. 2021 Apr;27(4):668-676.

doi: 10.1038/s41591-021-01310-z. Epub 2021 Apr 9.

Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19

Liam Gaziano^{1

2}, Claudia Giambartolomei^{3

4}, Alexandre C Pereira^{5

6}, Anna Gaulton⁷, Daniel C Posner¹, Sonja A Swanson⁸, Yuk-Lam Ho¹, Sudha K Iyengar^{9

10}, Nicole M Kosik¹, Marijana Vujkovic^{11

12}, David R Gagnon^{1

13}, A Patrícia Bento⁷, Inigo Barrio-Hernandez¹⁴, Lars Rönnblom¹⁵, Niklas Hagberg¹⁵, Christian Lundtoft¹⁵, Claudia Langenberg^{16

17}, Maik Pietzner¹⁷, Dennis Valentine^{18

19}, Stefano Gustincich³, Gian Gaetano Tartaglia³, Elias Allara², Praveen Surendran^{2

20

21

22}, Stephen Burgess^{2

23}, Jing Hua Zhao², James E Peters^{21

24}, Bram P Prins^{2

21}, Emanuele Di Angelantonio^{2

20

21

25

26}, Poornima Devineni¹, Yunling Shi¹, Kristine E Lynch^{27

28}, Scott L DuVall^{27

28}, Helene Garcon¹, Lauren O Thomann¹, Jin J Zhou^{29

30}, Bryan R Gorman¹, Jennifer E Huffman³¹, Christopher J O'Donnell^{32

33}, Philip S Tsao^{34

35}, Jean C Beckham^{36

37}, Saiju Pyarajan¹, Sumitra Muralidhar³⁸, Grant D Huang³⁸, Rachel Ramoni³⁸, Pedro Beltrao¹⁴, John Danesh^{2

20

21

25

26}, Adriana M Hung^{39

40}, Kyong-Mi Chang^{12

41}, Yan V Sun^{42

43}, Jacob Joseph^{1

44}, Andrew R Leach⁷, Todd L Edwards^{45

46}, Kelly Cho^{1

47}, J Michael Gaziano^{1

47}, Adam S Butterworth^{48

49

50

51

52}, Juan P Casas^{53

54}; VA Million Veteran Program COVID-19 Science Initiative

Affiliations

¹ Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.
² BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
³ Central RNA Lab, Istituto Italiano di Tecnologia, Genoa, Italy.
⁴ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
⁵ Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, São Paulo, Brazil.
⁶ Genetics Department, Harvard Medical School, Harvard University, Boston, MA, USA.
⁷ Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
⁸ Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.
⁹ Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA.
¹⁰ Department of Population and Quantitative Health Sciences, Case Western Reserve University and Louis Stoke, Cleveland VA, Cleveland, OH, USA.
¹¹ The Corporal Michael J. Crescenz VA Medical Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
¹² Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
¹³ Biostatistics, School of Public Health, Boston University, Boston, MA, USA.
¹⁴ European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
¹⁵ Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
¹⁶ Berlin Institute of Health, Charité University Medicine Berlin, Berlin, Germany.
¹⁷ MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
¹⁸ Institute of Health Informatics, University College London, London, UK.
¹⁹ Health Data Research, University College London, London, UK.
²⁰ British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
²¹ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
²² Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
²³ MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
²⁴ Centre for Inflammatory Disease, Dept of Immunology and Inflammation, Imperial College, London, UK.
²⁵ National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
²⁶ National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK.
²⁷ VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
²⁸ Department of Internal Medicine, Epidemiology, University of Utah, Salt Lake City, UT, USA.
²⁹ Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA.
³⁰ Phoenix VA Health Care System, Phoenix, AZ, USA.
³¹ Center for Population Genomics, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.
³² Cardiology, VA Boston Healthcare System, Boston, MA, USA.
³³ Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
³⁴ Epidemiology Research and Information Center (ERIC), VA Palo Alto Health Care System, Palo Alto, CA, USA.
³⁵ Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
³⁶ MIRECC, Durham VA Medical Center, Durham, NC, USA.
³⁷ Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA.
³⁸ Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA.
³⁹ VA Tennessee Valley Healthcare System, Nashville, TN, USA.
⁴⁰ Nephrology & Hypertension, Vanderbilt University, Nashville, TN, USA.
⁴¹ The Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.
⁴² Atlanta VA Health Care System, Decatur, GA, USA.
⁴³ Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA.
⁴⁴ Medicine, Cardiovascular, VA Boston Healthcare System and Brigham & Women's Hospital, Boston, MA, USA.
⁴⁵ Department of Veterans Affairs, Tennessee Valley Healthcare System, Vanderbilt University, Nashville, TN, USA.
⁴⁶ Medicine, Epidemiology, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
⁴⁷ Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁴⁸ BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. asb38@medschl.cam.ac.uk.
⁴⁹ British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK. asb38@medschl.cam.ac.uk.
⁵⁰ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK. asb38@medschl.cam.ac.uk.
⁵¹ National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK. asb38@medschl.cam.ac.uk.
⁵² National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK. asb38@medschl.cam.ac.uk.
⁵³ Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA. jpcasasromero@bwh.harvard.edu.
⁵⁴ Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. jpcasasromero@bwh.harvard.edu.

