Trans-ancestry analysis reveals genetic and nongenetic associations with COVID-19 susceptibility and severity

  1. Janie F. Shelton
  2. Anjali J. Shastri
  3. Chelsea Ye
  4. Catherine H. Weldon
  5. Teresa Filshtein-Sonmez
  6. Daniella Coker
  7. Antony Symons
  8. Jorge Esparza-Gordillo
  9. The 23andMe COVID-19 Team
  10. Adrian Chubb
  11. Alison Fitch
  12. Alison Kung
  13. Amanda Altman
  14. Andy Kill
  15. Jason Tan
  16. Jeff Pollard
  17. Jey McCreight
  18. Jess Bielenberg
  19. John Matthews
  20. Johnny Lee
  21. Lindsey Tran
  22. Michelle Agee
  23. Monica Royce
  24. Nate Tang
  25. Pooja Gandhi
  26. Raffaello d’Amore
  27. Ruth Tennen
  28. Scott Dvorak
  29. Scott Hadly
  30. Sungmin Park
  31. Taylor Morrow
  32. Trung Le
  33. Yiwen Zheng
  34. Stella Aslibekyan
  35. Adam Auton

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Abstract

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  1. Version published to 10.1038/s41588-021-00854-7
  2. ScreenIT

    SciScore for 10.1101/2020.09.04.20188318: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: All individuals included in the analyses provided informed consent and answered surveys online according to our human subjects research protocol, which was reviewed and approved by Ethical and Independent Review Services, a private institutional review board
    IRB: All individuals included in the analyses provided informed consent and answered surveys online according to our human subjects research protocol, which was reviewed and approved by Ethical and Independent Review Services, a private institutional review board
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableFor the nonpseudoautosomal region of the X chromosome, males and females were phased together in segments, treating the males as already phased; the pseudoautosomal regions were phased separately.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Participant genotype data were imputed using the Haplotype Reference Consortium (HRC) panel11, augmented by the Phase 3 1000 Genomes Project panel12 for variants not present in HRC.
    1000 Genomes Project
    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: We detected the following sentences addressing limitations in the study:
    However, there are notable caveats to relying on self-reported data for a disease with lethal outcomes. Namely, the cases identified in this study were healthy enough to respond to the survey therefore are likely biased towards a healthier case population than otherwise exists. In addition, 23andMe research participants are a self-selected group and may not reflect the general population. Furthermore, the scarcity of testing has likely further obscured the true picture of SARS-CoV-2 infections in the United States, leading to misclassification of true cases as controls in this study if they did not receive a positive test result. The effect of these types of error would bias the reported effect estimates towards the null, meaning that the true impact of risk factors reported here may be expected to be larger if the sample were randomly drawn from the broader population and had perfect case and control classification.

    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 10.1101/2020.09.04.20188318v1 on medRxiv