Human genetic factors associated with pneumonia risk, a cue for COVID-19 susceptibility

  1. Debleena Guin
  2. Saroj Yadav
  3. Priyanka Singh
  4. Pooja Singh
  5. Sarita Thakran
  6. Samiksha Kukal
  7. Neha Kanojia
  8. Priyanka Rani Paul
  9. Bijay Pattnaik
  10. Viren Sardana
  11. Sandeep Grover
  12. Yasha Hasija
  13. Luciano Saso
  14. Anurag Agrawal
  15. Ritushree Kukreti

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Abstract

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  1. Version published to 10.1016/j.meegid.2022.105299
  2. ScreenIT

    SciScore for 10.1101/2021.06.03.21258106: (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
    Bibliographic databases like MEDLINE (PubMed), Web of Science, and Cochrane database of systematic reviews were searched for all articles published till January 13, 2020.
    MEDLINE
    suggested: (MEDLINE, RRID:SCR_002185)
    Cochrane
    suggested: (Cochrane Library, RRID:SCR_013000)
    Studies (1) any other type of lung inflammation apart from pneumonia or pneumonia as a consequence of any exposure or pneumonia existing with comorbid conditions, (2) no defined diagnostic criteria for pneumonia, and (3) genotypic data not in accordance with Hardy Weinberg equilibrium were excluded.
    Hardy
    suggested: (HARDY, RRID:SCR_009107)
    The population specific allele frequency data for rs2606345 was obtained from 1000genome browser [16] on March 08, 2021.
    1000genome browser
    suggested: None
    The R version 3.4.2 (R Project for Statistical Computing) was used for statistical analyses and spatial analysis plots.
    R Project for Statistical
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)

    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: We detected the following sentences addressing limitations in the study:
    As a caveat, induction of CYP1A1 would be expected to be protective. This interpretation could explain counterintuitive lower mortality in regions with higher ambient air pollution, since pollutants are known to be a powerful inducer of CYP1A1 gene expression [42]. The unexpected associations of smoking (induces CYP1A1 gene expression [43, 44] with COVID-19 severity [45] and death [46] could also be understood in this framework. It is clearly noted that both air pollution and smoking are extremely detrimental to health and overall increase respiratory as well as non-respiratory morbidity and mortality. To conclude, we find that CYP1A1 alleles are associated with CAP mortality, presumably via altered xenobiotic metabolism. We speculate that gene-environment interactions governing CYP1A1 expression may influence COVID-19 mortality. Towards this by-product of the main conclusion, we fully acknowledge there are several other factors like demographics (average age of population, population density, sex of a person, ethnic diversity, genetic variability), comorbidities (cardiovascular, cancer, and chronic respiratory diseases), socio-economic factors (GDP per capita, healthcare infrastructure), and political regime (Govt. isolation policies, social distancing, stringency index) that contribute to SARS-CoV-2 mortality and could be potential confounders. Further, the uncertainty of estimation of mortality, while the pandemic is still on, the limitations of the meta-analysis in itself...

    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.

  3. Version published to 10.1101/2021.06.03.21258106v1 on medRxiv