Detailed stratified GWAS analysis for severe COVID-19 in four European populations
Abstract
Metabolic associated fatty liver disease (MAFLD) is the most prevalent form of liver disease worldwide, accounting for a high liver-related mortality and morbidity with extensive multi organ involvement. This entity has displaced viral hepatitis as the main cause of severe forms of hepatic diseases, although the onset and transition of MAFLD stages still remains unclear. Nevertheless, innate and adaptive immune responses seem to play an essential role in the establishment and further progression of this disease. The immune system is responsible of safeguard and preserves organs and systems function, and might be altered under different stimuli. Thus, the liver suffers from metabolic and immune changes leading to different injuries and loss of function. It has been stablished that cell-cell crosstalk is a key process in the hepatic homeostasis maintenance. There is mounting evidence suggesting that MAFLD pathogenesis is determined by a complex interaction of environmental, genetic and host factors that leads to a full plethora of outcomes. Therefore, herein we will revisit and discuss the interplay between immune mechanisms and MAFLD, highlighting the potential role of immunological markers in an attempt to clarify its relationship.
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Received: January 14, 2022. Revised: June 11, 2022. Accepted: July 26, 2022 © The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected] This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/ by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] Human Molecular Genetics, 2022, Vol. 31, 23, 3945–3966 https://doi.org/10.1093/hmg/ddac158 Advance access publication date 15 July 2022 Original Article Detailed stratified GWAS analysis for severe COVID-19 in four European populations Frauke Degenhardt 1,†,*,David Ellinghaus1,2,†,Simonas Juzenas1,3,†, Jon Lerga-Jaso4,†, Mareike Wendorff1,†, Douglas Maya-Miles5,6,7,†,Florian Uellendahl-Werth1,Hesham ElAbd1, Malte C. Rühlemann1,8,Jatin Arora9,10,11,12,13,Onur Özer14,15, Ole Bernt Lenning16,17, Ronny Myhre18,May Sissel Vadla17,19,Eike M. Wacker1,Lars Wienbrandt1,Aaron Blandino Ortiz20, Adolfo de Salazar21,22, Adolfo Garrido Chercoles23,Adriana Palom24,25, Agustín Ruiz26,27,Alba-Estela Garcia-Fernandez28, Albert Blanco-Grau28,Alberto Mantovani29,30,Alberto Zanella31,32,Aleksander Rygh Holten33,34,Alena Mayer35, Alessandra Bandera31,32,Alessandro Cherubini32,Alessandro Protti29,30,Alessio Aghemo29,30,Alessio Gerussi36,37, Alfredo Ramirez38,39,40,41,42,Alice Braun35,Almut Nebel1,Ana Barreira25,Ana Lleo29,30, Ana Teles14,15,Anders Benjamin Kildal 43, Andrea Biondi44, Andrea Caballero-Garralda45,Andrea Ganna46, Andrea Gori32,47,Andreas Glück48,Andreas Lind49,Anja Tanck1, Anke Hinney50,Anna Carreras Nolla51,Anna Ludovica Fracanzani31,32,Anna Peschuck1,Annalisa Cavallero52, Anne Ma Dyrhol-Riise34,53, Antonella Ruello54,Antonio Julià 24,Antonio Muscatello32,Antonio Pesenti31,32,Antonio Voza29,30, Ariadna Rando-Segura55,56, Aurora Solier57,58, Axel Schmidt59,Beatriz Cortes51,Beatriz Mateos6,60,Beatriz Nafria-Jimenez23, Benedikt Schaefer61,62,Björn Jensen63,Carla Bellinghausen64,Carlo Maj65,Carlos Ferrando66,67, Carmen de la Horra5,7,68,69,70, Carmen Quereda71, Carsten Skurk35,Charlotte Thibeault35,Chiara Scollo72,Christian Herr73, Christoph D. Spinner74, Christoph Gassner1,75,Christoph Lange76,77,78,Cinzia Hu32,Cinzia Paccapelo32,Clara Lehmann80,81,82,Claudio Angelini83, Claudio Cappadona30,Clinton Azuure14,15,COVICAT study group, Aachen Study (COVAS),Cristiana Bianco32,Cristina Cea28, Cristina Sancho84, Dag Arne Lihaug Hoff85,86,Daniela Galimberti31,32,Daniele Prati32,David Haschka87, David Jiménez57,58, David Pestaña88,David Toapanta67,Eduardo Muñiz-Diaz89,Elena Azzolini29,30,Elena Sandoval67,Eleonora Binatti36,37, Elio Scarpini31,32,Elisa T. Helbig35,Elisabetta Casalone90,Eloisa Urrechaga91,92,Elvezia Maria Paraboschi 29,30,Emanuele Pontali93, Enric Reverter67, Enrique J. Calderón5,7,68,69,70, Enrique Navas71,Erik Solligård94,95,Ernesto Contro96,Eunate Arana-Arri97, Fátima Aziz67,Federico Garcia21,22,98,Félix García Sánchez99, Ferruccio Ceriotti32,Filippo Martinelli-Boneschi100,101, Flora Peyvandi102,103,Florian Kurth35,104,105,Francesco Blasi106,107,Francesco Malvestiti31,Francisco J. Medrano7,68,69,70,108, Francisco Mesonero6,60,Francisco Rodriguez-Frias6,24,28,55,56,109,Frank Hanses110,111, Fredrik Müller34,49,Georg Hemmrich-Stanisak1, Giacomo Bellani112,113,Giacomo Grasselli31,32,Gianni Pezzoli114,Giorgio Costantino31,32,Giovanni Albano54, Giulia Cardamone30, Giuseppe Bellelli113,115, Giuseppe Citerio113,116, Giuseppe Foti112,113, Giuseppe Lamorte32, Giuseppe Matullo90,Guido Baselli32, Hayato Kurihara83, Holger Neb117,Ilaria My29,Ingo Kurth118,Isabel Hernández26,27,Isabell Pink119,Itziar de Rojas26,27, Iván Galván-Femenia51,Jan Cato Holter34,49,Jan Egil Afset84,120, Jan Heyckendorf76,77,78,Jan Kässens1,Jan Kristian Damås121,122, Jan Rybniker80,81,123,Janine Altmüller124, Javier Ampuero5,6,7,68,125,Javier Martín126,Jeanette Erdmann127,128,129, Jesus M. Banales6,130,131,Joan Ramon Badia132,Joaquin Dopazo 7,133, Jochen Schneider73,Jonas Bergan134,Jordi Barretina135, Jörn Walter136,Jose Hernández Quero21,137, Josune Goikoetxea138,Juan Delgado5,7,68,69,70,Juan M. Guerrero5,7,68,Julia Fazaal59, Julia Kraft35, Julia Schröder59,Kari Risnes122,139,Karina Banasik2,Karl Erik Müller140,Karoline I. Gaede141,142,143, Koldo Garcia-Etxebarria6,130,Kristian Tonby34,53,Lars Heggelund140,144, Laura Izquierdo-Sanchez6,130,145,Laura Rachele Bettini44, Lauro Sumoy135,Leif Erik Sander35, Lena J. Lippert35,Leonardo Terranova32, Lindokuhle Nkambule146,147,Lisa Knopp63, Lise Tuset Gustad94,148,Lucia Garbarino149,Luigi Santoro32, Luis Téllez6,60, Luisa Roade6,25,57,Mahnoosh Ostadreza32, Maider Intxausti84,Manolis Kogevinas69,150,151,152,Mar Riveiro-Barciela6,25,56,Marc M. Berger153, Marco Schaefer154,Mari E.K. Niemi46, María A. Gutiérrez-Stampa155,Maria Carrabba79,Maria E. Figuera Basso1,Maria Grazia Valsecchi156,María Hernandez-Tejero67, Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
3946 |Human Molecular Genetics, 2022, Vol. 31, No. 23 Maria J.G.T. Vehreschild157,Maria Manunta 32,Marialbert Acosta-Herrera 126,Mariella D’Angiò44,Marina Baldini32, Marina Cazzaniga158,Marit M. Grimsrud34,159,160,Markus Cornberg161,Markus M. Nöthen59,Marta Marquié 26,27, Massimo Castoldi54,Mattia Cordioli46, Maurizio Cecconi29,30, Mauro D’Amato131,162,163, Max Augustin80,81,82,Melissa Tomasi32, Mercè Boada26,27, Michael Dreher164, Michael J. Seilmaier165,Michael Joannidis166,Michael Wittig1, Michela Mazzocco149, Michele Ciccarelli83,Miguel Rodríguez-Gandía6,60,Monica Bocciolone83, Monica Miozzo31,79,Natale Imaz Ayo97,Natalia Blay 51, Natalia Chueca22,Nicola Montano31,32,Nicole Braun1,167,Nicole Ludwig168, Nikolaus Marx169,Nilda Martínez170,Norwegian SARS-CoV-2 Study group, Oliver A. Cornely40,80,82,171, Oliver Witzke172,Orazio Palmieri173,Pa Study Group,Paola Faverio113,174, Paoletta Preatoni83, Paolo Bonfanti113,175,Paolo Omodei83,Paolo Tentorio29,Pedro Castro67,Pedro M. Rodrigues6,130,131,145, Pedro Pablo España91,Per Hoffmann59,Philip Rosenstiel1,Philipp Schommers80,81,82,Phillip Suwalski35,Raúl de Pablo20, Ricard Ferrer176, Robert Bals73, Roberta Gualtierotti31,32, Rocío Gallego-Durán5,6,7, Rosa Nieto57,58,Rossana Carpani32, Rubén Morilla5,7,68,69,70,Salvatore Badalamenti29, Sammra Haider177, Sandra Ciesek178,179,Sandra May1, Sara Bombace29,30, Sara Marsal24,Sara Pigazzini46,Sebastian Klein166, Serena Pelusi31,32,Sibylle Wilfling111,180,181,Silvano Bosari31,32,Sonja Volland182, Søren Brunak2,Soumya Raychaudhuri9,10,11,12,13,183, Stefan Schreiber1,48,Stefanie Heilmann-Heimbach59,Stefano Aliberti29,30, Stephan Ripke35, Susanne Dudman 34,49,Tanja Wesse1,Tenghao Zheng184,The STORM Study group, The Humanitas Task Force, The Humanitas Gavazzeni Task Force, Thomas Bahmer48,Thomas Eggermann118,Thomas Illig182, Thorsten Brenner153, Tomas Pumarola185,186,Torsten Feldt63,Trine Folseraas34,159,160,187,Trinidad Gonzalez Cejudo188,Ulf Landmesser189, Ulrike Protzer190,191, Ute Hehr180, Valeria Rimoldi30,Valter Monzani32,Vegard Skogen192,193,Verena Keitel63, Verena Kopfnagel182, Vicente Friaza5,7,68,69,70, Victor Andrade38,39,Victor Moreno69,194,195,196, Wolfgang Albrecht1, Wolfgang Peter154,197, Wolfgang Poller35, Xavier Farre51,Xiaoli Yi1,Xiaomin Wang35,Yascha Khodamoradi157,Zehra Karadeniz35,Anna Latiano173, Siegfried Goerg198, Petra Bacher1,199,Philipp Koehler40,80,81,Florian Tran1,48,Heinz Zoller61,62,Eva C. Schulte190,200,201, Bettina Heidecker35, Kerstin U. Ludwig 59,Javier Fernández67,202,Manuel Romero-Gómez5,6,7,68,125,Agustín Albillos6,60,Pietro Invernizzi36,37, Maria Buti6,25,56,Stefano Duga29,30, Luis Bujanda6,130,Johannes R. Hov34,159,160,187,‡,Tobias L. Lenz14,15,‡,Rosanna Asselta29,30,‡, Rafael de Cid51,‡,Luca Valenti31,32,‡,Tom H. Karlsen34,159,160,187,‡, Mario Cáceres4,203,‡and Andre Franke1,204,‡,* 1Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany 2Novo Nordisk Foundation Center for Protein Research, Disease Systems Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark 3Life Science Centre, Institute of Biotechnology, Vilnius University, Vilnius, Lithuania 4Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain 5Hospital Universitario Virgen del Rocío de Sevilla, Sevilla, Spain 6Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid, Spain 7Instituto de Biomedicina de Sevilla (IBIS), Sevilla, Spain 8Institute for Medical Microbiology and Hospital Epidemiology, Hannover Medical School, Hannover, Germany 9Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA 10Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA 11Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA 12Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA 13Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA 14Research Group for Evolutionary Immunogenomics, Max Planck Institute for Evolutionary Biology, Plön, Germany 15Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany 16Research Department, Stavanger University Hospital, Stavanger, Norway 17Randaberg Municipality, Stavanger, Norway 18Division of Health Data and Digitalization, Department of Genetics and Bioinformatics (HDGB), Norwegian Institute of Public Health, Oslo, Norway 19Department of Quality and Health Technology, Faculty of Health Sciences, University of Stavanger, Stavanger, Norway 20Department of Intensive Care, Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), University of Alcalá, Madrid, Spain 21Ibs.Granada Instituto de Investigación Biosanitaria, Granada, Spain 22Microbiology Unit. Hospital Universitario Clinico San Cecilio, Granada, Spain 23Clinical Biochemistry Department, Osakidetza Basque Health Service, Donostialdea Integrated Health Organisation, San Sebastian, Spain 24Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Barcelona, Spain 25Liver Unit, Department of Internal Medicine, Hospital Universitari Vall d’Hebron, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain 26Research Center and Memory Clinic, Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain 27CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain 28Department of Biochemistry, University Hospital Vall d’Hebron, Barcelona, Spain 29IRCCS Humanitas Research Hospital, Milan, Italy 30Department of Biomedical Sciences, Humanitas University, Milan, Italy 31University of Milan, Milan, Italy 32Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy 33Department of Acute Medicine, Oslo University Hospital, Oslo, Norway 34Institute of Clinical Medicine, University of Oslo, Oslo, Norway 35Charite Universitätsmedizin Berlin, Berlin, Germany Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
Human Molecular Genetics, 2022, Vol. 31, No. 23 |3947 36European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy 37Division of Gastroenterology, School of Medicine and Surgery, Center for Autoimmune Liver Diseases, University of Milano-Bicocca, Milan, Italy 38Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany 39Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Medical Faculty, Bonn, Germany 40Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany 41German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany 42Department of Psychiatry, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, San Antonio, TX, USA 43Department of Anesthesiology and Intensive Care, University Hospital of North Norway, Tromsø, Norway 44Pediatric Departement, Centro Tettamanti-European Reference Network (ERN) PaedCan, EuroBloodNet, MetabERN-University of Milano-Bicocca-Fondazione MBBM/Ospedale, San Gerardo, Italy 45Biochemistry Department, Echevarne Laboratory, Barcelona, Spain 46Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland 47Centre for Multidisciplinary Research in Health Science (MACH), University of Milan, Milan, Italy 48Klinik für Innere Medizin I, Universitätsklinikum Schleswig-Holstein, Kiel, Germany 49Department of Microbiology, Oslo University Hospital, Oslo, Norway 50Department of Child and Adolescent Psychiatry, University Hospital Essen, University of Duisburg-Essen, Essen, Germany 51Genomes for Life-GCAT Lab, Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain 52Laboratory of Microbiology, San Gerardo Hospital, Monza, Italy 53Department of Infectious diseases, Oslo University Hospital, Oslo, Norway 54Humanitas Gavazzeni-Castelli, Bergamo, Italy 55Microbiology Department, Hospital Universitari Vall d’Hebron, Barcelona, Spain 56Universitat Autònoma de Barcelona, Bellatera, Spain 57Department of Respiratory Diseases, Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), University of Alcalá, Madrid, Spain 58Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), University of Alcalá, Madrid, Spain 59Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany 60Department of Gastroenterology, Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), University of Alcalá, Madrid, Spain 61Department of Medicine I, Gastroenterology, Hepatology and Endocrinology, Medical University of Innsbruck, Innsbruck, Austria 62Christian Doppler Laboratory of Iron and Phosphate Biology at the Department of Medicine I, Medical University of Innsbruck, Innsbruck, Austria 63Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Medical Faculty Heinrich Heine University, Duesseldorf, Germany 64Department of Respiratory Medicine and Allergology, University Hospital, Goethe University, Frankfurt am Main, Germany 65Institute of Genomic Statistics and Bioinformatics, University Hospital Bonn, Medical Faculty University of Bonn, Bonn, Germany 66Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain 67Hospital Clinic, University of Barcelona, and IDIBAPS, Barcelona, Spain 68University of Sevilla, Sevilla, Spain 69Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain 70Consejo Superior de Investigaciones Científicas, Sevilla, Spain 71Department of Infectious Diseases, Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), University of Alcalá, Madrid, Spain 72Department of Transfusion Medicine and Haematology Laboratory, San Gerardo Hospital, Monza, Italy 73Department of Internal Medicine V—Pneumology, Allergology and Intensive Care Medicine, University Hospital Saarland, Homburg, Germany 74Department of Internal Medicine II, School of Medicine, University Hospital rechts der Isar, Technical University of Munich, Munich, Germany 75Institute of Translational Medicine, Private University in the Principality of Liechtenstein (UFL), Triesen, Liechtenstein 76Respiratory Medicine and International Health, University of Lübeck, Lübeck, Germany 77Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany 78Clinical Tuberculosis Unit, German Center for Infection Research (DZIF), Borstel, Germany 79Internal Medicine Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy 80Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany 81Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany 82German Center for Infection Research (DZIF), Cologne, Germany 83Humanitas Clinical and Research Center, IRCCS, Milan, Italy 84Osakidetza Basque Health Service, Basurto University Hospital, Respiratory Service, Bilbao, Spain 85Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim,Norway 86Department of Medicine, Møre and Romsdal Hospital Trust, Ålesund, Norway 87Department of Internal Medicine II, Medical University of Innsbruck, Innsbruck, Austria 88Department of Anesthesiology and Critical Care, Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), University of Alcalá, Madrid, Spain 89Immunohematology Department, Banc de Sang i Teixits, Autonomous University of Barcelona, Barcelona, Spain 90Department of Medical Sciences, Università degli Studi di Torino, Turin, Italy 91Osakidetza Basque Health Service, Galdakao Hospital, Respiratory Service, Galdakao, Spain 92Biocruces Bizkaia Health Research Institute, Barakaldo, Spain 93Department of Infectious Diseases, E.O. Ospedali Galliera, Genova, Italy 94Geminicenter for Sepsis Research, Institute of Circulation and Medical Imaging (ISB), NTNU, Trondheim, Norway 95Clinic of Anesthesia and Intensive Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway 96Accident and Emergency and Emergency Medicine Unit, San Gerardo Hospital, Monza, Italy 97Biocruces Bizkaia Health Research Institute, Barakaldo, Spain 98Centro de Investigación Biomédica en Red en Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain 99Histocompatibilidad y Biologia Molecular, Centro de Transfusion de Madrid, Madrid, Spain 100Department of Pathophysiology and Transplantation, Dino Ferrari Center, University of Milan, Milan, Italy 101Neurology Unit, IRCCS Fondazione Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy 102Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy 103Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Angelo Bianchi Bonomi Hemophilia and Thrombosis Center, Milan, Italy Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
3948 |Human Molecular Genetics, 2022, Vol. 31, No. 23 104Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany 105Department of Medicine I, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany 106Respiratory Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy 107Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy 108Internal Medicine Department, Virgen del Rocio University Hospital, Sevilla, Spain 109Biochemistry Department, University Hospital Vall d’Hebron, Barcelona, Spain 110Emergency Department, University Hospital Regensburg, Regensburg, Germany 111Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Regensburg, Germany 112Department Emergency, Anesthesia and Intensive Care, San Gerardo Hospital, Monza, Italy 113School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy 114Fondazione Grigioni per il Morbo di Parkinson and Parkinson Institute, ASST Gaetano Pini-CTO, Milan, Italy 115Acute Geriatric Unit, San Gerardo Hospital, Monza, Italy 116Neurointensive Care Unit, San Gerardo Hospital, Monza, Italy 117Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Frankfurt am Main, Germany 118Institute of Human Genetics, Medical Faculty, RWTH Aachen University, Aachen, Germany 119Department of Pneumology, Hannover Medical School, Hannover, Germany 120Department of Medical Microbiology, Clinic of Laboratory Medicine, St. Olavs hospital, Trondheim, Norway 121Department of Infectious Diseases, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway 122Department of Clinical and Molecular Medicine, NTNU, Trondheim, Norway 123German Centre for Infection Research (DZIF), Cologne, Germany 124Cologne Center for Genomics (CCG), University of Cologne, Cologne, Germany 125Digestive Diseases Unit, Virgen del Rocio University Hospital, Institute of Biomedicine of Seville, University of Seville, Seville, Spain 126Institute of Parasitology and Biomedicine Lopez-Neyra, CSIC, Granada, Spain 127Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany 128German Research Center for Cardiovascular Research, Lübeck, Germany 129University Heart Center Lübeck, Lübeck, Germany 130Department of Liver and Gastrointestinal Diseases, Biodonostia Health Research Institute—Donostia