salamanca, Spain 32 Universidad de Salamanca, 37007 Salamanca, Spain 33 Escola Tecnica de Saúde, Laboratorio de Vigilancia Molecular Aplicada, 68515-000 Pará, Brazil 34 Federal University of Pernambuco, Genetics Postgraduate Program, Recife 50670-907, PE, Brazil 35 Hospital Universitario Mutua Terrassa, 08221 Terrassa, Spain 36 Instituto de Investigación Sanitaria de Santiago (IDIS), Xenética Cardiovascular, 15706 Santiago de Compostela, Spain 37 Centre for Biomedical Network Research on Cardiovascular Diseases (CIBERCV), Instituto de Salud Carlos III, 28029 Madrid, Spain 38 Cardiovascular Genetics Center, Institut d’Investigació Biomèdica Girona (IDIBGI), 17190 Girona, Spain 39 Medical Science Department, School of Medicine, University of Girona, 17190 Girona, Spain 40 Hospital Josep Trueta, Cardiology Service, 17190 Girona, Spain 41 Institute of Biomedicine of Seville (IBiS), Consejo Superior de Investigaciones Científicas (CSIC)- University of Seville- Virgen del Rocio University Hospital, 41013 Seville, Spain 42 Departemento de Medicina, Hospital Universitario Virgen del Rocío, Universidad de Sevilla, 41013 Seville, Spain 43 Centre for Biomedical Network Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029 Madrid, Spain 44 Instituto de Biomedicina de Sevilla, 41013 Seville, Spain 45 Hospital Universitario de Salamanca-IBSAL, Servicio de Medicina Interna, 37007 Salamanca, Spain 46 Osakidetza, Cruces University Hospital, 48903 Barakaldo, Bizkaia, Spain 47 Centre for Biomedical Network Research on Diabetes and Metabolic Associated Diseases (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain 48 University of Pais Vasco, UPV/EHU, 48903 Bizkaia, Spain 49 Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galego de Saúde, 36312 Vigo, Spain 50 Hospital Universitario Río Hortega, 47012 Valladolid, Spain 51 Servicio de Medicina intensiva, Complejo Hospitalario Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), 15009 A Coruña, Spain 52 Tecnológico de Monterrey, 64718 Monterrey, Mexico 53 Otto von Guericke University, Departament of Microgravity and Translational Regenerative Medicine, 39106 Magdeburg, Germany 54 Hospital Universitario Mostoles, Unidad de Genética, 28935 Madrid, Spain 55 Instituto Aragonés de Ciencias de la Salud (IACS), 50009 Zaragoza, Spain 56 Instituto Investigación Sanitaria Aragón (IIS-Aragon), 50009 Zaragoza, Spain 57 Preventive Medicine Department, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galego de Saúde, 36312 Vigo, Spain 58 Unidad Diagnóstico Molecular. Fundación Rioja Salud, 26006 La Rioja, Spain 59 Universidade Federal do Rio Grande do Norte, Departamento de Analises Clinicas e Toxicologicas, 59012-570 Natal, Brazil 60 Hospital Universitario de Salamanca-IBSAL, Servicio de Cardiología, 37007 Salamanca, Spain 61 Instituto Regional de Investigación en Salud-Universidad Nacional de Caaguazú, HH36+J3Q Caaguazú, Paraguay 62 IDIVAL, 39008 Cantabria, Spain 63 Universidad de Cantabria, 39008 Cantabria, Spain 64 Hospital U M Valdecilla, 39008 Cantabria, Spain 65 Urgencias Hospitalarias, Complejo Hospitalario Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), 15009 A Coruña, Spain 66 Grupo de Investigación en Interacciones Gen-Ambiente y Salud (GIIGAS) - Instituto de Biomedicina (IBIOMED), Universidad de León, 24071 León, Spain 67 Hospital Universitario de Getafe, Servicio de Genética, 28905 Madrid, Spain 68 Ministerio de Salud Ciudad de Buenos Aires, Buenos Aires C1425EFD CABA, Argentina 69 Hospital Universitario Virgen de las Nieves, Servicio de Análisis Clínicos e Inmunología, 18014 Granada, Spain 70 IIS La Fe, Plataforma de Farmacogenética, 46026 Valencia, Spain 71 Universidad de Valencia, Departamento de Farmacología, 46010 Valencia, Spain 72 Data Analysis Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), 28040 Madrid, Spain 73 Universidad de los Andes, Facultad de Ciencias, Bogotá 111711, Colombia Human Molecular Genetics, 2022, Vol. 31, No. 22 | 3791 74 SIGEN Alianza Universidad de los Andes - Fundación Santa Fe de Bogotá, Bogotá 111711, Clombia 75 Hospital General de Segovia, Medicina Intensiva, 40002 Segovia, Spain 76 Hospital Universitario 12 de Octubre, Department of Immunology, 28041 Madrid, Spain 77 Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Transplant Immunology and Immunodeficiencies Group, 28041 Madrid, Spain 78 Instituto de Biomedicina (IBIOMED), Universidad de León, 24071 León, Spain 79 Fundación Santa Fe de Bogota, Departamento Patologia y Laboratorios, Bogotá 111711, Colombia 80 Unidad de Genética y Genómica Islas Baleares, 07120 Islas Baleares, Spain 81 Hospital Universitario Son Espases, Unidad de Diagnóstico Molecular y Genética Clínica, 07120 Islas Baleares, Spain 82 Genomics of Complex Diseases Unit, Research Institute of Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, 08041 Barcelona, Spain 83 Faculdade de Medicina, Universidade de Brasília, Brasilia 70910-900, Brazil 84 Programa de Pós-Graduação em Ciências Médicas, Universidade de Brasília, Brasilia 70910-900, Brazil 85 Programa de Pós-Graduação em Ciências da Saúde, Universidade de Brasília, Brasilia 70910-900, Brazil 86 Hospital El Bierzo, Unidad Cuidados Intensivos, 24404 León, Spain 87 Hospital Universitario Mostoles, Medicina Interna, 28935 Madrid, Spain 88 Universidad Francisco de Vitoria, 28223 Madrid, Spain 89 Programa de Pós-Graduação em Biologia Animal, Universidade de Brasília, Brasília 70910-900, Brazil 90 Programa de Pós-Graduação Profissional em Ensino de Biologia, Universidade de Brasília, Brasília 70910-900, Brazil 91 Programa de Pós-Graduação Profissional em Ensino de Biologia (UnB), Universidade de Brasília, Brasília 70910-900, Brazil 92 Programa de Pós-Graduação em Ciências Médicas, Universidade de Brasília, Brasília 70910-900, Brazil 93 Universidad Complutense de Madrid, Department of Immunology, Ophthalmology and ENT, 28040 Madrid, Spain 94 Universidade Federal do Pará, Núcleo de Pesquisas em Oncologia, Belém, Pará 66075-110, Brazil 95 Infectious Diseases, Microbiota and Metabolism Unit, Center for Biomedical Research of La Rioja (CIBIR), 26006 Logroño, Spain 96 Inditex, 15141 A Coruña, Spain 97 GENYCA, 28220 Madrid, Spain 98 Instituto Mexicano del Seguro Social (IMSS), Centro Médico Nacional Siglo XXI, Unidad de Investigación Médica en Enfermedades Infecciosas y Parasitarias, Mexico City 02990, Mexico 99 Clinica Comfamiliar Risaralda, 660003 Pereira, Colombia 100 Bellvitge Biomedical Research Institute (IDIBELL), Neurometabolic Diseases Laboratory, 08908 L’Hospitalet de Llobregat, Spain 101 Catalan Institution of Research and Advanced Studies (ICREA), 08010 Barcelona, Spain 102 Hospital Ophir Loyola, Departamento de Ensino e Pesquisa, Belém, Pará 66063-240, Brazil 103 Unidad de Cuidados Intensivos, Hospital Clínico Universitario de Santiago (CHUS), Sistema Galego de Saúde (SERGAS), 15706 Santiago de Compostela, Spain 104 Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, 28049 Madrid, Spain 105 IdiPaz (Instituto de Investigación Sanitaria Hospital Universitario La Paz), 28046 Madrid, Spain 106 IMDEA-Food Institute, CEI UAM+CSIC, 28049 Madrid, Spain 107 Complejo Asistencial Universitario de León, 24071 León, Spain 108 Instituto de Investigación Biosanitaria de Granada (ibs GRANADA), 18012 Granada, Spain 109 Universidad de Granada, Departamento Bioquímica, Biología Molecular e Inmunología III, 18071 Granada, Spain 110 Hospital Infanta Elena, Allergy Unit, Valdemoro, 28342 Madrid, Spain 111 Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), 28040 Madrid, Spain 112 Faculty of Medicine, Universidad Francisco de Vitoria, 28223 Madrid, Spain 113 Hospital Universitario Infanta Leonor, 28031 Madrid, Spain 114 Complutense University of Madrid, 28040 Madrid, Spain 115 Gregorio Marañón Health Research Institute (IiSGM), 28007 Madrid, Spain 116 Haemostasis and Thrombosis Unit, Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, 08041 Barcelona, Spain 117 Hospital Clinico Universitario de Valladolid, Servicio de Anestesiologia y Reanimación, 47003 Valladolid, Spain 118 Universidad de Valladolid, Departamento de Cirugía, 47005 Valladolid, Spain 119 Hospital Clinico Universitario de Valladolid, Servicio de Hematologia y Hemoterapia, 47003 Valladolid, Spain 120 Hospital de Niños Ricardo Gutierrez, Buenos Aires C1425EFD CABA, Argentina 121 Fundación Universitaria de Ciencias de la Salud, 113827 Bogotá, Colombia 122 Spanish National Cancer Research Centre, Familial Cancer Clinical Unit, 28029 Madrid, Spain 123 University Hospital of Burgos, 09006 Burgos, Spain 124 Universidad Simón Bolívar, Facultad de Ciencias de la Salud, 080002 Barranquilla, Colombia 125 Centro para el Desarrollo de la Investigación Científica, 1255 Asunción, Paraguay 126 Institute for Molecular Medicine Finland (FIMM), 00014 Univerisity of Helsinki, Finland 127 McGill University, Department of Human Genetics, H3A 0G4 Montréal, Québec, Canada 128 Lady Davis Institute, Jewish General Hospital, McGill University, H3T 1E2 Montréal, Québec, Canada 129 Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, 606-8501 Kyoto, Japan 130 Research Fellow, Japan Society for the Promotion of Science, 102-0083 Tokyo, Japan 131 University of Milano-Bicocca, 20126 Milano, Italy 132 Institute for Molecular Medicine Finland, Univerisity of Helsinki, 00014 Helsinki, Finland 133 Institute of Clinical Molecular Biology, Christian-Albrechts-University, 24118 Kiel, Germany 134 University Hospital Schleswig-Holstein, Campus Kiel, 24118 Kiel, Germany 135 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, H3A 0G4 Montréal, Québec, Canada 136 Digestive Diseases Unit, Virgen del Rocio University Hospital, Institute of Biomedicine of Seville, University of Seville, 41103 Seville, Spain 137 Centre for Biomedical Network Research on Hepatic and Digestive Diseases (CIBEREHD), Instituto de Salud Carlos III, 28029 Madrid, Spain 138 Department of Liver and Gastrointestinal Diseases, Biodonostia Health Research Institute - Donostia University Hospital, University of the Basque Country (UPV/EHU), 20014 San Sebastian, Spain. 