Asthma is a complex disease that is reportedly associated with insomnia. However, the causal directionality of this association is still unclear. We used asthma and insomnia-associated single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) summary statistics to test the causal directionality between insomnia and asthma via Mendelian randomization (MR) analysis. We also performed a cross-trait meta-analysis using UK Biobank GWAS summary statistics and a gene–environment interaction study using data from UK Biobank. The interaction of genetic risk score for asthma (GRSasthma) with insomnia on asthma was tested by logistic regression. Insomnia was a risk factor for the incidence of asthma, as revealed by three different methods of MR analysis. However, asthma did not act as a risk factor for insomnia. The cross-trait meta-analysis identified 28 genetic loci shared between asthma and insomnia. In the gene–environment interaction study, GRSasthma interacted with insomnia to significantly affect the risk of asthma. The results of this study highlight the importance of insomnia as a risk factor of asthma, and warrant further analysis of the mechanism through which insomnia affects the risk of asthma.

Although asthma is one of the most common chronic diseases throughout all age groups, its etiology remains unknown, primarily due to its heterogeneous characteristics. We examined the causal effects of various environmental factors on asthma using Mendelian randomization and determined whether the susceptibility to asthma due to the causal effect of a risk factor differs between asthma subtypes, based on age of onset, severity of asthma, and sex. We performed Mendelian randomization analyses (inverse variance weighted, weighted median, and generalized summary-data-based Mendelian randomization) using UK Biobank data to estimate the causal effects of 69 environmental factors on asthma. Additional sensitivity analyses (MR-Egger regression, Cochran’s Q test, clumping, and reverse Mendelian randomization) were performed to ensure minimal or no pleiotropy. For confirmation, two-sample setting analyses were replicated using BMI SNPs that had been reported by a meta-genome-wide association study in Japanese and European (GIANT) populations and a genome-wide association study in control individuals from the UK Biobank. We found that BMI causally affects the development of asthma and that the adult-onset moderate-to-severe asthma subtype is the most susceptible to causal inference by BMI. Further, it is likely that the female subtype is more susceptible to BMI than males among adult asthma cases. Our findings provide evidence that obesity is a considerable risk factor in asthma patients, particularly in adult-onset moderate-to-severe asthma cases, and that weight loss is beneficial for reducing the burden of asthma.

Asthma is among the most common chronic diseases worldwide, creating a substantial healthcare burden. In late-onset asthma, there are wide global differences in asthma prevalence and low genetic heritability. It has been suggested as evidence for genetic susceptibility to asthma triggered by exposure to multiple environmental factors. Very few genome-wide interaction studies have identified gene-environment (G×E) interaction loci for asthma in adults. We evaluated genetic loci for late-onset asthma showing G×E interactions with multiple environmental factors, including alcohol intake, body mass index, insomnia, physical activity, mental status, sedentary behavior, and socioeconomic status. In gene-by-single environment interactions, we found no genome-wide significant single-nucleotide polymorphisms. However, in the gene-by-multi-environment interaction study, we identified three novel and genome-wide significant single-nucleotide polymorphisms: rs117996675, rs345749, and rs17704680. Bayes factor analysis suggested that for rs117996675 and rs17704680, body mass index is the most relevant environmental factor; for rs345749, insomnia and alcohol intake frequency are the most relevant factors in the G×E interactions of late-onset asthma. Functional annotations implicate the role of these three novel loci in regulating the immune system. In addition, the annotation for rs117996675 supports the body mass index as the most relevant environmental factor, as evidenced by the Bayes factor value. Our findings help to understand the role of the immune system in asthma and the role of environmental factors in late-onset asthma through G×E interactions. Ultimately, the enhanced understanding of asthma would contribute to better precision treatment depending on personal genetic and environmental information.