University Hospital, University of the Basque Country (UPV/EHU), CIBERehd, Ikerbasque, San Sebastian, Spain 131Ikerbasque, Basque Foundation for Science, Bilbao, Spain 132Respiratory ICU, Institut Clínic Respiratory, Hospital Clinic, University of Barcelona, and IDIBAPS, Barcelona, Spain 133Bioinformatics Area, Fundación Progreso y Salud, and Institute of Biomedicine of Sevilla (IBIS), Sevilla, Spain 134Department of Research, Ostfold Hospital Trust, Gralum, Norway 135Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain 136Department of Genetics & Epigenetics, Saarland University, Saarbrücken, Germany 137Department of Infectious Diseases, Hospital Universitario Clinico San Cecilio, Granada, Spain 138Infectious Diseases Service, Osakidetza, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain 139Department of Research, St. Olav Hospital, Trondheim University Hospital, Trondheim, Norway 140Medical Department, Drammen Hospital, Vestre Viken Hospital Trust, Drammen, Norway 141Research Center Borstel, BioMaterialBank Nord, Borstel, Germany 142German Center for Lung Research (DZL), Airway Research Center North (ARCN), Borstel, Germany 143Popgen 2.0 Network (P2N), Kiel, Germany 144Department of Clinical Science, University of Bergen, Bergen, Norway 145Biodonostia Health Research Institute, Donostia University Hospital, San Sebastian, Spain 146Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA 147Stanley Center for Psychiatric Research & Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA 148Clinic of Medicine and Rehabilitation, Levanger Hospital, Nord-Trondelag Hospital Trust, Levanger, Norway 149HLA Laboratory, E.O. Ospedali Galliera, Genova, Italy 150ISGlobal, Barcelona, Spain 151Universitat Pompeu Fabra (UPF), Barcelona, Spain 152IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain 153Department of Anesthesiology and Intensive Care Medicine, University Hospital Essen, University Duisburg-Essen, Essen, Germany 154Stefan-Morsch-Stiftung, Birkenfeld, Germany 155Osakidetza, OSI Donostialdea, Altza Primary Care, Biodonostia Health Research Institute, San Sebastián, Spain 156Center of Bioinformatics, Biostatistics and Bioimaging, School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy 157Department of Internal Medicine, Infectious Diseases, University Hospital Frankfurt & Goethe University Frankfurt, Frankfurt am Main, Germany 158Phase 1 Research Centre, ASST Monza, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy 159Division of Surgery, Inflammatory Diseases and Transplantation, Research Institute for Internal Medicine, Oslo University Hospital Rikshospitalet and University of Oslo, Oslo, Norway 160Division of Surgery, Inflammatory Diseases and Transplantation, Department of Transplantation Medicine, Norwegian PSC Research Center, Oslo University Hospital Rikshospitalet, Oslo, Norway 161Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany 162Gastrointestinal Genetics Lab, CIC bioGUNE—BRTA, Derio, Spain 163Department of Medicine and Surgery, LUM University, Casamassima, Italy 164Department of Pneumology and Intensive Care Medicine, University Hospital Aachen, Aachen, Germany 165Munich Clinic Schwabing, Academic Teaching Hospital, Ludwig-Maximilians-University (LMU), Munich, Germany 166Division of Intensive Care and Emergency Medicine, Department of Internal Medicine, Medical University Innsbruck, Innsbruck, Austria 167University Hospital Schleswig-Holstein, Kiel, Germany 168Department of Human Genetics, Center of Human and Molecular Biology, University Hospital Saarland, Homburg/Saar, Germany 169Department of Internal Medicine I, RWTH Aachen University Hospital, Aachen, Germany 170Department of Anesthesiology, Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain 171Faculty of Medicine and University Hospital Cologne, Clinical Trials Centre Cologne (ZKS Köln), University of Cologne, Cologne, Germany 172Department of Infectious Diseases, University Hospital Essen, University Duisburg-Essen, Essen, Germany 173Gastroenterology Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy 174Pulmonary Unit, San Gerardo Hospital, Monza, Italy 175Infectious Diseases Unit, San Gerardo Hospital, Monza, Italy 176Intensive Care Department, Vall d’Hebron University Hospital, SODIR-VHIR research group, Barcelona, Spain Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
Human Molecular Genetics, 2022, Vol. 31, No. 23 |3949 177Department of Medicine, Møre & Romsdal Hospital Trust, Molde, Norway 178Institute of Medical Virology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany 179German Centre for Infection Research (DZIF), Frankfurt am Main, Germany 180Zentrum für Humangenetik Regensburg, Regensburg, Germany 181Department of Neurology, Bezirksklinikum Regensburg, University of Regensburg, Regensburg, Germany 182Hannover Unified Biobank, Hannover Medical School, Hannover, Germany 183Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The Universityof Manchester, Manchester, UK 184School of Biological Sciences, Monash University, Clayton, VIC, Australia 185Department of Microbiology, University Hospital Vall d’Hebron, Barcelona, Spain 186Autonoma University of Barcelona, Barcelona, Spain 187Division for Cancer Medicine, Surgery and Transplantation, Section for Gastroenterology, Department of Transplantation Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway 188Biochemistry Unit, Hospital Universitario Clinico San Cecilio, Granada, Spain 189Berlin Institute of Health, Charite Universitätsmedizin Berlin, Berlin Germany 190Institute of Virology, Technical University Munich/Helmholtz Zentrum München, Munich, Germany 191German Center for Infection Research (DZIF), Munich, Germany 192Department of Infectious Diseases, University Hospital of North Norway, Tromsø, Norway 193Faculty of Health Sciences, UIT The Arctic University of Norway, Norway 194Catalan Institute of Oncology (ICO), Barcelona, Spain 195Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain 196Universitat de Barcelona (UB), Barcelona, Spain 197Faculty of Medicine and University Hospital Cologne, Institute for Transfusion Medicine, University of Cologne, Cologne, Germany 198Institute of Transfusionsmedicine, University Hospital Schleswig-Holstein (UKSH), Kiel, Germany 199Institute of Immunology, Christian-Albrechts-University of Kiel and UKSH Schleswig-Holstein, Kiel, Germany 200Institute of Psychiatric Phenomics and Genomics, University Medical Center, University of Munich, Munich, Germany 201Department of Psychiatry, University Medical Center, University of Munich, Munich, Germany 202European Foundation for the Study of Chronic Liver Failure (EF-CLIF), Barcelona, Spain 203ICREA, Barcelona, Spain 204University Hospital Schleswig-Holstein (UKSH), Kiel, Germany *To whom correspondence should be addressed. Email: [email protected] (Frauke Degenhardt) and a.frank[email protected] (Andre Franke) †The authors wish it to be known that, in their opinion, the first six authors should be regarded as joint First Authors. ‡The authors wish it to be known that, in their opinion, the last seven authors should be regarded as joint Last Authors. Abstract Given the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), a deeper analysis of the host genetic contribution to severe COVID-19 is important to improve our understanding of underlying disease mechanisms. Here, we describe an extended genome-wide association meta-analysis of a well-characterized cohort of 3255 COVID-19 patients with respiratory failure and 12488 population controls from Italy, Spain, Norway and Germany/Austria, including stratified analyses based on age, sex and disease severity, as well as targeted analyses of chromosome Y haplotypes, the human leukocyte antigen region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a ∼0.9-Mb inversion polymorphism that creates two highly differentiated haplotypes and characterized the potential effects of the inversion in detail. Our data, together with the 5th release of summary statistics from the COVID-19 Host Genetics Initiative including non-Caucasian individuals, also identified a new locus at 19q13.33, including NAPSA, a gene which is expressed primarily in alveolar cells responsible for gas exchange in the lung. Introduction In the past year, Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has evolved into a global pandemic with more than 532 million confirmed cases and 6.3 million COVID-19-related deaths worldwide (frequencies reported by the World Health Organization, June 11, 2022). The clinical manifestations of COVID-19 are variable and range from complete absence of symptoms to severe respiratory failure and death. Severe COVID- 19 requires intensive medical care with respiratory support and can result in long-term damages detrimental to the individual. The pathogenesis of severe COVID-19 is, however, still poorly understood. This condition has been associated with clinical risk factors such as old age, male sex and comorbidities including diabetes, active cancer, hypertension and coronary artery disease (CAD) as well as solid organ transplant or other conditions that promote an immunosuppressive state (1–4). Different studies, including the first genome-wide association analysis (GWAS) by of our Severe COVID-19 GWAS study group, have shown that genetic predisposition plays a role in COVID-19 susceptibility and severity (5–7). In particular, we reported significant associations between genetic variants at loci 3p21.31 (around LZTF1) and 9q34.2 (ABO blood group locus) to severe respiratory failure and SARS-CoV-2 infection in 1610 severe COVID- 19 patients and 2205 population controls of European ancestry. Since then, these loci have been replicated in subsequent studies and extended also to non- European cohorts (8–10). Eleven additional genome-wide significant loci, associated with SARS-CoV-2 infection or COVID-19 manifestations, have been reported by various studies, including the Genetics Of Mortality In Critical Care (GenOMICC) Initiative and, more recently, the largest genetic consortium for COVID-19, the COVID- 19 Host Genetics initiative (HGI) (6,7). Six of these loci have been linked to critical illness by COVID-19 and Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
