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Meta-Analysis
. 2022 Nov 28;31(23):3945-3966.
doi: 10.1093/hmg/ddac158.

Detailed stratified GWAS analysis for severe COVID-19 in four European populations

Frauke Degenhardt  1 David Ellinghaus  1   2 Simonas Juzenas  1   3 Jon Lerga-Jaso  4 Mareike Wendorff  1 Douglas Maya-Miles  5   6   7 Florian Uellendahl-Werth  1 Hesham ElAbd  1 Malte C Rühlemann  1   8 Jatin Arora  9   10   11   12   13 Onur Özer  14   15 Ole Bernt Lenning  16   17 Ronny Myhre  18 May Sissel Vadla  17   19 Eike M Wacker  1 Lars Wienbrandt  1 Aaron Blandino Ortiz  20 Adolfo de Salazar  21   22 Adolfo Garrido Chercoles  23 Adriana Palom  24   25 Agustín Ruiz  26   27 Alba-Estela Garcia-Fernandez  28 Albert Blanco-Grau  28 Alberto Mantovani  29   30 Alberto Zanella  31   32 Aleksander Rygh Holten  33   34 Alena Mayer  35 Alessandra Bandera  31   32 Alessandro Cherubini  32 Alessandro Protti  29   30 Alessio Aghemo  29   30 Alessio Gerussi  36   37 Alfredo Ramirez  38   39   40   41   42 Alice Braun  35 Almut Nebel  1 Ana Barreira  25 Ana Lleo  29   30 Ana Teles  14   15 Anders Benjamin Kildal  43 Andrea Biondi  44 Andrea Caballero-Garralda  45 Andrea Ganna  46 Andrea Gori  32   47 Andreas Glück  48 Andreas Lind  49 Anja Tanck  1 Anke Hinney  50 Anna Carreras Nolla  51 Anna Ludovica Fracanzani  31   32 Anna Peschuck  1 Annalisa Cavallero  52 Anne Ma Dyrhol-Riise  34   53 Antonella Ruello  54 Antonio Julià  24 Antonio Muscatello  32 Antonio Pesenti  31   32 Antonio Voza  29   30 Ariadna Rando-Segura  55   56 Aurora Solier  57   58 Axel Schmidt  59 Beatriz Cortes  51 Beatriz Mateos  6   60 Beatriz Nafria-Jimenez  23 Benedikt Schaefer  61   62 Björn Jensen  63 Carla Bellinghausen  64 Carlo Maj  65 Carlos Ferrando  66   67 Carmen de la Horra  5   7   68   69   70 Carmen Quereda  71 Carsten Skurk  35 Charlotte Thibeault  35 Chiara Scollo  72 Christian Herr  73 Christoph D Spinner  74 Christoph Gassner  1   75 Christoph Lange  76   77   78 Cinzia Hu  32 Cinzia Paccapelo  32 Clara Lehmann  79   80   81 Claudio Angelini  82 Claudio Cappadona  30 Clinton Azuure  14   15 COVICAT study group, Aachen Study (COVAS)Cristiana Bianco  32 Cristina Cea  28 Cristina Sancho  83 Dag Arne Lihaug Hoff  84   85 Daniela Galimberti  31   32 Daniele Prati  32 David Haschka  86 David Jiménez  57   58 David Pestaña  87 David Toapanta  67 Eduardo Muñiz-Diaz  88 Elena Azzolini  29   30 Elena Sandoval  67 Eleonora Binatti  36   37 Elio Scarpini  31   32 Elisa T Helbig  35 Elisabetta Casalone  89 Eloisa Urrechaga  90   91 Elvezia Maria Paraboschi  29   30 Emanuele Pontali  92 Enric Reverter  67 Enrique J Calderón  5   7   68   69   70 Enrique Navas  71 Erik Solligård  93   94 Ernesto Contro  