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Article: An integrated strategy to identify COVID-19 causal genes and characteristics represented by LRRC37A2

TitleAn integrated strategy to identify COVID-19 causal genes and characteristics represented by LRRC37A2
Authors
Keywordscasual genes
COVID-19
GWAS-eQTL
integrated strategy
SARS-CoV-2
Issue Date16-Feb-2023
PublisherWiley
Citation
Journal of Medical Virology, 2023, v. 95, n. 2 How to Cite?
AbstractGenome-wide association study (GWAS) could identify host genetic factors associated with coronavirus disease 2019 (COVID-19). The genes or functional DNA elements through which genetic factors affect COVID-19 remain uncharted. The expression quantitative trait locus (eQTL) provides a path to assess the correlation between genetic variations and gene expression. Here, we firstly annotated GWAS data to describe genetic effects, obtaining genome-wide mapped genes. Subsequently, the genetic mechanisms and characteristics of COVID-19 were investigated by an integrated strategy that included three GWAS-eQTL analysis approaches. It was found that 20 genes were significantly associated with immunity and neurological disorders, including prior and novel genes such as OAS3 and LRRC37A2. The findings were then replicated in single-cell datasets to explore the cell-specific expression of causal genes. Furthermore, associations between COVID-19 and neurological disorders were assessed as a causal relationship. Finally, the effects of causal protein-coding genes of COVID-19 were discussed using cell experiments. The results revealed some novel COVID-19-related genes to emphasize disease characteristics, offering a broader insight into the genetic architecture underlying the pathophysiology of COVID-19.
Persistent Identifierhttp://hdl.handle.net/10722/347536
ISSN
2023 Impact Factor: 6.8
2023 SCImago Journal Rankings: 1.560

 

DC FieldValueLanguage
dc.contributor.authorZhu, Z-
dc.contributor.authorChen, X-
dc.contributor.authorWang, C-
dc.contributor.authorZhang, S-
dc.contributor.authorYu, R-
dc.contributor.authorXie, Y-
dc.contributor.authorYuan, S-
dc.contributor.authorCheng, L-
dc.contributor.authorShi, L-
dc.contributor.authorZhang, X-
dc.date.accessioned2024-09-25T00:30:35Z-
dc.date.available2024-09-25T00:30:35Z-
dc.date.issued2023-02-16-
dc.identifier.citationJournal of Medical Virology, 2023, v. 95, n. 2-
dc.identifier.issn0146-6615-
dc.identifier.urihttp://hdl.handle.net/10722/347536-
dc.description.abstractGenome-wide association study (GWAS) could identify host genetic factors associated with coronavirus disease 2019 (COVID-19). The genes or functional DNA elements through which genetic factors affect COVID-19 remain uncharted. The expression quantitative trait locus (eQTL) provides a path to assess the correlation between genetic variations and gene expression. Here, we firstly annotated GWAS data to describe genetic effects, obtaining genome-wide mapped genes. Subsequently, the genetic mechanisms and characteristics of COVID-19 were investigated by an integrated strategy that included three GWAS-eQTL analysis approaches. It was found that 20 genes were significantly associated with immunity and neurological disorders, including prior and novel genes such as OAS3 and LRRC37A2. The findings were then replicated in single-cell datasets to explore the cell-specific expression of causal genes. Furthermore, associations between COVID-19 and neurological disorders were assessed as a causal relationship. Finally, the effects of causal protein-coding genes of COVID-19 were discussed using cell experiments. The results revealed some novel COVID-19-related genes to emphasize disease characteristics, offering a broader insight into the genetic architecture underlying the pathophysiology of COVID-19.-
dc.languageeng-
dc.publisherWiley-
dc.relation.ispartofJournal of Medical Virology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectcasual genes-
dc.subjectCOVID-19-
dc.subjectGWAS-eQTL-
dc.subjectintegrated strategy-
dc.subjectSARS-CoV-2-
dc.titleAn integrated strategy to identify COVID-19 causal genes and characteristics represented by LRRC37A2-
dc.typeArticle-
dc.identifier.doi10.1002/jmv.28585-
dc.identifier.scopuseid_2-s2.0-85148658251-
dc.identifier.volume95-
dc.identifier.issue2-
dc.identifier.eissn1096-9071-
dc.identifier.issnl0146-6615-

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