Athanasios Kousathanas, Erola Pairo-Castineira, Konrad Rawlik, Alex Stuckey, Christopher A Odhams, Susan Walker, Clark D Russell, Tomas Malinauskas, Yang Wu, Jonathan Millar, Xia Shen, Katherine S Elliott, Fiona Griffiths, Wilna Oosthuyzen, Kirstie Morrice, Sean Keating, Bo Wang, Daniel Rhodes, Lucija Klaric, Marie Zechner, Nick Parkinson, Afshan Siddiq, Peter Goddard, Sally Donovan , David Maslove, Alistair Nichol, Malcolm G Semple, Tala Zainy, Fiona Maleady-Crowe, Linda Todd, Shahla Salehi, Julian Knight, Greg Elgar, Georgia Chan, Prabhu Arumugam, Christine Patch, Augusto Rendon, David Bentley, Clare Kingsley, Jack A Kosmicki , Julie E Horowitz , Aris Baras, Goncalo R Abecasis , Manuel A R Ferreira, Anne Justice, Tooraj Mirshahi, Matthew Oetjens, Daniel J Rader, Marylyn D Ritchie, Anurag Verma, Tom A Fowler , Manu Shankar-Hari, Charlotte Summers, Charles Hinds, Peter Horby, Lowell Ling, Danny McAuley, Hugh Montgomery, Peter J M Openshaw, Paul Elliott, Timothy Walsh , Albert Tenesa; GenOMICC investigators; 23andMe investigators; COVID-19 Human Genetics Initiative; Angie Fawkes, Lee Murphy, Kathy Rowan,  Chris P Ponting, Veronique Vitart, James F Wilson, Jian Yang, Andrew D Bretherick, Richard H Scott, Sara Clohisey Hendry, Loukas Moutsianas, Andy Law, Mark J Caulfield, J Kenneth Baillie.

Nature; 2022 Jul;607(7917):97-103.  doi: 10.1038/s41586-022-04576-6. 

Abstract

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease.

Analysis workflow for GWAS and AVT analyses of this study.

The cohorts displayed in yellow and green in the top box were processed with Genomics England Pipeline 2.0 and Illumina NSV4, respectively (see Methods on WGS Alignment and variant calling for details on differences between pipelines). We used individuals that were processed with either pipeline for the GWAS analyses and individuals processed only with Genomics England Pipeline 2.0 for the AVT analyses. The definition of the cases and controls was the same for GWAS and AVT, cases were the COVID-19 severe individuals for both, and controls included individuals from the 100,000 Genomes Project (100,000 Genomes Project) and also COVID-19 positive individuals that were recruited for this study and experienced only mild symptoms (COVID-mild).

See https://pubmed.ncbi.nlm.nih.gov/35255492/

Figure 1: GWAS results for the EUR ancestry group, and multi-ancestry meta-analysis.

Manhattan plots are shown on the left and quantile–quantile (QQ) plots of observed versus expected P values on the right, with genomic inflation (λ) displayed for each analysis. Highlighted results in blue in the Manhattan plots indicate variants that are LD-clumped (r² = 0.1, P₂ = 0.01, EUR LD) with the lead variants at each locus. Gene name annotation indicates genes that are affected by the predicted worst consequence type of each lead variant (annotation by Variant Effect Predictor (VEP)). For the HLA locus, the gene that was identified by HLA allele analysis is annotated. The GWAS was performed using logistic regression and meta-analysed by the inverse variant method. The red dashed line shows the Bonferroni-corrected P value: P = 2.2 × 10⁻⁸.