Whole genome sequencing identifies multiple loci for critical illness caused by COVID-19

Kousathanas, A.; Pairo-Castineira, E.; Rawlik, K.; Stuckey, A.; Odhams, C. A.; Walker, S.; Russell, C. D.; Malinauskas, T.; Millar, J.; Elliott, K. S.; Griffiths, F.; Oosthuyzen, W.; Morrice, K.; Keating, S.; Wang, B.; Rhodes, D.; Klaric, L.; Zechner, M.; Parkinson, N.; Bretherick, A. D.; Siddiq, A.; Goddard, P.; Donovan, S.; Maslove, D.; Nichol, A.; Semple, M. G.; Zainy, T.; Maleady-Crowe, F.; Todd, L.; Salehi, S.; Knight, J.; Elgar, G.; Chan, G.; Arumugam, P.; Fowler, T. A.; Rendon, A.; Shankar-Hari, M.; Summers, C.; Elliott, P.; Yang, J.; Wu, Y.; GenOMICC Investigators,; Investiga, 23andMe

Critical illness in COVID-19 is caused by inflammatory lung injury, mediated by the host immune system. We and others have shown that host genetic variation influences the development of illness requiring critical care or hospitalisation following SARS-Co-V2 infection. The GenOMICC (Genetics of Mortality in Critical Care) study is designed to compare genetic variants in critically-ill cases with population controls in order to find underlying disease mechanisms. Here, we use whole genome sequencing and statistical fine mapping in 7,491 critically-ill cases compared with 48,400 population controls to discover and replicate 22 independent variants that significantly predispose to life-threatening COVID-19. We identified 15 new independent associations with severe COVID-19, including variants within genes involved in interferon signalling (IL10RB, PLSCR1), leucocyte differentiation (BCL11A), and blood type secretor status (FUT2). Using transcriptome-wide association and colocalisation to infer the effect of gene expression on disease severity, we find evidence implicating expression of multiple genes, including reduced expression of a membrane flippase (ATP11A), and increased mucin expression (MUC1), in severe disease. We show that comparison between critically-ill cases and population controls is highly efficient for genetic association analysis and enables detection of therapeutically-relevant mechanisms of disease. Therapeutic predictions arising from these findings require testing in clinical trials.