Rare loss-of-function variants in type I IFN immunity genes are not associated with severe COVID-19

  1. Gundula Povysil
  2. Guillaume Butler-Laporte
  3. Ning Shang
  4. Chen Wang
  5. Atlas Khan
  6. Manal Alaamery
  7. Tomoko Nakanishi
  8. Sirui Zhou
  9. Vincenzo Forgetta
  10. Robert J.M. Eveleigh
  11. Mathieu Bourgey
  12. Naveed Aziz
  13. Steven J.M. Jones
  14. Bartha Knoppers
  15. Stephen W. Scherer
  16. Lisa J. Strug
  17. Pierre Lepage
  18. Jiannis Ragoussis
  19. Guillaume Bourque
  20. Jahad Alghamdi
  21. Nora Aljawini
  22. Nour Albes
  23. Hani M. Al-Afghani
  24. Bader Alghamdi
  25. Mansour S. Almutairi
  26. Ebrahim Sabri Mahmoud
  27. Leen Abu-Safieh
  28. Hadeel El Bardisy
  29. Fawz S. Al Harthi
  30. Abdulraheem Alshareef
  31. Bandar Ali Suliman
  32. Saleh A. Alqahtani
  33. Abdulaziz Almalik
  34. May M. Alrashed
  35. Salam Massadeh
  36. Vincent Mooser
  37. Mark Lathrop
  38. Mohamed Fawzy
  39. Yaseen M. Arabi
  40. Hamdi Mbarek
  41. Chadi Saad
  42. Wadha Al-Muftah
  43. Junghyun Jung
  44. Serghei Mangul
  45. Radja Badji
  46. Asma Al Thani
  47. Said I. Ismail
  48. Ali G. Gharavi
  49. Malak S. Abedalthagafi
  50. J. Brent Richards
  51. David B. Goldstein
  52. Krzysztof Kiryluk

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Abstract

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  1. Version published to 10.1172/jci147834
  2. ScreenIT

    SciScore for 10.1101/2020.12.18.20248226: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was conducted in accordance with the ethical principles of the National Bioethical committee at KACST and approved by the Institutional Review Board Committee at King Abdullah International Medical Research Centre, Ministry of National Guard-Health Affairs, Riyadh, Ministry of Health, and King Fahad Medical City.
    Consent: The Institutional Review Boards of all participating hospitals also approved the study and all patients provided written informed consent.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableThis cohort was composed of 288 males and 192 females; the average age was 67 (range 2-101); 45% of the participants were predicted to be Hispanic/Latino, 40% African, 6% Middle Eastern, 6% European, 2% East Asian, 0.6% South Asian, and 0.2 % Admixed (see below for details about ancestry determination).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    In brief, reads were aligned to human reference GRCh37 using DRAGEN (Edico Genome, San Diego, CA, USA)5 and duplicates were marked with Picard (Broad Institute, Boston, MA, USA).
    Picard
    suggested: (Picard, RRID:SCR_006525)
    Finally, variants were annotated with ClinEff8 and the IGM’s in-house ATAV9 (https://github.com/igm-team/atav) was used to add custom annotations including gnomAD v2.1 frequencies10 and clinical annotations provided by the Human Gene Mutation Database (HGMD)11, ClinVar12,13, and Online Mendelian Inheritance in Man (OMIM).
    gnomAD
    suggested: (Genome Aggregation Database, RRID:SCR_014964)
    Human Gene Mutation Database
    suggested: (Human Gene Mutation Database, RRID:SCR_001621)
    Online Mendelian Inheritance in Man
    suggested: None
    For this study, we used results from patients with available WGS data and who were recruited at the Jewish General Hospital (JGH) in Montreal.
    WGS
    suggested: None
    Sequencing results were analyzed using the McGill Genome Center bioinformatics pipelines23, in accordance with Genome Analysis Toolkit Best Practices (GATK) recommendations7.
    Genome Analysis Toolkit
    suggested: None
    Variant quality control was performed using the variantRecalibrator and applyVQSR functions from GATK.
    GATK
    suggested: (GATK, RRID:SCR_001876)
    Saudi Arabian COVID-19 Cohort: The Saudi Human Genome Program (SHGP) aims to sequence the genomes of the Saudi patients with COVID-19 confirmed through SARS-CoV-2 PCR as part of COVID19 KACST response (https://covid19.kacst.edu.sa/en.html).
    Human Genome Program
    suggested: None
    Variant files were annotated using the VEP version 100 and referencing the GRCh37/hg19 build within dbSNP, the NCBI database of genetic variation.
    dbSNP
    suggested: (dbSNP, RRID:SCR_002338)
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    Qatar Genome Program COVID-19 Cohort: The Qatar Genome Program (QGP) is a population-based project launched by the Qatar Foundation to generate a large whole genome sequence (WGS) dataset, in combination with comprehensive phenotypic information collected by the Qatar Biobank.
    Qatar Genome Program
    suggested: None
    Quality control of Fastq files was performed using FastQC (v0.11.2).
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    The secondary analyses (read mapping and joint variant calling) were performed using Sentieon’s DNASeq pipeline25 v201808.03 (Sentieon, San Jose, CA, USA), following the GATK 3.8 best practices6,7.
    DNASeq
    suggested: None

    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.

    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 10.1101/2020.12.18.20248226v2 on medRxiv
  4. Version published to 10.1101/2020.12.18.20248226v1 on medRxiv