Genetic differences according to onset age and lung function in asthma: A cluster analysis.

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.

Development of a Breast Cancer Risk Prediction Model Incorporating Polygenic Risk Scores and Nongenetic Risk Factors for Korean Women.

BACKGROUND: To develop a breast cancer prediction model for Korean women using published polygenic risk scores (PRS) combined with nongenetic risk factors (NGRF). METHODS: Thirteen PRS models generated from single or multiple combinations of the Asian and European PRSs were evaluated among 20,434 Korean women. The AUC and increase in OR per SD were compared for each PRS. The PRSs with the highest predictive power were combined with NGRFs; then, an integrated prediction model was established using the Individualized Coherent Absolute Risk Estimation (iCARE) tool. The absolute breast cancer risk was stratified for 18,142 women with available follow-up data. RESULTS: PRS38_ASN+PRS190_EB, a combination of Asian and European PRSs, had the highest AUC (0.621) among PRSs, with an OR per SD increase of 1.45 (95% confidence interval: 1.31-1.61). Compared with the average risk group (35%-65%), women in the top 5% had a 2.5-fold higher risk of breast cancer. Incorporating NGRFs yielded a modest increase in the AUC of women ages >50 years. For PRS38_ASN+PRS190_EB+NGRF, the average absolute risk was 5.06%. The lifetime absolute risk at age 80 years for women in the top 5% was 9.93%, whereas that of women in the lowest 5% was 2.22%. Women at higher risks were more sensitive to NGRF incorporation. CONCLUSIONS: Combined Asian and European PRSs were predictive of breast cancer in Korean women. Our findings support the use of these models for personalized screening and prevention of breast cancer. IMPACT: Our study provides insights into genetic susceptibility and NGRFs for predicting breast cancer in Korean women.

Gene-environment interaction explains a part of missing heritability in human body mass index.

Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships-software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity.

Smoking-Interaction Loci Affect Obesity Traits: A Gene-Smoking Stratified Meta-Analysis of 545,131 Europeans.

INTRODUCTION: 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. METHODS: We performed stratified analysis based on the smoking status using both the UK Biobank (UKB) data (N = 334,808) and the Genetic Investigation of ANthropometric Traits (GIANT) data (N = 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 p values (pstratified) based on the differences between meta-analyzed estimated beta coefficients with standard errors in each group. RESULTS: We identified four genome-wide significant loci in interactions with the smoking status (pstratified < 5 × 10-8): rs336396 (INPP4B) and rs12899135 (near CHRNB4) for BMI, and rs998584 (near VEGFA) and rs6916318 (near RSPO3) 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. CONCLUSIONS: Our findings suggest that obesity traits can be modified by the smoking status via interactions with genetic variants through various biological pathways.

A cardiac-null mutation of Prdm16 causes hypotension in mice with cardiac hypertrophy via increased nitric oxide synthase 1.

Hypertension or hypotension prevails as a comorbidity in patients with heart failure (HF). Although blood pressure (BP) is an important factor in managing the mortality of HF, the molecular mechanisms of changes in BP have not been clearly understood in cases of HF. We and others have demonstrated that a loss in PRDM16 causes hypertrophic cardiomyopathy, leading to HF. We aimed to determine whether BP is altered in mice that experience cardiac loss of Prdm16 and identify the underlying mechanism of BP-associated changes. BP decreased significantly only in female mice with a cardiac-null mutation of Prdm16 compared with controls, by an invasive protocol under anesthesia and by telemetric method during conscious, unrestrained status. Mice with a cardiac loss of Prdm16 had higher heart-to-body weight ratios and upregulated atrial natriuretic peptide, suggesting cardiac hypertrophy. Plasma aldosterone-to-renin activity ratios and plasma sodium levels decreased in Prdm16-deficient mice versus control. By RNA-seq and in subsequent functional analyses, Prdm16-null hearts were enriched in factors that regulate BP, including Adra1a, Nos1, Nppa, and Nppb. The inhibition of nitric oxide synthase 1 (NOS1) reverted the decrease in BP in cardiac-specific Prdm16 knockout mice. Mice with cardiac deficiency of Prdm16 present with hypotension and cardiac hypertrophy. Further, our findings suggest that the increased expression of NOS1 causes hypotension in mice with a cardiac-null mutation of Prdm16. These results provide novel insights into the molecular mechanisms of hypotension in subjects with HF and contribute to our understanding of how hypotension develops in patients with HF.

Genome-Wide Interaction Study of Late-Onset Asthma With Seven Environmental Factors Using a Structured Linear Mixed Model in Europeans.