139 Centre de Génétique Humaine, Hôpital Erasme, Université Libre de Bruxelles, 1070 Brussels, Belgium 140 Liver Unit, Department of Internal Medicine, Hospital Universitari Vall d’Hebron, Vall d’Hebron Barcelona Hospital Campus, 08035 Barcelona, Spain 141 Universitat Autònoma de Barcelona, Departament de Medicina, Bellatera, 08193 Barcelona, Spain 142 Vall d’Hebron Institut de Recerca (VHIR), Liver Diseases, 08035 Barcelona, Spain 143 Novo Nordisk Foundation Center for Protein Research, Disease Systems Biology, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark 144 GENYO, Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, 18016 Granada, Spain 145 Institute of Human Genetics, University Hospital Bonn, Medical Faculty University of Bonn, 53127 Bonn, Germany 146 Genetica Medica, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy 3792 | Human Molecular Genetics, 2022, Vol. 31, No. 22 147 Universitat Autònoma de Barcelona, Departament de Medicina, Bellatera, 08193 Barcelona, Spain 148 University of Siena, Medical Genetics, 53100 Siena, Italy 149 Azienda Ospedaliero-Universitaria Senese, Genetica Medica, 53100 Siena, Italy 150 , Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy 151 Technical University of Munich, School of Medicine, University Hospital rechts der Isar, Department of Internal Medicine II, 80333 Munich, Germany 152 Department of Transfusion Medicine and Hematology, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Università degli Studi di Milano, 20126 Milano, Italy 153 Mucosal Immunology Lab, Unidad de Excelencia del Instituto de Biomedicina y Genética Molecular (IBGM, Universidad de Valladolid-CSIC), 47005 Valladolid, Spain 154 Hospital Universitario Clinico San Cecilio, 18016 Granada, Spain 155 Instituto de Investigación Ibs, Granada, 18012 Granada, Spain 156 University of Liege. GIGA-Insitute, B- 4000 Liege, Belgium 157 Liege University Hospital (CHU of Liege), B- 4000 Liege, Belgium 158 Biomedical Research Institute Sant Pau (IIB Sant Pau), Stroke Pharmacogenomics and Genetics Group, 08041 Barcelona, Spain 159 Oslo University Hospital, Department of Microbiology, 0424 Oslo, Norway 160 Institute of Clinical Medicine, University of Oslo, 0424 Oslo, Norway 161 Department of Liver and Gastrointestinal Diseases, Biodonostia Health Research Institute - Donostia University Hospital, University of the Basque Country (UPV/EHU), Ikerbasque, 20014 San Sebastian, Spain 162 Department of Surgical Sciences, Anaesthesiology and Intensive Care Medicine, Uppsala University, 751 05 Uppsala, Sweden 163 Humanitas University, Department of Biomedical Sciences, 20089 Milan, Italy 164 IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy 165 Heart Institute (InCor)/University of Sao Paulo Medical School, 05508-070 Sao Paulo, Brazil 166 Osakidetza Basque Health Service, Donostialdea Integrated Health Organisation, Clinical Biochemistry Department, 20006 San Sebastian, Spain 167 Norwegian PSC Research Center and Section of Gastroenterology, Dept Transplantation Medicine, Oslo University Hospital, 0424 Oslo, Norway 168 Research Institute of Internal Medicine, Oslo University Hospital, 0424 Oslo, Norway 169 Fonds de la Recherche Scientifique (FNRS), B – 1000 Brussels 170 Institute for Biomedical Research of Barcelona (IIBB), National Spanish Research Council (CSIC), 08028 Barcelona, Spain 171 Institut d’Investigacions Biomediques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain 172 Institute for Environmental Medicine, Karolinska Institutet, 171 65 Solna, Sweden 173 Institute of Virology, Technical University Munich/Helmholtz Zentrum München, D-85764 Munich, Germany 174 Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich University, 80539 Munich, Germany 175 Department of Psychiatry, University Hospital, LMU Munich University, 80539 Munich, Germany 176 Research Institute of Internal Medicine, Oslo University Hospital, 0318 Oslo, Norway 177 Università degli Studi di Milano, Department of Pathopgysiology and Transplantation, 20126 Milano, Italy 178 Department of Transfusion Medicine and Hematology, Precision Medicine, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milano, Italy 179 Karolinska Institutet, Department of Neuroscience, 171 77 Stockholm, Sweden 180 Max-Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany 181 Department of Human Genetics, Epidemiology, Biostatistics and Occupational Health, McGill University, H3A 0G4 Montréal, Québec, Canada 182 King’s College London, Department of Twin Research, London, WC2R 2LS, United Kingdom 183 Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA 184 Research Center and Memory clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, 08028 Barcelona, Spain 185 Centre for Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, 28029 Madrid, Spain 186 CIEN Foundation/Queen Sofia Foundation Alzheimer Center, 28031 Madrid, Spain 187 Hospital Universitario de Valme, Unidad Clínica de Enfermedades Infecciosas y Microbiología, 41014 Sevilla, Spain 188 Sección Genética Médica - Servicio de Pediatría, Hospital Clínico Universitario Virgen de la Arrixaca, Servicio Murciano de Salud, 30120 Murcia, Spain 189 Departamento Cirugía, Pediatría, Obstetricia y Ginecología, Facultad de Medicina, Universidad de Murcia (UMU), 30100 Murcia, Spain 190 Grupo Clínico Vinculado, Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, 28029 Madrid, Spain 191 Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, 64718 Monterrey, Mexico 192 Research Unit, Hospital Universitario N.S. de Candelaria, 38010 Santa Cruz de Tenerife, Spain 193 Centre for Biomedical Network Research on Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain 194 Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, 35450 Las Palmas de Gran Canaria, Spain *To whom correspondence should be addressed at: Unidad de Investigación, Hospital Universitario Nuestra Señora de Candelaria, Carretera del Rosario s/n, 38 010 Santa Cruz de Tenerife, Spain. Tel: +34 922602938; Fax: +34 922600545; Email: cflores@ull.edu.es (Carlos Flores); Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, Paseo de la Castellana 261, Madrid 28 046, Spain. Tel: +34 917277217; Fax: +34 912071030; Email: pablo.lapunzina@salud.madrid.org (Pablo Lapunzina); Center for Research in Molecular Medicine and Chronic Diseases (CIMUS), Av. Barcelona s/n, 15 782 Santiago de Compostela (A Coruña), Spain. Tel: +34 981951490; Fax: +34 881815403; Email: angel.carracedo@usc.es (Ángel Carracedo) † These authors contributed equally: Raquel Cruz, Silvia Diz-de Almeida. ‡ These authors contributed equally: Carlos Flores, Pablo Lapunzina, Angel Carracedo. Abstract Here, we describe the results of a genome-wide study conducted in 11 939 coronavirus disease 2019 (COVID-19) positive cases with an extensive clinical information that were recruited from 34 hospitals across Spain (SCOURGE consortium). In sex-disaggregated genome-wide association studies for COVID-19 hospitalization, genome-wide significance (P < 5 × 10−8 ) was crossed for variants in 3p21.31 and 21q22.11 loci only among males (P = 1.3 × 10−22 and P = 8.1 × 10−12 , respectively), and for variants in 9q21.32 near TLE1 only among females (P = 4.4 × 10−8 ). In a second phase, results were combined with an independent Spanish cohort (1598 COVID-19 cases and 1068 population controls), revealing in the overall analysis two novel risk loci in 9p13.3 and 19q13.12, with fine-mapping prioritized variants functionally associated with AQP3 (P = 2.7 × 10−8 ) and ARHGAP33 (P = 1.3 × 10−8 ), respectively. The meta-analysis of both phases with four European studies stratified by sex from the Host Genetics Initiative (HGI) confirmed the association of the 3p21.31 and 21q22.11 loci predominantly in males and replicated a recently reported variant in 11p13 (ELF5, P = 4.1 × 10−8 ). Six of the COVID-19 HGI discovered loci were replicated and an HGI-based genetic risk score predicted the severity strata in SCOURGE. We also found more SNP-heritability and larger heritability differences by age (<60 or ≥60 years) among males than among females. Parallel genome-wide screening of inbreeding depression in SCOURGE also showed an effect of homozygosity in COVID-19 hospitalization and severity and this effect was stronger among older males. In summary, new candidate genes for COVID-19 severity and evidence supporting genetic disparities among sexes are provided. Human Molecular Genetics, 2022, Vol. 31, No. 22 | 3793 Introduction GWAS to assess both the additive and dominant Coronavirus disease 2019 (COVID-19)—caused by the components of the genetic architecture of COVID-19 severe acute respiratory syndrome coronavirus 2 (SARS- severity. Likewise, the ID analysis could also help to CoV-2)—develops with wide clinical variability, ranging explain the strong effect of age in COVID-19 severity. from asymptomatic infection to a life-threatening Results condition (1). Advanced age and the presence of comor- Discovery phase bidities are well-known major risk factors of COVID- In the SCOURGE study, 11 939 COVID-19 positive cases 19 severity (2,3). In addition, male sex is another risk were recruited from 34 centers (Supplementary Mate- factor associated with COVID-19 severity, regardless of rial, Table S1) between March and December 2020. All comorbidities (4). diagnosed cases were classified in a five-level sever- International genetic studies based on genome-wide ity scale (Table 1). Two untested Spanish sample col- association studies (GWAS) and/or comparative genome lections were used as general population controls in sequencing analyses have been conducted to identify some analyses: 3437 samples from the Spanish DNA genetic variants associated with COVID-19 severity (5,6). biobank (https://www.bancoadn.org) and 2506 samples These studies have revealed the role of genes of the from the GR@CE consortium (17). The discovery phase type-I interferon (IFN) signaling pathway as key players samples were genotyped with the Axiom Spain Biobank underlying disease severity (7–9). Besides, they have also Array (Thermo Fisher Scientific). Details of quality con- identified other potential loci previously linked to lung trol (QC), ancestry inference and imputation are shown function, respiratory diseases and autoimmunity (9). in the Materials and Methods section. Individuals with Regarding COVID-19 severity in males, sex-disaggregated inferred European ancestry were used for association genetic analyses have received limited attention despite testing. After post-imputation filtering, 15 045 individ- the importance of sex disparities in clinical severity (10). uals (9371 COVID-19 positive cases and 5674 popula- Early studies suggested immune deficits presumably tion controls) and 8 933 154 genetic markers remained in because of pre-existing neutralizing autoantibodies the SCOURGE European study (discovery). Clinical and against type-I IFN in older male patients (11). demographic characteristics of European patients from The effects of autozygosity, measured as the change of SCOURGE included in the analysis are shown in Table 2. the mean value of a complex trait because of inbreed- Population controls were 46.3% females with a mean age ing, have been useful to identify alternative genetic risk of 55.5 years (standard deviation, SD = 16.2) and 53.7% explanations and effects that traditionally are not cap- males, with a mean age of 51 years (SD = 13.04). tured by GWAS (12). By analyzing the contribution of the The discovery phase of the GWAS was carried out inbreeding depression (ID) through the lens of the runs of with infection susceptibility and three severity outcomes homozygosity (ROH: genomic tracts where homozygous (hospitalization, severe illness and critical illness), which markers occur in an uninterrupted sequence), it is pos- were tested using three different control definitions (see sible to assess the importance of directional dominance Supplementary Material, Table S2). or overdominance in the genetic architecture of com- plex traits (13). Even though this is a relatively modern • A1 analysis: COVID-19 positive not satisfying the case approach, different studies have shown the importance condition and control samples from the general pop- of homozygosity in a large range of complex phenotypes, ulation (COVID-19 untested). including anthropometric, cardiometabolic and mental • A2 analysis: control samples from the general popu- traits (14–16). lation. Through diverse nested sub-studies, the Spanish • C analysis: COVID-19 positive not satisfying the case Coalition to Unlock Research on Host Genetics on condition. COVID-19 (SCOURGE) consortium was launched in The GWAS was carried on by fitting logistic mixed May 2020 aiming to find biomarkers of evolution and regression models adjusting for age, sex and the first 10 prognosis that can have an immediate impact on clinical principal components (PCs; see Materials and Methods). management and therapeutic decisions in SARS-CoV- Summary statistics can be accessed from https://github. 