Search

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. [ABSTRACT FROM AUTHOR]

Summary: Background: Asthma‐chronic obstructive pulmonary disease (COPD) overlap syndrome (ACOS), which has received much attention, has not been unanimously defined. Objective: In this study, we tried to demonstrate that longitudinally defined ACOS is more useful in the real world than blending patients with asthma and COPD. Methods: The study patients had undergone two consecutive pulmonary function tests measured at least 3 months apart (n = 1889). We selected the patients who had positive bronchodilator response or methacholine provocation tests (n = 959). Next, we defined ACOS as a patient with a persistent airflow obstruction [forced expiratory volume in 1 second (FEV1)/forced vital capacity <0.7] that was identified twice consecutively by an interval of at least 3 months (n = 228). Results: The proportions of patients who were older, male and smokers were significantly higher, and baseline lung function was lower in patients with ACOS. In the longitudinal analysis, the mean change in lung function was high, and a greater decline in FEV1 was observed in patients with ACOS. In addition, we compared ACOS and severe asthma, and we also performed a cluster analysis and compared the results with our definition of ACOS. According to our definition, ACOS is an independent subtype with distinctive characteristics. Finally, a genome‐wide association study (GWAS) was performed to identify genetic variations associated with ACOS, but no significant single nucleotide polymorphisms were identified. Conclusion: Our findings suggest that ACOS should be defined longitudinally and considered as an independent subgroup distinguished by inherited environmental factors rather than as a genetically distinct independent group. [ABSTRACT FROM AUTHOR]

Although genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with common diseases, these observations are limited for fully explaining "missing heritability". Determining gene–gene interactions (GGI) are one possible avenue for addressing the missing heritability problem. While many statistical approaches have been proposed to detect GGI, most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available. In this study, we proposed a new statistical approach for gene-based GGI analysis, "Hierarchical structural CoMponent analysis of Gene–Gene Interactions" (HisCoM-GGI). HisCoM-GGI is based on generalized structured component analysis, and can consider hierarchical structural relationships between genes and SNPs. For a pair of genes, HisCoM-GGI first effectively summarizes all possible pairwise SNP–SNP interactions into a latent variable, from which it then performs GGI analysis. HisCoM-GGI can evaluate both gene-level and SNP-level interactions. Through simulation studies, HisCoM-GGI demonstrated higher statistical power than existing gene-based GGI methods, in analyzing a GWAS of a Korean population for identifying GGI associated with body mass index. Resultantly, HisCoM-GGI successfully identified 14 potential GGI, two of which, (NCOR2 × SPOCK1) and (LINGO2 × ZNF385D) were successfully replicated in independent datasets. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand the biological genetic mechanisms of complex traits. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand biological genetic mechanisms of complex traits. An implementation of HisCoM-GGI can be downloaded from the website (http://statgen.snu.ac.kr/software/hiscom-ggi). [ABSTRACT FROM AUTHOR]

Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the "missing heritability" problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data. [ABSTRACT FROM AUTHOR]