3950 |Human Molecular Genetics, 2022, Vol. 31, No. 23 include genes that were previously associated with pulmonary or autoimmune and inflammatory diseases (7). Since our primary publication, the Severe COVID-19 GWAS study group dataset has been extended to 3255 severe COVID-19 patients with respiratory failure (5) and 12488 controls with unknown COVID-19 status from 30 study centers across Italy, Spain, Norway and Germany/Austria (after quality control (QC); Supplementary Material, Table S1A and D). Compared with our previous study, we increased the number of patients and controls by approximate factors of 2 and 5.5, respectively (from 1610 and 2205). Furthermore, detailed information on age, sex and comorbidities as hypertension, diabetes and CAD is available in this dataset. The control cohort included predominantly population controls recruited at the respective centers as blood or bone marrow donors with unknown or negative COVID-19 status (Supplementary Material, Table S1C). With this unique resource at hand, we performed an extended genomewide association study (GWAS) and meta-analysis of severe COVID-19 as well as a meta-analysis with the COVID-19 HGI release 5 GWAS summary statistics, followed by stratified analyses of age, sex and disease and a detailed analysis of ABO secretor status. Using Y- chromosomal genotype calls from the Global Screening Array (GSA), we additionally performed an imputation and disease association of chromosome Y haplotypes. Compared with our first study, we also performed an even more detailed analysis of the human leukocyte antigen (HLA) which includes classical fine-mapping of the HLA region based on local imputation of SNP, amino acid and classical allele information, as well as a broad range of other approaches, including a peptidomewide association study (PepWAS) (11) computational prediction of SARS-CoV-2 peptide presentation, HLA class I supertype association analysis and tests for heterozygote advantage, divergent allele advantage and molecular mimicry. Finally, by inversion imputation, we present a functional analysis of the established 17q21.31 locus (7), which has been described by COVID-19 HGI but not characterized in depth. Results GWAS meta-analyses for severe COVID-19 with respiratory failure Genotyping was carried out using Illumina’s GSA, followed by genotype QC analysis and TOPMed genotype imputation (Material and Methods, Supplementary Material, Figure S1, patient numbers before and after QC are shown in Supplementary Material, Table S1A and C). Respiratory failure was defined as respiratory support with supplemental oxygen [class 1] or non-invasive and invasive ventilation [classes 2 and 3, respectively], or by extracorporeal membrane oxygenation (ECMO) [class 4]).(5) Analogously to the COVID-19 HGI (7), we conducted two GWAS discovery meta-analyses for two main categories of COVID-19 disease state: First, ‘hospitalization with respiratory support’ (respiratory support classes 1–4 with a total of 3255 patients and 12488 controls, main analysis), and second, a more stringent definition of severe COVID-19 ‘hospitalization with mechanical ventilation’ (classes 2–4 with 1911 critically ill individuals and 12226 controls, subtype analysis). Details of per cohort patient numbers are shown in Supplementary Material, Table S1E.The characteristics of patients and controls included in our analyses are shown in Table 1 and Supplementary Material, Table S2. After imputation, we carried out a GWAS of 9223 806 and 9175283 high-quality genetic variants [imputation R2≥0.6 and minor allele frequency (MAF) ≥1%] stratified by ancestry (Italy, Spain, Norway and Germany/Austria) using a logistic mixed model analysis as implemented in SAIGE (12), followed by a fixed-effect inverse variance-weighted meta-analysis using METAL (13) (low genomic inflation of 1.017; Supplementary Material, Figs S2 and S3). Genome-wide comparison of the case–control frequencies [conservatively adjusted for age, sex, age∗age, sex∗age and top 10 principal components (PCs) from principal component analysis (PCA) as employed by the COVID-19 HGI; Material and Methods],revealed genome-wide significant associations (P<5×10−8;Table 2 and Supplementary Material, Table S3,Figs S4 and S5) at three known loci in the main analysis: chr3:45848457:C:T (rs35731912) within LZTFL1 at the 3p21.31 locus, chr9:133261703:A:G (rs687289)within ABO at 9q34.2, chr19:10351837:C:T (rs11085725) within TYK2 at 19p13.2. In addition to the same TYK2 variant, chr19:4717660:A:G (rs12610495) within DPP9 at 19p13.3 was genome-wide significant in the subtype analysis. Additional suggestive loci (P<10−6) from both analyses include previously reported associations at 17q21.31 (MAPT), 19p13.3 (DPP9) and 21q22.11 (IFNAR2)(Table 2 and Supplementary Material, Table S3)(7). Of note, the respective lead risk variants (this study versus COVID- 19 HGI) at reported loci are linked with r2or D’>0.9 across our study cohorts (Supplementary Material, Table S4). Although hierarchical mixture model analysis with MAMBA (14)(Materials and Methods) showed a high posterior probability of replication (PPR>0.8) for suggestive associations at PCDH7 at 4p15.1, FREM1 at 9p22.3 (main analysis), OLMF4 at 13q21.1 and PTPRM at 18p11.23 (subtype analysis), they were not observed as significantly associated with COVID-19 in the COVID-19 HGI 5 release dataset, which is why we did not consider them further (forest plots including all data are shown in Supplementary Material, Fig. S6). Future analyses will be required to determine the significance of these associations to COVID-19. Next, we performed a replication analysis of the 13 variants reported to be genome-wide significantly associated with COVID-19 by the COVID-19 HGI. Since data from the Severe COVID-19 group where shared with the COVID-19 HGI, release 5 of the HGI contains 3815 individuals from our previous analysis. We therefore included only individuals from the Severe COVID-19 GWAS group not contained in the COVID- 19 HGI release 5 datasets (Material and Methods and Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
Human Molecular Genetics, 2022, Vol. 31, No. 23 |3951 Table 1. Overview of patients included in the genome-wide discovery analysis Italy Spain Norway Germany/Austria Total Nsevere COVID-19 patients 1536 1421 262 241 Respiratory support categories N(%) Supplemental oxygen only (1) 276 (17.97) 897 (63.53) 80.65 109 (45.23) Non-invasive ventilation (2) 933 (60.74) 122 (8.62) 3.23 21 (8.71) Ventilator (3) 320 (20.83) 381 (26.91) 16.13 86 (35.68) ECMO (4) 7 (0.46) 16 (1.13) 025 (10.37) Median age (IQR)—years (1) 75 (21) 69 (21) 59 (20) 63 (23) Median age (IQR)—years (2) 66 (23) 72 (17) 68.5 (10) 68 (20) Median age (IQR)—years (3) 63 (14) 65 (15) 66 (19) 64 (18) Median age (IQR)—years (4) 49 (12) 57 (10) /56 (12) Main analysis Nsevere COVID-19 patients 1536 1421 262 241 Female sex—% 32.81 35.88 33.87 26.97 Median age (IQR)—year 67 (22) 68 (18) 59.5 (21) 63 (19) Hypertension—% affected (% missing) 40.55 (21.16) 49.6 (1.91) 41.3 (25.81) 53.09 (32.78) CAD—% affected (% missing) 17.09 (21.16) 10.16 (1.98) 25.81 (0) 15.23 (37.34) Diabetes—% affected (% missing) 14.86 (21.16) 24.55 (1.91) 14.52 (0) 19.63 (32.37) Subtype analysis Nsevere COVID-19 patients 1260 519 132 Female sex—% 29.68 27.55 /22.73 Hypertension % affected (% missing) 38.26 (23.25) 52.19 (3.28) /62.96 (38.64) CAD—% affected (% missing) 16.03 (23.25) 12.55 (3.28) /18.75 (39.39) Diabetes—% affected (% missing) 14.58 (23.25) 25.3 (3.28) /19.75 (38.64) Overview of patients included in our first analysis (3260 patients) and second analysis (1911 patients). Individuals of the Italian, Spanish, Norwegian and German/Austrian cohorts were recruited at 5, 7, 8 and 10 different hospitals/centers, respectively. Shown are respiratory support status groups 1–4, age and median age across all individuals as well as within each respiratory support group, percentage of females within each cohort, as well as percentage of individuals affected by known comorbidities of COVID-19. Commonly reported comorbidities in COVID-19 are shown, hypertension, CAD and diabetes. Characteristics of control individuals are shown in Supplementary Material, Table S2. Table 2. Candidate variants from the main and subtype analysis snpid rsid A1 A2 FRQ OR OR_95L OR_95U PPR Pgene Main analysis chr3:45848457:C:T rs35731912 T C 0.09 1.784 1.723 1.847 1.000 2.32E−21 LZTFL1 chr19:10351837:C:T rs11085725 T C 0.273 1.269 1.254 1.283 1.000 1.02E−10 TYK2 chr9:133261703:A:G rs687289 A G 0.358 1.240 1.219 1.262 1.000 4.49E−10 ABO chr19:4717660:A:G rs12610495 G A 0.27 1.216 1.188 1.244 0.990 9.91E−08 DPP9 chr9:14754866:A:G rs7023573 A G 0.285 1.207 1.185 1.231 0.954 5.21E−07 FREM1 chr17:45933112:G:A rs8065800 G A 0.328 1.194 1.145 1.247 0.925 8.90E−07 MAPT chr4:30946048:T:C rs12512667 C T 0.444 0.850 0.828 0.873 0.922 8.95E−07 PCDH7 Subtype analysis chr19:10351837:C:T rs11085725 T C 0.272 1.309 1.294 1.325 0.999 3.24E−09 TYK2 chr19:4717660:A:G rs12610495 G A 0.267 1.286 1.257 1.316 0.993 2.87E−08 DPP9 chr13:55031860:T:C rs111671068 C T 0.012 2.802 2.794 2.810 0.806 1.43E−07 OLFM4 chr9:133271182:T:C rs550057 T C 0.266 1.262 1.238 1.286 0.939 4.56E−07 ABO chr18:7897309:A:C rs17565758 C A 0.078 1.510 1.470 1.550 0.938 4.84E−07 PTPRM chr21:33247902:C:T rs2834161 C T 0.371 1.250 1.175 1.331 0.923 6.10E−07 IFNAR2 We show only variants that have a PPR>0.8 in the MAMBA analysis. A more detailed overview of all variants is shown in Supplementary Material, Table S3. snpid: id of the marker as chr:pos:alleles, genome build hg38; A1: minor allele from TopMed imputation; A2: major allele from TopMed imputation; OR/OR_95L/OR_95U: OR and 95% CI of A1; PPR: posterior probability of replication; P:Pvalue of association. Supplementary Methods) for this analysis. 8 of the 13 variants reported as genome-wide significant by the COVID-19 HGI (7) replicated at least a nominal P-value of 0.05 (located near LZTFL1,ABO,TYK2,DDP9,IFNAR2, MAPT/KANSL1,OAS1;Supplementary Material, Table S5). Subsequently, we performed a fixed-effect inverse variance-weighted meta-analysis using METAL (13) across the main (hospitalized with respiratory support) analysis and COVID-19 HGI B2 statistics as well as the subtype analysis (critically ill: hospitalized with mechanical ventilation) and COVID-19 A2 statistics, resulting in a total of 1320760 (14467 and cases and 1306 293 controls) and 725601 (6526 cases and 719075 controls) analysed individuals in the respective analyses as well as 9 163456 and 9309 373 high quality variants (imputation R2≥0.6 in our cohorts and overall MAF≥1%; Supplementary Material, Figs S7 and S8). This analysis prioritized rs1819040 at the 17q21.31 locus within the KANSL1 gene as the most strongly associated variant in this region (P= 3.27×10−11 for rs1819040; OR= 0.88 for minor allele T; 95%CI=0.84–0.92) over rs8065800 from the main analysis (Supplementary Material, Tables S3,S4 and S6). The meta-analysis of our subtype analysis with the COVID-19 HGI A2 summary statistics revealed an additional genome-wide significant locus not previously associated with severe COVID-19,chr19:50379362:T:C (rs1405655,P=3.25 ×10−8; OR= 1.09; 95%CI= 1.06–1.13) located near the NAPSA Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
