A Bayesian approach to infer recombination patterns in coronaviruses

  1. Nicola F. Müller
  2. Kathryn E. Kistler
  3. Trevor Bedford

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Abstract

As shown during the SARS-CoV-2 pandemic, phylogenetic and phylodynamic methods are essential tools to study the spread and evolution of pathogens. One of the central assumptions of these methods is that the shared history of pathogens isolated from different hosts can be described by a branching phylogenetic tree. Recombination breaks this assumption. This makes it problematic to apply phylogenetic methods to study recombining pathogens, including, for example, coronaviruses. Here, we introduce a Markov chain Monte Carlo approach that allows inference of recombination networks from genetic sequence data under a template switching model of recombination. Using this method, we first show that recombination is extremely common in the evolutionary history of SARS-like coronaviruses. We then show how recombination rates across the genome of the human seasonal coronaviruses 229E, OC43 and NL63 vary with rates of adaptation. This suggests that recombination could be beneficial to fitness of human seasonal coronaviruses. Additionally, this work sets the stage for Bayesian phylogenetic tracking of the spread and evolution of SARS-CoV-2 in the future, even as recombinant viruses become prevalent.

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  1. Version published to 10.1038/s41467-022-31749-8
  2. Version published to 10.1101/2021.04.28.441806v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.04.28.441806: (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
    Recombination network summary: We implemented an algorithm to summarize distributions of recombination networks similar to the maximum clade credibility framework typically used to summarize trees in BEAST (Heled and Bouckaert, 2013).
    BEAST
    suggested: (BEAST, RRID:SCR_010228)
    All other plots are done in R using ggplot2 (Wickham, 2016).
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Sequence data: The genetic sequence data for OC43, NL63 and 229e were obtained from ViPR (http://www.viprbrc.org) as described in Kistler and Bedford (2021).
    ViPR
    suggested: (vipR, RRID:SCR_010685)

    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 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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  4. Version published to 10.1101/2021.04.28.441806v1 on bioRxiv