95 Eunate Arana-Arri  91 Fátima Aziz  67 Federico Garcia  21   22   96 Félix García Sánchez  97 Ferruccio Ceriotti  32 Filippo Martinelli-Boneschi  98   99 Flora Peyvandi  100   101 Florian Kurth  35   102   103 Francesco Blasi  104   105 Francesco Malvestiti  31 Francisco J Medrano  7   68   69   70   106 Francisco Mesonero  6   60 Francisco Rodriguez-Frias  6   24   28   55   56   107 Frank Hanses  108   109 Fredrik Müller  34   49 Georg Hemmrich-Stanisak  1 Giacomo Bellani  110   111 Giacomo Grasselli  31   32 Gianni Pezzoli  112 Giorgio Costantino  31   32 Giovanni Albano  54 Giulia Cardamone  30 Giuseppe Bellelli  111   113 Giuseppe Citerio  111   114 Giuseppe Foti  110   111 Giuseppe Lamorte  32 Giuseppe Matullo  89 Guido Baselli  32 Hayato Kurihara  82 Holger Neb  115 Ilaria My  29 Ingo Kurth  116 Isabel Hernández  26   27 Isabell Pink  117 Itziar de Rojas  26   27 Iván Galván-Femenia  51 Jan Cato Holter  34   49 Jan Egil Afset  83   118 Jan Heyckendorf  76   77   78 Jan Kässens  1 Jan Kristian Damås  119   120 Jan Rybniker  79   80   121 Janine Altmüller  122 Javier Ampuero  5   6   7   68   123 Javier Martín  124 Jeanette Erdmann  125   126   127 Jesus M Banales  6   128   129 Joan Ramon Badia  130 Joaquin Dopazo  7   131 Jochen Schneider  73 Jonas Bergan  132 Jordi Barretina  133 Jörn Walter  134 Jose Hernández Quero  21   135 Josune Goikoetxea  136 Juan Delgado  5   7   68   69   70 Juan M Guerrero  5   7   68 Julia Fazaal  59 Julia Kraft  35 Julia Schröder  59 Kari Risnes  120   137 Karina Banasik  2 Karl Erik Müller  138 Karoline I Gaede  139   140   141 Koldo Garcia-Etxebarria  6   128 Kristian Tonby  34   53 Lars Heggelund  138   142 Laura Izquierdo-Sanchez  6   128   143 Laura Rachele Bettini  44 Lauro Sumoy  133 Leif Erik Sander  35 Lena J Lippert  35 Leonardo Terranova  32 Lindokuhle Nkambule  144   145 Lisa Knopp  63 Lise Tuset Gustad  93   146 Lucia Garbarino  147 Luigi Santoro  32 Luis Téllez  6   60 Luisa Roade  6   25   57 Mahnoosh Ostadreza  32 Maider Intxausti  83 Manolis Kogevinas  69   148   149   150 Mar Riveiro-Barciela  6   25   56 Marc M Berger  151 Marco Schaefer  152 Mari E K Niemi  46 María A Gutiérrez-Stampa  153 Maria Carrabba  154 Maria E Figuera Basso  1 Maria Grazia Valsecchi  155 María Hernandez-Tejero  67 Maria J G T Vehreschild  156 Maria Manunta  32 Marialbert Acosta-Herrera  124 Mariella D'Angiò  44 Marina Baldini  32 Marina Cazzaniga  157 Marit M Grimsrud  34   158   159 Markus Cornberg  160 Markus M Nöthen  59 Marta Marquié  26   27 Massimo Castoldi  54 Mattia Cordioli  46 Maurizio Cecconi  29   30 Mauro D'Amato  129   161   162 Max Augustin  79   80   81 Melissa Tomasi  32 Mercè Boada  26   27 Michael Dreher  163 Michael J Seilmaier  