3952 |Human Molecular Genetics, 2022, Vol. 31, No. 23 Figure 1. Forest plot of candidates from the in-depth stratified analysis. The plots show the variants chr3:45823240, chr3:45848457:C:T, chr17:46142465:T:A which were significantly associated with age (interaction P-value [P_FDR(I)<0.05]) when comparing age groups ≤60 and >60. We additionally show variants chr19:4717660:A:G and chr21:33242905:T:C with insignificant, though strong trends for association with age. ORs and their respective 95% confidence intervals (CIs) are visualized for each of our four cohorts separately. The size of the dots indicates the size of the respective cohort (N). The OR value is displayed in addition as a numerical value. Only cohorts in which N>50 in both cases and controls are shown. The headers are built as follows: gene variant id (chr:pos(hg38):allele)—rs-id—effect allele ∗/∗∗.∗indicates a variant that was observed as associated from data in this study; ∗∗indicates a variant that was observed as associated in the release 5 of the COVID-19 HGI. gene at 19q13.33 (regional association and forest plots are shown in Supplementary Material, Fig. S9,Table S6). Bayesian fine-mapping analysis of this locus with FINEMAP (15)(Material and Methods) identified a total of 15 (log10(Bayes factor)= 5.95) variants that belong to the 95% most likely to be causal (Fig. 1,Supplementary Material, Table S7), indicating that a small number of SNPs could be considered causal in subsequent analyses. In-depth stratified analysis of lead variants To estimate differences in genetic effects across different age groups and different biological sex categories, we performed an in-depth stratified analysis for selected variants. These included genome-wide and suggestively significant variants from this study (LZTFL1,ABO,TYK2, DPP9;MAPT,IFNAR2; 7 lead variants in total from the main and the subtype analysis). We additionally included all of the 13 variants from the COVID-19 HGI release 5 analysis (7) and the novel association, chr19:50379362:T:C, at the NAPSA locus. A detailed overview of these loci is shown in Supplementary Material, Table S8. We additionally investigated the association of these variants to known comorbidities such as hypertension, CAD and diabetes in cases only and performed sex-stratified analysis within age groups and with respect to disease severity groups. We performed interaction analysis of the SNP with sex and age (age group ≤60 and >60), respectively, to investigate sex-specific and age-specific effects, i.e. to determine whether differences in effects within these groups were statistically significant (Materials and Methods and Supplementary Methods). Results of these analyses are shown in Figure 1,Supplementary Material, Tables S8 and S9,Figures S10 and S11.We confirmed our previously described associations with age (Fig. 1) and with disease severity at 3p21.31 for chr3:45848457:C:T (severity: P=9.73 ×10−7, OR= 1.65; 95%CI=1.35–2.03), which is also discussed in detail in a study by Nakanishi et al. (16). Differential association with age was also observed for chr17:46142465:T:A (rs1819040, MAPT)(Fig. 1). Strong but non-significant trends for an association were also observed for chr19:4717660:A:G (rs12610495; DPP9)at the 19p13.3 locus, and chr21:33242905:T:C (rs13050728; IFNAR2) which was significantly associated with severe COVID-19 only in younger individuals before correction for multiple testing (Fig. 1). We analysed these 5 variants Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
Human Molecular Genetics, 2022, Vol. 31, No. 23 |3953 even more in-depth within the age groups of 41–50, 51– 60 and 61–70 as well as 41–60 and 61–80 (Supplementary Material, Fig. S10C) and observed a tendency for stronger effects in the younger population at the four loci. Interestingly, the German population showed opposite effect sizes than the other cohorts in the broad age group analysis for chr17:46142465:T:A (see also below). We did not observe any statistically significant associations with age, sex, severity or comorbidities for the remaining analysed lead variants. Additional age and sex stratified forest plots of this analysis are shown in Supplementary Material, Figure S11A and B. As the genome-wide significant association near LTZF1,TYK2,DDP9 and ABO have been followed up in detail before, we next focused on the novel association at the 19q13.33 (NAPSA) locus from the meta-analysis with COVID-19 HGI release 5 summary statistics and performed an in-depth analysis of the 17q21.31 (MAPT, KANSL1) locus associated with age. The 17q21.31 locus has been previously reported to be linked to a common inversion polymorphism of two highly divergent haplotypes H1 and H2 (17). Indeed, Bayesian fine-mapping (15) at this locus showed that the 95% credible set includes 1530 variants among these chr17:45933112:G:A (rs8065800) from our initial discovery analysis and chr17:46142465:T:A (rs1819040) from the COVID-19 HGI analysis with certainty <0.3%, indicating, based on the overlap of LD boundaries of highly associated SNP variants with the inversion boundaries (Fig. 2A), that the individual SNP associations may be proxy variants for the associated inversion polymorphism. We therefore imputed the inversion haplotypes H1 and H2 for our cohorts by genotype imputation with IMPUTE2 (18), employing as reference 109 individuals from the 1000 Genomes Project for which 17q21.31 inversion genotypes were obtained experimentally by FISH and droplet digital polymerasechain-reaction (PCR; Material and Methods). LD between the chr17:46142465:T:A (rs1819040) variant, which was prioritized over chr17:45933112:G:A (rs8065800) in the meta-analysis with the COVID-19 HGI summary statistics, and the inversion in our cohorts is near perfect (r2=0.98, D= 0.99). Genome-wide significant association with severe respiratory COVID-19 for the inversion was confirmed using logistic regression followed by meta-analysis across this study’s panels and summary statistics derived for the from the COVID-19 HGI data (Materials and Methods) (meta-analysis main analysis and COVID-19 HGI release 5 B2: P=7.61×10−10, OR= 0.89; 95%CI= 0.84–0.92; meta-analysis subtype analysis and COVID-19 HGI release 5 A2: P=1.5×10−4, OR =0.90; 95%CI= 0.85–0.95; Fig. 2B,Supplementary Material, Table S10). Using stratified analysis, we also show an age effect for the inversion (Supplementary Material, Table S7, Fig. S10). The opposite effects direction observed for the German cohort for linking SNP chr17:46142465:T:A was shifted for individuals aged 40–61 when analysing the inversion. We did not observe any association of the inversion with disease severity. Functional analysis of 17q21.31 and 19q13.33 using publicly available datasets We next performed several follow-up analyses to better understand possible functional implications of the associations at the 17q21.31 inversion and at the novel 19q13.33 locus, including a phenome-wide association study (PheWas) and exploratory gene expression analysis. We queried variants in high LD (r2>0.9) with chr19:50- 379362:T:C (rs1405655) or the 17q21.31 inversion, using a wide range of phenotypes from the NHGRI GWAS Catalog (Material and Methods)(19). While no known phenotypes were found to be linked to chr19:50379362:T:C (rs1405655) or its proxy variants, 161 GWAS associations were identified for variants in high LD (r2>0.9) with the 17q21.31 inversion, illustrating its multiple effects (Supplementary Material, Table S11). These associations included several traits potentially related to COVID-19 pathology, such as blood and immune cell composition or lung function (Fig. 2C). Variants within the credible set from Bayesian fine mapping at 19q13.33 overlap with several genes, including for instance NAPSA,NR1H2 and KCNC3, while the 17q21.31 inversion spans multiple genes including MAPT, KANSL1, FMNL1 and CRHR1 (Supplementary Material, Figs S4,S5 and S9 and Fig. 2).To identify possible target genes relevant in COVID-19 disease pathology, we performed an exploratory gene expression analysis using several publicly available datasets to: (i) examine the direct effect of both loci on gene expression by analysing colocalization of lead SNPs with expression and splicing quantitative trait loci (eQTL and sQTL), (ii) identify in which tissues or cell types our candidate genes are expressed by analysing their RNA expression at bulk and single-cell levels and (iii) infer the possible contribution of these genes to COVID-19 pathology by looking at their expression patterns in (a) monocytes exposed to different viral and non-viral immune stimulators; (b) organoids infected with COVID-19 and (c) single-cell RNA-seq of lung and other tissues from patients who died after experiencing a SARS-CoV-2 severe disease (Material and Methods). Results of these analyses are displayed in Figure 3. The potential functional role of the 17q21.31 inversion is supported by the fact that the inversion locus (chr17:45883776±75 kb) strongly colocalizes (regional probability >0.9) with eQTLs and sQTLs (i.e. displaying the strongest association with the target), for 36 (eQTL) and 15 (sQTL) genes,respectively (Fig. 3A,Supplementary Material, Table S12,Fig. S12). Expression patterns of protein-coding genes with eQTLs associated to the inversion are shown in Supplementary Material, Figure S13, with the best candidates to play a role in the effects of the inversion on COVID-19, including MAPT, KANSL1, FMNL1 and CRHR1, being summarized in Figure 3. Many of these genes are highly expressed in neural tissues and testis (Supplementary Material, Fig. S13). However, several of them are also expressed in major immune cell types in COVID-19 relevant tissues Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