PMID: 33837377
PMCID: PMC7612986
DOI: 10.1038/s41591-021-01310-z

Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19

Liam Gaziano et al. Nat Med. 2021 Apr.

. 2021 Apr;27(4):668-676.

doi: 10.1038/s41591-021-01310-z. Epub 2021 Apr 9.

Authors

Affiliations

¹ Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.
² BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
³ Central RNA Lab, Istituto Italiano di Tecnologia, Genoa, Italy.
⁴ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
⁵ Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, São Paulo, Brazil.
⁶ Genetics Department, Harvard Medical School, Harvard University, Boston, MA, USA.
⁷ Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
⁸ Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.
⁹ Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA.
¹⁰ Department of Population and Quantitative Health Sciences, Case Western Reserve University and Louis Stoke, Cleveland VA, Cleveland, OH, USA.
¹¹ The Corporal Michael J. Crescenz VA Medical Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
¹² Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
¹³ Biostatistics, School of Public Health, Boston University, Boston, MA, USA.
¹⁴ European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
¹⁵ Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
¹⁶ Berlin Institute of Health, Charité University Medicine Berlin, Berlin, Germany.
¹⁷ MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
¹⁸ Institute of Health Informatics, University College London, London, UK.
¹⁹ Health Data Research, University College London, London, UK.
²⁰ British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
²¹ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
²² Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
²³ MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
²⁴ Centre for Inflammatory Disease, Dept of Immunology and Inflammation, Imperial College, London, UK.
²⁵ National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
²⁶ National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK.
²⁷ VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
²⁸ Department of Internal Medicine, Epidemiology, University of Utah, Salt Lake City, UT, USA.
²⁹ Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA.
³⁰ Phoenix VA Health Care System, Phoenix, AZ, USA.
³¹ Center for Population Genomics, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.
³² Cardiology, VA Boston Healthcare System, Boston, MA, USA.
³³ Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
³⁴ Epidemiology Research and Information Center (ERIC), VA Palo Alto Health Care System, Palo Alto, CA, USA.
³⁵ Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
³⁶ MIRECC, Durham VA Medical Center, Durham, NC, USA.
³⁷ Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA.
³⁸ Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA.
³⁹ VA Tennessee Valley Healthcare System, Nashville, TN, USA.
⁴⁰ Nephrology & Hypertension, Vanderbilt University, Nashville, TN, USA.
⁴¹ The Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.
⁴² Atlanta VA Health Care System, Decatur, GA, USA.
⁴³ Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA.
⁴⁴ Medicine, Cardiovascular, VA Boston Healthcare System and Brigham & Women's Hospital, Boston, MA, USA.
⁴⁵ Department of Veterans Affairs, Tennessee Valley Healthcare System, Vanderbilt University, Nashville, TN, USA.
⁴⁶ Medicine, Epidemiology, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
⁴⁷ Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁴⁸ BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. asb38@medschl.cam.ac.uk.
⁴⁹ British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK. asb38@medschl.cam.ac.uk.
⁵⁰ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK. asb38@medschl.cam.ac.uk.
⁵¹ National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK. asb38@medschl.cam.ac.uk.
⁵² National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK. asb38@medschl.cam.ac.uk.
⁵³ Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA. jpcasasromero@bwh.harvard.edu.
⁵⁴ Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. jpcasasromero@bwh.harvard.edu.

