Genetic liability to systemic inflammatory regulators is causally linked to COVID-19 diverse phenotypes: Insights from multi-omics association study and single-cell RNA sequencing analysis

Coronavirus disease 2019 (COVID-19) brings heavy burden to patients and society globally. Observational research suggest inflammatory regulators are related to COVID-19. Nevertheless, their causal effects are still unclear. Herein, we performed a Mendelian randomization (MR) analysis to estimate the causality between systemic inflammatory regulators and COVID-19 diverse phenotypes. Genetic instrumental variables associated with systemic inflammatory regulators were extracted from genome-wide association study (GWAS) data involving 8293 European participants. Summary statistics for COVID-19 diverse subtypes were obtained from the COVID-19 host genetic initiative (54,071 cases and 4,905,697 controls). We performed multi-omics approach and MR study to detect the causal links through integrating GWAS and protein quantity trait loci data. Inverse variance weighted method was applied as the main analysis approach. Additionally, MR-Egger intercept regression and Cochran Q test were employed to verify pleiotropy and heterogeneity. Lastly, single-cell RNA sequencing analysis was performed to reveal the expression of significant genes. The MR analysis of the integrated GWAS and protein quantity trait loci data demonstrated that CCL27 and CXCL9 were causally associated with a higher risk of COVID-19 susceptibility (CCL27: odds ratio [OR]: 1.06, 95% confidence interval [CI]: 1.01-1.10, P = .015; CXCL9: OR: 1.04, 95% CI: 1.01-1.08, P = .023), while hepatocyte growth factor was causally linked to a lower risk of COVID-19 susceptibility (OR: 0.89, 95% CI: 0.83-0.96, P = .002). In terms of hospitalization, the inverse variance weighted approach provided evidence to support that genetically predicted CXCL9 and interleukin-16 had negative associations with the risk of COVID-19 hospitalization (interleukin-16: OR: 0.92, 95% CI: 0.85-0.99, P = .034; CXCL9: OR: 0.85, 95% CI: 0.74-0.98, P = .043). No heterogeneity and directional pleiotropy were detected through sensitivity analysis. Our results supported the causal associations between specific inflammatory regulators and COVID-19 diverse phenotypes, thereby providing promising biomarkers of severity stratification and new insights for the therapeutic targets of COVID-19.

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Journal
Medicine
Published
2025-08-22
DOI
https://doi.org/10.1097/md.0000000000044089
Primary Topic
COVID-19 Clinical Research Studies
Type
article
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article

Genetic liability to systemic inflammatory regulators is causally linked to COVID-19 diverse phenotypes: Insights from multi-omics association study and single-cell RNA sequencing analysis

Renbing Pan, Jingwen Liu
Medicine
COVID-19 Clinical Research Studies
article

Genetic liability to systemic inflammatory regulators is causally linked to COVID-19 diverse phenotypes: Insights from multi-omics association study and single-cell RNA sequencing analysis

Renbing Pan, Jingwen Liu
article en

Abstract

Coronavirus disease 2019 (COVID-19) brings heavy burden to patients and society globally. Observational research suggest inflammatory regulators are related to COVID-19. Nevertheless, their causal effects are still unclear. Herein, we performed a Mendelian randomization (MR) analysis to estimate the causality between systemic inflammatory regulators and COVID-19 diverse phenotypes. Genetic instrumental variables associated with systemic inflammatory regulators were extracted from genome-wide association study (GWAS) data involving 8293 European participants. Summary statistics for COVID-19 diverse subtypes were obtained from the COVID-19 host genetic initiative (54,071 cases and 4,905,697 controls). We performed multi-omics approach and MR study to detect the causal links through integrating GWAS and protein quantity trait loci data. Inverse variance weighted method was applied as the main analysis approach. Additionally, MR-Egger intercept regression and Cochran Q test were employed to verify pleiotropy and heterogeneity. Lastly, single-cell RNA sequencing analysis was performed to reveal the expression of significant genes. The MR analysis of the integrated GWAS and protein quantity trait loci data demonstrated that CCL27 and CXCL9 were causally associated with a higher risk of COVID-19 susceptibility (CCL27: odds ratio [OR]: 1.06, 95% confidence interval [CI]: 1.01-1.10, P = .015; CXCL9: OR: 1.04, 95% CI: 1.01-1.08, P = .023), while hepatocyte growth factor was causally linked to a lower risk of COVID-19 susceptibility (OR: 0.89, 95% CI: 0.83-0.96, P = .002). In terms of hospitalization, the inverse variance weighted approach provided evidence to support that genetically predicted CXCL9 and interleukin-16 had negative associations with the risk of COVID-19 hospitalization (interleukin-16: OR: 0.92, 95% CI: 0.85-0.99, P = .034; CXCL9: OR: 0.85, 95% CI: 0.74-0.98, P = .043). No heterogeneity and directional pleiotropy were detected through sensitivity analysis. Our results supported the causal associations between specific inflammatory regulators and COVID-19 diverse phenotypes, thereby providing promising biomarkers of severity stratification and new insights for the therapeutic targets of COVID-19.

MedicineVol. 104(34)
Wenzhou Medical University (CN), Sir Run Run Shaw Hospital (CN), Plan International (US), Quzhou City People's Hospital (CN), Quzhou University (CN)
Openalex Percentile: Top 72%
COVID-19 Clinical Research Studies
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