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Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19

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...

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Autores principales: Zhu, Dongliang, Zhao, Renjia, Yuan, Huangbo, Xie, Yijing, Jiang, Yanfeng, Xu, Kelin, Zhang, Tiejun, Chen, Xingdong, Suo, Chen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169198/
https://www.ncbi.nlm.nih.gov/pubmed/37160831
http://dx.doi.org/10.1007/s44197-023-00106-3
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author Zhu, Dongliang
Zhao, Renjia
Yuan, Huangbo
Xie, Yijing
Jiang, Yanfeng
Xu, Kelin
Zhang, Tiejun
Chen, Xingdong
Suo, Chen
author_facet Zhu, Dongliang
Zhao, Renjia
Yuan, Huangbo
Xie, Yijing
Jiang, Yanfeng
Xu, Kelin
Zhang, Tiejun
Chen, Xingdong
Suo, Chen
author_sort Zhu, Dongliang
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s44197-023-00106-3.
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spelling pubmed-101691982023-05-11 Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19 Zhu, Dongliang Zhao, Renjia Yuan, Huangbo Xie, Yijing Jiang, Yanfeng Xu, Kelin Zhang, Tiejun Chen, Xingdong Suo, Chen J Epidemiol Glob Health Research Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s44197-023-00106-3. Springer Netherlands 2023-05-09 /pmc/articles/PMC10169198/ /pubmed/37160831 http://dx.doi.org/10.1007/s44197-023-00106-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Zhu, Dongliang
Zhao, Renjia
Yuan, Huangbo
Xie, Yijing
Jiang, Yanfeng
Xu, Kelin
Zhang, Tiejun
Chen, Xingdong
Suo, Chen
Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19
title Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19
title_full Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19
title_fullStr Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19
title_full_unstemmed Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19
title_short Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19
title_sort host genetic factors, comorbidities and the risk of severe covid-19
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169198/
https://www.ncbi.nlm.nih.gov/pubmed/37160831
http://dx.doi.org/10.1007/s44197-023-00106-3
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