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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, ; 23andMe Investiga

2021-09-02 intensive care and critical care medicine
10.1101/2021.09.02.21262965 medRxiv
Show abstract

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 care1 or hospitalisation2;3;4 following SARS-Co-V2 infection. The GenOMICC (Genetics of Mortality in Critical Care) study recruits critically-ill cases and compares their genomes 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 identify 15 new independent associations with critical COVID-19, including variants within genes involved in interferon signalling (IL10RB, PLSCR1), leucocyte differentiation (BCL11A), and blood type antigen 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 critical 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.

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