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. 2023 Jul 31;24(15):12266.
doi: 10.3390/ijms241512266.

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

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

Junho Cha et al. Int J Mol Sci. .

Abstract

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.

Keywords: Korean Genome and Epidemiology Study; asthma; gene–environment interactions; genome-wide association study; smoking status.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic representation of the sample selection and study design.
Figure 2
Figure 2
Bubble plot showing GO enrichment analysis (three sub-ontologies and top five enriched GO terms). Dot color corresponds to the Bonferroni-adjusted p-values and dot size to the number of genes mapped to the GO terms.
Figure 3
Figure 3
SETDB1 plays an important role in asthma induction through histone 3 lysine 9 (H3K9) methylation. ERVs: Endogenous retroviruses.
Figure 4
Figure 4
The ZNF8 gene plays an important role in asthma induction by Smad1 interaction.

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