2 infections. This consortium has recruited patients com/CIBERER/Scourge-COVID19. The SCOURGE Board of from hospitals across Spain and Latin America in Directors has agreed to aggregate the GWAS summaries close collaboration with the STOP-Coronavirus initiative with those from the COVID-19 Host Genetics Initiative (https://www.scourge-covid.org). Here, we describe the (HGI) in the data freeze 7 that has not been used for results of the first SCOURGE genome-wide studies of any published article to date. Supplementary Material, COVID-19 conducted in patients recruited in Spain. Table S3 shows the independent significant associated This dataset has not been used in any previous GWAS loci for hospitalization, severity, critical illness and infec- of COVID-19 that has been published to date. To the tion susceptibility risk, for global and sex-stratified anal- best of our knowledge, this is the first time that the ysis in the SCOURGE dataset. However, considering the impact of homozygosity is considered in COVID-19 overlap between the findings for these analyses, only the studies, serving as a complement to the traditional main results for the A1 analysis are presented. 3794 | Human Molecular Genetics, 2022, Vol. 31, No. 22 Table 1. Five-level severity scale used to classify SCOURGE patients Level Clinical findings Severity 0 (asymptomatic) Asymptomatic Severity 1 (mild) With symptoms, but without pulmonary infiltrates or need of oxygen therapy Severity 2 (moderate) With pulmonary infiltrates affecting <50% of the lungs or need of supplemental oxygen therapy Severity 3 (severe) Hospitalized with any of the following criteria: • PaO2 < 65 mmHg or SaO2 < 90% • PaO2 /FiO2 < 300 • SaO2 /FiO2 < 440 • Dyspnea • Respiratory frequency ≥ 22 bpm • Infiltrates affecting > 50% of the lungs Severity 4 (critical) Admission to the ICU or need of mechanical ventilation (invasive or non-invasive) Note: PaO2 , partial pressure of oxygen in arterial blood; SaO2 , saturation of oxygen in arterial blood; FiO2 , fraction of inspired oxygen. Table 2. Baseline characteristics of European patients from SCOURGE included in the analysis Variable Global N = 9371 Males N = 4343 Females N = 5028 Age—mean years (SD) 62.6 (17.9) 64.3 (16.3) 61.1 (19.1) Severity—N (%) 0—asymptomatic 582 (6.6) 161 (3.9) 421 (8.9) 1—mild 2689 (30.3) 748 (18.2) 1941 (40.8) 2—intermediate 2099 (23.6) 1093 (26.5) 1006 (21.1) 3—severe 2379 (26.8) 1300 (31.6) 1079 (22.7) 4—critical illness 1128 (12.7) 817 (19.8) 311 (6.5) Hospitalization—N (%) 5966 (63.8) 3436 (79.3) 2530 (50.5) Severe COVID-19—N (%) 3507 (39.2) 2117 (51.2) 1390 (28.9) Critical illness—N (%) 1128 (12.6) 817 (19.8) 311 (6.5) Comorbidities—N (%) Vascular/endocrinological 4099 (43.7) 2207 (50.8) 1892 (37.6) Cardiac 1057 (11.3) 634 (14.6) 423 (8.4) Nervous 773 (8.3) 341 (7.9) 432 (8.6) Digestive 264 (2.8) 153 (3.5) 111 (2.2) Onco-hematological 647 (6.9) 411 (9.5) 236 (4.7) Respiratory 905 (9.7) 565 (13.0) 340 (6.8) All analyses support the association of two known Material, Fig. S3), a Bayesian fine-mapping on the 9q21.32 loci, i.e. 3p21.31 and 21q22.11. However, other suggestive did not allow to prioritize variants by their role as associations were also found (Fig. 1 and Supplementary expression quantitative trait loci (eQTLs) or anticipate Material, Fig. S1). Strikingly, the leading signals found in the function (Fig. 2). To assess if a higher prevalence of the global (sex-aggregated) analysis were genome-wide comorbidities in males may underlie these differential significant in the analyses among males but not among findings, we performed an additional C analysis in which females. Association in 3p21.31 was also found in the the presence of comorbidities was tested for association C analyses (rs10490770, P = 3.8 × 10−12 ) and once again, within hospitalized patients. No significant association association was genome-wide significant only among was found in either males or females (Supplementary males (males: P = 3.9 × 10−9 , females: P = 4.6 × 10−5 ). Material, Fig. S4). Further explorations of the genetic However, the leading variant of 9q21.32 (near TLE1 gene) associations with comorbidities are presented in the reached genome-wide significance among females only Supplementary Note. (similarly, in the C analysis for females, rs140152223, This GWAS phase was also performed disaggregated by P = 2.11 × 10−6 ). Several variants (rs17763742 near LZTFL1, age (<60/≥60 years old), and by age and sex simultane- rs2834164 in IFNAR2 and rs1826292621 near TLE1) ously. Differences in effect sizes between both age groups showed a significant difference in effect sizes (SNP∗sex were tested for the SNPs shown in the Supplementary interaction P < 0.0031, adjusted probability for 16 com- Material, Table S3, in global and sex-specific analysis parisons) linked not only to hospitalization, but also (Supplementary Material, Table S4). Significant findings to critical illness and infection risk. The A2 and C were only found in the subgroup of males with <60 years analyses did not reveal any additional significant loci old. This was also found in the C analysis for hospital- (Supplementary Material, Fig. S2). Although fine-mapping ization where association in 3p21.31 was significant only studies in 3p21.31 and 21q22.11 have led to gene and in males <60 years old (P = 3.32 × 10−9 ). Differences in variant prioritization within these loci (Supplementary effect size (significant age-interaction) were significant Human Molecular Genetics, 2022, Vol. 31, No. 22 | 3795 Figure 1. Association results of SCOURGE for global and sex-disaggregated A1 hospitalization analysis. (A) Manhattan plot of results from global analysis. A quantile–quantile plot of the global analysis is also shown as an inset. (B) Miami plot of results from sex-disaggregated analyses (top: males and bottom: females). Figure 2. Regional plot of a novel association at 9q21.32 found among females from the SCOURGE study. The x axis ref lects the chromosomal position, and the y axis the −log(P-value). The sentinel variant is indicated by a diamond and all other variants are colour coded by their degree of LD with the sentinel variant in Europeans. Credible set for this signal is shown within a dashed square. The horizontal dotted blue line corresponds to the threshold for genome-wide significance (P = 5 × 10−8 ). 3796 | Human Molecular Genetics, 2022, Vol. 31, No. 22 at 3p21.31 for severity and critical illness, and suggestive in hospitalization. Lookup of previously found COVID-19 host risk factors in the SCOURGE study Known significant loci for COVID-19 severity in 3p21.31 (near SLC6A20 and LZTFL1) and 21q22.11 (in IFNAR2) were clearly replicated at genome-wide significance in this study, specifically for risk of infection, hospitaliza- tion and severity. Three other loci, in 9q34.2 (in ABO), 12q24.13 (in OAS1) and 19p13.2 (near RAVER1 and TYK2), did not reach the genome-wide significance threshold but they were significant after correcting for the 390 tests performed in a lookup (13 SNPs and 30 analyses, significance threshold P < 1.3 × 10−4 ). In agreement with previous results, ABO was mainly associated with the risk of infection. However, other loci as those in 3q12.3 (near RPL24) and 19p13.3 (near DPP9), previously found associated with COVID-19 severity, were not replicated in the SCOURGE Europeans. The complete list of results for the list of COVID-19 HGI significant loci (9) is shown in Figure 3 and in the Supplementary Material, Table S5. Figure 3 also reinforces the clear sex differences found in this study. Genetic risk score and the COVID-19 severity scale We developed a genetic risk score (GRS) combining the 13 leading variants associated with infection risk, hospital- ization or severity in the meta-analysis performed by the COVID-19 HGI (9). This GRS predicted the severity scale in SCOURGE but supporting the differentiation in three classes: (i) controls/asymptomatic/mild cases; (ii) moder- ate and severe cases and (iii) critical cases. (Supplemen- tary Material, Fig. S5). Simultaneously disaggregating by age (<60/≥60 years old) and sex, we identify the three severity classes in the subgroup of males with <60 years old, supporting the importance of this group in the over- all findings (Supplementary Material, Fig. S5). Details of this analysis can be found in Supplementary Note. Figure 3. Lookup of previously found COVID-19 host risk factors in Second study phase and meta-analysis with the the SCOURGE study. Heatmap illustrating the results in all analyses discovery performed in this study (rows) for the 13 leading variants in the COVID- Results for hospitalization risk were meta-analysed with 19 HGI study (columns). Each box illustrates the top associated variant within the focal region. The color (gray to dark red) indicates the strength a second Spanish cohort, the CNIO study (see Materials (significance level) of the association in SCOURGE. Note: In three cases and Methods). This study was filtered following the same (chr12: 112919388, chr19: 4719431 and chr21: 33242905), the imputed QC and imputation procedures. The final dataset of the variants did not pass QC filters in SCOURGE and they were replaced by the nearest QC-ed imputed variant (chr12:112919404, chr19:4719822 and CNIO study included 2446 European individuals (1378 chr21:33241950, respectively). COVID-19 positive cases and 1068 population controls) and 8 895 721 markers. Table 3 shows the results that were genome-wide failed to prioritize a credible set of variants, hindering significant either in global or sex-stratified meta- functional links of the locus. Functional assessments of analysis with SCOURGE. Besides the widely replicated the prioritized variants by the Bayesian fine-mapping loci at 3p21.31 and 