164 Michael Joannidis  165 Michael Wittig  1 Michela Mazzocco  147 Michele Ciccarelli  82 Miguel Rodríguez-Gandía  6   60 Monica Bocciolone  82 Monica Miozzo  31   154 Natale Imaz Ayo  91 Natalia Blay  51 Natalia Chueca  22 Nicola Montano  31   32 Nicole Braun  1   166 Nicole Ludwig  167 Nikolaus Marx  168 Nilda Martínez  169 Norwegian SARS-CoV-2 Study groupOliver A Cornely  40   79   81   170 Oliver Witzke  171 Orazio Palmieri  172 Pa Study GroupPaola Faverio  111   173 Paoletta Preatoni  82 Paolo Bonfanti  111   174 Paolo Omodei  82 Paolo Tentorio  29 Pedro Castro  67 Pedro M Rodrigues  6   128   129   143 Pedro Pablo España  90 Per Hoffmann  59 Philip Rosenstiel  1 Philipp Schommers  79   80   81 Phillip Suwalski  35 Raúl de Pablo  20 Ricard Ferrer  175 Robert Bals  73 Roberta Gualtierotti  31   32 Rocío Gallego-Durán  5   6   7 Rosa Nieto  57   58 Rossana Carpani  32 Rubén Morilla  5   7   68   69   70 Salvatore Badalamenti  29 Sammra Haider  176 Sandra Ciesek  177   178 Sandra May  1 Sara Bombace  29   30 Sara Marsal  24 Sara Pigazzini  46 Sebastian Klein  165 Serena Pelusi  31   32 Sibylle Wilfling  109   179   180 Silvano Bosari  31   32 Sonja Volland  181 Søren Brunak  2 Soumya Raychaudhuri  9   10   11   12   13   182 Stefan Schreiber  1   48 Stefanie Heilmann-Heimbach  59 Stefano Aliberti  29   30 Stephan Ripke  35 Susanne Dudman  34   49 Tanja Wesse  1 Tenghao Zheng  183 STORM Study group, The Humanitas Task Force, The Humanitas Gavazzeni Task ForceThomas Bahmer  48 Thomas Eggermann  116 Thomas Illig  181 Thorsten Brenner  151 Tomas Pumarola  184   185 Torsten Feldt  63 Trine Folseraas  34   158   159   186 Trinidad Gonzalez Cejudo  187 Ulf Landmesser  188 Ulrike Protzer  189   190 Ute Hehr  179 Valeria Rimoldi  30 Valter Monzani  32 Vegard Skogen  191   192 Verena Keitel  63 Verena Kopfnagel  181 Vicente Friaza  5   7   68   69   70 Victor Andrade  38   39 Victor Moreno  69   193   194   195 Wolfgang Albrecht  1 Wolfgang Peter  152   196 Wolfgang Poller  35 Xavier Farre  51 Xiaoli Yi  1 Xiaomin Wang  35 Yascha Khodamoradi  156 Zehra Karadeniz  35 Anna Latiano  172 Siegfried Goerg  197 Petra Bacher  1   198 Philipp Koehler  40   79   80 Florian Tran  1   48 Heinz Zoller  61   62 Eva C Schulte  189   199   200 Bettina Heidecker  35 Kerstin U Ludwig  59 Javier Fernández  67   201 Manuel Romero-Gómez  5   6   7   68   123 Agustín Albillos  6   60 Pietro Invernizzi  36   37 Maria Buti  6   25   56 Stefano Duga  29   30 Luis Bujanda  6   128 Johannes R Hov  34   158   159   186 Tobias L Lenz  14   15 Rosanna Asselta  29   30 Rafael de Cid  51 Luca Valenti  31   32 Tom H Karlsen  34   158   159   186 Mario Cáceres  4   202 Andre Franke  1   203
Affiliations
Meta-Analysis

