Expanded COVID-19 phenotype definitions reveal distinct patterns of genetic association and protective effects

  1. Genevieve H. L. Roberts
  2. Raghavendran Partha
  3. Brooke Rhead
  4. Spencer C. Knight
  5. Danny S. Park
  6. Marie V. Coignet
  7. Miao Zhang
  8. Nathan Berkowitz
  9. David A. Turrisini
  10. Michael Gaddis
  11. Shannon R. McCurdy
  12. Milos Pavlovic
  13. Luong Ruiz
  14. Chodon Sass
  15. AncestryDNA Science Team
  16. Asher K. Haug Baltzell
  17. Harendra Guturu
  18. Ahna R. Girshick
  19. Catherine A. Ball
  20. Eurie L. Hong
  21. Kristin A. Rand

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Abstract

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  1. Version published to 10.1038/s41588-022-01042-x
  2. ScreenIT

    SciScore for 10.1101/2021.01.24.21250324: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Ethics statement: All data for this research project were from subjects who provided prior informed consent to participate in AncestryDNA’s Human Diversity Project, as reviewed and approved by our external institutional review board, Advarra (formerly Quorum).
    IRB: Ethics statement: All data for this research project were from subjects who provided prior informed consent to participate in AncestryDNA’s Human Diversity Project, as reviewed and approved by our external institutional review board, Advarra (formerly Quorum).
    RandomizationFor all close relative pairs, one individual was randomly selected for exclusion from our study.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableThe same association procedure that was used for the discovery study was applied for replication cohorts, except sample sizes for these cohorts were smaller (Supplementary Table 2), and thus a single GWAS was conducted for males and females together with genetic sex included as a covariate.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Calculation of principal components (PCs): For each population described above, genetic PCs were calculated to include in the association studies to control residual population structure and were computed using FlashPCA 2.0.14 Input genotypes were linkage disequilibrium (LD)-pruned using PLINK 1.9 command --indep-pairwise 100 5 0.2 -- maf 0.05 --geno 0.001. Imputation: Samples were imputed to the Haplotype Reference Consortium (HRC) reference panel version 1.1, which consists of 27,165 total individuals and 36 million variants.
    PLINK
    suggested: (PLINK, RRID:SCR_001757)
    We determined best-guess haplotypes with Eagle15 version 2.4.1 and performed imputation with Minimac4 version 1.0.1.
    Eagle15
    suggested: None
    Minimac4
    suggested: None
    Trans-Ancestry Meta-Analysis: For each phenotype, we additionally performed a trans-ancestry meta-analysis of the discovery EUR cohort, AA, and LAT summary statistics, again using fixed-effect inverse-variance weighting implemented in METAL.
    METAL
    suggested: (METAL, RRID:SCR_002013)
    From the resulting matrix of −log10(P-values), we generated a heatmap with R package pheatmap, and used hierarchical clustering to order the phenotype rows and the SNP columns in an unsupervised fashion.
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)

    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.

    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 10.1101/2021.01.24.21250324v1 on medRxiv