21q22.11, three additional signals analysis in the other two regions supported that they were found: chr9:33426577:A:G (intergenic to AQP7 and were eQTLs of the AQP3 and ARGAP33 genes, including AQP3), chr17:45422978:G:C (intronic to ARHGAP27) and whole blood and lung tissues (Fig. 4). chr19:35687796:G:A (intergenic to UPK1A and ZBTB32). These variants were also associated with the three Bayesian fine-mapping around chr17:45422978:G:C severity groups previously outlined in SCOURGE by the Human Molecular Genetics, 2022, Vol. 31, No. 22 | 3797 Table 3. Genome-wide significant variants in global or sex-stratified meta-analysis between the SCOURGE and CNIO studies SNP chr:position Meta-ALL Meta-males Meta-females Nearest gene EA NEA beta SE P-value beta SE P-value beta SE P-value rs115679256 3:45587795 G A 0.43 0.08 1.1E−08 0.48 0.10 2.3E−06 0.40 0.11 2.9E−04 LIMD1 rs17763742 3:45805277 A G 0.60 0.05 4.1E−29 0.74 0.07 3.3E−25 0.43 0.08 4.5E−08 LZTFL1 rs35477280 3:45932600 G A 0.39 0.05 1.4E−17 0.48 0.06 6.3E−15 0.28 0.07 1.6E−05 FYCO1 rs4443214 3:46136372 T C 0.25 0.04 9.0E−09 0.26 0.06 1.4E−05 0.26 0.06 4.4E−05 XCR1 rs115102354 3:46180545 A G 0.41 0.07 1.6E−08 0.52 0.10 2.1E−07 0.32 0.10 2.0E−03 CCR3 rs10813976 9:33426577 A G 0.18 0.03 2.7E−08 0.16 0.04 2.5E−04 0.19 0.05 3.5E−05 AQP3 rs1230082 17:45422978 C G 0.16 0.03 2.1E−08 0.17 0.04 2.8E−05 −0.15 0.04 2.5E−04 ARHGAP27 rs77127536 19:35687796 G A −0.22 0.04 1.3E−08 −0.25 0.05 2.1E−06 −0.19 0.05 4.3E−04 UPK1A/ZTBT32 rs17860169 21:33240996 A G 0.19 0.03 2.3E−11 0.27 0.04 1.4E−11 0.12 0.04 3.7E−03 IFNAR2 Note: Representative SNPs were selected using the clump function of PLINK 1.9 (clumping parameters r2 = 0.5, Pval = 5 × 10−8 and Pval2 = 0.05). EA, effect allele; NEA, non-effect allele; beta, effect coefficient; SE, standard error. Table 4. Results of European meta-analysis for hospitalization risk Meta-all Meta-males Meta-females SNP chr:position EA NEA beta SE P-value beta SE P-value beta SE P-value Nearest gene rs115679256 3:45587795 G A 0.37 0.06 1.3E−08 0.41 0.08 5.6E−07 0.36 0.09 1.6E−04 LIMD1 rs13078854 3:45820440 G A 0.53 0.04 6.7E−34 0.64 0.05 2.7E−33 0.38 0.06 1.0E−09 LZTFL1 rs41289622 3:45973053 T G 0.36 0.04 3.6E−21 0.44 0.05 3.4E−20 0.27 0.05 7.2E−07 FYCO1 rs115102354 3:46180545 A G 0.40 0.06 8.9E−12 0.48 0.07 6.8E−11 0.26 0.08 1.8E−03 XCR1 rs61882275 11:34482745 G A −0.12 0.02 1.0E−06 −0.17 0.03 4.1E−08 −0.08 0.03 1.3E−02 ELF5 rs4767028 12:112921383 A G −0.16 0.02 1.6E−10 −0.19 0.03 2.5E−09 −0.11 0.04 8.7E−04 OAS1 rs12609134 19:35687796 G A −0.19 0.03 2.3E−08 −0.22 0.04 9.5E−08 −0.13 0.05 6.0E−03 UPK1A/ZBTB32 rs17860169 21:33240996 A G 0.18 0.03 3.9E−12 0.21 0.03 1.6E−10 0.15 0.04 2.9E−05 IFNAR2 Note: Summary statistics of both phases (SCOURGE and CNIO) were meta-analysed with four additional sex-disaggregated European studies from the COVID-19 HGI consortium. EA, effect allele; NEA, non-effect allele; beta, effect coefficient; SE, standard error. GRS under a multinomial model (Supplementary Mate- Heritability of COVID-19 hospitalization rial, Table S6). In the hospitalization risk analysis, we found that common variants (minor allele frequency, MAF > 1%) explain 27.1% (95% confidence interval, CI: 19.0–35.3%) of heritability on the observed scale (corresponding to 13.1% [95%CI: 9.2–17.0%] on the liability scale, assuming Meta-analysis in independent European studies a prevalence of 0.5%; Fig. 5). We observed less heritability Hospitalization risk was meta-analysed with other Euro- among females than males (2.9% [95%CI: 0.00–10.6%] pean studies combining both Spanish cohorts (SCOURGE in females and 17.0% [95%CI: 9.2–24.9%] in males on and CNIO) with other four sex-disaggregated studies the liability scale). In agreement with observations from the COVID-19 HGI consortium, namely: BelCOVID, suggesting an accumulation of non-genetic risk factors GenCOVID, Hostage-Spain and Hostage-Italy (Table 4). with age, especially among males (11,18), we observed Once again, the most outstanding significant loci were larger heritability differences by age groups among males found at 3p21.31 and 21q22.11 (in global and male- (40.2% [95%CI: 22.8–57.5%] in <60 years versus 17.6% specific analyses), and three additional loci reached [95%CI: 0.00–38.0%] in ≥60 years on the liability scale) genome-wide significance in the meta-analysis of males: than among females (9.1% [0.00–31.3%] in <60 years chr12:11292383:A:G (in OAS1), chr19:35687796:G:A (inter- versus 13.7% [0.00–29.6%] in ≥60 years on the liability genic to UPK1A and ZBTB32) and chr11:34482745:G:A scale). (in ELF5). The 3p21.31 variants reached genome-wide significance in females, although at significantly lower level than in males despite the similar sample sizes ID and COVID-19 outcomes (Z = 3.33, P = 5 × 10−4 ). ROH calling was performed in the European QC-ed Significance of two interesting loci revealed in the genotyped dataset. Inbreeding depression analyses Spanish studies was reduced in the meta-analysis with are described in Materials and Methods section and other European studies, although still showed suggestive Supplemental Note. associations: that of 9q21.32 near TLE1 previously found The median genomic inbreeding coefficient, FROH , for only in females (female meta-analysis P = 5.4 × 10−7 ), the entire SCOURGE study was 0.0048 (IQR = 0.004). No and that of 9p13.3 near AQP3 (global meta-analysis, differences were detected between males (FROH = 0.004, P = 1.23 × 10−7 ). IQR = 0.0035) and females (FROH = 0.0056, IQR = 0.0038), or 3798 | Human Molecular Genetics, 2022, Vol. 31, No. 22 Figure 5. Forest plot of the SNP-heritability estimates for the COVID-19 hospitalization risk analysis on the liability scale. between younger and older individuals (FROH individuals < 60 years old = 0.004, IQR = 0.0035; FROH individuals ≥ 60 years old = 0.0052, IQR = 0.0047, respectively; Supplementary Mate- rial, Fig. S6). Regarding the ID in COVID-19 outcomes, we detected a positive association of the FROH in COVID- 19 hospitalization risk (Fig. 6), severity risk and risk for critical illness (Supplementary Material, Table S7). Our results showed that the hospitalization odds for COVID- 19 patients with an FROH = 0.0039 were 380% higher than individuals with FROH = 0. No effect of the genomic relationship matrix (FGRM ) was found. To assess whether ID in COVID-19 hospitalization in SCOURGE was different between sexes, we first tested the interaction between FROH and biological sex. FROH , sex and the interaction of both (FROH :Sex) were significant (FROH = 4.7 × 10−3 , Sex = 1.0 × 10−112 , FROH :Sex = 1.2 × 10−3 ). This interaction was significant when comparing the hospitalized COVID-19 patients with different controls (A2 and C analyses, see Supplementary Material, Table S8). This interaction was also found significant with severity, but not with critical illness (Supplementary Material, Table S8). In sex-disaggregated analyses, we observed a sex-specific effect of inbreeding. FROH was significant in hospitalized males but not in females (Fig. 6 and Supplementary Material, Table S8). This sex- Figure 4. Regional plots of novel association signals found from the meta- specific effect was also observed with severity and for analysis between the SCOURGE and CNIO studies. Regional plots of novel critical illness (Supplementary Material, Table S8). We association signals found in 9p13.3 (A–C), 17q21.31 (D–F) and 19q13.12 then assessed whether ID in COVID-19 hospitalization (G–I). The x axes ref lect the chromosomal position, and the y axes the −log(P-value) of the SCOURGE-CNIO meta-analysis. On A, D and G the was different by age. We detected a significant inter- sentinel variant is indicated by a diamond and all other variants are action between age and FROH for the three outcomes color coded by their degree of LD with the sentinel variant in Europeans. considered (hospitalization, severity and critical illness) Whenever a concise set of variants was prioritized, a credible set is shown within a dashed square. The horizontal dotted blue line corresponds to (Supplementary Material, Table S9). After disaggregating the threshold for genome-wide significance (P = 5 × 10−8 ). In the rest of SCOURGE by sex and age (<60, ≥60), we found that panels, the x axes ref lect the chromosomal position, and the y axes the the ID for hospitalization and severity were signifi- −log(P-value) resulting from the eQTL analyses in whole blood (B, E and H) and in the lung (C, F and I) whenever a significant finding is available cant mainly in older males (Fig. 6 and Supplemen- from GTEx v8. tary Material, Table S9). We detected significant ID for hospitalization and severity in males ≥ 60 years old, but it was marginally significant in females (Fig. 6 and Human Molecular Genetics, 2022, Vol. 31, No. 22 | 3799 Figure 6. Effect of the ID on COVID-19 hospitalization in the SCOURGE cohort. Forest plots are shown for global analyses as well as for sex and age- disaggregated analyses. Supplementary Material, Table S9). Age and sex-specific unique for hospitalized COVID-19 individuals (males effects in hospitalization and severity were robust across and females) and non-hospitalized males, respectively different experimental designs using different control (Supplementary Note, Supplementary Material, Table groups (Supplementary Material, Fig. S7). S11). Enrichment analysis of pathways based on the Finally, we then aimed to replicate the ID results with protein coding genes present in ROH islands were hospitalization, assessing sex and age-specific effects, in also different between sexes (Supplementary Note, a 4418 case-enriched European cohort made of 16 studies Supplementary Material, Table S12), revealing links with from nine countries. Median FROH in this other European coagulation and complement pathways in males. cohort was slightly higher than that of SCOURGE, 0.05 (0.009–0.0577). A positive and significant ID in COVID- 19 hospitalization was detected in this other European Discussion cohort when the entire cohort was considered (FROH Here we report the replication of six COVID-19 loci Beta = 18.22, P = 3.33 × 10−3 ). FGRM was not significant across analyses and provide evidence supporting three (FGRM Beta = −7.34, P = 0.240). ID was also detected additional loci, one of them specifically detected among in hospitalized COVID-19 males but not in females females. Besides, our analyses provide new insights (Male FROH Beta = 16.22, P = 3.31 × 10−3 ; Female FROH into disease severity disparities by sex and age and Beta = 15.65, P = 0.269). FGRM was not significant in males support the necessity of similarly stratified studies or in female analyses. When disaggregating by age, it was to increase the possibility of detecting additional risk possible to detect significant ID in hospitalization only variants. Our GWAS constitutes the largest study on in males ≥60 years old (FROH Beta = 36.16, P = 3.34 × 10−3 ) COVID-19 genetic risk factors