The rise and spread of the SARS-CoV-2 AY.122 lineage in Russia

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Abstract

Delta has outcompeted most preexisting variants of SARS-CoV-2, becoming the globally predominant lineage by mid-2021. Its subsequent evolution has led to the emergence of multiple sublineages, most of which are well-mixed between countries. By contrast, here we show that nearly the entire Delta epidemic in Russia has probably descended from a single import event, or from multiple closely timed imports from a single poorly sampled geographic location. Indeed, over 90 per cent of Delta samples in Russia are characterized by the nsp2:K81N + ORF7a:P45L pair of mutations which is rare outside Russia, putting them in the AY.122 sublineage. The AY.122 lineage was frequent in Russia among Delta samples from the start, and has not increased in frequency in other countries where it has been observed, suggesting that its high prevalence in Russia has probably resulted from a random founder effect rather than a transmission advantage. The apartness of the genetic composition of the Delta epidemic in Russia makes Russia somewhat unusual, although not exceptional, among other countries.

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  1. SciScore for 10.1101/2021.12.02.21267168: (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
    UShER phylogenetic tree: We downloaded the public UShER mutation-annotated tree together with metadata on September 21, 2021 from the UCSC browser (http://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2/).
    UCSC browser
    suggested: None
    Branch lengths were corrected using mutation paths obtained by matUtils [20].
    matUtils
    suggested: None
    For each dataset, we built a maximum likelihood phylogenetic tree using the FastTreeDbl algorithm of FastTree 2.1.11 [21] with the GTR substitution model and gamma model for heterogeneity of evolutionary rates across sites.
    FastTree
    suggested: (FastTree, RRID:SCR_015501)
    The python script for finding imports is available on GitHub: https://github.com/GalkaKlink/Delta-lineage-in-Russia Estimation of the logistic growth rates: Logistic growth rates of the Delta lineage were estimated with the nls() function of the R language [22].
    python
    suggested: (IPython, RRID:SCR_001658)
    Estimation of the effective reproduction number: We used the skyline birth-death model (BDSKY) [24] with continuous sampling, or ψ-sampling, implemented in BEAST2 [25] to infer the dynamics of the effective reproduction number Re.
    BEAST2
    suggested: (BEAST2, RRID:SCR_017307)

    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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