Abstract
Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls. We found significant Mendelian randomization results for three proteins (ACE2, P = 1.6 × 10−6; IFNAR2, P = 9.8 × 10−11 and IL-10RB, P = 2.3 × 10−14) using cis-expression quantitative trait loci genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared expression quantitative trait loci signal for IL10RB and IFNAR2, we conducted phenome-wide association scans and pathway enrichment analysis, which suggested that IFNAR2 is more likely to play a role in COVID-19 hospitalization. Our findings prioritize trials of drugs targeting IFNAR2 and ACE2 for early management of COVID-19.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
GTEx project v.8 data are available at https://gtexportal.org/home/. CheMBL database data are available at https://www.ebi.ac.uk/chembl/. Fenland-SomaLogic protein GWAS data are available at https://omicscience.org/apps/covidpgwas/. HGI COVID-19 hospitalization summary statistics are available at https://www.covid19hg.org/. PhenoScanner results are available at http://www.phenoscanner.medschl.cam.ac.uk/.
References
Horby, P. et al. Dexamethasone in hospitalized patients with COVID-19—preliminary report. N. Engl. J. Med. https://doi.org/10.1056/NEJMoa2021436 (2020).
Beigel, J. H. et al. Remdesivir for the treatment of COVID-19—final report. N. Engl. J. Med. https://doi.org/10.1056/NEJMoa2007764 (2020).
Kalil, A. C. et al. Baricitinib plus remdesivir for hospitalized adults with COVID-19. N. Engl. J. Med. https://doi.org/10.1056/NEJMoa2031994 (2020).
Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).
Cohen, J. C., Boerwinkle, E., Mosley, T. H. Jr. & Hobbs, H. H. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N. Engl. J. Med. 354, 1264–1272 (2006).
Lopalco, L. CCR5: from natural resistance to a new anti-HIV strategy. Viruses 2, 574–600 (2010).
Finan, C. et al. The druggable genome and support for target identification and validation in drug development. Sci. Transl. Med. https://doi.org/10.1126/scitranslmed.aag1166 (2017).
Swerdlow, D. I. et al. The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. Lancet 379, 1214–1224 (2012).
The COVID-19 Host Genetics Initiative The COVID-19 Host Genetics Initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic. Eur. J. Hum. Genet. 28, 715–718 (2020).
Gaziano, J. M. et al. Million veteran program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).
Labrecque, J. & Swanson, S. A. Understanding the assumptions underlying instrumental variable analyses: a brief review of falsification strategies and related tools. Curr. Epidemiol. Rep. 5, 214–220 (2018).
Lonsdale, J. & et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).
Pietzner, M. et al. Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat. Commun. 11, 6397 (2020).
Borden, E. C. et al. Interferons at age 50: past, current and future impact on biomedicine. Nat. Rev. Drug Disco. 6, 975–990 (2007).
Emilsson, V. et al. Co-regulatory networks of human serum proteins link genetics to disease. Science 361, 769–773 (2018).
Staley, J. R. et al. PhenoScanner: a database of human genotype–phenotype associations. Bioinformatics 32, 3207–3209 (2016).
von Marschall, Z. et al. Effects of interferon-α on vascular endothelial growth factor gene transcription and tumor angiogenesis. J. Natl Cancer Inst. 95, 437–448 (2003).
Jia, H. et al. Endothelial cell functions impaired by interferon in vitro: insights into the molecular mechanism of thrombotic microangiopathy associated with interferon therapy. Thromb. Res. 163, 105–116 (2018).
Casassus, P. et al. Treatment of adult systemic mastocytosis with interferon-α: results of a multicentre phase II trial on 20 patients. Br. J. Haematol. 119, 1090–1097 (2002).
Swanson, S. A., Tiemeier, H., Ikram, M. A. & Hernán, M. A. Nature as a trialist?: deconstructing the analogy between Mendelian randomization and randomized trials. Epidemiology 28, 653–659 (2017).
