Genomic variations in SARS-CoV-2 genomes from Gujarat: Underlying role of variants in disease epidemiology

  1. Madhvi Joshi
  2. Apurvasinh Puvar
  3. Dinesh Kumar
  4. Afzal Ansari
  5. Maharshi Pandya
  6. Janvi Raval
  7. Zarna Patel
  8. Pinal Trivedi
  9. Monika Gandhi
  10. Labdhi Pandya
  11. Komal Patel
  12. Nitin Savaliya
  13. Snehal Bagatharia
  14. Sachin Kumar
  15. Chaitanya Joshi

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Abstract

Humanity has seen numerous pandemics during its course of evolution. The list includes many such as measles, Ebola, SARS, MERS, etc. Latest edition to this pandemic list is COVID-19, caused by the novel coronavirus, SARS-CoV-2. As of 4th July 2020, COVID-19 has affected over 10 million people from 170+ countries, and 5,28,364 deaths. Genomic technologies have enabled us to understand the genomic constitution of the pathogens, their virulence, evolution, rate of mutations, etc. To date, more than 60,000 virus genomes have been deposited in the public depositories like GISAID and NCBI. While we are writing this, India is the 3rd most-affected country with COVID-19 with 0.6 million cases, and >18000 deaths. Gujarat is the fourth highest affected state with 5.44 percent death rate compared to national average of 2.8 percent.

Here, 361 SARS-CoV-2 genomes from across Gujarat have been sequenced and analyzed in order to understand its phylogenetic distribution and variants against global and national sequences. Further, variants were analyzed from diseased and recovered patients from Gujarat and the World to understand its role in pathogenesis. From missense mutations, found from Gujarat SARS-CoV-2 genomes, C28854T, deleterious mutation in nucleocapsid (N) gene was found to be significantly associated with mortality in patients. The other significant deleterious variant found in diseased patients from Gujarat and the world is G25563T, which is located in Orf3a and has a potential role in viral pathogenesis. SARS-CoV-2 genomes from Gujarat are forming distinct cluster under GH clade of GISAID.

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    SciScore for 10.1101/2020.07.10.197095: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Sample collection and processing: Nasopharyngeal and oropharyngeal swabs from a total of 277 individuals tested positive for COVID-19 from 38 locations representing 18 districts of Gujarat were collected after obtaining informed consent and appropriate ethics approval.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Raw data quality assessment and filtering: Quality of data was assessed using FASTQC v.
    FASTQC
    suggested: (FastQC, RRID:SCR_014583)
    The multiple sequence alignment was performed using MAFFT (Katoh and Standley 2013) implemented via a phylodynamic alignment pipeline provided by Augur (https://github.com/nextstrain/augur).
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    The selected metadata information is plotted in the time resolved phylogenetic tree was constructed using TreeTime (Sagulenko et al. 2018), annotated and visualized in the FigTree (Rambaut et al., 2018).
    FigTree
    suggested: (FigTree, RRID:SCR_008515)

    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 10.1101/2020.07.10.197095 on bioRxiv