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.

A Wireless, CRISPR-Polymer Dot Electrochemical Sensor for the Diagnosis of Bacterial Pneumonia and Multi-Drug Resistance.

Rapid and accurate diagnosis is crucial for managing the global health threat posed by multidrug-resistant bacterial infections; however, current methods have limitations in either being time-consuming, labor-intensive, or requiring instruments with high costs. Addressing these challenges, we introduce a wireless electrochemical sensor integrating the CRISPR/Cas system with electroconductive polymer dot (PD) nanoparticles to rapidly detect bacterial pathogens from human sputum. To enhance the electroconductive properties, we synthesized copper-ion-immobilized PD (PD-Cu), followed by conjugation of the deactivated Cas9 protein (dCas9) onto PD-Cu-coated Si electrodes to generate the dCas9-PD-Cu sensor. The dCas9-PD-Cu sensor integrated with isothermal amplification can specifically detect target nucleic acids of multidrug-resistant bacteria, such as the antibiotic resistance genes kpc-2 and mecA. The dCas9-PD-Cu sensor exhibits high sensitivity, allowing for the detection of ∼54 femtograms of target nucleic acids, based on measuring the changes in resistivity of the Si electrodes through target capture by dCas9. Furthermore, a wireless sensing platform of the dCas9-PD-Cu sensor was established using a Bluetooth module and a microcontroller unit for detection using a smartphone. We demonstrate the feasibility of the platform in diagnosing multidrug-resistant bacterial pneumonia in patients' sputum samples, achieving 92% accuracy. The current study presents a versatile biosensor platform that can overcome the limitations of conventional diagnostics in the clinic.

An Antibody-CRISPR/Cas Conjugate Platform for Target-Specific Delivery and Gene Editing in Cancer.

The CRISPR/Cas system has been introduced as an innovative tool for therapy, however achieving specific delivery to the target has been a major challenge. Here, an antibody-CRISPR/Cas conjugate platform that enables specific delivery and target gene editing in HER2-positive cancer is introduced. The CRISPR/Cas system by replacing specific residues of Cas9 with an unnatural amino acid is engineered, that can be complexed with a nanocarrier and bioorthogonally functionalized with a monoclonal antibody targeting HER2. The resultant antibody-conjugated CRISPR/Cas nanocomplexes can be specifically delivered and induce gene editing in HER2-positive cancer cells in vitro. It is demonstrated that the in vivo delivery of the antibody-CRISPR/Cas nanocomplexes can effectively disrupt the plk1 gene in HER2-positive ovarian cancer, resulting in substantial suppression of tumor growth. The current study presents a useful therapeutic platform for antibody-mediated delivery of CRISPR/Cas for the treatment of various cancers and genetic diseases.

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 R 2 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. However, some studies show that PRS models exhibit heteroscedasticity between PRS and traits. This study analyzes whether heteroscedasticity exists in PRS models of diverse disease-related traits and, if any, it affects the accuracy of PRS-based prediction in 354,761 Europeans from the UK Biobank. We constructed PRSs for 15 quantitative traits using LDpred2 and estimated the existence of heteroscedasticity between PRSs and 15 traits using three different tests of the Breusch-Pagan (BP) test, score test, and F test. Thirteen out of fifteen traits show significant heteroscedasticity. Further replication using new PRSs from the PGS catalog and independent samples (N = 23,620) from the UK Biobank confirmed the heteroscedasticity in ten traits. As a result, ten out of fifteen quantitative traits show statistically significant heteroscedasticity between the PRS and each trait. There was a greater variance of residuals as PRS increased, and the prediction accuracy at each level of PRS tended to decrease as the variance of residuals increased. In conclusion, heteroscedasticity was frequently observed in the PRS-based prediction models of quantitative traits, and the accuracy of the predictive model may differ according to PRS values. Therefore, prediction models using the PRS should be constructed by considering heteroscedasticity.

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.

