A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program

  1. Anurag Verma
  2. Noah L. Tsao
  3. Lauren O. Thomann
  4. Yuk-Lam Ho
  5. Sudha K. Iyengar
  6. Shiuh-Wen Luoh
  7. Rotonya Carr
  8. Dana C. Crawford
  9. Jimmy T. Efird
  10. Jennifer E. Huffman
  11. Adriana Hung
  12. Kerry L. Ivey
  13. Michael G. Levin
  14. Julie Lynch
  15. Pradeep Natarajan
  16. Saiju Pyarajan
  17. Alexander G. Bick
  18. Lauren Costa
  19. Giulio Genovese
  20. Richard Hauger
  21. Ravi Madduri
  22. Gita A. Pathak
  23. Renato Polimanti
  24. Benjamin Voight
  25. Marijana Vujkovic
  26. Seyedeh Maryam Zekavat
  27. Hongyu Zhao
  28. Marylyn D. Ritchie
  29. VA Million Veteran Program COVID-19 Science Initiative
  30. Kyong-Mi Chang
  31. Kelly Cho
  32. Juan P. Casas
  33. Philip S. Tsao
  34. J. Michael Gaziano
  35. Christopher O’Donnell
  36. Scott M. Damrauer
  37. Katherine P. Liao

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Abstract

The study aims to determine the shared genetic architecture between COVID-19 severity with existing medical conditions using electronic health record (EHR) data. We conducted a Phenome-Wide Association Study (PheWAS) of genetic variants associated with critical illness (n = 35) or hospitalization (n = 42) due to severe COVID-19 using genome-wide association summary data from the Host Genetics Initiative. PheWAS analysis was performed using genotype-phenotype data from the Veterans Affairs Million Veteran Program (MVP). Phenotypes were defined by International Classification of Diseases (ICD) codes mapped to clinically relevant groups using published PheWAS methods. Among 658,582 Veterans, variants associated with severe COVID-19 were tested for association across 1,559 phenotypes. Variants at the ABO locus (rs495828, rs505922) associated with the largest number of phenotypes (n rs495828 = 53 and n rs505922 = 59); strongest association with venous embolism, odds ratio (OR rs495828 1.33 (p = 1.32 x 10 −199 ), and thrombosis OR rs505922 1.33, p = 2.2 x10 -265 . Among 67 respiratory conditions tested, 11 had significant associations including MUC5B locus (rs35705950) with increased risk of idiopathic fibrosing alveolitis OR 2.83, p = 4.12 × 10 −191 ; CRHR1 ( rs61667602) associated with reduced risk of pulmonary fibrosis, OR 0.84, p = 2.26× 10 −12 . The TYK2 locus (rs11085727) associated with reduced risk for autoimmune conditions, e.g., psoriasis OR 0.88, p = 6.48 x10 -23 , lupus OR 0.84, p = 3.97 x 10 −06 . PheWAS stratified by ancestry demonstrated differences in genotype-phenotype associations. LMNA (rs581342) associated with neutropenia OR 1.29 p = 4.1 x 10 −13 among Veterans of African and Hispanic ancestry but not European. Overall, we observed a shared genetic architecture between COVID-19 severity and conditions related to underlying risk factors for severe and poor COVID-19 outcomes. Differing associations between genotype-phenotype across ancestries may inform heterogenous outcomes observed with COVID-19. Divergent associations between risk for severe COVID-19 with autoimmune inflammatory conditions both respiratory and non-respiratory highlights the shared pathways and fine balance of immune host response and autoimmunity and caution required when considering treatment targets.

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  1. Version published to 10.1371/journal.pgen.1010113
  2. Version published to 10.1101/2021.05.18.21257396v3 on medRxiv
  3. Version published to 10.1101/2021.05.18.21257396v2 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2021.05.18.21257396: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The quality control steps and genotyping imputation using 1000 Genomes cosmopolitan reference panel on the MVP cohort has been described previously.
    1000 Genomes
    suggested: (1000 Genomes Project and AWS, RRID:SCR_008801)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  5. Version published to 10.1101/2021.05.18.21257396v1 on medRxiv