Genetic Diversity and Spatiotemporal Distribution of SARS-CoV-2 Alpha Variant in India

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Abstract

After the spill to humans, in the evolutionary timeline of SARS-CoV-2, several positively selected variants have emerged. A phylogeographic study on these variants can reveal their spatial and temporal distribution. In December 2020, the alpha variant of the severe acute respiratory syndrome coronavirus (SARS-CoV-2), which has been designated as a variant of concern (VOC) by the WHO, was discovered in the south-eastern United Kingdom (UK). Slowly, it expanded across India, with a considerable number of cases, particularly in North India. This study focuses on determining the prevalence and expansion of the Alpha variants in various parts of India mainly by using phylospatial analysis. The genetic diversity estimation has helped us to understand various evolutionary forces that have shaped the spatial distribution of this variant during its peak. Overall, our study paves the way to understanding the evolution and expansion of a virus variant, which may help to mitigate in the case of any future wave.

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  1. SciScore for 10.1101/2022.04.20.22274084: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Multiple Alignment using Fast Fourier Transform (MAFFT, 2020) was used to align genome sequences to reference sequences.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Frequency maps were generated by Datawrapper (2012), and haplotype diversity was derived from DnaSP (Julio Rozas et al., 2019) 1131 genomic sequences of the alpha variant were extracted on June 11/2021, with high coverage and aligned to the reference sequence (Wuhan/2019-EPI_ISL_402124).
    DnaSP
    suggested: (DnaSP, RRID:SCR_003067)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    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

    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.