3960 |Human Molecular Genetics, 2022, Vol. 31, No. 23 5 A2 summary statistics and meta-analysis of our subtype analysis and COVID-19 release 5 B2 summary statistics. We used the common threshold of P<5×10−8 to determine genome-wide significance. Bayesian fine-mapping analysis Statistical fine-mapping analysis was conducted using FINEMAP (15) Version 1.4 for each of the loci of interest to calculate the posterior inclusion probability for each lead SNP and every other SNP within 250 kb flanking regions. In the case where the association signal extended over a larger region, as in the case of 17q21.21, the region was expanded to include this. FINEMAP determined the 95% credible set of SNPs assuming a single causal variant using shotgun stochastic search (—ncausal-snps 1—sss),i.e.the minimum set of variants containing the causal variant with ≥95% certainty. The union of the genotypes of the Italian, Spanish, Norwegian and German/Austrian cohorts was used as the LD reference population. Meta-analysis model-based assessment of replicability We tested the replicability of our candidate variants from the meta-analysis using the Meta-Analysis Modelbased Assessment of replicability (14) (MAMBA) method, as single outlier studies can drive a false positive metaanalysis association. MAMBA takes the genetic effects and standard deviations from participating studies to test each SNP for a true non-zero effect. This is achieved by fitting a two-level mixture model to genome-wide LD-pruned SNPs that reduces to a fixed effect metaanalysis in absence of outliers, in which case it has similar power. Since this is a likelihood model, the result is a posterior probability of replicability (PPR) that the SNP has a non-zero replicable effect. If the PPR is low, MAMBA tests for excess in variation of effects sizes and outputs the probability that each study is an outlier. For each of the first and second analyses, we used the lead variants and a genome-wide set of LD-pruned SNPs (PLINK v1.90b6.16 64-bit, —indep-pairwise 500 kb 1 0.1) (40,41) from each participating study for the algorithm to estimate the null distribution. Suggestive variants and LD-pruned background SNPs are entered as a single table and the algorithm has no prior knowledge of which SNPs are considered significant. MAMBA was run with default settings and always terminated before 10000 iterations. We considered a PPR value >0.8 to indicate a high probability of replication. Association analysis of candidate SNPs We performed age- and sex-stratified on 7 candidate SNPs from our main and subtype analysis and 13 candidate SNPs from the COVID-19 HGI analysis, as well as the novel variant identified in this study (7). We additionally analysed disease severity and comorbidities hypertension, CAD and diabetes in cases only. We carried out a logistic regression analysis including age, biological sex, age∗age, biological sex∗age and the first 10 PCs from PCA as covariates dependent on the stratification level (i.e. for sex-stratified analyses, only age and PCs were included; for age-stratified analysis, only the biological sex and PCs were included; detailed formulas are shown in the Supplementary Methods). All analysis was done in R version 3.6.2. An inverse-variance weighted fixed-effects metaanalysis was conducted using the R-package metafor (47) to combine cohort-specific summary statistics,including only statistics from cohorts with NCase and NControl >50. Age was analysed in groups ≤60 to >60, which were chosen in reference to the analyses performed by the COVID-19 HGI. We additionally performed an interaction analysis of SNP and sex as well as SNP and binarized age (≤60 versus >60), to determine statistical significance of differences between the groups, respectively (Supplementary Methods).For variants that showed a significant age effect, we additionally analysed age groups 40–51, 51–60 and 61–70 for the Italian and Spanish cohorts and performed a meta-analysis on the summary statistics from these two cohorts only (Supplementary Material, Fig. S10). We were interested in observing more detailed age effects in these age intervals with sufficient data for analysis. For the meta-analyses, P-values were adjusted for multiple testing using the P-value correction of Benjamini–Hochberg. Imputation of the ∼0.9-Mb inversion polymorphism at 17q21.31 In 2005, Stefansson and colleagues discovered a 900 kb inversion polymorphism at 17q21.31, a region that contains several genes, including those encoding corticotropin releasing hormone receptor 1 (CRHR1) and microtubule-associated protein tau (MAPT)(17). Chromosomes with the inverted segment in different orientations represent two distinct lineages, H1 and H2, that have diverged for up to 3 million years and show no evidence of recombination (17). For the Italian, Spanish, Norwegian and German/Austrian GWAS discovery cohorts, we inferred the 17q21.31 inversion status (H1 or H2) with IMPUTE v2.3.2 (18) using genotype information and an imputation reference panel consisting of 109 individuals (from different continents [EUR, EAS, AFR]) from the 1000 Genomes Project Phase 3 (43), for which 17q21.31 inversion genotypes, obtained experimentally by FISH (48,49) and droplet digital PCR (50), as well as SNP genotype data are available for the region of the inversion.Imputed inversion genotypes were determined according to the highest posterior probability and were further confirmed by examining consensus genotypes of known inversion tag SNPs in perfect LD (r2= 1) with the inversion in the imputation reference panel. H1 and H2 alleles were coded according to an additive model as 0,1 and 2. We additionally imputed the 17q21.31 inversion for the COVID-19 HGI release 5 A2 and B2 analyses from the files COVID19_HGI_A2_ALL_leave_23andme_20210107.txt.gz and COVID19_HGI_B2_ALL_leave_23andme_20210107.- Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
Human Molecular Genetics, 2022, Vol. 31, No. 23 |3961 txt.gz. Here, the inversion P-value was imputed from summary statistics using Fast and accurate P-value Imputation for genome-wide association study (FAPI) (51). The odds ratio (OR) and 95% confidence intervals (CIs) were estimated from the SNP rs62061809, which is in perfect LD with the inversion (r2=1).The inversion was coded as 0 for the major allele H1 and 1 for the minor allele H2. All association analyses were carried out as described above on the minor allele H2. Functional analysis Phenome-wide Association study (PheWAS) Phenotype associations with the different variants were obtained from the NHGRI-EBI GWAS Catalog curated collection of established genome-wide significant disease/trait associations (http://www.ebi.ac.uk/gwas/; release 2020-12-02) (19). We queried GWAS hits in high LD (r2>0.9) with the inversion as well as hits in high LD (r2>0.9) with rs1405655 (19q13.33) and P-value<10−7. Since each GWAS study is focused on populations from different origins, the LD patterns employed to evaluate the association between the inversion and GWAS signals were based on individuals with the corresponding ancestry or the closest one available from our inversion imputation panel (of 109 experimentally genotyped individuals), whereas the global LD was selected if populations from underrepresented ancestries were studied .27 proxy variants were used for r1405655 and 2904 proxy variants were used for the inversion. Colocalization of GWAS and tissue-specific expression and splicing quantitative trait loci (eQTL, sQTL) Colocalization of GWAS and QTL (eQTL and sQTL) data from 49 different human tissues of the GTEx Project (GTEx Analysis Release V8) (19) was performed using the HyPrColoc method (52) implemented in the ezQTL tool (53). The analysis was conducted including all cis-QTL variants falling within ±75 kb from coordinates (GRCh38) of the two variants of interest: chr17:46142465:T:A— lead SNP for the 17q21.31 inversion—and chr19:50379362 (rs1405655—lead SNP of the 19q13.33 locus). LD values used in the colocalization analysis were calculated based on the 1000 Genomes population European (2) population only, to account for the ethnic bias of GTEx donors. The loci of tested traits were considered to be colocalized when a regional probability of colocalization was greater than 0.9. Additionally, to assess the actual effect (and its direction) of the variants on neighboring gene expression, normalized effect score (NES) and P-values of the two variants (inversion tagged by rs62061809) were retrieved from the GTEx portal. Selection and definition of candidate genes at 17q21.31 and 19q13.33 To identify candidate genes most likely to play a causative role at 17q21.31 and 19q13.33, all protein-coding genes that are located within locus boundaries or that are candidates based on GWAS colocalization with eQTL (eGenes) or sQTL (sGenes)-variants were chosen. More precisely, the boundaries for the 19q13.33 locus were defined by Bayesian fine mapping (GRCh38: chr19:50344768–50379362), while extended boundaries (GRCh38: chr17:45495836–46707123) (54) were used for the 17q21.21.31 locus, since it lies within a ∼0.9 Mb inversion region of high LD that affects