Abstract
Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2-4 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.
Original language | English |
---|---|
Pages (from-to) | 97-103 |
Number of pages | 7 |
Journal | NATURE |
Volume | 607 |
Issue number | 7917 |
Early online date | 7 Mar 2022 |
DOIs | |
Publication status | Published - 7 Jul 2022 |
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In: NATURE, Vol. 607, No. 7917, 07.07.2022, p. 97-103.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Whole-genome sequencing reveals host factors underlying critical COVID-19
AU - GenOMICC investigators
AU - Covid-19
AU - covid-19 host genetic initiative
AU - Kousathanas, Athanasios
AU - Pairo-Castineira, Erola
AU - Rawlik, Konrad
AU - Stuckey, Alex
AU - Odhams, Christopher A
AU - Walker, Susan
AU - Russell, Clark D
AU - Malinauskas, Tomas
AU - Wu, Yang
AU - Millar, Jonathan
AU - Shen, Xia
AU - Elliott, Katherine S
AU - Griffiths, Fiona
AU - Oosthuyzen, Wilna
AU - Morrice, Kirstie
AU - Keating, Sean
AU - Wang, Bo
AU - Rhodes, Daniel
AU - Klaric, Lucija
AU - Zechner, Marie
AU - Parkinson, Nick
AU - Siddiq, Afshan
AU - Goddard, Peter
AU - Donovan, Sally
AU - Maslove, David
AU - Nichol, Alistair
AU - Semple, Malcolm G
AU - Zainy, Tala
AU - Maleady-Crowe, Fiona
AU - Todd, Linda
AU - Salehi, Shahla
AU - Knight, Julian
AU - Elgar, Greg
AU - Chan, Georgia
AU - Arumugam, Prabhu
AU - Patch, Christine
AU - Rendon, Augusto
AU - Bentley, David
AU - Kingsley, Clare
AU - Kosmicki, Jack A
AU - Horowitz, Julie E
AU - Baras, Aris
AU - Abecasis, Goncalo R
AU - Ferreira, Manuel A R
AU - Justice, Anne
AU - Mirshahi, Tooraj
AU - Oetjens, Matthew
AU - Rader, Daniel J
AU - Shankar-Hari, Manu
AU - van de Beek, Diederik
AU - GenOMICC co-investigators
AU - COVID-19 Human Genetics Initiative
AU - 23andMe investigators
AU - Nivard, Michel
AU - de Geus, Eco
AU - Bartels, Meike
AU - Jan Hottenga, Jouke
N1 - Funding Information: We thank the patients and their loved ones who volunteered to contribute to this study at one of the most difficult times in their lives, and the research staff in every intensive care unit who recruited patients at personal risk under challenging conditions. GenOMICC was funded by the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council (MRC), UKRI, Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (J.K.B., 223164/Z/21/Z) a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. WGS was performed by Illumina at Illumina Laboratory Services and was overseen by Genomics England. We would like to thank all at Genomics England who have contributed to the sequencing, clinical and genomic data analysis. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref. MC_PC_20029). A.D.B. would like to acknowledge funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z) and the Edinburgh Clinical Academic Track (ECAT) programme. We thank the research participants and employees of 23andMe for making this work possible. Genomics England and the 100,000 Genomes Project were funded by the National Institute for Health Research, the Wellcome Trust, the MRC, Cancer Research UK, the DHSC and NHS England. We are grateful for the support from S. Hill and the team in NHS England and the 13 Genomic Medicine Centres that delivered the 100,000 Genomes Project, which provided most of the control genome sequences for this study. We thank the participants in the 100,000 Genomes Project, who made this study possible, and the Genomics England Participant Panel for their strategic advice, involvement and engagement. We acknowledge NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre, who provided life-course longitudinal clinical data on the participants. This work forms part of the portfolio of research of the National Institute for Health Research Barts Biomedical Research Centre. Mark Caulfield is an NIHR Senior Investigator. This study owes a great deal to the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. Additional replication was conducted using the UK Biobank Resource (project 26041). The Penn Medicine BioBank is funded by a gift from the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health under CTSA award number UL1TR001878; and the Perelman School of Medicine at the University of Pennsylvania. We thank the AncestryDNA customers who voluntarily contributed information in the COVID-19 survey. HRS (dbGaP accession: phs000428.v1.p1): HRS was supported by the National Institute on Aging (NIA U01AG009740). The genotyping was funded separately by the National Institute on Aging (RC2 AG036495, RC4 AG039029). Genotyping was conducted by the NIH Center for Inherited Disease Research (CIDR) at Johns Hopkins University. Genotyping quality control and final preparation of the data were performed by the Genetics Coordinating Center at the University of Washington. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by the NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on 22 August 2021 (GTEx Analysis Release v.8 (dbGaP Accession phs000424.v8.p2). We thank the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available at https://www.covid19hg.org/acknowledgements . The views expressed are those of the authors and not necessarily those of the DHSC, NHS, Department for International Development (DID), NIHR, MRC, Wellcome Trust or Public Health England. Funding Information: We thank the patients and their loved ones who volunteered to contribute to this study at one of the most difficult times in their lives, and the research staff in every intensive care unit who recruited patients at personal risk under challenging conditions. GenOMICC was funded by the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council (MRC), UKRI, Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (J.K.B., 223164/Z/21/Z) a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. WGS was performed by Illumina at Illumina Laboratory Services and was overseen by Genomics England. We would like to thank all at Genomics England who have contributed to the sequencing, clinical and genomic data analysis. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref. MC_PC_20029). A.D.B. would like to acknowledge funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z) and the Edinburgh Clinical Academic Track (ECAT) programme. We thank the research participants and employees of 23andMe for making this work possible. Genomics England and the 100,000 Genomes Project were funded by the National Institute for Health Research, the Wellcome Trust, the MRC, Cancer Research UK, the DHSC and NHS England. We are grateful for the support from S. Hill and the team in NHS England and the 13 Genomic Medicine Centres that delivered the 100,000 Genomes Project, which provided most of the control genome sequences for this study. We thank the participants in the 100,000 Genomes Project, who made this study possible, and the Genomics England Participant Panel for their strategic advice, involvement and engagement. We acknowledge NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre, who provided life-course longitudinal clinical data on the participants. This work forms part of the portfolio of research of the National Institute for Health Research Barts Biomedical Research Centre. Mark Caulfield is an NIHR Senior Investigator. This study owes a great deal to the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. Additional replication was conducted using the UK Biobank Resource (project 26041). The Penn Medicine BioBank is funded by a gift from the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health under CTSA award number UL1TR001878; and the Perelman School of Medicine at the University of Pennsylvania. We thank the AncestryDNA customers who voluntarily contributed information in the COVID-19 survey. HRS (dbGaP accession: phs000428.v1.p1): HRS was supported by the National Institute on Aging (NIA U01AG009740). The genotyping was funded separately by the National Institute on Aging (RC2 AG036495, RC4 AG039029). Genotyping was conducted by the NIH Center for Inherited Disease Research (CIDR) at Johns Hopkins University. Genotyping quality control and final preparation of the data were performed by the Genetics Coordinating Center at the University of Washington. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by the NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on 22 August 2021 (GTEx Analysis Release v.8 (dbGaP Accession phs000424.v8.p2). We thank the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available at https://www.covid19hg.org/acknowledgements. The views expressed are those of the authors and not necessarily those of the DHSC, NHS, Department for International Development (DID), NIHR, MRC, Wellcome Trust or Public Health England. Publisher Copyright: © 2022, The Author(s).
PY - 2022/7/7
Y1 - 2022/7/7
N2 - Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2-4 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.
AB - Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2-4 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85134361378&partnerID=8YFLogxK
U2 - https://doi.org/10.1038/s41586-022-04576-6
DO - https://doi.org/10.1038/s41586-022-04576-6
M3 - Article
C2 - 35255492
SN - 0028-0836
VL - 607
SP - 97
EP - 103
JO - NATURE
JF - NATURE
IS - 7917
ER -