Genomic heterogeneity and clinical characterization of SARS-CoV-2 in Oregon

  1. Alexa K Dowdell
  2. Kevin Matlock
  3. Fred L. Robinson
  4. Roshanthi Weerasinghe
  5. Rogan Rattray
  6. Marina Pukay
  7. Melvin Lathara
  8. Anastacia Harlan
  9. Thomas R. Ward
  10. Mary Campbell
  11. Walter Urba
  12. Ganapati Srinivasa
  13. Carlo B. Bifulco
  14. Brian D. Piening

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Abstract

The first reported case of COVID-19 in the State of Oregon occurred in late February 2020, with subsequent outbreaks occurring in the populous Portland metro area but also with significant outbreaks in less-populous and rural areas. Here we report viral sequences from 188 patients across the hospitals and associated clinics in the Providence Health System in the State of Oregon dating back to the early days of the outbreak. We show a significant shift in dominant clade lineages over time in Oregon, with the rapid emergence and dominance of Spike D614G-positive variants. We also highlight significant diversity in SARS-CoV-2 sequences in Oregon, including a large number of rare mutations, indicative that these genomes could be utilized for outbreak tracing. Lastly, we show that SARS-CoV-2 genomic information may offer additional utility in combination with clinical covariates in the prediction of acute disease phenotypes.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.30.20160069: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Patients: All studies were conducted on discarded specimens and associated deidentified clinical information under IRB approval (study protocol 2020000127) with approved waiver of consent.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    : Raw sequencing reads were mapped against the reference sequence MN908947.3 using BWA-MEM v0.7.12-r1039 (Li and Durbin, 2009).
    BWA-MEM
    suggested: (Sniffles, RRID:SCR_017619)
    Mapping statistics were calculated via samtools flagstat (samtools v1.10) (Li et al., 2009).
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Coverage was assessed by generating a coverage plot for each sample illustrating percent coverage across the genome via deepTools plotCoverage v3.4.3 (Ramirez et al., 2014).
    deepTools
    suggested: (Deeptools, RRID:SCR_016366)
    Variant calling was performed using Freebayes v1.3.2-40-gcce27fc with standard filters and ploidy of 1.
    Freebayes
    suggested: (FreeBayes, RRID:SCR_010761)
    Remaining variants passing filter were annotated via SnpEff v4.3 (Cingolani et al., 2012).
    SnpEff
    suggested: (SnpEff, RRID:SCR_005191)
    SARS-CoV-2 genotype diversity was assessed by extracting variant calls from annotated VCFs using a custom Python script and generating plots using custom R scripts.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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.

  2. Version published to 10.1101/2020.07.30.20160069v1 on medRxiv