Nelson, C. P. et al. Genetic associations with plasma angiotensin converting enzyme 2 concentration: potential relevance to COVID-19 risk. Circulation 142, 1117–1119 (2020).
Wallace, C. Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLoS Genet. 16, e1008720 (2020).
Hemnes, A. R. et al. A potential therapeutic role for angiotensin-converting enzyme 2 in human pulmonary arterial hypertension. Eur. Respir. J. https://doi.org/10.1183/13993003.02638-2017 (2018).
Kuba, K., Imai, Y., Rao, S., Jiang, C. & Penninger, J. M. Lessons from SARS: control of acute lung failure by the SARS receptor ACE2. J. Mol. Med. 84, 814–820 (2006).
Monteil, V. et al. Inhibition of SARS-CoV-2 infections in engineered human tissues using clinical-grade soluble human ACE2. Cell 181, 905–913 (2020).
Monteil, V. et al. Human soluble ACE2 improves the effect of remdesivir in SARS-CoV-2 infection. EMBO Mol. Med. 13, e13426 (2020).
Zoufaly, A. et al. Human recombinant soluble ACE2 in severe COVID-19. Lancet Respir. Med. https://doi.org/10.1016/S2213-2600(20)30418-5 (2020).
Onabajo, O. O. et al. Interferons and viruses induce a novel truncated ACE2 isoform and not the full-length SARS-CoV-2 receptor. Nat. Genet. 52, 1283–1293 (2020).
Blanco-Melo, D. et al. Imbalanced host response to SARS-CoV-2 drives development of COVID-19. Cell 181, 1036–1045 (2020).
Chu, H. et al. Comparative replication and immune activation profiles of SARS-CoV-2 and SARS-CoV in human lungs: an ex vivo study with implications for the pathogenesis of COVID-19. Clin. Infect. Dis. 71, 1400–1409 (2020).
Hadjadj, J. et al. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science 369, 718–724 (2020).
Bastard, P. et al. Auto-antibodies against type I IFNs in patients with life-threatening COVID-19. Science https://doi.org/10.1126/science.abd4585 (2020).
Zhang, Q. et al. Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science https://doi.org/10.1126/science.abd4570 (2020).
van der Made, C. I. et al. Presence of genetic variants among young men with severe COVID-19. JAMA 324, 1–11 (2020).
Pairo-Castineira, E. et al. Genetic mechanisms of critical illness in COVID-19. Nature https://doi.org/10.1038/s41586-020-03065-y (2020).
Lokugamage, K. G. et al. Type I interferon susceptibility distinguishes SARS-CoV-2 from SARS-CoV. J. Virol. 94, e01410–e01420 (2020).
Mantlo, E., Bukreyeva, N., Maruyama, J., Paessler, S. & Huang, C. Antiviral activities of type I interferons to SARS-CoV-2 infection. Antivir. Res. 179, 104811 (2020).
Clementi, N. et al. Interferon-β-1a inhibition of severe acute respiratory syndrome-coronavirus 2 in vitro when administered after virus infection. J. Infect. Dis. 222, 722–725 (2020).
Dinnon, K. H. et al. A mouse-adapted model of SARS-CoV-2 to test COVID-19 countermeasures. Nature 586, 560–566 (2020).
Hung, I. F. et al. Triple combination of interferon-β-1b, lopinavir-ritonavir, and ribavirin in the treatment of patients admitted to hospital with COVID-19: an open-label, randomised, phase 2 trial. Lancet 395, 1695–1704 (2020).
WHO SOLIDARITY Trial Consortium. Repurposed antiviral drugs for COVID-19: interim WHO solidarity trial results. N. Engl. J. Med. https://doi.org/10.1056/NEJMoa2023184 (2021).
FDA News. NIAID stops COVID-19 trial enrollment over adverse events. https://www.fdanews.com/articles/199319-niaid-stops-covid-19-trial-enrollment-over-adverse-events (2020).
Liu, D. et al. Mendelian randomization analysis identified genes pleiotropically associated with the risk and prognosis of COVID-19. J. Infect. https://doi.org/10.1016/j.jinf.2020.11.031 (2021).
