SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load

  1. Martin Skarzynski
  2. Erin M. McAuley
  3. Ezekiel J. Maier
  4. Anthony C. Fries
  5. Jameson D. Voss
  6. Richard R. Chapleau

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Abstract

The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically derived PCR-based viral load of 716 viral genomes. For those samples predicted to be “severe” (probability of severe illness >0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the “mild” category (severity probability <0.5) had an average Ct of 20.4 ( P = 0.0017 ). We also found a nontrivial correlation between predicted severity probability and cycle threshold (r = −0.199). Finally, when divided into severity probability quartiles, the group most likely to experience severe illness (≥75% probability) had a Ct of 16.6 (n = 10), whereas the group least likely to experience severe illness (<25% probability) had a Ct of 21.4 (n = 350) ( P = 0.0045 ). Taken together, our results suggest that the severity predicted by a genome-based algorithm can be related to clinical diagnostic tests and that relative severity may be inferred from diagnostic values.

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  1. Version published to 10.1155/2022/6499217
  2. ScreenIT

    SciScore for 10.1101/2021.11.22.21266688: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This study was approved as a portion of the study FWR20190037N, reviewed and approved by the Air Force Research Laboratory’s Institutional Review Board.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    This algorithm was used to generate a predicted severity score from 0 to 1 in the Python environment.
    Python
    suggested: (IPython, RRID:SCR_001658)
    The genomic sequences were first downloaded and aligned to the Wuhan reference strain (NCBI: NC_045512.2; GISAID: EPI_ISL_402125) using MiniMap2 (version 2.17) [Li, 2018].
    MiniMap2
    suggested: (Minimap2, RRID:SCR_018550)
    Sequences were aligned using MAFFT [Katoh 2002] and variants were called using SNP-sites [Page 2016].
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    We used GraphPad Prism 7.0c for performing the t tests and Pearson’s correlation.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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

    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 10.1101/2021.11.22.21266688v1 on medRxiv