conducted in Spain, with (Supplementary Material, Table S10). an intrinsic design benefit that SCOURGE includes No evidence was found of major loci that may be exert- detailed clinical and genetic information gathered ing large effects. Rather, polygenicity seems to underlie homogeneously across the country. Besides, the study the ID association. Different islands of ROH (ROHi) included patients from the whole spectrum of COVID-19 and regions of heterozygosity (RHZ) were found to be severity covering from asymptomatic to life-threatening 3800 | Human Molecular Genetics, 2022, Vol. 31, No. 22 disease. To date, most research on COVID-19 disease has reported as a new locus associated with critical illness in focused on respiratory failure. However, the inclusion Europeans (25). Variants of this locus reached genome- of a severity scale provided a unique opportunity to wide significance in our male meta-analysis of European assess whether previously reported loci combined into cohorts (P = 4.1 × 10−8 ). As regards of TLE1, this locus a GRS model were associated with differential risk by should be taken as speculative as the signal did not reach strata. We warn, however, that the GRS model findings the standard genome-wide significance in the study. should be interpreted with caution as sex and age- However, given that the meta-analysis involved a low differential results in some of the severity strata needs number of studies (and the top marker was not imputed appropriate replication. Association was tested for four in one of them), this result should be taken with caution main variables: infection, hospitalization, severe illness as further sex-specific studies will be needed to validate and critical illness, and using different definitions of this finding. controls to align with the COVID-19 HGI. Irrespective TLE1 encodes for the transducin-like enhancer of split of the tested outcomes or the definition of controls, 1, a co-repressor of other transcription factors and sig- the results were very similar, supporting the use of naling pathways. Besides repressing the transcriptional population controls to increase power in these studies activity of FOXA2 and of the Wnt signaling, TLE1 has and the utility of using hospitalization as a proxy of been shown to negatively regulate NF-κB, which is fun- severity. However, our results from the GRS analysis damental in controlling inflammation and the immune reported a need to maintain separated categories for response. The deficiency of TLE1 activity in mice results medium–severe and critical illness. in enhancement of the NF-κB-mediated inflammatory We observed larger heritability differences by age response in diverse tissues including the lung (26). Inter- groups among males than among females for COVID- estingly, TLE1 is one of the 332 high-confidence SARS- 19 hospitalization, which have diverse support from the CoV-2 protein–human protein interactions identified so literature. On the one hand, there is robust evidence far (27). Taken together, SARS-CoV-2 would be directly supporting that the presence of X-linked deleterious targeting the innate immunity and inflammation signal- variants in the TLR7 gene are causal for life-threatening ing pathways by interfering with the NF-κB activity. Thus, COVID-19 only affecting males (19–21). Of note, most of it is not surprising that TLE1 is a top-ranking regulator these severe COVID-19 male patients were younger than of inflammation that allows to transcriptionally distin- 60 years (21). On the other hand, autoantibodies impair- guish mild from severe COVID-19 (28). ing type-I interferon signaling, which are supported to In the 19q13.12 locus, the most biologically plausible be strong determinants of critical COVID-19 pneumonia, genes are ARHGAP33 (also showing the best functional are preferentially found among males older than 65 years support based on the fine-mapping variants) and ZBTB32. (11,18). Taken together, this reconciles with the idea that ARHGAP33 is transcriptionally regulated by IRF1—a non-genetic factors involved in severe COVID-19 are prominent antiviral effector and IFN-stimulated gene expected among older males. (29). It also harbors NF-κB binding site that modifies its We clearly replicated previously reported associations expression in human lymphoblastoid cell lines by the at 3p21.31 (near SLC6A20 and LZTFL1-FYCO1) (7,9,22,23) presence of genetic variants in the binding site linked and 21q22.11 (in IFNAR2) (7,9), and other findings in ABO, to many inflammatory and immune-related diseases OAS1, TYK2 and ARHGAP27. We also found a differential including sepsis, and bacterial and viral infection (30). Its effect between males and females for SNPs in 3p21.31 expression is also altered in human induced pluripotent and 21q22.11. Although in the meta-analysis with other stem cells-derived pancreatic cultures in response to European studies the leading variants of 3p21.31 reached SARS-CoV-2 infection (31). ARHGAP33 was identified genome-wide significance in females, there was still a in an unbiased genome-wide CRISPR-based knockout difference in effect size that, considering its magnitude, screen in human Huh7.5.1 hepatoma cells infected by should not be disregarded. It is important to remark coronaviruses including SARS-CoV-2 and further inter- that these association signals found in males were not actome studies (32). With respect to the transcription associated with the presence of comorbidities (see Sup- factor ZBTB32, it has been shown to impair antiviral plementary Material, Fig. S4). In fact, genetic effects were immune responses by affecting cytokine production and only found for younger males (<60 years old), consistent the proliferation of natural killer and T cells, and the with other studies (24) and strongly supporting those generation of memory cells (33). In single cell studies, comorbidities outweigh genetic effects in disease out- transcripts of ZBTB32 were enriched in T follicular helper comes in the older patients. cells and were also expressed at significantly higher Some novel genome-wide significant signals were levels in hospitalized COVID-19 patients (34). found in our study, one in chromosome 19q13.12 AQP3 is expressed most strongly in the kidney (intergenic to UPK1A and ZBTB32, and also linked to the collecting duct, the gastrointestinal tract, large airways transcriptional regulation of ARHGAP33), and another (in basal epithelial cells and the nasopharynx), skin in chromosome 9p13.3 (intergenic to AQP7 and AQP3). and the urinary bladder; whereas AQP7 is expressed Interestingly, we also found two sex-specific signals: ELF5 primarily in the testis, fat cells and, to a lesser extent in males and TLE1 in females. ELF5 has been recently in a subsegment of the kidney proximal tubule (35). In Human Molecular Genetics, 2022, Vol. 31, No. 22 | 3801 addition, AQP3 is upregulated in the lung tissues during individuals, both males and females, hospitalized for viral or bacterial-induced diffuse alveolar damage (36). severe COVID-19 have androgenetic alopecia [AGA; (47)] Based on this, in the fact that SARS-CoV-2 interacts and (iii) Most of the genes on COVID-19 severity in this with host proteins with the highest expression in lung study have been identified in male-only analyses, and tissues (27), and the functional evidence linking the fine- these genes have been shown to interact with the AR. mapped variants with eQTLs in lung tissues, our data The following lines of evidence suggest the AR pathway support AQP3 as the most likely 9p13.3 gene driving is a mechanism responsible for some identified genes- the association with COVID-19 hospitalization. Many COVID-19 severity relationship: (i) FYCO1 is regulated by patients develop acute respiratory distress syndrome the AR (48), and binding sites between the sex hormone (ARDS) during severe COVID-19 (37), and one of the receptor AR and FYCO1 have been demonstrated (48,49); hallmarks of ARDS is the increase of f luid volume (ii) There is a cross-talk between the IFN pathways and (edema) in the airspaces of the lung because of an the androgen signaling pathways (50), and androgens increase in the alveolo-capillary membrane permeability. are regulated by IFNs in human prostate cells (51); (iii) Some of the aquaporins, including AQP3, essentially TMPRSS2, another gene associated with COVID-19 sever- function as water transport pores between the airways ity in other studies, is induced by androgens through and the pulmonary capillaries (38), are key in lung fluid a distal AR binding enhancer (52); (iv) AR induces the clearance and the formation of this lung edema as a expression of chemokine receptors such as CCR1; (v) consequence of the lung injury (35). In fact, the use of Variants of LZTFL1 gene are likely pathogenic for male aquaporin modulators in lung inf lammation and edema reproductive system diseases (53) and (vi) Genetic poly- has been proposed for potential use for the treatment of morphisms in the AR (long polyQ alleles ≥23) and higher COVID-19 respiratory comorbidity (39). testosterone levels in subjects with AR long-polyQ appear We have also shown for the first time that COVID- to predispose some men to develop more severe dis- 19 severity risk suffers from ID, where individuals with ease (54). Thus, it is not unexpected to find that antian- higher levels of homozygosity associate with higher risk drogen treatments are under the focus as treatment of being hospitalized and of developing severe COVID- options and prophylaxis of severe COVID-19 (47) and that 19. Our results also suggested that autozygous rare randomized controlled clinical trials with bicalutamide recessive variants found in ROH across the genome, (NCT04374279), degarelix (NCT04397718) and spirono- rather than homozygous common variants in strong LD, lactone (NCT04345887) are currently underway. are underlying the ID. Furthermore, the ID is stronger in males than in females suffering from COVID-19 hospitalizations, especially in males ≥ 60 years old. Materials and Methods Although these results may be found counterintuitive Recruitment of cases and phenotype definitions with the rest of findings, they are supported by the for the discovery phase mutation accumulation senescence theory. Under this In Spain, 11 939 COVID-19 positive cases were recruited theory, alleles with detrimental effects that act in late life as part of SCOURGE study from 34 centers in 25 cities are expected to accumulate and cause senescence, thus between March and December 2020. The complete list of increasing the ID (40). We detected further sex-specific hospitals or research centers and the number of samples effects of homozygosity through ROHi. In hospitalized that each contributed to the study is shown in Sup- males, coagulation and complement pathways, which plementary Material, Table S1. Study samples and data have been previously associated with severe COVID- were collected by the