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. In order to reduce the impact on human life, it is critical to rapidly identify which genetic variants result in increased virulence or transmission. To address the former, we evaluated if a genome-based predictive algorithm designed to predict clinical severity could predict polymerase chain reaction (PCR) results, as a surrogate for viral load and severity. 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” (predicted severity score > 0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the “mild” category (severity prediction < 0.5) had an average Ct of 20.4 ( P = 0.0017). We found a non-trivial correlation between predicted severity probability and cycle threshold ( r = −0.199). Additionally, when divided into quartiles by prediction severity probability, the most probable quartile (≥75% probability) had a Ct of 16.6 (n=10) as compared to those least probable to be severe (<25%) 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 the metrics from the clinical diagnostic test, and that relative severity may be inferred from diagnostic values.

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    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

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  2. Version published to 10.1101/2021.11.22.21266688v1 on medRxiv