Xiao, F. et al. Evidence for gastrointestinal infection of SARS-CoV-2. Gastroenterology 158, 1831–1833 (2020).
Mao, L. et al. Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China. JAMA Neurol. 77, 683–690 (2020).
Song, E. et al. Neuroinvasion of SARS-CoV-2 in human and mouse brain. J. Exp. Med. https://doi.org/10.1084/jem.20202135 (2021).
Puelles, V. G. et al. Multiorgan and renal tropism of SARS-CoV-2. N. Engl. J. Med. 383, 590–592 (2020).
Mendez, D. et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 47, D930–D940 (2019).
Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).
Rohloff, J. C. et al. Nucleic acid ligands with protein-like side chains: modified aptamers and their use as diagnostic and therapeutic agents. Mol. Ther. Nucleic Acids 3, e201 (2014).
Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE 5, e15004 (2010).
Chapman, A. B. et al. A Natural Language Processing System for National COVID-19 Surveillance in the US Department of Veterans Affairs. https://www.aclweb.org/anthology/2020.nlpcovid19-acl.10 (Association for Computational Linguistics, 2020).
Hunter-Zinck, H. et al. Genotyping array design and data quality control in the million veteran program. Am. J. Hum. Genet 106, 535–548 (2020).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Giambartolomei, C. et al. A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics 34, 2538–2545 (2018).
Lundberg, M., Eriksson, A., Tran, B., Assarsson, E. & Fredriksson, S. Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood. Nucleic Acids Res. 39, e102 (2011).
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).
Wareham, N. Fenland Study (Version 1) Dataset. https://doi.org/10.22025/2017.10.101.00001 (MRC Epidemiology Unit, University of Cambridge, 2016).
Acknowledgements
We are grateful to the Host Genetic Initiative for making their data publicly available (full acknowledgements can be found at https://www.covid19hg.org/acknowledgements/). This research is based on data from the MVP, Office of Research and Development, VA and was supported by award no. MVP035. This research was also supported by additional Department of Veterans Affairs awards grant no. MVP001. This publication does not represent the views of the Department of Veteran Affairs or the US Government. Full acknowledgements for the VA MVP COVID-19 Science Initiative can be found in Supplementary Methods. C.G. has received funding from the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie grant agreement no. 754490, MINDED project. A.G., P.B. and A.R.L. are funded by the Member States of the European Molecular Biology Laboratory. I.B.-H. received funding from Open Targets (grant agreement OTAR-044). The Fenland study59 is funded by the Medical Research Council (MC_UU_12015/1); we are grateful to all the volunteers and to the General Practitioners and practice staff for assistance with recruitment; we thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams; we further acknowledge support for genomics from the Medical Research Council (MC_PC_13046); proteomic measurements were supported and governed by a collaboration agreement between the University of Cambridge and Somalogic. J.E.P. is supported by UK Research and Innovation Fellowship at Health Data Research UK (MR/S004068/2). L.R., N.H. and C.L. are supported by the Swedish Research Council. E.A. was supported by the EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart grant no. 116074 and by the British Heart Foundation Programme grant RG/18/13/33946. We thank A. Wood for feedback on statistical analyses used in the paper. We thank the INTERVAL Study investigators, co-ordination team and the epidemiology field, data and laboratory teams, who were supported by core funding from the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194; RG/18/13/33946), the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014) (the views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care) and the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation and Wellcome. J.D. holds a British Heart Foundation Professorship and an NIHR Senior Investigator Award.