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. To identify asthma-related genes, we carried out colocalization, Summary-based Mendelian Randomization (SMR) analysis, and Transcriptome-Wide Association Study (TWAS) using the results of asthma GWASs and eQTL data. In addition, we compared the results of disease eQTL data and asthma-related genes with two normal blood eQTL data from Genotype-Tissue Expression (GTEx) project and a Japanese study. RESULTS: We identified 340,274 cis-eQTL and 2,875 eGenes from asthmatic eQTL analysis. We compared the disease eQTL results with GTEx and a Japanese study and found that 64.1% of the 2,875 eGenes overlapped with the GTEx eGenes and 39.0% with the Japanese eGenes. Following the integrated analysis of the asthmatic eQTL data with asthma GWASs, using colocalization and SMR methods, we identified 15 asthma-related genes specific to the Korean asthmatic eQTL data. CONCLUSIONS: We provided Korean asthmatic cis-eQTL data and identified asthma-related genes by integrating them with GWAS data. In addition, we suggested these asthma-related genes as therapeutic targets for asthma. We envisage that our findings will contribute to understanding the etiological mechanisms of asthma and provide novel therapeutic targets.

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. We conducted stratified analyses between young and old groups using genome-wide association studies of 355,335 United Kingom Biobank participants for five obesity-related phenotypes, including body mass index, body fat percentage, waist-hip ratio, waist circumference, and hip circumference. Using t-statistic, we identified five significant lead single nucleotide polymorphisms: rs2258461 with body mass index, rs9861311 and rs429358 with body fat percentage, rs2870099 with waist-hip ratio, and rs145500243 with waist circumference. Among these single nucleotide polymorphisms, rs429358, located in APOE gene was associated with diverse age-related diseases, such as Alzheimer's disease, coronary artery disease, age-related degenerative macular diseases, and cognitive decline. The C allele of rs429358 gradually decreases body fat percentage as one grows older in the range of 40-69 years. In conclusion, we identified five genetic variants with differential effects on obesity-related phenotypes based on age using a stratified analysis between young and old groups, which may help to elucidate the mechanisms by which age influences the development 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.

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. Moreover, obesity is a major risk factor for diabetes mellitus, coronary heart disease, and cardiovascular disease. Recently, GWAS examining obesity and body mass index (BMI) have increasingly unveiled many aspects of the genetic architecture of obesity and BMI. Information on genome-wide genetic variants has been used to estimate the genome-wide polygenic score (GPS) for a personalized prediction of obesity. However, the prediction power of GPS is affected by various factors, including the unequal variance in the distribution of a phenotype, known as heteroscedasticity. Here, we calculated a GPS for BMI using LDpred2, which was based on the BMI GWAS summary statistics from a European meta-analysis. Then, we tested the GPS in 354,761 European samples from the UK Biobank and found an effective prediction power of the GPS on BMI. To study a change in the variance of BMI, we investigated the heteroscedasticity of BMI across the GPS via graphical and statistical methods. We also studied the homoscedastic samples for BMI compared to the heteroscedastic sample, randomly selecting samples with various standard deviations of BMI residuals. Further, we examined the effect of the genetic interaction of GPS with environment (GPS×E) on the heteroscedasticity of BMI. We observed the changing variance (i.e., heteroscedasticity) of BMI along the GPS. The heteroscedasticity of BMI was confirmed by both the Breusch-Pagan test and the Score test. Compared to the heteroscedastic sample, the homoscedastic samples from small standard deviation of BMI residuals showed a decreased heteroscedasticity and an improved prediction accuracy, suggesting a quantitatively negative correlation between the phenotypic heteroscedasticity and the prediction accuracy of GPS. To further test the effects of the GPS×E on heteroscedasticity, first we tested the genetic interactions of the GPS with 21 environments and found 8 significant GPS×E interactions on BMI. However, the heteroscedasticity of BMI was not ameliorated after adjusting for the GPS×E interactions. Taken together, our findings suggest that the heteroscedasticity of BMI exists along the GPS and is not affected by the GPS×E interaction.