Population genomics insights into the recent evolution of SARS-CoV-2

This article has been Reviewed by the following groups

Read the full article

Abstract

The current coronavirus disease 2019 (COVID-19) pandemic is caused by the SARS-CoV-2 virus and is still spreading rapidly worldwide. Full-genome-sequence computational analysis of the SARS-CoV-2 genome will allow us to understand the recent evolutionary events and adaptability mechanisms more accurately, as there is still neither effective therapeutic nor prophylactic strategy. In this study, we used population genetics analysis to infer the mutation rate and plausible recombination events that may have contributed to the evolution of the SARS-CoV-2 virus. Furthermore, we localized targets of recent and strong positive selection. The genomic regions that appear to be under positive selection are largely co-localized with regions in which recombination from non-human hosts appeared to have taken place in the past. Our results suggest that the pangolin coronavirus genome may have contributed to the SARS-CoV-2 genome by recombination with the bat coronavirus genome. However, we find evidence for additional recombination events that involve coronavirus genomes from other hosts, i.e., Hedgehog and Sparrow. Even though recombination events within human hosts cannot be directly assessed, due to the high similarity of SARS-CoV-2 genomes, we infer that recombinations may have recently occurred within human hosts using a linkage disequilibrium analysis. In addition, we employed an Approximate Bayesian Computation approach to estimate the parameters of a demographic scenario involving an exponential growth of the size of the SARS-CoV-2 populations that have infected European, Asian and Northern American cohorts, and we demonstrated that a rapid exponential growth in population size can support the observed polymorphism patterns in SARS-CoV-2 genomes.

Article activity feed

  1. SciScore for 10.1101/2020.04.21.054122: (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

    No key resources detected.


    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 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.
    • 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.