Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness

  1. Fritz Obermeyer
  2. Martin Jankowiak
  3. Nikolaos Barkas
  4. Stephen F. Schaffner
  5. Jesse D. Pyle
  6. Leonid Yurkovetskiy
  7. Matteo Bosso
  8. Daniel J. Park
  9. Mehrtash Babadi
  10. Bronwyn L. MacInnis
  11. Jeremy Luban
  12. Pardis C. Sabeti
  13. Jacob E. Lemieux

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Abstract

Repeated emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with increased fitness underscores the value of rapid detection and characterization of new lineages. We have developed PyR 0 , a hierarchical Bayesian multinomial logistic regression model that infers relative prevalence of all viral lineages across geographic regions, detects lineages increasing in prevalence, and identifies mutations relevant to fitness. Applying PyR 0 to all publicly available SARS-CoV-2 genomes, we identify numerous substitutions that increase fitness, including previously identified spike mutations and many nonspike mutations within the nucleocapsid and nonstructural proteins. PyR 0 forecasts growth of new lineages from their mutational profile, ranks the fitness of lineages as new sequences become available, and prioritizes mutations of biological and public health concern for functional characterization.

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  1. Version published to 10.1126/science.abm1208
  2. Version published to 10.1101/2021.09.07.21263228v2 on medRxiv
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    SciScore for 10.1101/2021.09.07.21263228: (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
    When all new lineages have been generated and all nodes assigned a lineage, a global multinomial logistic regression is performed, using the Python package sklearn.linear_model, yielding estimates for the relative growth rates of all lineages.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and 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: Please consider improving the rainbow (“jet”) colormap(s) used on page 26. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

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  4. Version published to 10.1101/2021.09.07.21263228v1 on medRxiv