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.

Gene-environment interaction in type 2 diabetes in Korean cohorts: Interaction of a type 2 diabetes polygenic risk score with triglyceride and cholesterol on fasting glucose levels.

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: ß ± SE = 0.0005 ± 0.0001, p = 1.06 × 10-19 in HEXA, ß ± SE = 0.0008 ± 0.0001, p = 2.08 × 10-8 in KARE for TG; ß ± SE = 0.0006 ± 0.0001, p = 2.00 × 10-6 in HEXA, ß ± SE = 0.0020 ± 0.0004, p = 2.11 × 10-6 in KARE, ß ± 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.

Characterisation of insomnia as an environmental risk factor for asthma via Mendelian randomization and gene environment interaction.

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.

Association Between Environmental Factors and Asthma Using Mendelian Randomization: Increased Effect of Body Mass Index on Adult-Onset Moderate-to-Severe Asthma Subtypes.

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.

Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model.

Multiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10-6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10-6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10-9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10-10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene-environment interaction affecting disease.

Electrocardiogram Recordings in Anesthetized Mice using Lead II.

The electrocardiogram is a valuable tool for evaluating the cardiac conduction system. Animal research has helped generate novel genetic and pharmacological information regarding the electrocardiogram. However, making electrocardiogram measurements in small animals in vivo, such as mice, has been challenging. To this end, we used an electrocardiogram recording method in anesthetized mice with many advantages: it is a technically simple procedure, is inexpensive, has short measuring time, and is affordable, even in young mice. Despite the limitations with using anesthesia, comparisons between control and experimental groups can be performed with enhanced sensitivity. We treated mice with agonists and antagonists of the autonomic nervous system to determine the validity of this protocol and compared our results with previous reports. Our ECG protocol detected increased heart rates and QTc intervals on treatment with atropine, decreased heart rates and QTc intervals after carbachol treatment, and higher heart rates and QTc intervals with isoprenaline but did not note any change in ECG parameters on administration of propranolol. These results are supported by previous reports, confirming the reliability of this ECG protocol. Thus, this method can be used as a screening approach to making ECG measurements that otherwise would not be attempted due to high cost and technical difficulties.

Cardiac-specific inactivation of Prdm16 effects cardiac conduction abnormalities and cardiomyopathy-associated phenotypes.

A variant in the PRDM16 locus has been correlated with QRS duration in an electrocardiogram genome-wide association study, and the deletion of PRDM16 has been implicated as a causal factor of the dilated cardiomyopathy that is linked to 1p36 deletion syndrome. We aimed to determine how a null mutation of Prdm16 affects cardiac function and study the underlying mechanism of the resulting phenotype in an appropriate mouse model. We used cardiac-specific Prdm16 conditional knockout mice to examine cardiac function by electrocardiography. QRS duration and QTc interval increased significantly in cardiac-specific Prdm16 knockout animals compared with wild-type mice. Further, we assessed cardiomyopathy-associated features by trichrome staining, densitometry, and hydroxyproline assay. Prdm16-null hearts showed greater fibrosis and cardiomyocyte hypertrophy. By quantitative real-time PCR, Prdm16-null hearts upregulated extracellular matrix-related genes (Ctgf, Timp1) and α-smooth muscle actin (Acta2), a myofibroblast marker. Moreover, TGF-ß signaling was activated in Prdm16-null hearts, as evidenced by increased Tgfb1-3 transcript levels and phosphorylated Smad2. However, the inhibition of TGF-ß receptor did not reverse the aberrations in conduction in cardiac-specific Prdm16 knockout mice. To determine the underlying mechanisms, we performed RNA-seq using mouse left ventricular tissue. By functional analysis, Prdm16-null hearts experienced dysregulated expression of ion channel genes, including Kcne1, Scn5a, Cacna1h, and Cacna2d2. Mice with Prdm16-null hearts develop abnormalities in cardiac conduction and cardiomyopathy-associated phenotypes, including fibrosis and cellular hypertrophy. Further, the RNA-seq findings suggest that impairments in ion homeostasis (Ca2+, K+, and Na+) may at least partially underlie the abnormal conduction in cardiac-specific Prdm16 knockout mice.NEW & NOTEWORTHY This is the first study that describes aberrant cardiac function and cardiomyopathy-associated phenotypes in an appropriate murine genetic model with cardiomyocyte-specific Prdm16-null mutation. It is noteworthy that the correlation of PRDM16 with QRS duration is replicated in a murine animal model and the potential underlying mechanism may be the impairment of ion homeostasis.