Background The extent of differences between genetic risks associated with various asthma subtypes is still unknown. To better understand the heterogeneity of asthma, we employed an unsupervised method to identify genetic variants specifically associated with asthma subtypes. Our goal was to gain insight into the genetic basis of asthma. Methods In this study, we utilized the UK Biobank dataset to select asthma patients (All asthma, n = 50,517) and controls (n = 283,410). We excluded 14,431 individuals who had no information on predicted values of forced expiratory volume in one second percent (FEV1%) and onset age, resulting in a final total of 36,086 asthma cases. We conducted k‐means clustering based on asthma onset age and predicted FEV1% using these samples (n = 36,086). Cluster‐specific genome‐wide association studies were then performed, and heritability was estimated via linkage disequilibrium score regression. To further investigate the pathophysiology, we conducted eQTL analysis with GTEx and gene‐set enrichment analysis with FUMA. Results Clustering resulted in four distinct clusters: early onset asthmanormalLF (early onset with normal lung function, n = 8172), early onset asthmareducedLF (early onset with reduced lung function, n = 8925), late‐onset asthmanormalLF (late‐onset with normal lung function, n = 12,481), and late‐onset asthmareducedLF (late‐onset with reduced lung function, n = 6508). Our GWASs in four clusters and in All asthma sample identified 5 novel loci, 14 novel signals, and 51 cluster‐specific signals. Among clusters, early onset asthmanormalLF and late‐onset asthmareducedLF were the least correlated (rg = 0.37). Early onset asthmareducedLF showed the highest heritability explained by common variants (h2 = 0.212) and was associated with the largest number of variants (71 single nucleotide polymorphisms). Further, the pathway analysis conducted through eQTL and gene‐set enrichment analysis showed that the worsening of symptoms in early onset asthma correlated with lymphocyte activation, pathogen recognition, cytokine receptor activation, and lymphocyte differentiation. Conclusions Our findings suggest that early onset asthmareducedLF was the most genetically predisposed cluster, and that asthma clusters with reduced lung function were genetically distinct from clusters with normal lung function. Our study revealed the genetic variation between clusters that were segmented based on onset age and lung function, providing an important clue for the genetic mechanism of asthma heterogeneity.

Type 2 diabetes (T2D) is caused by genetic and environmental factors as well as gene‐environment interactions. However, these interactions have not been systematically investigated. We analyzed these interactions for T2D and fasting glucose levels in three Korean cohorts, HEXA, KARE, and CAVAS, using the baseline data with a multiple regression model. Two polygenic risk scores for T2D (PRST2D) and fasting glucose (PRSFG) were calculated using 488 and 82 single nucleotide polymorphisms (SNP) for T2D and fasting glucose, respectively, which were extracted from large‐scaled genome‐wide association studies with multiethnic data. Both lifestyle risk factors and T2D‐related biochemical measurements were assessed. The effect of interactions between PRST2D‐triglyceride (TG) and PRST2D‐total cholesterol (TC) on fasting glucose levels was observed as follows: β ± SE = 0.0005 ± 0.0001, p = 1.06 × 10−19 in HEXA, β ± SE = 0.0008 ± 0.0001, p = 2.08 × 10−8 in KARE for TG; β ± SE = 0.0006 ± 0.0001, p = 2.00 × 10−6 in HEXA, β ± SE = 0.0020 ± 0.0004, p = 2.11 × 10−6 in KARE, β ± SE = 0.0007 ± 0.0004, p = 0.045 in CAVAS for TC. PRST2D‐based classification of the participants into four groups showed that the fasting glucose levels in groups with higher PRST2D were more adversely affected by both the TG and TC. In conclusion, blood TG and TC levels may affect the fasting glucose level through interaction with T2D genetic factors, suggesting the importance of consideration of gene‐environment interaction in the preventive medicine of T2D.

<b><i>Introduction:</i></b> Although many studies have investigated the association between smoking and obesity, very few have analyzed how obesity traits are affected by interactions between genetic factors and smoking. Here, we aimed to identify the loci that affect obesity traits via smoking status-related interactions in European samples. <b><i>Methods:</i></b> We performed stratified analysis based on the smoking status using both the UK Biobank (UKB) data (<i>N</i> = 334,808) and the Genetic Investigation of ANthropometric Traits (GIANT) data (<i>N</i> = 210,323) to identify gene-smoking interaction for obesity traits. We divided the UKB subjects into two groups, current smokers and nonsmokers, based on the smoking status, and performed genome-wide association study (GWAS) for body mass index (BMI), waist circumference adjusted for BMI (WCadjBMI), and waist-hip ratio adjusted for BMI (WHRadjBMI) in each group. And then we carried out the meta-analysis using both GWAS summary statistics of UKB and GIANT for BMI, WCadjBMI, and WHRadjBMI and computed the stratified <i>p</i> values (<i>p</i>_stratified) based on the differences between meta-analyzed estimated beta coefficients with standard errors in each group. <b><i>Results:</i></b> We identified four genome-wide significant loci in interactions with the smoking status (<i>p</i>_stratified &#x3c; 5 × 10⁻⁸): rs336396 (<i>INPP4B</i>) and rs12899135 (near <i>CHRNB4</i>) for BMI, and rs998584 (near <i>VEGFA</i>) and rs6916318 (near <i>RSPO3</i>) for WHRadjBMI. Moreover, we annotated the biological functions of the SNPs using expression quantitative trait loci (eQTL) and GWAS databases, along with publications, which revealed possible mechanisms underlying the association between the smoking status-related genetic variants and obesity. <b><i>Conclusions:</i></b> Our findings suggest that obesity traits can be modified by the smoking status via interactions with genetic variants through various biological pathways.