Analysis of Genetic Host Response Risk Factors in Severe COVID-19 Patients

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

BACKGROUND

Epidemiological studies indicate that as many as 20% of individuals who test positive for COVID-19 develop severe symptoms that can require hospitalization. These symptoms include low platelet count, severe hypoxia, increased inflammatory cytokines and reduced glomerular filtration rate. Additionally, severe COVID-19 is associated with several chronic co-morbidities, including cardiovascular disease, hypertension and type 2 diabetes mellitus.

The identification of genetic risk factors that impact differential host responses to SARS-CoV-2, resulting in the development of severe COVID-19, is important in gaining greater understanding into the biological mechanisms underpinning life-threatening responses to the virus. These insights could be used in the identification of high-risk individuals and for the development of treatment strategies for these patients.

METHODS

As of June 6, 2020, there were 976 patients who tested positive for COVID-19 and were hospitalized, indicating they had a severe response to SARS-CoV-2. To overcome the limited number of patients with a mild form of COVID-19, we used similar control criteria to our previous study looking at shared genetic risk factors between severe COVID-19 and sepsis, selecting controls who had not developed sepsis despite having maximum co-morbidity risk and exposure to sepsis-causing pathogens.

RESULTS

Using a combinatorial (high-order epistasis) analysis approach, we identified 68 protein-coding genes that were highly associated with severe COVID-19. At the time of analysis, nine of these genes have been linked to differential response to viral pathogens including SARS-CoV-2. We also found many novel targets that are involved in key biological pathways associated with the development of severe COVID-19, including production of pro-inflammatory cytokines, endothelial cell dysfunction, lipid droplets, neurodegeneration and viral susceptibility factors.

CONCLUSION

The variants we found in genes relating to immune response pathways and cytokine production cascades, were in equal proportions across all severe COVID-19 patients, regardless of their co-morbidities. This suggests that such variants are not associated with any specific co-morbidity, but are common amongst patients who develop severe COVID-19. This is consistent with being able to find and validate severe disease biomarker signatures when larger patient datasets become available.

Several of the genes identified relate to lipid programming, beta-catenin and protein kinase C signalling. These processes converge in a central pathway involved in plasma membrane repair, clotting and wound healing. This pathway is largely driven by Ca 2+ activation, which is a known serum biomarker associated with severe COVID-19 and ARDS. This suggests that aberrant calcium ion signalling may be responsible for driving severe COVID-19 responses in patients with variants in genes that regulate the expression and activity of this ion. We intend to perform further analyses to confirm this hypothesis.

Among the 68 severe COVID-19 risk-associated genes, we found several druggable protein targets and pathways. Nine are targeted by drugs that have reached at least Phase I clinical trials, and a further eight have active chemical starting points for novel drug development.

Several of these targets were particularly enriched in specific co-morbidities, providing insights into shared pathological mechanisms underlying both the development of severe COVID-19, ARDS and these predisposing co-morbidities. We can use these insights to identify patients who are at greatest risk of contracting severe COVID-19 and develop targeted therapeutic strategies for them, with the aim of improving disease burden and survival rates.

Article activity feed

  1. SciScore for 10.1101/2020.06.17.20134015: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationIn comparison, these co-morbidities were found to be less prevalent in the mild cases and an age and gender-matched random selection of the UK Biobank population (Figure 1).
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableAfter quality control and removal of samples with missing data, we assigned as cases 779 patients (442 males, 337 females) who had been hospitalized with severe COVID-19.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    We performed a SNP-based blood group analysis of the UK Biobank cohort by determining the blood groups (A, B, O and AB) of the cohort based on allele combinations of three SNPs (rs8176747, rs8176746, rs8176719) in the ABO gene9.
    AB
    suggested: None

    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: 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.