Ancestral origins are associated with SARS-CoV-2 susceptibility and protection in a Florida patient population

  1. Yiran Shen
  2. Bhuwan Khatri
  3. Santosh Rananaware
  4. Danmeng Li
  5. David A. Ostrov
  6. Piyush K Jain
  7. Christopher J. Lessard
  8. Cuong Q. Nguyen

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Abstract

COVID-19 is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The severity of COVID-19 is highly variable and related to known (e.g., age, obesity, immune deficiency) and unknown risk factors. The widespread clinical symptoms encompass a large group of asymptomatic COVID-19 patients, raising a crucial question regarding genetic susceptibility, e.g., whether individual differences in immunity play a role in patient symptomatology and how much human leukocyte antigen (HLA) contributes to this. To reveal genetic determinants of susceptibility to COVID-19 severity in the population and further explore potential immune-related factors, we performed a genome-wide association study on 284 confirmed COVID-19 patients (cases) and 95 healthy individuals (controls). We compared cases and controls of European (EUR) ancestry and African American (AFR) ancestry separately. We identified two loci on chromosomes 5q32 and 11p12, which reach the significance threshold of suggestive association (p<1×10 -5 threshold adjusted for multiple trait testing) and are associated with the COVID-19 susceptibility in the European ancestry (index rs17448496: odds ratio [OR] = 0.173; 95% confidence interval [CI], 0.08–0.36 for G allele; p=5.15× 10 -5 and index rs768632395: OR = 0.166; 95% CI, 0.07–0.35 for A allele; p= 4.25×10 -6 , respectively), which were associated with two genes, PPP2R2B at 5q32, and LRRC4C at 11p12, respectively. To explore the linkage between HLA and COVID-19 severity, we applied fine-mapping analysis to dissect the HLA association with mild and severe cases. Using In-silico binding predictions to map the binding of risk/protective HLA to the viral structural proteins, we found the differential presentation of viral peptides in both ancestries. Lastly, extrapolation of the identified HLA from the cohort to the worldwide population revealed notable correlations. The study uncovers possible differences in susceptibility to COVID-19 in different ancestral origins in the genetic background, which may provide new insights into the pathogenesis and clinical treatment of the disease.

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  1. ScreenIT

    SciScore for 10.1101/2022.03.30.486345: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The study was approved by the Institutional Review Board of the University of Florida.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Isolated genomic DNA was quantified by NanoDrop™ One/OneC
    NanoDrop™
    suggested: None
    Quality Control: PLINK (v1.9) [20] was used for quality control and logistic analysis of the data.
    PLINK
    suggested: (PLINK, RRID:SCR_001757)
    The data were phased using Eagle version 2.4 and imputed using Minimac4.
    Eagle
    suggested: (Eagle, RRID:SCR_017262)
    Full-length amino acid sequences of structural proteins from SARS-CoV-2 whole-genome proteome (SnapGene, EPI_ISL_7196120_B.1.1.529, EPI_ISL_7196121_B.1.1.529) were used to infer all possible potentially relevant peptides (9mers for class I and 15mers for class II).
    SnapGene
    suggested: (SnapGene, RRID:SCR_015052)
    The peptide in the template structure was mutated in COOT, too.
    COOT
    suggested: (Coot, RRID:SCR_014222)
    The geometry of the result complexes was regularized in PHENIX.
    PHENIX
    suggested: (Phenix, RRID:SCR_014224)
    Statistical analysis: The association between the allele frequency of each HLA gene and cases and mortality were assessed by linear regression in GraphPad Prism (v9.3.1).
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.

    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.

  2. Version published to 10.1101/2022.03.30.486345v1 on bioRxiv