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

  1. Liam Gaziano
  2. Claudia Giambartolomei
  3. Alexandre C. Pereira
  4. Anna Gaulton
  5. Daniel C. Posner
  6. Sonja A. Swanson
  7. Yuk-Lam Ho
  8. Sudha K. Iyengar
  9. Nicole M. Kosik
  10. Marijana Vujkovic
  11. David R. Gagnon
  12. A. Patrícia Bento
  13. Inigo Barrio-Hernandez
  14. Lars Rönnblom
  15. Niklas Hagberg
  16. Christian Lundtoft
  17. Claudia Langenberg
  18. Maik Pietzner
  19. Dennis Valentine
  20. Stefano Gustincich
  21. Gian Gaetano Tartaglia
  22. Elias Allara
  23. Praveen Surendran
  24. Stephen Burgess
  25. Jing Hua Zhao
  26. James E. Peters
  27. Bram P. Prins
  28. Emanuele Di Angelantonio
  29. Poornima Devineni
  30. Yunling Shi
  31. Kristine E. Lynch
  32. Scott L. DuVall
  33. Helene Garcon
  34. Lauren O. Thomann
  35. Jin J. Zhou
  36. Bryan R. Gorman
  37. Jennifer E. Huffman
  38. Christopher J. O’Donnell
  39. Philip S. Tsao
  40. Jean C. Beckham
  41. Saiju Pyarajan
  42. Sumitra Muralidhar
  43. Grant D. Huang
  44. Rachel Ramoni
  45. Pedro Beltrao
  46. John Danesh
  47. Adriana M. Hung
  48. Kyong-Mi Chang
  49. Yan V. Sun
  50. Jacob Joseph
  51. Andrew R. Leach
  52. Todd L. Edwards
  53. Kelly Cho
  54. J. Michael Gaziano
  55. Adam S. Butterworth
  56. Juan P. Casas
  57. VA Million Veteran Program COVID-19 Science Initiative

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1038/s41591-021-01310-z
  2. ScreenIT

    SciScore for 10.1101/2020.11.19.20234120: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: The MVP received ethical and study protocol approval by the Veterans Affairs Central Institutional Review Board and informed consent was obtained for all participants.
    Consent: The MVP received ethical and study protocol approval by the Veterans Affairs Central Institutional Review Board and informed consent was obtained for all participants.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Identification of actionable druggable genes suitable for repurposing against COVID-19: Information about drugs and clinical candidates, and their therapeutic targets, was obtained from the ChEMBL database (release 2651, Supplementary Methods).
    ChEMBL
    suggested: (ChEMBL, RRID:SCR_014042)
    Selection of proposed instruments: Estimates for COVID-19 hospitalization: To generate outcome summary-statistics, we meta-analyzed results from the Million Veteran Program (MVP), an ongoing, prospective cohort recruiting from 63 Veterans Health Administration (VA) medical facilities
    Million Veteran Program
    suggested: None
    In total, HGI accumulated 6,492 cases of COVID-19 hospitalization through collaboration from 16 contributing studies (Supplementary Table 1), which were asked to define cases as “hospitalized laboratory confirmed SARS-CoV-2 infection (RNA and/or serology based), hospitalization due to corona-related symptoms” versus population controls (https://docs.google.com/document/d/1okamrqYmJfa35ClLvCt_vEe4PkvrTwggHq7T3jbeyCI/view) and use a model that adjusts for age, age2, sex, age*sex, PCs, and study specific covariates (https://docs.google.com/document/d/16ethjgi4MzlQeO0KAW_yDYyUHdB9kKbtfuGW4XYVKQg/view) Results for each ancestry-stratum were meta-analyzed along with the HGI (summary statistics already meta-analyzed from contributing studies) for COVID hospitalization using METAL software56 with inverse-variance weighting and fixed effects.
    METAL
    suggested: (METAL, RRID:SCR_002013)
    Alleles were tested against Olink proteins using SNPTEST v2.5.2 and adjusted for age, sex, plate, time from blood draw to processing, season and the first 5 principal components.
    SNPTEST
    suggested: (SNPTEST, RRID:SCR_009406)
    For enrichment analysis we have used the corpus from WikiPathways, Gene Ontology and Reactome.
    WikiPathways
    suggested: (WikiPathways, RRID:SCR_002134)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our analysis also has limitations. Though we make use of instrumental variants from multiple data sources, none cover the entire actionable druggable genome, were ancestry-specific or were derived from COVID-19 patients. However, we managed to recover credible biological targets from our analysis that were consistent across ancestral groups. Identifying the most relevant tissue or cell-type can be challenging for interpreting MR analyses of gene expression. In our case, a relevant tissue could be: one invaded by SARS-CoV-2, an organ associated with clinical complications of COVID-19, a tissue where the COVID-19-relevant protein is produced, or a tissue that would be the likely site of action for the target drug. We opted to use a data-driven strategy that incorporates all tissues available in GTEx V8. For IFNAR2, we recovered fibroblasts (the main cell type responsible for IFN-beta production), esophageal mucosa46,47 (a tissue invaded by SARS-CoV-2), and skeletal muscle48 (associated with the neurological manifestations of COVID-19). For ACE2, we recovered brain tissue, an organ known to be invaded by SARS-CoV-2 and associated with clinical manifestations.49,50 In conclusion, our trans-ancestry MR analysis covering all actionable druggable genes identified two drug repurposing opportunities (type-I IFNs and hsrACE2) as interventions that need to be evaluated in adequately powered randomized trials to investigate their efficacy and safety for early management of COVID-19.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.11.19.20234120v1 on medRxiv