expression and splicing of numerous genes in GTEx (55). To retrieve candidate genes that overlap the boundaries, GENCODE v36 (56) annotations for genome build GRCh38 were used. A complete list of candidate protein-coding genes from both loci is provided in Supplementary Material, Table S21. Gene expression analysis for candidate genes from genome-wide significant susceptibility loci Publicly available bulk tissue and immune cell type RNA- seq data for all available candidate genes were retrieved from GTEx v8 (55) and from the Expression Atlas (57) (BLUEPRINT consortium data [accession E-MTAB- 3827] (58)) portals, respectively. Gene-level expression values (transcripts per million, TPM) by tissue or by cell type were obtained as median-summarized in the case of the GTEx data and as mean-summarized in the case of the BLUEPRINT data. The summarized TPM values were centered gene-wise and z-score scaled for visualization using the ggplot2 R-package. The single-cell RNA-seq (scRNA-seq) data in COVID-19 relevant tissues from non-diseased individuals, such as lung and upper airways (59) or brain (60), were obtained from the COVID- 19 Cell Atlas (61). The pre-processed and cell type annotated scRNA-seq datasets were retrieved as AnnData objects in.h5ad format files. Log-normalized average expression values of available candidate genes by cell type were visualized using the scanpy v1.4.6 package (62). For gene expression analysis of candidate genes in SARS-CoV-2 infected brain organoids, pre-processed and cell-annotated scRNA-seq data were obtained upon request from Song et al. (63). Differential gene expression analysis of scRNA-seq data was performed using the R package MAST (64). More precisely, hurdle models were used to evaluate differentially expressed genes in each brain organoid cell type (neural progenitors, interneurons, neurons and cortical neurons) comparing SARS-CoV-2 infected and mock (non-infected) cells. The models were fitted using the condition, sample identification, number of detected genes (centered) and total counts of SARS-CoV-2 transcripts (centered) as covariates, thus adjusting for the cellular detection rate, batch effects and viral load. Genes with PFDR<0.01 and absolute value of log2 fold change >0.1 were considered as significantly differentially expressed. Finally, the status, log2 fold change and P-values of candidate gene differential expression in COVID-19-infected lung cells were obtained from pseudo-bulk differential expression analysis performed by Delorey et al.(20) Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
3962 |Human Molecular Genetics, 2022, Vol. 31, No. 23 Analysis and fine mapping of the HLA Detailed descriptions of this analysis can be found in the Supplementary Methods. In brief, quality-controlled genotypes at the HLA region (chr6:29-34 Mb) were extracted. HLA allele, amino acid and SNP imputation were performed using the random-forest-based HLA genotype imputation with attribute bagging (HIBAG) and applying specially tailored as well as publicly available reference panels (5,65,66). The resulting data were used as a basis for several subsequent analyses, including: (i) basic association analysis (fine mapping) as described in the section Statistical Analysis and the Supplementary Methods, (ii) a peptidome-wide association study (11) (pepWAS), to screen for disease-relevant peptides from SARS-CoV-2, that may present a possible functional link between severe COVID-19 and variation at classical HLA loci, (iii) quantitative HLA analyses directed at the number of peptides bound by an HLA allele as well as (iv) an analysis of HLA-presentation of shared peptides (‘molecular mimicry’). Analysis of the Y-chromosome haplotypes First, we produced high-quality Y-chromosome genotypes by manually calling and visually inspecting Y- chromosome SNPs in the male individuals from all cohorts only. Next, we used 22 Y-chromosome SNPs to distinguish known Y-chromosome haplogroups as described in the Supplementary Methods at different haplogroup resolutions. We here focused on haplogroup R, the most prevalent Y-chromosome haplogroup across Europe. Association analysis was carried out as described in the Section Statistical Analysis—Association Analysis of Candidate SNPs on Y-chromosome haplogroups coded as absent (0) or present (1). We additionally analysed the end-point mortality (Supplementary Methods). The coefficient of variation (CV) across frequencies was calculated for Y-chromosome haplogroups between individual batches (i.e. contributing center/hospital or different versions of the GSA). Here, only frequencies calculated from batches including more than 100 individuals were considered. Analysis of the ABO secretor status ABO blood group typing was performed as described by Ellinghaus et al.( 5). Briefly, genotypes of the SNPs rs8176747 (c.803C>T, ABO∗B), rs41302905 (c.802G>A, ABO∗O2) and rs8176719 (c.261delG, ABO∗O1) were extracted from the imputed data (R2=1 for all SNPs and cohorts) and used to infer the A, B and O blood types. The ABO-‘secretor’ status was inferred from the genotypes of the rs601338 SNP (c.428G>A, FUT2∗01 N.02) at the FUT2 gene, located at 19q13.33 and extracted from the imputed data (R2=0.98–0.99 for all cohorts). Individuals carrying genotypes GA or GG were assigned secretor status and individuals carrying genotype AA were assigned non-secretor status based on the genotype dosages, ranging from 0 to 2, retrieved from the imputed data. Individuals with allelic dosages 1.3–1.7 were called as ‘no call’, individuals with dosages ≤1.3 were called ‘secretors’ and individuals with dosages ≥1.7 were called ‘non-secretors’. Association analysis was carried out as described in the Section Statistical Analysis—Association Analysis of Candidate SNPs on blood type or blood type secretor status coded as absent (0) or present (1). Availability of summary statistics The GWAS summary statistics are available through GWAS Catalog (https://www.ebi.ac.uk/gwas/), study accession number GCST90129579. Supplementary Material Supplementary Material is available at HMG online. Acknowledgements We would like to thank the COVID-19 Human Genetics Initiative (HGI) for collecting and openly sharing extensive summary statistics with the community. For the cohort from Milan Fondazione Ca’ Granda, we would like to acknowledge specifically Rossana Carpani. A full list of the investigators who contributed to the generation of the GCAT data is available from www. genomesforlife.com/. We thank Dr Luis Puig and Vanessa Pleguezuelos on behalf of the Blood and Tissue Bank from Catalonia (BST) who collaborated in the GCAT/COVICAT recruitment, and all the GCAT volunteers that participated in the study. We would also like to acknowledge the Task Force Humanitas and the Task Force Humanitas Gavazzeni Castelli (Supplementary Note). We would like to acknowledge all the participants from the ‘Grupo de Trabajo en Medicina Personalizada contra el COVID-19 de Andalucia’. We also thank ‘Consejeria de Salud y Familias’ and ‘Junta de Andalucía’ for their support and funding that is currently supporting the COVID-19-GWAS and the COVID-PREMED initiatives. We are indebted to the HCB-IDIBAPS Biobank for the biological human samples and data procurement and to the Fundació Glòria Soler for its support to the COVIDBANK collection. We acknowledge the work of the Norwegian SARS-CoV-2 Study group (Supplementary Note).We also acknowledge Benedicte A. Lie at the Department of Immunology, Oslo University Hospital and the Norwegian Bone Marrow Donor Registry are acknowledged for providing healthy controls. We thank the support of the German COVID-19 multiomics initiative (decoi.eu). We thank the members of the COVID-19 use and access committee (Prof. Reinhold Förster, Prof. Christine Falk, Prof. Thomas Fühner, Prof. Christoph Höner zu Siederdissen, Prof. Marius Höper, Prof. Thomas Friedrich Schulz, Prof. Markus Cornberg, Prof. Thomas Illig and Prof. Michael Manns), as well as the study nurses, medical doctors and biobank staff members involved in the project. For funding, we thank the Ministry of Science and Culture (MWK) of Lower-Saxony. We thank everybody involved in the COVID-19 registry at the University Hospital Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
Human Molecular Genetics, 2022, Vol. 31, No. 23 |3963 Regensburg (COVUR). Genotyping of the German control dataset was performed at the Regeneron Genetics Center. Genotyping of the COMRI study was performed by the Genotyping laboratory of Institute for Molecular Medicine Finland FIMM Technology Centre, University of Helsinki. We are very grateful to Prof. Akiko Iwasaki and Eric Song for generously sharing pre-processed singlecell RNA-seq data generated from SARS-CoV-2-infected brain organoids. Conflict of Interest statement. P.K. has received lecture honoraria from or is advisor to Akademie für Infektionsmedizin e.V., Astellas Pharma, European Confederation of Medical Mycology, Gilead Sciences, GPR Academy Ruesselsheim, MSD Sharp & Dohme GmbH, Noxxon N.V. and University Hospital, LMU Munich outside the submitted work. He also received nonfinancial scientific grants from Miltenyi Biotec GmbH. O.A.C. reports grants or contracts from Amplyx, Basilea, BMBF, Cidara, DZIF, EU-DG RTD (101037867), F2G, Gilead, Matinas, MedPace, MSD, Mundipharma, Octapharma, Pfizer, Scynexis; Consulting fees from Amplyx, Biocon, Biosys, Cidara, Da Volterra, Gilead, Matinas, Med- Pace, Menarini, Molecular Partners, MSG-ERC, Noxxon, Octapharma, PSI, Scynexis, Seres; Honoraria for lectures from Abbott,Al-Jazeera Pharmaceuticals,Astellas,Grupo Biotoscana/United Medical/Knight, Hikma, MedScape, MedUpdate, Merck/MSD, Mylan, Pfizer; Payment for expert testimony from Cidara; Participation on a Data Safety Monitoring Board or Advisory Board from Actelion, Allecra, Cidara, Entasis, IQVIA, Jannsen, MedPace, Paratek, PSI, Shionogi; a pending patent currently reviewed at the German Patent and Trade Mark Office; other interests from DGHO, DGI, ECMM, ISHAM, MSGERC, Wiley. S.D. received funding from the Banca Intesa San Paolo. A.M. has