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Gene–Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors

Asthma is a complex heterogeneous disease caused by gene–environment interactions. Although numerous genome-wide association studies have been conducted, these interactions have not been systemically investigated. We sought to identify genetic factors associated with the asthma phenotype in 66,857 s...

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Autores principales: Cha, Junho, Choi, Sungkyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10419280/
https://www.ncbi.nlm.nih.gov/pubmed/37569643
http://dx.doi.org/10.3390/ijms241512266
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author Cha, Junho
Choi, Sungkyoung
author_facet Cha, Junho
Choi, Sungkyoung
author_sort Cha, Junho
collection PubMed
description Asthma is a complex heterogeneous disease caused by gene–environment interactions. Although numerous genome-wide association studies have been conducted, these interactions have not been systemically investigated. We sought to identify genetic factors associated with the asthma phenotype in 66,857 subjects from the Health Examination Study, Cardiovascular Disease Association Study, and Korea Association Resource Study cohorts. We investigated asthma-associated gene–environment (smoking status) interactions at the level of single nucleotide polymorphisms, genes, and gene sets. We identified two potentially novel (SETDB1 and ZNF8) and five previously reported (DM4C, DOCK8, MMP20, MYL7, and ADCY9) genes associated with increased asthma risk. Numerous gene ontology processes, including regulation of T cell differentiation in the thymus (GO:0033081), were significantly enriched for asthma risk. Functional annotation analysis confirmed the causal relationship between five genes (two potentially novel and three previously reported genes) and asthma through genome-wide functional prediction scores (combined annotation-dependent depletion, deleterious annotation of genetic variants using neural networks, and RegulomeDB). Our findings elucidate the genetic architecture of asthma and improve the understanding of its biological mechanisms. However, further studies are necessary for developing preventive treatments based on environmental factors and understanding the immune system mechanisms that contribute to the etiology of asthma.
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spelling pubmed-104192802023-08-12 Gene–Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors Cha, Junho Choi, Sungkyoung Int J Mol Sci Article Asthma is a complex heterogeneous disease caused by gene–environment interactions. Although numerous genome-wide association studies have been conducted, these interactions have not been systemically investigated. We sought to identify genetic factors associated with the asthma phenotype in 66,857 subjects from the Health Examination Study, Cardiovascular Disease Association Study, and Korea Association Resource Study cohorts. We investigated asthma-associated gene–environment (smoking status) interactions at the level of single nucleotide polymorphisms, genes, and gene sets. We identified two potentially novel (SETDB1 and ZNF8) and five previously reported (DM4C, DOCK8, MMP20, MYL7, and ADCY9) genes associated with increased asthma risk. Numerous gene ontology processes, including regulation of T cell differentiation in the thymus (GO:0033081), were significantly enriched for asthma risk. Functional annotation analysis confirmed the causal relationship between five genes (two potentially novel and three previously reported genes) and asthma through genome-wide functional prediction scores (combined annotation-dependent depletion, deleterious annotation of genetic variants using neural networks, and RegulomeDB). Our findings elucidate the genetic architecture of asthma and improve the understanding of its biological mechanisms. However, further studies are necessary for developing preventive treatments based on environmental factors and understanding the immune system mechanisms that contribute to the etiology of asthma. MDPI 2023-07-31 /pmc/articles/PMC10419280/ /pubmed/37569643 http://dx.doi.org/10.3390/ijms241512266 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cha, Junho
Choi, Sungkyoung
Gene–Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors
title Gene–Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors
title_full Gene–Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors
title_fullStr Gene–Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors
title_full_unstemmed Gene–Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors
title_short Gene–Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors
title_sort gene–smoking interaction analysis for the identification of novel asthma-associated genetic factors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10419280/
https://www.ncbi.nlm.nih.gov/pubmed/37569643
http://dx.doi.org/10.3390/ijms241512266
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