Global Geographic and Temporal Analysis of SARS-CoV-2 Haplotypes Normalized by COVID-19 Cases during the Pandemic

  1. Santiago Justo Arevalo
  2. Daniela Zapata Sifuentes
  3. Cesar Huallpa Robles
  4. Gianfranco Landa Bianchi
  5. Adriana Castillo Chavez
  6. Romina Garavito-Salini Casas
  7. Guillermo Uceda-Campos
  8. Roberto Pineda Chavarria

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Abstract

Since the identification of SARS-CoV-2, a large number of genomes have been sequenced with unprecedented speed around the world. This marks a unique opportunity to analyze virus spreading and evolution in a worldwide context. Currently, there is not a useful haplotype description to help to track important and globally scattered mutations. Also, differences in the number of sequenced genomes between countries and/or months make it difficult to identify the emergence of haplotypes in regions where few genomes are sequenced but a large number of cases are reported. We propose an approach based on the normalization by COVID-19 cases of relative frequencies of mutations using all the available data to identify major haplotypes. Furthermore, we can use a similar normalization approach to tracking the temporal and geographic distribution of haplotypes in the world. Using 171 461 genomes, we identify five major haplotypes (OTUs) based on nine high-frequency mutations. OTU_3 characterized by mutations R203K and G204R is currently the most frequent haplotype circulating in four of the six continents analyzed. On the other hand, during almost all months analyzed, OTU_5 characterized by the mutation T85I in nsp2 is the most frequent in North America. Recently (since September), OTU_2 has been established as the most frequent in Europe. OTU_1, the ancestor haplotype is near to extinction showed by its low number of isolations since May. Also, we analyzed whether age, gender, or patient status is more related to a specific OTU. We did not find OTU’s preference for any age group, gender, or patient status. Finally, we discuss structural and functional hypotheses in the most frequently identified mutations, none of those mutations show a clear effect on the transmissibility or pathogenicity.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.12.199414: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationAnalysis of OTUs geographical distribution: In this analysis, we randomly separate the genomes into 6 samples of 28 576 genomes each.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableFor the gender analysis, we selected sample populations with at least 250 genomes in the category to analyze and at least two times the total number of genomes (for example, USA – March has 2 079 genomes from female patients and 9287 genomes with or without gender information).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Phylogenetic tree construction: Using an alignment of the 109 953 complete, high coverage genomes without ambiguities, we estimated a maximum likelihood tree with Fasttree v2.1.10 with the next parameters: -nt -gtr - gamma -sprlength 1000 -spr 10 -refresh 0.8 -topm 1.5 close 0.75 (Price et al. 2009, Price et al. 2010), after the generation of the tree we improved topology using -boot 1000 and the first output tree as an input using -intree option.
    Fasttree
    suggested: (FastTree, RRID:SCR_015501)
    The number of cases of each country was obtained from the European Centre for Disease Prevention and Control: https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide.
    Control
    suggested: None
    All plots were produced in R using “ggpubr” and ggplot2.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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.
    • 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 10.1101/2020.07.12.199414 on bioRxiv