participating centers, through their 19 (41), were enriched among the protein coding genes respective biobanks after informed consent, with the located in ROHi, highlighting the role of homozygosity approval of the respective Ethic and Scientific Commit- whereas the Lectin pathway of complement activation tees. The whole project was approved by the Galician Eth- is reported to be involved in the response to SARS- ical Committee Ref 2020/197. All samples and data were CoV-2 infection (42–44). In hospitalized females, PI3K- processed following normalized procedures. Study data Akt signaling genes were found to be enriched in ROH were collected and managed using REDCap electronic islands, whose networks are affected by a great variety of data capture tools hosted at Centro de Investigación viruses (45). Biomédica en Red [CIBER; (55,56); Supplementary Mate- Given that the effect of the genetic variants in SARS- rial, Supplemental Note]. Individuals were diagnosed as CoV-2 severity is clearer among males and previous evi- COVID-19 positive through a PCR-based test (81.7% of dence on this regard, we elucubrate on the role of andro- cases) or according to local clinical (3.4%) and laboratory gens in COVID-19 severity. Androgenic hormones have procedures (antibody test: 14.2%; other microbiological been suggested to be responsible of the excess male tests: 0.7%). All cases were classified in a five-level sever- mortality observed in COVID-19 patients (46), and several ity scale (Table 1). lines of evidence suggest that the androgen receptor Two Spanish sample collections with unknown COVID- (AR) pathway is involved in the severity of SARS-CoV-2 19 status were included as general population controls infection: (i) A higher mortality rate among men has in some analyses: 3437 samples from the Spanish been established (47); (ii) A substantial proportion of DNA biobank (https://www.bancoadn.org) and 2506 3802 | Human Molecular Genetics, 2022, Vol. 31, No. 22 samples from the GR@CE consortium (17). General using PLINK 1.9 (58) and a custom R script. We excluded population controls were collected from branches of the variants with MAF < 1%, call rate < 98%, a difference in National Blood Service from adult unrelated individuals missing rate between cases and controls >0.02, or devi- self-reporting Spanish origin and absence of personal ating from Hardy–Weinberg equilibrium (HWE) expecta- and familial history of diseases including infectious, tions [P < 1 × 10−6 in controls, P < 1 × 10−10 in cases, with cancerous, blood and circulatory, endocrine, mental a mid-P adjustment (59)]. Samples with a call rate < 98% or behavioral, nervous, vision, hearing, respiratory, and those in which heterozygosity rate deviated >5 SD immunological, bone, congenital, skin and digestive. from the mean heterozygosity of the study were also removed. Second phase: the CNIO study To assess kinship and assign ancestries, autosomal A total of 1598 COVID-19 cases from six different Spanish SNPs (MAF > 5%) were pruned with PLINK using a Biobanks (Biobanco CNIO, Biobanco Vasco, Biobanco Hos- window of 1000 markers, a step size of 80 and a r2 pital Ramón y Cajal, Biobanco Hospital Puerta de Hierro, of 0.1. In addition, high-linkage disequilibrium (LD) Biobanco Hospital San Carlos, and Banco Nacional de regions described in Price et al. (60) were also excluded. ADN) were obtained according to the ethical committee A subset of 131 937 independent SNPs was used to approval CEI PI 34_2020-v2. In addition, 1068 individuals evaluate kinship (IBD estimation) in PLINK. Given the from Spanish DNA biobank with unknown COVID-19 possible confusion between relatedness and ancestry, status were included as healthy controls in the anal- we removed only one individual from each pair of ysis whenever necessary. Classification as healthy was individuals with PI_HAT > 0.25 (second-degree relatives) based on self-reported absence of cardiovascular, renal, that showed a Z0, Z1 and Z2 coherent pattern (according pulmonary, hepatic, hematological illnesses or any other to theoretical expected values for each relatedness chronic conditions, which require continuous treatment, level). The unrelated SCOURGE individuals were merged hepatitis B, C infections or acquired immunodeficiency with samples from 1KGP and the common SNPs were syndrome (AIDS). No clinical characterization was per- LD-pruned as previously indicated. Ancestry was then formed on any subject, no information from medical inferred with Admixture (61) using the defined 1KGP record was incorporated and no medical testing was per- superpopulations. Those individuals with an estimated formed on these individuals. We will refer to these cases probability >80% of pertaining to European ancestry and controls as the Centro Nacional de Investigaciones were defined as European (N = 15 571). Oncológicas (CNIO) study. Genomic PCs were also computed using a LD-pruned (r2 < 0.1 with a window size of 1000 markers) subset of Genotyping genotyped SNPs passing quality check for controlling the The discovery phase samples were genotyped with the population structure in the GWAS. Axiom Spain Biobank Array (Thermo Fisher Scientific) The CNIO study data was filtered following the same following the manufacturer’s instructions in the San- QC procedures, where 220 individuals were identified as tiago de Compostela Node of the National Genotyping non-European and, therefore, were excluded from fur- Center (CeGen-ISCIII; http://www.usc.es/cegen). This ther analysis. array contains 757 836 markers, including rare variants selected in the Spanish population. Genomic DNA was Variant imputation obtained from peripheral blood and isolated using the Imputation was conducted based on the TOPMed version Chemagic DNA Blood100 kit (PerkinElmer Chemagen r2 reference panel [GRCh38; (62)] in the TOPMed Impu- Technologies GmbH), following the manufacturer’s tation Server. After post-imputation filtering (Rsq > 0.3, recommendations. HWE P > 1 × 10−6 , MAF > 1%), 15 045 individuals (9371 For the second phase study samples, a total of 250 ng COVID-19 positive cases and 5674 population controls) of DNA was processed according to the Infinium HTS and 8 933 154 genetic markers remained in the SCOURGE assay Protocol (Part # 15045738 Rev. A, Illumina), includ- European study (discovery). The final dataset of the ing amplification, fragmentation and hybridization using CNIO study (replication) included 2446 individuals (1378 the Global Screening Array Multi-disease v3.0. This array COVID-19 positive cases and 1068 population controls) contains a total of 730 059 markers and was scanned and 8 895 721 markers. For directly genotyped variants, on an iScan platform (Illumina, Inc.). Clustering and the original genotype was maintained in place of the genotype calling were performed using Genome Studio imputed data. v2.0.4 (Illumina, Inc.). Statistical analysis Quality control Association testing was computed by fitting logistic A QC procedure was carried out for the SCOURGE study mixed regression models adjusting for age, sex and samples and control datasets. First, a list of probe sets the first 10 ancestry-specific PCs. SNPRelate (63) was was removed based on poor cluster separation or exces- used for prior LD-pruning and data management. sive MAF difference from The 1000 Genomes Project data Association analyses were performed in SAIGEgds (64), (1KGP) (57). Then, the following QC steps were applied which implements the SAIGE (65) two-step mixed model Human Molecular Genetics, 2022, Vol. 31, No. 22 | 3803 methodology and the SPA test while using more efficient the most prominent biological effects of the variants in objects for genotype storage. A null model was fitted in the credible sets. the first step using the LD-pruned genotyped variants (MAF > 0.5%, missing rate < 98%) to estimate variance Genetic risk score components and the genetic relationship matrix. Then, A GRS was created for the SCOURGE cohort individuals in a second step, association analyses were performed and population controls using the list of SNPs associated for both genotyped and imputed SNPs. Significance was with hospitalization, severity or risk in the meta-analysis established at P < 5 × 10−8 after meta-analysis of the performed by the COVID-19 HGI [see Supplementary discovery and the second study phases. Material, Table S2 in (9)] to appraise its prediction power To align the results with those from the COVID-19 HGI, of the severity scale in SCOURGE. Details of this analysis three outcomes were evaluated in relation to severity: can be found in Supplementary Note. hospitalization, severe COVID-19 (severity ≥ 3) and very severe COVID-19 (severity = 4, critical illness). For each SNP-heritability of COVID-19 severity comparison, three control definitions (A1, A2 and C) were We relied on GCTA-GREML 1.93.2beta (68) to assess used (Supplementary Material, Table S2). the heritability of severe COVID-19 symptoms among In addition, the risk to COVID-19 infection was also SCOURGE patients, excluding those with cryptic related- analysed by comparing all COVID-19 positive cases with ness or with missing genotypes above 0.5% and assuming control samples from the general population. a prevalence of COVID-19 hospitalization of 0.5%. This All analyses were conducted for each complete dataset analysis considered all patients (modelling for age, sex, and stratified by sex and age (<60 years, ≥60 years). The sex∗age and the 10 first PCs), and males and females SNP∗sex and SNP∗age-interaction terms were tested for separately (modelling for age and the 10 first PCs). We each SNP in the subset of clumped signals, adjusting the also partitioned the variance to assess the heritability models for the same covariates. changes among the patients <60 or ≥60 years old. Then, the main results of both Spanish cohorts We focused on the 547 206 autosomal variants with (SCOURGE and CNIO) for the overall and sex-stratified MAF > 1% and missingness <0.5%. Assuming 0.5% of analyses were meta-analysed assuming a fixed-effects prevalence of severe COVID-19, and at least 1500 cases model using METAL (66). and 1500 controls per stratum, we estimate >97.9% Because of the similarity of both the SCOURGE and power to detect a heritability >0.2. CNIO studies in the clinical variables recorded and, more importantly, in the definition of the severity scale, the ROH calling leading variants from the significant and validated loci The ROH segments longer than 300 Kb were called in the hospitalization analysis were also analysed under in SCOURGE using PLINK 1.9 in the European QC- a multinomial model (Supplementary Material, Supple- ed genotyped dataset (before imputation) with the mental Note). following parameters: homozyg-snp 30, homozyg-kb 300, homozyg-density 30, homozyg-window-sn 30, homozyg-gap Meta-analysis in independent European studies 1000, homozyg-window-het 1, homozyg-window-missing 5 In order to validate