tetano
Editor, Senior Moderator
J Epidemiol Glob Health
. 2023 May 9.
doi: 10.1007/s44197-023-00106-3. Online ahead of print. Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19
Dongliang Zhu[SUP] 1 [/SUP], Renjia Zhao[SUP] 2 [/SUP], Huangbo Yuan[SUP] 2 [/SUP], Yijing Xie[SUP] 1 [/SUP], Yanfeng Jiang[SUP] 2 3 [/SUP], Kelin Xu[SUP] 3 4 [/SUP], Tiejun Zhang[SUP] 1 5 3 [/SUP], Xingdong Chen[SUP] 2 3 6 7 [/SUP], Chen Suo[SUP] 8 9 10 [/SUP]
Affiliations
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.
Keywords: COVID-19; Comorbidity; GWAS; Host genetics; Polygenic risk score; Predictive model.
. 2023 May 9.
doi: 10.1007/s44197-023-00106-3. Online ahead of print. Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19
Dongliang Zhu[SUP] 1 [/SUP], Renjia Zhao[SUP] 2 [/SUP], Huangbo Yuan[SUP] 2 [/SUP], Yijing Xie[SUP] 1 [/SUP], Yanfeng Jiang[SUP] 2 3 [/SUP], Kelin Xu[SUP] 3 4 [/SUP], Tiejun Zhang[SUP] 1 5 3 [/SUP], Xingdong Chen[SUP] 2 3 6 7 [/SUP], Chen Suo[SUP] 8 9 10 [/SUP]
Affiliations
- PMID: 37160831
- DOI: 10.1007/s44197-023-00106-3
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.
Keywords: COVID-19; Comorbidity; GWAS; Host genetics; Polygenic risk score; Predictive model.