Integrative genomics analysis reveals a 21q22.11 locus contributing risk to COVID-19
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Abstract
The systematic identification of host genetic risk factors is essential for the understanding and treatment of coronavirus disease 2019 (COVID-19). By performing a meta-analysis of two independent genome-wide association summary datasets (N = 680 128), a novel locus at 21q22.11 was identified to be associated with COVID-19 infection (rs9976829 in IFNAR2-IL10RB, odds ratio = 1.16, 95% confidence interval = 1.09–1.23, P = 2.57 × 10−6). The rs9976829 represents a strong splicing quantitative trait locus for both IFNAR2 and IL10RB genes, especially in lung tissue (P = 1.8 × 10−24). Integrative genomics analysis of combining genome-wide association study with expression quantitative trait locus data showed the expression variations of IFNAR2 and IL10RB have prominent effects on COVID-19 in various types of tissues, especially in lung tissue. The majority of IFNAR2-expressing cells were dendritic cells (40%) and plasmacytoid dendritic cells (38.5%), and IL10RB-expressing cells were mainly nonclassical monocytes (29.6%). IFNAR2 and IL10RB are targeted by several interferons-related drugs. Together, our results uncover 21q22.11 as a novel susceptibility locus for COVID-19, in which individuals with G alleles of rs9976829 have a higher probability of COVID-19 susceptibility than those with non-G alleles.
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SciScore for 10.1101/2020.09.16.20195685: (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 Sentences Resources After removing low-quality and non-matched SNPs, there were 8,424,883 high-quality SNPs with a MAF ≥ 1% and ≥ imputation R2 0.6 that were common to both datasets with the use of effect-size estimates (BETA) and their standard errors (SE) for the meta-analysis. BETAsuggested: (BETA, RRID:SCR_007556)As reported in Ellinghaus et al. [8], we used the combined P value and combined effect (E) with its SE generated by the METAL to compute the odds ratio (OR) and its 95% confidence interval (CI): 1) OR = exp (E); 2) the upper confidence limit (OR_95 U) = exp (E + 1.96*SE); 3) the lower … SciScore for 10.1101/2020.09.16.20195685: (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 Sentences Resources After removing low-quality and non-matched SNPs, there were 8,424,883 high-quality SNPs with a MAF ≥ 1% and ≥ imputation R2 0.6 that were common to both datasets with the use of effect-size estimates (BETA) and their standard errors (SE) for the meta-analysis. BETAsuggested: (BETA, RRID:SCR_007556)As reported in Ellinghaus et al. [8], we used the combined P value and combined effect (E) with its SE generated by the METAL to compute the odds ratio (OR) and its 95% confidence interval (CI): 1) OR = exp (E); 2) the upper confidence limit (OR_95 U) = exp (E + 1.96*SE); 3) the lower confidence limit (OR_95L) = exp (E - 1.96*SE). METALsuggested: (METAL, RRID:SCR_002013)The web-access tool of LocusZoom [31] was used to visualize regional association plots (http://locuszoom.sph.umich.edu/). LocusZoomsuggested: (LOCUSZOOM, RRID:SCR_009257)Using the overrepresentation analysis, the WebGestalt could identify functional association between COVID-19-associated genes and KEGG pathways. WebGestaltsuggested: NoneKEGGsuggested: (KEGG, RRID:SCR_012773)These estimated weights are processed with beta values and standard errors from meta-GWAS summary data on COVID-19 to predict gene expression from GWAS summary statistics, while combining the variance and co-variance of SNPs from an LD reference panel based on the 1000 Genomes Project Phase 3 genotypes [33]. 1000 Genomes Projectsuggested: (1000 Genomes Project and AWS, RRID:SCR_008801)Drug-gene interaction analysis: We submitted these 11 COVID-19-associated genes into the widely-used Drug Gene Interaction Database (DGIdb v.3.0.2; http://www.dgidb.org/) to identify drug-gene interactions with Food and Drug Administration (FDA)-approved pharmaceutical compounds as well as antineoplastic and immunotherapies drugs depended on 20 databases with 51 known interaction types, and search 10 databases to find genes with potential drug abilities. DGIdbsuggested: (DGIdb, RRID:SCR_006608)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: We found the following clinical trial numbers in your paper:
Identifier Status Title NCT04331899 Active, not recruiting Single-Blind Study of a Single Dose of Peginterferon Lambda-… 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.
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- No protocol registration statement was detected.
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