The Genetic Risk for COVID-19 Severity Is Associated With Defective Immune Responses

  1. Yunus Kuijpers
  2. Xiaojing Chu
  3. Martin Jaeger
  4. Simone J. C. F. M. Moorlag
  5. Valerie A. C. M. Koeken
  6. Bowen Zhang
  7. Aline de Nooijer
  8. Inge Grondman
  9. Manoj Kumar Gupta
  10. Nico Janssen
  11. Vera P. Mourits
  12. L. Charlotte J. de Bree
  13. Quirijn de Mast
  14. Frank L. van de Veerdonk
  15. Leo A. B. Joosten
  16. Yang Li
  17. Mihai G. Netea
  18. Cheng-Jian Xu

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Abstract

Recent genome-wide association studies (GWASs) of COVID-19 patients of European ancestry have identified genetic loci significantly associated with disease severity. Here, we employed the detailed clinical, immunological and multi-omics dataset of the Human Functional Genomics Project (HFGP) to explore the physiological significance of the host genetic variants that influence susceptibility to severe COVID-19. A genomics investigation intersected with functional characterization of individuals with high genetic risk for severe COVID-19 susceptibility identified several major patterns: i. a large impact of genetically determined innate immune responses in COVID-19, with ii. increased susceptibility for severe disease in individuals with defective cytokine production; iii. genetic susceptibility related to ABO blood groups is probably mediated through the von Willebrand factor (VWF) and endothelial dysfunction. We further validated these identified associations at transcript and protein levels by using independent disease cohorts. These insights allow a physiological understanding of genetic susceptibility to severe COVID-19, and indicate pathways that could be targeted for prevention and therapy.

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  1. Version published to 10.3389/fimmu.2022.859387
  2. Version published to 10.1101/2020.11.10.20229203v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.11.10.20229203: (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
    The cohorts were phased using Eagle v2.4 with the European population of HRC 1.1 2016 hg 2019 reference panel.
    Eagle
    suggested: (Eagle, RRID:SCR_017262)
    All quality control steps were performed using Plink v1.9.
    Plink
    suggested: (PLINK, RRID:SCR_001757)
    Gene-sets were obtained from MsigDB, WikiPathways, and GWAS-catalog reported gene-sets.
    WikiPathways
    suggested: (WikiPathways, RRID:SCR_002134)
    Roadmap epigenetic state enrichment: Based on the Roadmap 15-core epigenetic state database(20), we used data obtained from 23 blood samples spanning 127 epigenomes to map the QTLs in the summary statistics to their respective epigenetic states.
    Roadmap
    suggested: (Roadmap, RRID:SCR_017207)
    Visualization: R package ggplot2 was used to perform bar charts, box plots and scatter plots.
    Visualization
    suggested: (LONI Visualization Tool, RRID:SCR_000765)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    We used R package pheatmap to generate heat maps.
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)

    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:
    While our study sheds further light on how COVID-19 genetic risk affects the human immune system, there are several limitations of this study: firstly, due to different sets of stimuli used in measuring cytokine production to stimulations in the two healthy cohorts, we are not able to replicate all our findings of genetic associations with cytokine responses from the 500FG cohort in the 300BCG cohort. Secondly, young adults (< 30 years) are overrepresented in both healthy cohorts (500FG and 300BCG), which may lead to a biased conclusion which cannot be generalized to the whole population, especially since the severe COVID-19 cases often occur in the elderly population. Thirdly, 500FG and 300BCG cohorts are designed to understand the genetic regulation of immune function in healthy individuals. Therefore, a COVID-19 patients’ cohort will be needed for better characterization of disease mechanism, which will be our future research goal. Collectively, our data demonstrate that genetic variability explains an important component of the increased susceptibility to severe COVID-19. The genetic risk for severe COVID-19 is associated with defective innate immune responses (low cytokine production), dysregulated endothelial function, and is strongly influenced by polymorphisms in sex chromosomes. These findings may contribute to the development of novel treatment and prevention strategies for severe COVID-19.

    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

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  4. Version published to 10.1101/2020.11.10.20229203v1 on medRxiv