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Zhu D, Zhao R, Yuan H, Xie Y, Jiang Y, Xu K, Zhang T, Chen X, Suo C. Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19. J Epidemiol Glob Health 2023; 13:279-291. [PMID: 37160831 PMCID: PMC10169198 DOI: 10.1007/s44197-023-00106-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 04/17/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was varied in disease symptoms. We aim to explore the effect of host genetic factors and comorbidities on severe COVID-19 risk. METHODS A total of 20,320 COVID-19 patients in the UK Biobank cohort were included. Genome-wide association analysis (GWAS) was used to identify host genetic factors in the progression of COVID-19 and a polygenic risk score (PRS) consisted of 86 SNPs was constructed to summarize genetic susceptibility. Colocalization analysis and Logistic regression model were used to assess the association of host genetic factors and comorbidities with COVID-19 severity. All cases were randomly split into training and validation set (1:1). Four algorithms were used to develop predictive models and predict COVID-19 severity. Demographic characteristics, comorbidities and PRS were included in the model to predict the risk of severe COVID-19. The area under the receiver operating characteristic curve (AUROC) was applied to assess the models' performance. RESULTS We detected an association with rs73064425 at locus 3p21.31 reached the genome-wide level in GWAS (odds ratio: 1.55, 95% confidence interval: 1.36-1.78). Colocalization analysis found that two genes (SLC6A20 and LZTFL1) may affect the progression of COVID-19. In the predictive model, logistic regression models were selected due to simplicity and high performance. Predictive model consisting of demographic characteristics, comorbidities and genetic factors could precisely predict the patient's progression (AUROC = 82.1%, 95% CI 80.6-83.7%). Nearly 20% of severe COVID-19 events could be attributed to genetic risk. CONCLUSION In this study, we identified two 3p21.31 genes as genetic susceptibility loci in patients with severe COVID-19. The predictive model includes demographic characteristics, comorbidities and genetic factors is useful to identify individuals who are predisposed to develop subsequent critical conditions among COVID-19 patients.
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Affiliation(s)
- Dongliang Zhu
- Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
| | - Renjia Zhao
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, Shanghai, China
| | - Huangbo Yuan
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, Shanghai, China
| | - Yijing Xie
- Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
| | - Yanfeng Jiang
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Yaocheng Road 799, Taizhou, Jiangsu, China
| | - Kelin Xu
- Fudan University Taizhou Institute of Health Sciences, Yaocheng Road 799, Taizhou, Jiangsu, China
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
| | - Tiejun Zhang
- Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
- Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Yaocheng Road 799, Taizhou, Jiangsu, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Yaocheng Road 799, Taizhou, Jiangsu, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Yiwu Research Institute of Fudan University, Yiwu, Zhejiang, China
| | - Chen Suo
- Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China.
- Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, China.
- Fudan University Taizhou Institute of Health Sciences, Yaocheng Road 799, Taizhou, Jiangsu, China.
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