Author information
Authors and Affiliations
Consortia
Contributions
J.P.C., A.S.B. and J.M.G. conceived the study design. A.G., A.P.B. and A.R.L. defined the actionable genome and identified and curated drug information relating to SARS-CoV-2; P.B. and I.B.-H. provided biological annotation relating to SARS-CoV-2; C.G. performed stepwise conditional analysis on GTEx raw data; D.P. tested associations for COVID-19 in MVP; L.G. and J.H.Z. performed meta-analysis of HGI and MVP; L.G. performed MR analysis; L.G. and C.G. performed colocalization analyses; L.G. and B.P.P. performed conditional analysis on Olink proteins; L.G. and E.A. performed phenome-wide scans; A.C.P. performed pathway enrichment analysis; J.N.D., A.S.B. and J.E.P. provided INTERVAL data; several authors were involved in the curation of MVP data; L.G., C.G., A.C.P., A.G., D.P., A.S.B. and J.P.C. wrote the manuscript. J.P.C. oversaw all analyses. All authors critically reviewed the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Peer review information Nature Medicine thanks David Evans, Steven Wolinsky and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Joao Monteiro was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Regional association plots for rs2266590 and rs2239573 for plasma IL-10RB, IL10RB gene expression, and COVID-19 hospitalization.
This region was investigated further using IL-10RB pQTL data because it was available on an independent proteomic platform (Olink) and results using eQTL instruments for IL10RB passed our Mendelian randomization P value threshold (0.05 Bonferroni-corrected for 1,263 actionable druggable genes) and colocalization threshold (PP.H4 > 0.8). a, rs2266590 as pQTL for plasma IL-10RB measured by Olink in 4,998 INTERVAL participants. b, rs2266590 as an eQTL for IL10RB expression tibial artery tissue (N = 584). c, rs2266590 in COVID-19 hospitalization. d, rs2239573 as pQTL for plasma IL-10RB (after adjusting for rs2266590) measured by Olink in 4,998 INTERVAL participants. e, rs2239573 as an eQTL for IL10RB expression in whole blood (N = 670). f, rs2239573 in COVID-19 hospitalization. a colocalizes with b (PP.H4 = 0.97), and d colocalizes with e (PP.H4 = 1.00). The two variants highlighted in this figure (rs2266590 and rs2239573), which are associated with gene expression and plasma protein levels of IL-10RB, are not associated with COVID-19 hospitalization (P = 0.85 for rs2266590, P = 0.66 for rs2239573).
Extended Data Fig. 2 Enrichment analysis of peak eQTLs for IFNAR2-IL10RB and ACE2 regions.
Results obtained from association analysis using all 49 tissues from GTEx V8 contrasted against variant genotypes in an additive model. Dotplot of over-representation analysis using all significant (p < 0.05) differentially expressed (DE) genes (476 for rs13050728; 1,397 for rs4830976) for a, rs13050728, peak eQTL in the IFNAR2-IL10RB region and b, rs4830976, peak eQTL in the ACE2 region. Count = number of DE genes part of the enriched pathway. Gene ratio is the rate of DE genes represented in each pathway.
Extended Data Fig. 3 Regional association plots for cis-variants associated with ACE2 gene expression or ACE2 plasma protein levels, and their association with COVID-19 hospitalization.
a, rs4830976 as an eQTL for ACE2 expression in brain frontal cortex tissue (N = 175). b, rs4830976 in COVID-19 hospitalization (N = 1,377,758). c, rs5935998 as the primary pQTL for plasma ACE2 measured by Oink in 4,998 INTERVAL participants d, rs5935998 in COVID-19 hospitalization e, rs4646156 as the secondary pQTL (that is after adjusting for rs5935998) for plasma ACE2 measured by Oink in 4,998 INTERVAL participants. f, rs4646156 in COVID-19 hospitalization. a colocalizes with b (PP.H4 = 0.95), but c does not colocalize with d (PP.H4 = 0.49) and e does not colocalize with f (PP.H4 = 0.08).
Supplementary information
Supplementary Information
Supplementary Figs. 1 and 2, Supplementary Methods and VA Million Veteran Program COVID-19 Science Initiative members and their affiliations.
Supplementary Tables
Supplementary Tables 1–16
Rights and permissions
About this article
Cite this article
Gaziano, L., Giambartolomei, C., Pereira, A.C. et al. Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19. Nat Med 27, 668–676 (2021). https://doi.org/10.1038/s41591-021-01310-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41591-021-01310-z
