Emerging phylogenetic structure of the SARS-CoV-2 pandemic

  1. Nicholas M Fountain-Jones
  2. Raima Carol Appaw
  3. Scott Carver
  4. Xavier Didelot
  5. Erik Volz
  6. Michael Charleston

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Abstract

Since spilling over into humans, SARS-CoV-2 has rapidly spread across the globe, accumulating significant genetic diversity. The structure of this genetic diversity and whether it reveals epidemiological insights are fundamental questions for understanding the evolutionary trajectory of this virus. Here, we use a recently developed phylodynamic approach to uncover phylogenetic structures underlying the SARS-CoV-2 pandemic. We find support for three SARS-CoV-2 lineages co-circulating, each with significantly different demographic dynamics concordant with known epidemiological factors. For example, Lineage C emerged in Europe with a high growth rate in late February, just prior to the exponential increase in cases in several European countries. Non-synonymous mutations that characterize Lineage C occur in functionally important gene regions responsible for viral replication and cell entry. Even though Lineages A and B had distinct demographic patterns, they were much more difficult to distinguish. Continuous application of phylogenetic approaches to track the evolutionary epidemiology of SARS-CoV-2 lineages will be increasingly important to validate the efficacy of control efforts and monitor significant evolutionary events in the future.

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  1. Version published to 10.1093/ve/veaa082
  2. ScreenIT

    SciScore for 10.1101/2020.05.19.103846: (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

    Software and Algorithms
    SentencesResources
    We aligned these sequences with MAFFT 18 using the CIPRES 19 server and visually checked the results.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    We removed sequences from Washington State and China that likely had some sequence error as they were strong outliers in the TempEst analysis.
    TempEst
    suggested: (TempEst, RRID:SCR_017304)
    We employed the computationally intensive Bayesian methodology (BEAST version 1.10.4 25 with BEAGLE 26 computational enhancement) to validate our maximum likelihood MRCA estimates and to provide dating estimates for internal nodes of interest.
    BEAGLE
    suggested: (BEAGLE, RRID:SCR_001789)
    For the BEAST analysis, as there is strong evidence that the pandemic is growing, we assumed an exponential growth coalescent model.
    BEAST
    suggested: (BEAST, RRID:SCR_010228)

    Results from OddPub: Thank you for sharing your code.

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

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  3. Version published to 10.1101/2020.05.19.103846v1 on bioRxiv