Detailed stratified GWAS analysis for severe COVID-19 in four European populations

Frauke Degenhardt et al. Hum Mol Genet. .

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 12 488 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.

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Figures

Figure 1
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.
Figure 2
Figure 2
Association of the 17q21.31 locus with severe COVID-19 with respiratory failure. (A) Regional association plot showing the variant most strongly associated with severe COVID-19 (rs1819040, purple diamond) a ~ 0.9 Mb inversion polymorphism at 17q21.21 (56) (white line with blue rectangles representing the variable segmental duplication (SD) blocks at the breakpoints), and the large credible set obtained by statistical fine-mapping including 2178 SNPs in high LD (median [LD] = 0.97) with the inversion (Supplementary Material, Table S7). Pairwise LD values (r2) with lead variant rs1819040 were calculated from merged Italian, Spanish, German/Austrian and Norwegian GWAS main anlysis datasets. The dotted line indicates the genome-wide significance threshold (P = 5 × 10−8). Below, organization of the 17q21.31 inversion genomic region, with the extended haplotypes associated with each orientation (H1 and H2) shown as red and blue arrows, respectively, and breakpoint SDs as dark rectangles. Protein-coding genes for which the inversion is a lead eQTL in at least one GTEx tissue are shown as pointed rectangles indicating the direction of transcription. (B) Forest plot and extended meta-analysis of our first discovery analysis and the COVID-19 HGI release 5 analysis B2 dataset (Material and Methods) of the association between severe COVID-19 and the 17q21.31 inversion based on the presence relative to the absence of the inversion haplotype H2. We visualized the ORs and their 95% confidence invervals (CIs) across all analysed cohorts of the main analysis and the COVID-19 HGI release 5 analysis B2 data. In this analysis, the overlap between the main analysis cohort and the COVID-19 HGI data was excluded from the main analysis cohort. The OR value is displayed in addition as a numerical value. The size of the dots indicates the size of the respective cohort (N). (C) Phenome-wide association study (PheWAS) results for the 17q21.31 inversion allele H2 showing only potentially COVID-19-related phenotypes from the GWAS Catalog (P = 10−7) grouped by disease categories using different colors. The effect direction of known SNP-trait associations from the corresponding GWAS is shown using triangles pointing upward (increase) and downward (decrease), whereas dots represent unknown effect direction. Phenotypes shown were selected according to previously reported COVID-19 links with lung damage, blood cell alterations and exacerbated immune response, as well as some potential co-morbidities. The whole list of phenotypic associations is included in Supplementary Material, Table S11.
Figure 3
Figure 3
Expression analysis of the most plausible candidate genes associated with the 17q.21.31 and 19q.13.33 loci in organ tissues and COVID-19 relevant cell types. (A) GTEx tissue-specific expression QTL (eQTL, upper panel) and splicing QTL (sQTL, middle panel) effects of the 17q21.31 and 19q.13.33 loci on selected candidate genes as well as expression of these genes in GTEx (20) tissues (lower panel). The direction of the normalized eQTL and sQTL effect size (NES) of the lead SNP rs1405655 and the inversion tagger rs62055540 in perfect LD with the inversion is represented by color intensities, and statistical significance by dot size. Black rectangles indicate genes for which the expression colocalizes (regional probability > 0.9) with GWAS loci in a given human tissue from the GTEx dataset. Heatmap displays gene-wise centered median by tissue expression values (represented by color intensities), showing in which tissues candidate genes are mostly enriched. (B) Expression levels of candidate genes in scRNA-seq datasets from healthy upper airways (nasal, bronchi) and lung (parenchyma) cells (47) and adult human brain cells from recently deceased, non-diseased donors (48). Figure displays log-normalized mean expression (represented by color) and fraction of cells expressing those genes (indicated by dot size). Processed and cell-type-annotated gene expression levels from studies were retrieved from COVID-19 Cell Atlas (49). (C) The figure shows differential expression of candidate genes in lung cells of COVID-19 patients compared with healthy controls. Log2 fold change (log2FC) values are presented as color gradient. Nominal P-values in −log10 scale are shown proportionally to dot size. Black-bordered circles indicate significantly differentially expressed genes after FDR correction. Results were obtained from pseudo-bulk differential expression analysis by Delorey et al. (21). More detailed figures are shown in Supplementary Material, Figures S11, S12 and S15.

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