the findings in independent study and homozyg-window-threshold 0.05. No LD pruning was samples of European ancestry, a meta-analysis of hos- performed. pitalization risk was performed for the overall and sex- stratified summary statistics of both Spanish cohorts Calculating genomic inbreeding coefficients (SCOURGE and CNIO) and other four sex-stratified Euro- Different genomic inbreeding coefficients were calcu- peans studies from the COVID-19 HGI consortium (Bel- lated (69): COVID, GenCOVID, Hostage-Spain and Hostage-Italy). FROH measures the actual proportion of the autosomal genome that is autozygous above a specific threshold of Bayesian fine-mapping of GWAS findings minimum ROH length. Credible sets were calculated for the GWAS loci to identify a subset of variants most likely containing the n i=1 ROH > 1.5 Mb causal variant at 95% confidence level, assuming that FROH = 3 Gb there is a single causal variant and that it has been tested. We used corrcoverage for R (67) to calculate the posterior probabilities of the variant being causal for all variants with r2 > 0.1 with the leading SNP and within FGRM is an alternative genomic inbreeding coefficient 1 Mb. Variants were added to the credible set until the that was obtained using PLINK’s parameter -ibc (Fhat3). sum of the posterior probabilities was ≥0.95. VEP (https:// This coefficient is described by Yang et al. (68) where N is www.ensembl.org/info/docs/tools/vep/index.html) and the number of SNPs, pi is the reference allele frequency of the V2G aggregate scoring from Open Targets Genetics the ith SNP, and xi is the number of copies of the reference (https://genetics.opentargets.org) were used to annotate allele. The reference allele frequencies were site-specific 3804 | Human Molecular Genetics, 2022, Vol. 31, No. 22 and included only variants with MAF > 0.05. Sistema de Salud de Aragón, Sistema Sanitario Público de Andalucía, and Banco Nacional de ADN. n     SCOURGE Cohort Group members and affiliations, 1  x2i − 1 + 2pi xi + 2p2i FGRM =   HOSTAGE Cohort Group and GR@ACE Cohort Group N 2pi 1 − pi i (Supplementary Material). Conflict of Interest statement: The authors declare no com- peting interests. Testing and replicating the ID Inbreeding depression is defined as the change in the mean phenotypic value in a population because of Funding inbreeding (12,13). The ID was modelled in SCOURGE Instituto de Salud Carlos III (COV20_00622 to A.C., by a multiple logistic regression. The covariables used in COV20/00792 to M.B., COV20_00181 to C.A., COV20_1144 this study were sex, age and the first 10 PCs. to M.A.J.S., PI20/00876 to C.F.); European Union (ERDF) The results were replicated in a cohort gathered by ‘A way of making Europe’. Fundación Amancio Ortega, Nakanishi et al. (24). This consists of clinical and genomic Banco de Santander (to A.C.), Estrella de Levante S.A. data from 4418 individuals of European ancestry (3946 and Colabora Mujer Association (to E.G.-N.) and Obra hospitalized COVID-19 cases and 422 controls): 2597 Social La Caixa (to R.B.); Agencia Estatal de Investigación males (1072 males < 60 years old, 1525 males ≥ 60 years (RTC-2017-6471-1 to C.F.), Cabildo Insular de Tenerife old) and 1821 females (808 females < 60 years old, (CGIEU0000219140 ‘Apuestas científicas del ITER para 1013 females ≥ 60 years old). The cohort was built by colaborar en la lucha contra la COVID-19’ to C.F.) and harmonizing individual-level data from 16 different Fundación Canaria Instituto de Investigación Sanitaria studies (24). The FROH and FGRM coefficients were obtained de Canarias (PIFIISC20/57 to C.F.). following the procedure explained previously. The model described previously with the same covariables (age, sex and the first then PCs) was applied in this cohort. References Genome-specific effects on COVID-19 severity and 1. Tang, D., Komish, P. and Kang, R. (2020) The hallmarks hospitalization were tested through ID in genomic win- of COVID-19 disease. PLoS Pathog., 16, e1008536. dows, ROH islands (ROHi) and regions of heterozygosity https://doi.org/10.1371/journal.ppat.1008536. (RHZ) (Supplementary Material, Supplemental Note). 2. Goyal, P., Choi, J., Pinheiro, L., Schenck, E., Chen, R., Jabri, A., Satlin, M., Campion, R., Nahid, M., Ringel, J. et al. (2020) Clinical characteristics of Covid-19 in new York City. N. Engl. J. Med., 382, Supplementary Material 2372–2374. Supplementary Material is available at HMGJ online. 3. Richardson, S., Hirsch, J., Narasimhan, M., Crawford, J., McGinn, T., Davidson, K. and the Northwell COVID-19 Research Con- sortium (2020) Presenting characteristics, comorbidities, and Acknowledgements outcomes among 5700 patients hospitalized with COVID-19 in R.L.-R. is granted by Cátedra de Medicina Genómica IIS- the New York City area. JAMA, 323, 2052–2059. 4. Vahidy, F., Pan, A., Ahnstedt, H., Munshi, Y., Choi, H., Tiruneh, Fundación Jiménez Díaz-UAM, M.B. by Nextgeneration Y., Nasir, K., Kash, B., Andrieni, J. and McCullough, L. (2021) EU funds. M.C., P.M., M.A.J.S., A.F.R. are granted by the Sex differences in susceptibility, severity, and outcomes Miguel Servet Programme (CP17/00006, CP16/00116, of coronavirus disease 2019: cross-sectional analysis from CPII20CIII/0001, CPII20CIII/0001 respectively) and B.A. by a diverse US metropolitan area. PLoS One, 16, e0245556. the Juan Rodés Programme (JR17/00020), all of them from https://doi.org/10.1371/journal.pone.0245556. Instituto de Salud Carlos III, and cofunded by European 5. The COVID-19 Host Genetics Initiative (2020) The COVID-19 host Union (ERDF) ‘A way of making Europe’. genetics initiative, a global initiative to elucidate the role of host The contribution of the Centro National de Genotipado genetic factors in susceptibility and severity of the SARS-CoV-2 (CEGEN), and Centro de Supercomputación de Galicia virus pandemic. Eur. J. Hum. Genet., 28, 715–718. (CESGA) for funding this project by providing supercom- 6. Casanova, C., Su, H. and COVID Human Genetic Effort (2020) A puting infrastructures, is also acknowledged. Authors are global effort to define the human genetics of protective immu- nity to SARS-CoV-2 infection. Cell, 181, 1194–1199. also particularly grateful for the supply of material and 7. Pairo-Castineira, E., Clohisey, S., Klaric, L., Bretherick, A., Rawlik, the collaboration of patients, health professionals from K., Pasko, D., Walker, S., Parkinson, N., Fourman, M., Russell, C. participating centers and biobanks. Namely Biobanc- et al. (2021) Genetic mechanisms of critical illness in COVID-19. Mur, and biobancs of the Complexo Hospitalario Univer- Nature, 591, 92–98. sitario de A Coruña, Complexo Hospitalario Universitario 8. Zhang, Q., Bastard, B., Liu, Z., Le Pen, J., Moncada-Velez, M., Chen, de Santiago, Hospital Clínico San Carlos, Hospital La Fe, J., Ogishi, M., Sabli, I., Hodeib, S., Korol, C. et al. (2020) Inborn Hospital Universitario Puerta de Hierro Majadahonda— errors in type I IFN immunity in patients. Science, 370, eabd4570. Instituto de Investigación Sanitaria Puerta de Hierro— https://doi.org/10.1126/science.abd4570. Segovia de Arana, Hospital Ramón y Cajal, IDIBGI, IdISBa, 9. COVID-19 Host Genetics Initiative (2021) Mapping the human IIS Biocruces Bizkaia, IIS Galicia Sur. Also biobanks of the genetic architecture of COVID-19. Nature, 600, 472–477. Human Molecular Genetics, 2022, Vol. 31, No. 22 | 3805 10. Brady, E., Nielsen, M., Andersen, J. and Oertelt-Prigione, 23. Shelton, J., Shastri, A., Ye, C., Weldon, C., Filshtein-Sonmez, T., S. (2021) Lack of consideration of sex and gender Coker, D., Symons, A., Esparza-Gordillo, J., 23andMe COVID-19 in COVID-19 clinical studies. Nat. Commun., 12, 4015. Team, Aslibekyan, S. et al. (2021) Trans-ancestry analysis reveals https://doi.org/10.1038/s41467-021-24265-8. genetic and nongenetic associations with COVID-19 susceptibil- 11. Bastard, P., Rosen, L., Zhang, Q., Michailidis, E., Hoffmann, ity and severity. Nat. Genet., 53, 801–808. H., Dorgham, Z., Philippot, Q., Rosain, J., Béziat, V., Manry, J. 24. Nakanishi, T., Pigazzini, S., Degenhardt, F., Cordioli, M., Butler- et al. (2020) Autoantibodies against type I IFNs in patients Laporte, G., Maya-Miles, D., Bujanda, L., Bouysran, Y., Niemi, M., with life-threatening COVID-19. Science, 370, eabd4570. Palom, A. et al. (2021) Age-dependent impact of the major com- https://doi.org/10.1126/science.abd4585. mon genetic risk factor for COVID-19 on severity and mortality. 12. Charlesworth, D. and Willis, H. (2009) The genetics of inbreeding J. Clin. Invest., 131, e152386. https://doi.org/10.1172/JCI152386. depression. Nat. Rev. Genet., 10, 783–796. 25. Kousathanas, A., Pairo-Castineira, E., Rawlik, K., Stuckey, A., 13. Ceballos, F., Joshi, P., Clark, D., Ramsay, M. and Wilson, J. (2018) Odhams, C., Walker, S., Russell, C., Malinauskas, T., Mil- Runs of homozygosity: windows into population history and lar, J., Elliott, K. et al. (2022) Whole genome sequencing trait architecture. Nat. Rev. Genet., 19, 220–234. reveals host factors underlying critival Covid-19. Nature. 14. Ceballos, F., Hazelhurst, S., Clark, D., Agongo, G., Asiki, G., https://doi.org/10.1038/s41586-022-04576-6. Boua, P., Gómez-Olivé, X., Mashinya, F., Norris, S., Wilson, J. 26. Ramasamy, S., Saez, B., Mukhopadhyay, S., Ding, D., Ahmed, A., et al. (2020) Autozygosity influences cardiometabolic disease- Chen, X., Pucci, F., Yamin, R., Wang, J., Pittet, M. et al. (2016) associated traits in the AWI-gen sub-Saharan African study. Nat. Tle1 tumor suppressor negatively regulates inflammation in Commun., 11, 5754. https://doi.org/10.1038/s41467-020-19595-y. vivo and modulates NF-κB inflammatory pathway. PNAS, 113, 15. Clark, D., Okada, Y., Moore, K., Mason, D., Pirastu, N., Gandin, I., 1871–1876. Mattsson, H., Barnes, C., Lin, K., Zhao, J. et al. (2019) Associations 27. Gordon, D., Jang, G., Bouhaddou, M., Xu, J., Obernier, K., White, K., of autozygosity with a broad range of human phenotypes. Nat. O’Meara, M., Rezelj, V., Guo, J., Swaney, D. et al. (2020) A SARS-CoV- Commun., 10, 4957. https://doi.org/10.1038/s41467-019-12283-6. 2 protein interaction map reveals targets for drug repurposing. 16. Moreno-Grau, S., Fernández, M., de Rojas, I., Garcia-González, Nature, 583, 459–468. P., Hernández, I., Farias, F., Budde, J., Quintela, I., Madrid, L., 28. de Jong, T., Guryev, V. and Moshkin, Y. (2021) Estimates of gene González-Pérez, A. et al. (2021) Long runs of homozygosity are ensemble noise highlight critical pathways and predict disease associated with Alzheimer’s disease. Transl. Psychiatry, 11, 142. severity in H1N1, COVID-19 and mortality in sepsis patients. Sci. https://doi.org/10.1038/s41398-020-01145-1. Rep., 11, 10793. https://doi.org/10.1038/s41598-021-90192-9. 