received research funding from or reports personal fees from Dolce&Gabbana Fashion Firm, Ventana, Pierre Fabre, Verily, AbbVie, Astra Zeneca, Verseau Therapeutics, Compugen, Myeloid Therapeutics,Third Rock Venture,Imcheck Therapeutics, Ellipses, Novartis, Roche, Macrophage Pharma, BiovelocIta, Merck, Principia, Cedarlane Laboratories Ltd, HyCult Biotechnology, eBioscience, Biolegend, ABCAM Plc, Novus Biologicals, Enzo Life (ex Alexis Corp.), Affymetrix, BMS, Johnson&Johnson; in addition, he has a patent WO2019057780 ‘Anti-human migration stimulating factor (MSF) and uses thereof’ issued, a patent WO2019081591 ‘NK or T cells and uses thereof’ issued, a patent WO2020127471 ‘Use of SAP for the treatment of Euromycetes fungi infections’ issued and a patent EP20182181.6 ‘PTX3 as prognostic marker in Covid-19’ pending. A.L. has consulted by Intercept Pharma, Takeda, AlfaSigma, Abbvie, Gilead, and Merck Sharp, and Dohme. M.R.G. has served as a speaker for AbbVie, Bristol-Myers Squibb, GENFIT, Gilead Sciences, Intercept, MSD and Roche; an advisory board member for GENFIT, Gilead Sciences, Intercept, Janssen-Cilag, Kaleido, NovoNordisk, Medimmune and Prosceinto; and has received research grants from Abbvie, Gilead Sciences and Intercept. J.C.H. received a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner during the conduct of this study. C.D.S. reports grants, personal fees from AstraZeneca, personal fees and nonfinancial support from BBraun Melsungen, personal fees from BioNtech, grants, personal fees and nonfinancial support from Gilead Sciences, grants and personal fees from Janssen-Cilag, personal fees from Eli Lilly, personal fees from Formycon, personal fees from Roche, other from Apeiron, grants and personal fees from MSD, grants from Cepheid, personal fees from GSK, personal fees from Molecular partners, other from Eli Lilly, personal fees from SOBI during the conduct of the study; personal fees from AbbVie, personal fees from Synairgen, grants and personal fees from ViiV Healthcare, outside the submitted work. F.B. reports grants and personal fees from Astrazeneca, grants and personal fees from Chiesi, grants and personal fees from GSK, personal fees from Grifols, personal fees from Guidotti, personal fees from Insmed, grants and personal fees from Menarini,personal fees from Novartis, grants and personal fees from Pfizer, personal fees from Zambon, personal fees from Vertex and personal fees from Viatris outside the submitted work. C.L. reports personal fees from Chiesi, Gilead, Janssen, Novartis, Oxfordimmunotec and Insmed outside the submitted work. J.H. reports personal fees from Chiesi, Gilead and Janssen outside the submitted work. T.H.K. has received consulting fees from Engitix and Intercept and speaker fees from Gilead and Alfasigma. Funding S.E.H. and C.A.S. partially supported genotyping through a philanthropic donation.A.F. and D.E. were supported by a grant from the German Federal Ministry of Education and COVID-19 grant Research (BMBF; ID:01KI20197); A.F., D.E.and F.D.were supported by the Deutsche Forschungsgemeinschaft Cluster of Excellence ‘Precision Medicine in Chronic Inflammation’ (EXC2167). D.E. was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the Computational Life Sciences funding concept (CompLS grant 031L0165). D.E., K.B. and S.B. acknowledge the Novo Nordisk Foundation (NNF14CC0001 and NNF17OC0027594). T.L.L., A.T. and O.Ö. were funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project numbers 279645989; 433116033; 437857095. M.W. and H.E. are supported by the German Research Foundation (DFG) through the Research Training Group 1743, ‘Genes, Environment and Inflammation’. L.V. received funding from: Ricerca Finalizzata Ministero della Salute (RF-2016-02364358), Italian Ministry of Health ‘CV PREVITAL’—strategie di prevenzione primaria cardiovascolare primaria nella popolazione italiana; The European Union (EU) Programme Horizon 2020 (under grant agreement No. 777377) for the project Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
3964 |Human Molecular Genetics, 2022, Vol. 31, No. 23 LITMUS- and for the project ‘REVEAL’; Fondazione IRCCS Ca’ Granda ‘Ricerca corrente’, Fondazione Sviluppo Ca’ Granda ‘Liver-BIBLE’ (PR-0391), Fondazione IRCCS Ca’ Granda ‘5permille’ ‘COVID-19 Biobank’ (RC100017A). A.B. was supported by a grant from Fondazione Cariplo to Fondazione Tettamanti: ‘Bio-banking of Covid-19 patient samples to support national and international research (Covid-Bank). This research was partly funded by an MIUR grant to the Department of Medical Sciences,under the program ‘Dipartimenti di Eccellenza 2018–2022’. This study makes use of data generated by the GCAT-Genomes for Life. Cohort study of the Genomes of Catalonia, Fundació IGTP (The Institute for Health Science Research Germans Trias i Pujol) IGTP is part of the CERCA Program/Generalitat de Catalunya. GCAT is supported by Acción de Dinamización del ISCIII-MINECO and the Ministry of Health of the Generalitat of Catalunya (ADE 10/00026); the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) (2017-SGR 529). M.M. received research funding from grant PI19/00335 Acción Estratégica en Salud, integrated in the Spanish National RDI Plan and financed by ISCIII-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (European Regional Development Fund (FEDER)-Una manera de hacer Europa’). B.C. is supported by national grants PI18/01512. X.F. is supported by the VEIS project (001- P-001647) (co-funded by the European Regional Development Fund (ERDF), ‘A way to build Europe’). Additional data included in this study were obtained in part by the COVICAT Study Group (Cohort Covid de Catalunya) supported by IsGlobal and IGTP, European Institute of Innovation & Technology (EIT), a body of the European Union, COVID-19 Rapid Response activity 73A and SR20- 01024 La Caixa Foundation. A.J. and S.M. were supported by the SpanishMinistry of Economy and Competitiveness (grant numbers: PSE-010000-2006-6 and IPT-010000- 2010-36). A.J. was also supported by national grant PI17/00019 from the Acción Estratégica en Salud (ISCIII) and the European Regional Development Fund (FEDER). The Basque Biobank, a hospital-related platform that also involves all Osakidetza health centres, the Basque government’s Department of Health and Onkologikoa, is operated by the Basque Foundation for Health Innovation and Research-BIOEF. M.C. received Grants BFU2016- 77244-R and PID2019-107836RB-I00 funded by the Agencia Estatal de Investigación (AEI,Spain) and the European Regional Development Fund (FEDER, EU). M.R.G., J.A.H., R.G.D. and D.M.M. are supported by the ‘Spanish Ministry of Economy, Innovation and Competition, the Instituto de Salud Carlos III’ (PI19/01404, PI16/01842, PI19/00589, PI17/00535 and GLD19/00100) and by the Andalussian government (Proyectos Estratégicos-Fondos Feder PE- 0451-2018, COVID-Premed, COVID GWAs). The position held by Itziar de Rojas Salarich is funded by grant FI20/00215, PFIS Contratos Predoctorales de Formación en Investigación en Salud. Enrique Calderón’s team is supported by CIBER of Epidemiology and Public Health (CIBERESP), ‘Instituto de Salud Carlos III’. J.C.H. reports grants from Research Council of Norway grant no 312780 during the conduct of the study. E.S. reports grants from Research Council of Norway grant no. 312769. The BioMaterialBank Nord is supported by the German Center for Lung Research (DZL), Airway Research Center North (ARCN). The BioMaterialBank Nord is member of popgen 2.0 network (P2N). P.K. Bergisch Gladbach, Germany and the Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany. He is supported by the German Federal Ministry of Education and Research (BMBF). O.A.C. is supported by the German Federal Ministry of Research and Education and is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—CECAD, EXC 2030–390661388. The COMRI cohort is funded by Technical University of Munich, Munich, Germany. This work was supported by grants of the Rolf M. Schwiete Stiftung, the Saarland University, BMBF and The States of Saarland and Lower Saxony. K.U.L. is supported by the German Research Foundation (DFG, LU-1944/3-1). Genotyping for the BoSCO study is funded by the Institute of Human Genetics, University Hospital Bonn. F.H. was supported by the Bavarian State Ministry for Science and Arts. Part of the genotyping was supported by a grant to A.R. from the German Federal Ministry of Education and Research (BMBF, grant: 01ED1619A, European Alzheimer DNA BioBank, EADB) within the context of the EU Joint Programme— Neurodegenerative Disease Research (JPND). Additional funding was derived from the German Research Foundation (DFG) grant: RA 1971/6-1 to A.R. P.R. is supported by the DFG (CCGA Sequencing Centre and DFG ExC2167 PMI and by SH state funds for COVID19 research). F.T. is supported by the Clinician Scientist Program of the Deutsche Forschungsgemeinschaft Cluster of Excellence ‘Precision Medicine in Chronic Inflammation’ (EXC2167). C.L. and J.H. are supported by the German Center for Infection Research (DZIF). T.B., M.M.B., O.W. und A.H. are supported by the Stiftung Universitätsmedizin Essen. M.A.-H. was supported by Juan de la Cierva Incorporacion program, grant IJC2018-035131-I funded by MCIN/AEI/10.13039/501100011033. E.C.S. is supported by the Deutsche Forschungsgemeinschaft (DFG; SCHU 2419/2-1). Authors’ contributions Contributions are shown in the Supplementary Note. References 1. Booth, A., Reed, A.B., Ponzo, S., Yassaee, A., Aral, M., Plans, D., Labrique, A. and Mohan, D. (2021) Population risk factors for severe disease and mortality in COVID-19: a global systematic review and meta-analysis. PLoS One,16, e0247461. 2. Wong, C.K.H., Wong, J.Y.H., Tang, E.H.M., Au, C.H. and Wai, A.K.C. (2020) Clinical presentations, laboratory and radiological findings, and treatments for 11,028 COVID-19 patients: a systematic review and meta-analysis. Sci. Rep.,10, 19765. Downloaded from https://academic.oup.com/hmg/article/31/23/3945/6644888 by Universidad de Sevilla user on 23 May 2023
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