Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction.

Polygenic risk score (PRS) is useful for capturing an individual's genetic susceptibility. However, previous studies have not fully exploited the potential of the risk factor PRS (RFPRS) for disease prediction. We explored the potential of integrating disease-related RFPRSs with disease PRS to enhance disease prediction performance.

Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients.

Background: More than 200 asthma-associated genetic variants have been identified in genome-wide association studies (GWASs). Expression quantitative trait loci (eQTL) data resources can help identify causal genes of the GWAS signals, but it can be difficult to find an eQTL that reflects the disease state because most eQTL data are obtained from normal healthy subjects.

Methods: We performed a blood eQTL analysis using transcriptomic and genotypic data from 433 Korean asthma patients.

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.

Investigation of heteroscedasticity in polygenic risk scores across 15 quantitative traits.

The polygenic risk score (PRS) could be used to stratify individuals with high risk of diseases and predict complex trait of individual in a population. Previous studies developed a PRS-based prediction model using linear regression and evaluated the predictive performance of the model using the value. One of the key assumptions of linear regression is that the variance of the residual should be constant at each level of the predictor variables, called homoscedasticity.

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 effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index.

Globally, more than 1.9 billion adults are overweight. Thus, obesity is a serious public health issue.

Identification of five genetic variants with differential effects on obesity-related traits based on age.

Obesity is a major public health concern, and its prevalence generally increases with age. As the number of elderly people is increasing in the aging population, the age-dependent increase in obesity has raised interest in the underlying mechanism. To understand the genetic basis of age-related increase in obesity, we identified genetic variants showing age-dependent differential effects on obesity.

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.

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.

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.

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.

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.

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.

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.

Analysis of the Interaction between Polygenic Risk Score and Calorie Intake in Obesity in the Korean Population.

Introduction: Obesity results from an imbalance in the intake and expenditure of calories that leads to lifestyle-related diseases. Although genome-wide association studies (GWAS) have revealed many obesity-related genetic factors, the interactions of these factors and calorie intake remain unknown. This study aimed to investigate interactions between calorie intake and the polygenic risk score (PRS) of BMI.