The impact of super-spreaders in COVID-19: mapping genome variation worldwide

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

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

The human pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the major pandemic of the 21 st century. We analyzed >4,700 SARS-CoV-2 genomes and associated meta-data retrieved from public repositories. SARS-CoV-2 sequences have a high sequence identity (>99.9%), which drops to >96% when compared to bat coronavirus. We built a mutation-annotated reference SARS-CoV-2 phylogeny with two main macro-haplogroups, A and B, both of Asian origin, and >160 sub-branches representing virus strains of variable geographical origins worldwide, revealing a uniform mutation occurrence along branches that could complicate the design of future vaccines. The root of SARS-CoV-2 genomes locates at the Chinese haplogroup B1, with a TMRCA dating to 12 November 2019 - thus matching epidemiological records. Sub-haplogroup A2a originates in China and represents the major non-Asian outbreak. Multiple founder effect episodes, most likely associated with super-spreader hosts, explain COVID-19 pandemic to a large extent.

Article activity feed

  1. 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.

    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 10.1101/2020.05.19.097410 on bioRxiv