PMID: 33837377
PMCID: PMC7612986
DOI: 10.1038/s41591-021-01310-z

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.

PubMed Disclaimer

Conflict of interest statement

Competing interests

The authors declare no competing interests.

Figures

**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 (i.e. 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).

**Figure 1. Outline of the analyses performed.**
Using multiple data sources, this study tested *cis*-pQTL and *cis*-eQTL proposed instruments for actionable druggable proteins against COVID-19 hospitalization summary statistics meta-analyzed from the Host Genetics Initiative and the Million Veteran Program. Significant MR associations that also showed evidence for colocalization were investigated further with an independent platform (Olink), phenome-wide scans of relevant variants, and pathway enrichment.

**Figure 2. Manhattan plot of results from actionable druggable genome-wide Mendelian randomization analysis.**
Mendelian randomization estimates were calculated using inverse-variance weighting and fixed effects for instruments that contained more than one variant, and Wald-ratio for instruments with one variant. Blue solid line indicates the P value threshold for significance (P<3.96×10^-5, 0.05 Bonferroni-corrected for 1,263 actionable druggable genes) and red dashed line indicates a suggestive (P<5.00×10^-4) threshold. Genes are labeled by their most significant MR association. For example, the results for *IL10RB* is most significant with *cis*-eQTL proposed instruments derived in skeletal muscle tissue (P=2.31x10^-14), which is the point labeled. Results are plotted by the gene start position. All MR results with P value less than 5.00×10^-4 used the GTEx *cis*-eQTLs as proposed instruments.

**Figure 3. Genomic context, local association plot and LD structure of the *IFNAR2/IL10RB* region.**
a, Local association plot (N=1,377,758) of the interval defined by all unique eQTLs for *IL10RB* or *IFNAR2.* Color code represents the degree of linkage disequilibrium with the most associated marker in 1000G Europeans. b, Genomic context of the region. Coding genes are represented by the refseq transcript. Bars represent epigenome Roadmap layered H3K27 acetylation markers. Connecting lines represent significant Hi-C interactions. c, Set of rsIDs used as proposed instruments for Mendelian Randomization analysis. Color code represents instruments for *IL10RB* (blue circles), *IFNAR2* (green circles). Orange half-circles represent pQTLs for IL-10RB. d, Linkage disequilibrium structure and blocks defined using European populations from 1000G project.

**Figure 4. Regional association plots of the *IFNAR2-IL10RB* locus.**
Regional association plots for a, *IL10RB* gene expression in tibial nerve tissue from GTEx (N=532), b, COVID-19 hospitalization from HGI and MVP trans-ancestry meta-analysis (N=1,377,758) **c, d,** Vascular endothelial growth factor receptor 2 (VEGFR2) and tryptase gamma (TPSG1), respectively, measured by SomaLogic in 3,301 INTERVAL participants. All show the correlation (1000G European ancestry) for rs13050728, the *cis*-eQTL most associated with COVID-19 hospitalization in the *IL10RB-IFNAR2* region. All colocalize with each other (PP.H4>0.96 for all).

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References

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Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19

Affiliations

Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Medical