17. Moreno-Grau, S., de Rojas, I., Hernández, I., Quintela, I., Mon- 29. Schoggins, J. and Rice, C. (2011) Interferon-stimulated genes and trreal, L., Alegret, M., Hernández-Olasagarre, B., Madrid, L., their antiviral effector functions. Curr. Op. Virol., 1, 519–525. González-Perez, A., Maroñas, O. et al. (2019) Genome-wide asso- 30. Karczewski, K., Dudley, J., Kukurba, K., Chen, R., Butte, A., Mont- ciation analysis of dementia and its clinical endophenotypes gomery, S. and Snyder, M. (2013) Systematic functional regu- reveal novel loci associated with Alzheimer’s disease and three latory assessment of disease-associated variants. PNAS, 110, causality networks: the GR@ACE project. Alzheimers Dement., 15, 9607–9612. 1333–1347. 31. Shaharuddin, S., Wang, V., Santos, R., Gross, A., Wang, Y., 18. Bastard, P., Gervais, A., Le Voyer, T., Rosain, J., Philippot, Q., Jawanda, H., Zhang, Y., Hasan, W., Garcia, G., Jr., Arumugaswami, Manry, J., Michailidis, E., Hoffmann, H., Eto, S., Garcia-Prat, V. et al. (2021) Deleterious effects of SARS-CoV-2 infection on M. et al. (2021) Autoantibodies neutralizing type I IFNs are Human pancreatic cells. Front. Cell. Infect. Microbiol., 11, 678482. present in ∼4% of uninfected individuals over 70 years old https://doi.org/10.3389/fcimb.2021.678482. and account for ∼20% of COVID-19 deaths. Sci. Immunol., 6, 32. Wang, R., Simoneau, C., Kulsuptrakul, J., Bouhaddou, M., Trav- EABL4340. https://doi.org/10.1126/sciimmunol.abl4340. isano, K., Hayashi, J., Carlson-Stevermer, J., Zengel, J., Richards, C., 19. van der Made, C., Simons, A., Schuurs-Hoeijmakers, J., van den Fozouni, P. et al. (2021) Genetic screens identify host factors for Heuvel, G., Mantere, T., Kersten, S., van Deuren, R., Steehouwer, SARS-CoV-2 and common cold coronaviruses. Cell, 184, 106–119. M., van Reijmersdal, S., Jaeger, M. et al. (2020) Presence of genetic 33. Shin, H., Kapoor, V., Kim, G., Li, P., Kim, H., Suresh, M., variants among young men with severe COVID-19. JAMA, 324, Kaech, S., Wherry, E., Selin, L., Leonard, W., Welsh, R.M. and 663–673. Berg, L.J. (2017) Transient expression of ZBTB32 in anti-viral 20. Fallerini, C., Daga, S., Mantovani, S., Benetti, E., Picchiotti, CD8+ T cells limits the magnitude of the effector response N., Francisci, D., Paciosi, F., Schiaroli, E., Baldassarri, M., and the generation of memory. PLoS Pathog., 13, e1006544. Fava, F. et al. (2021) Association of Toll-like receptor 7 vari- https://doi.org/10.1371/journal.ppat.1006544. ants with life-threatening COVID-19 disease in males: find- 34. Beaulieu, A., Zawislak, C., Nakayama, T. and Sun, J. (2014) The ings from a nested case-control study. Elife, 10, e67569. transcription factor Zbtb32 controls the proliferative burst of https://doi.org/10.7554/eLife.67569. virus-specific natural killer cells responding to infection. Nat. 21. Asano, T., Boisson, B., Onodi, F., Matuozzo, D., Moncada- Immunol., 15, 546–553. Velez, M., Maglorius Renkilaraj, M., Zhang, P., Meertens, 35. Song, Y., Fukuda, N., Bai, C., Ma, T., Matthay, M. and Verkman, A. L., Bolze, A., Materna, M. et al. (2021) X-linked recessive (2000) Role of aquaporins in alveolar fluid clearance in neonatal TLR7 deficiency in ∼1% of men under 60 years old and adult lung, and in oedema formation following acute lung with life-threatening COVID-19. Sci. Immunol., 6, eabl4348. injury: studies in transgenic aquaporin null mice. J. Physiol., 525, https://doi.org/10.1126/sciimmunol.abl4348. 771–779. 22. Severe Covid-19 GWAS Group, Ellinghaus, D., Degenhardt, F., 36. Pires-Neto, R., Del Carlo Bernardi, F., Alves de Araujo, P., Bujanda, L., Buti, M., Albillos, A., Invernizzi, P., Fernández, J., Mauad, T. and Dolhnikoff, M. (2016) The expression of Prati, D., Baselli, G. et al. (2020) Genomewide association study water and ion channels in diffuse alveolar damage is of severe Covid-19 with respiratory failure. N. Engl. J. Med., 383, not dependent on DAD Etiology. PLoS One, 11, e0166184. 1522–1534. https://doi.org/10.1371/journal.pone.0166184. 3806 | Human Molecular Genetics, 2022, Vol. 31, No. 22 37. Ferrando, C., Suarez-Sipmann, F., Mellado-Artigas, R., Hernán- intraflagellar transporter protein 27 (IFT27) associated protein, dez, M., Gea, A., Arruti, E., Aldecoa, C., Martínez-Pallí, G., is required for normal sperm function and male fertility. Dev. Martínez-González, M., Slutsky, A. et al. (2020) Clinical features, Biol., 477, 164–176. ventilatory management, and outcome of ARDS caused by 54. Baldassarri, M., Picchiotti, N., Fava, F., Fallerini, C., Benetti, COVID-19 are similar to other causes of ARDS. Intensive Care E., Daga, S., Valentino, F., Doddato, G., Furini, S., Gilib- Med., 46, 2200–2211. erti, A. et al. (2021) Shorter androgen receptor polyQ alle- 38. Mariajoseph-Antony, L.F., Kannan, A., Panneerselvam, A., les protect against life-threatening COVID-19 disease in Euro- Loganathan, C., Anbarasu, K. and Prahalathan, C. (2020) pean males. EBioMedicine, 65, 103246. https://doi.org/10.1016/j. Aquaporin water channels and lung physiology. Am. J. Physiol. ebiom.2021.103246. Lung Cell Mol., 278, L867–L879. 55. Harris, P., Taylor, R., Thielke, R., Payne, J., Gonzalez, N. and 39. Mariajoseph-Antony, L., Kannan, A., Panneerselvam, A., Conde, J. (2009) Research electronic data capture (REDCap)—a Loganathan, C., Anbarasu, K. and Prahalathan, C. (2020) metadata-driven methodology and workflow process for provid- Could aquaporin modulators be employed as prospective drugs ing translational research informatics support. J. Biomed. Inform., for COVID-19 related pulmonary comorbidity? Med. Hypotheses, 42, 377–381. 143, 110201. https://doi.org/10.1016/j.mehy.2020.110201. 56. Harris, P., Taylor, R., Minor, B., Elliott, V., Fernandez, M., O’Neal, 40. Charlesworth, B. (2001) Patterns of age-specific means and L., McLeod, L., Delacqua, G., Delacqua, F., Kirby, J. et al. genetic variances of mortality rates predicted by the mutation- (2019) The REDCap consortium: building an international com- accumulation theory of ageing. J. Theor. Biol., 210, 47–65. munity of software partners. J. Biomed. Inform., 95, 103208. 41. Perico, L., Benigni, A., Casiraghi, F., Ng, L., Renia, L. and Remuzzi, https://doi.org/10.1016/j.jbi.2019.103208. G. (2021) Immunity, endothelial injury and complement-induced 57. The 1000 Genomes Project Consortium (2015) A global reference coagulopathy in COVID-19. Nat. Rev. Nephrol., 17, 46–64. for human genetic variation. Nature, 526, 68–74. 42. Java, A., Apicelli, A., Liszewski, M., Coler-Reilly, A., Atkin- 58. Purcell, S., Ben, N., Todd-Brown, K., Thomas, L., Ferreira, M., son, J., Kim, A. and Kulkarni, H. (2020) The complement sys- Bender, D., Maller, J., Sklar, P., de Bakker, P., Daly, M. et al. tem in COVID-19: friend and foe? JCI Insight, 5, e140711. (2007) PLINK: a tool set for whole-genome association and https://doi.org/10.1172/jci.insight.140711. population-based linkage analyses. Am. J. Hum. Genet., 81, 43. Lo, M., Kemper, C. and Woodruff, T. (2020) COVID-19: com- 559–575. plement, coagulation, and collateral damage. J. Inmunol., 205, 59. Graffelman, J. and Moreno, V. (2013) The mid P-value in exact 1488–1495. tests for hardy-Weinberg equilibrium. Stat. Appl. Genet. Mol. Biol., 44. Noris, M., Benigni, A. and Remuzzi, G. (2020) The case of comple- 12, 433–448. ment activation in COVID-19 multiorgan impact. Kidney Int., 98, 60. Price, A., Weale, M., Patterson, N., Myers, S., Need, A., Shianna, 314–322. K., Ge, D., Rotter, J., Torres, E., Taylor, Q. et al. (2008) Long-range 45. Dunn, E. and Connor, J. (2012) Chapter 9—HijAkt: the PI3K/Akt LD can confound genome scans in admixed populations. Am. J. pathway in virus replication and pathogenesis. Prog. Mol. Biol. Hum. Genet., 83, 132–139. Transl. Sci., 106, 223–250. 61. Alexander, D., Novembre, J. and Lange, K. (2009) Fast model- 46. Lamy, P., Rébillard, X., Vacherot, F. and de la Taille, A. (2021) based estimation of ancestry in unrelated individuals. Genome Androgenic hormones and the excess male mortality observed Res., 19, 1655–1664. in COVID-19 patients: new convergent data. World J. Urol., 39, 62. Taliun, D., Harris, D., Kessler, M., Carlson, J., Szpiech, Z., Torres, R., 3121–3123. Galiano Taliun, S., Corvelo, A., Gogarten, S., Kang, H. et al. (2021) 47. Wambier, C., Goren, A., Vaño-Galván, S., Müller, P., Ossimetha, Sequencing of 53,831 diverse genomes from the NHLBI TOPMed A., Nau, G., Herrera, S. and McCoy, J. (2020) Androgen sensitivity program. Nature, 590, 290–299. gateway to COVID-19 disease severity. Drug Dev. Res., 81, 771–776. 63. Zheng, X., Levine, D., Shen, J., Gogarten, S., Laurie, C. and Weir, 48. Shang, D., Wang, L., Klionsky, D., Cheng, H. and Zhou, R. (2021) Sex B. (2012) A high-performance computing toolset for relatedness differences in autophagy-mediated diseases: toward precision and principal component analysis of SNP data. Bioinformatics, 28, medicine. Autophagy, 17, 1065–1076. 3326–3328. 49. Wyce, A., Bai, Y., Nagpal, S. and Thompson, C. (2010) Research 64. Zheng, X. and Davis, W. (2021) SAIGEgds—an efficient statistical resource: the androgen receptor modulates expression of genes tool for large-scale PheWAS with mixed models. Bioinformatics, with critical roles in muscle development and function. Mol. 37, 728–730. Endocrinol., 24, 1665–1674. 65. Zhou, W., Nielsen, J., Fritsche, L., Dey, R., Gabrielsen, M., Wolford, 50. Bettoun, D., Scafonas, A., Rutledge, S., Hodor, P., Chen, O., Gam- B., LeFaive, J., VandeHaar, P., Gagliano, S., Figgord, A. et al. (2018) bone, C., Vogel, R., McElwee-Witmer, S., Bai, C., Freedman, L. et al. Efficiently controlling for case-control imbalance and sample (2005) Interaction between the androgen receptor and RNase relatedness in large-scale genetic association studies. Nat. Genet., L mediates a cross-talk between the interferon and androgen 50, 1335–1341. signaling pathways. J. Biol. Chem., 280, 38898–38901. 66. Willer, C., Li, Y. and Abecasis, G. (2010) METAL: fast and efficient 51. Basrawala, Z., Alimirah, F., Xin, H., Mohideen, N., Campbell, S., meta-analysis of genomewide association scans. Bioinformatics, Flanigan, R. and Choubey, D. (2006) Androgen receptor levels 26, 2190–2191. are increased by interferons in human prostate stromal and 67. Hutchinson, A., Watson, H. and Wallace, C. (2020) epithelial cells. Oncogene, 25, 2812–2817. Improving the coverage of credible sets in Bayesian 52. Lin, B., Ferguson, C., White, J., Wang, S., Vessella, R., True, L., genetic fine-mapping. PLoS Comput. Biol., 16, e1007829. Hood, L. and Nelson, P.S. (1999) Prostate-localized and androgen- https://doi.org/10.1371/journal.pcbi.1007829. regulated expression of the membrane-bound serine protease 68. Yang, J., Lee, H., Goddard, E. and Visscher, P. (2011) GCTA: a tool TMPRSS2. Cancer Res., 17, 4180–4184. for genome-wide complex trait analysis. Am. J. Hum. Genet., 88, 53. Huang, Q., Li, W., Zhou, Q., Awasthi, P., Cazin, C., Yap, Y., 76–82. Mladenovic-Lucas, L., Hu, B., Jeyasuria, P., Zhang, L. et al. 69. Templeton, A. and Read, B. (1994) Inbreeding: one word, several (2021) Leucine zipper transcription factor-like 1 (LZTFL1), an meanings, much confusion. EXS, 68, 91–105.