A Monte Carlo Estimation of the Narrow-Sense Heritability of COVID-19 Infection and Severity from AncestryDNA Survey Data

  1. Amelia J. Averitt
  2. Deepika Sharma
  3. Michael Cantor

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Respiratory infectious diseases, such as COVID-19, demonstrate a host genetic component that contributes to interindividual differences of susceptibility and infection. At present, the relative effect of environmental and genetic factors of COVID-19 is unknown. This research presents a Monte Carlo (MC) estimation of the genetic narrow-sense heritability of COVID-19 infection and severity from AncestryDNA survey data. The results suggest a moderate genetic contribution to COVID-19 infection and a low genetic contribution for COVID-19 severity.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2022.05.23.22275364: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    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.
    • Thank you for including a protocol registration statement.

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

    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.

  2. Version published to 10.1101/2022.05.23.22275364v1 on medRxiv