Mapping genome variation of SARS-CoV-2 worldwide highlights the impact of COVID-19 super-spreaders

  1. Alberto Gómez-Carballa
  2. Xabier Bello
  3. Jacobo Pardo-Seco
  4. Federico Martinón-Torres
  5. Antonio Salas

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Abstract

The human pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the major pandemic of the twenty-first century. We analyzed more than 4700 SARS-CoV-2 genomes and associated metadata retrieved from public repositories. SARS-CoV-2 sequences have a high sequence identity (>99.9%), which drops to >96% when compared to bat coronavirus genome. We built a mutation-annotated reference SARS-CoV-2 phylogeny with two main macro-haplogroups, A and B, both of Asian origin, and more than 160 sub-branches representing virus strains of variable geographical origins worldwide, revealing a rather uniform mutation occurrence along branches that could have implications for diagnostics and the design of future vaccines. Identification of the root of SARS-CoV-2 genomes is not without problems, owing to conflicting interpretations derived from either using the bat coronavirus genomes as an outgroup or relying on the sampling chronology of the SARS-CoV-2 genomes and TMRCA estimates; however, the overall scenario favors haplogroup A as the ancestral node. Phylogenetic analysis indicates a TMRCA for SARS-CoV-2 genomes dating to November 12, 2019, thus matching epidemiological records. Sub-haplogroup A2 most likely originated in Europe from an Asian ancestor and gave rise to subclade A2a, which represents the major non-Asian outbreak, especially in Africa and Europe. Multiple founder effect episodes, most likely associated with super-spreader hosts, might explain COVID-19 pandemic to a large extent.

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  1. Version published to 10.1101/gr.266221.120
  2. Version published to 10.1101/2020.05.19.097410v3 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.05.19.097410: (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
    Alignment of SARS-CoV-2 genomes against the reference sequence was carried out using MUSCLE v3.8.31 program (Edgar 2004).
    MUSCLE
    suggested: (MUSCLE, RRID:SCR_011812)
    Interspecific alignment was carried out using MAFFT program (Katoh et al. 2002) with default parameters.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    For this, we used the function cmdscale (library stats) from the statistical software R Project for Statistical Computing v. 3.3.1 (https://www.r-project.org/; (Team 2012)).
    R Project for Statistical
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)
    The best maximum likelihood tree was visualized and edited using FigTree v.
    FigTree
    suggested: (FigTree, RRID:SCR_008515)
    The demography of SARS-CoV-2 sequences was inferred using the Extended Bayesian Skyline Plot method (EBSP) (Heled and Drummond 2008) implemented in BEAST v2.6.2 (Drummond and Rambaut 2007).
    BEAST
    suggested: (BEAST, RRID:SCR_010228)

    Results from OddPub: Thank you for sharing your data.

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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  4. Version published to 10.1101/2020.05.19.097410v2 on bioRxiv
  5. Version published to 10.1101/2020.05.19.097410v1 on bioRxiv