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.

Genome-wide gene and serum ferritin interaction in the development of type 2 diabetes in adults aged 40 years or older.

BACKGROUND AND AIMS: Elevated serum ferritin is associated with incident Type 2 diabetes (T2D), but the interactions between serum ferritin and genetic factors which may improve understanding underlying mechanism in the development of T2D are still unclear. We determined the gene-ferritin interactions on the development of T2D by genome-wide gene-ferritin interaction analyses. METHODS AND RESULTS: A total of 3405 participants from two prospective cohorts of community living residents were included, and the median follow-time was 3.99 years. Genome-wide gene-ferritin interactions were analyzed using the joint test with two degrees of freedom and the interaction test with one degree of freedom. There were 18 SNPs selected in the joint test. Finally, four independent variants [rs355140 (LINC00312), rs4075576 (nearby PDGFA), rs1332202 (PTPRD), and rs713157 (nearby LINC00900)] with low pairwise linkage disequilibrium (r2<0.2) and located at least 1000 kb from the index SNP showed interactions with serum ferritin level. In the association analyses between serum ferritin levels (tertiles of ferritin and ferritin status) and the incidence of T2D according to genotype, the Incidence Rate Ratios (IRRs) in the highest tertile of ferritin level (vs. the lowest tertile) were greater for participants with heterozygotes of risk alleles of each of the four SNP than IRRs for those with wild type. Compared with the normal group, the elevated ferritin group also had a higher risk of T2D for all genetic variants of risk alleles, particularly its homozygotes. CONCLUSION: Serum ferritin level interacts with genetic variants (rs355140, rs4075576, rs1332202, and rs713157) in the development of T2D.

Genome-wide interaction study of single-nucleotide polymorphisms and alcohol consumption on blood pressure: The Ansan and Ansung study of the Korean Genome and Epidemiology Study (KoGES).

Hypertension is a common disease worldwide. Alcohol consumption is one of the risk factors for hypertension, however, it is unclear how alcohol consumption elevates blood pressure. Blood pressure could be affected by interactions between genetic variations and alcohol consumption. Thus, we performed a genome-wide interaction study (GWIS) to assess the effect of gene-alcohol consumption interaction on blood pressure among adults aged ≥40 years from the Ansan and Ansung cohort study (n = 6,176), a part of the Korean Genome Epidemiology Study (KoGES). As a result, rs1297184, single-nucleotide polymorphism (SNP) in locus LGR5 was significant (PGWIS = 8.78 × 10-9 ) in GWIS analysis on diastolic blood pressure, but not on systolic blood pressure. However, there was a heteroscedasticity of alcohol consumption. In the GWIS analysis, applying the inverse-variance weighting to correct the systematic inflation slightly attenuated the strength of interaction (PGWIS_IVW = 7.14 × 10-8 ). This interaction was replicated in the Health Examinees cohort (p = .026), a large-scale community-based cohort (n = 18,708). In conclusion, we identified a possible novel interaction between an SNP (rs1297184) and alcohol consumption on blood pressure.

Gene-environment interactions related to blood pressure traits in two community-based Korean cohorts.

Hypertension is a complex disorder caused by genetic and environmental risk factors. Recently, genome-wide association studies (GWASs) identified more than 100 genetic variants for blood pressure traits and hypertension. However, the interactions between these genetic variants and environmental factors have not been systematically investigated. Therefore, we examined the interaction between genetic and environmental risk factors in blood pressure traits using the genetic risk score (GRS). Two Korean community-based cohorts, Cohort I (KARE; N = 8,840) and Cohort II (CAVAS; N = 9,599), were used for this study, and GRSs were calculated from 42 GWAS single-nucleotide polymorphisms (SNPs) that were validated for their association in these cohorts. We calculated GRSs in both ways by considering the effect sizes of each SNP (weighted GRS) and not considering the effect sizes (unweighted GRS). The unweighted GRS was strongly associated with systolic blood pressure, diastolic blood pressure, and hypertension (p = 9.03 × 10 -47 , p = 9.41 × 10 -48 , and p = 3.22 × 10 -55 by meta-analysis, respectively) and the weighted GRS showed the similar results. The environmental factors of body mass index, waist circumference, and drinking status were significantly associated with blood pressure traits, and the interaction between these factors and GRSs were examined. However, no interactions were found with either the GRS or the individual SNPs considered for the GRS. Our findings show that it is challenging to find GRS-environment interactions regarding blood pressure traits.

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.

Longitudinal analysis to better characterize Asthma-COPD overlap syndrome: Findings from an adult asthma cohort in Korea (COREA).

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.

Identification of five novel genetic loci related to facial morphology by genome-wide association studies.

BACKGROUND: Face morphology is strongly determined by genetic factors. However, only a small number of genes related to face morphology have been identified to date. Here, we performed a two-stage genome-wide association study (GWAS) of 85 face morphological traits in 7569 Koreans (5643 in the discovery set and 1926 in the replication set). RESULTS: In this study, we analyzed 85 facial traits, including facial angles. After discovery GWAS, 128 single nucleotide polymorphisms (SNPs) showing an association of P < 5 × 10- 6 were selected to determine the replication of the associations, and meta-analysis of discovery GWAS and the replication analysis resulted in five genome-wide significant loci. The OSR1-WDR35 [rs7567283, G allele, beta (se) = -0.536 (0.096), P = 2.75 × 10- 8] locus was associated with the facial frontal contour; the HOXD1-MTX2 [rs970797, A allele, beta (se) = 0.015 (0.003), P = 3.97 × 10- 9] and WDR27 [rs3736712, C allele, beta (se) = 0.293 (0.048), P = 8.44 × 10- 10] loci were associated with eye shape; and the SOX9 [rs2193054, C allele, beta (se) (ln-transformed) = -0.007 (0.001), P = 6.17 × 10- 17] and DHX35 [rs2206437, A allele, beta (se) = -0.283 (0.047), P = 1.61 × 10- 9] loci were associated with nose shape. WDR35 and SOX9 were related to known craniofacial malformations, i.e., cranioectodermal dysplasia 2 and campomelic dysplasia, respectively. In addition, we found three independent association signals in the SOX9 locus, and six known loci for nose size and shape were replicated in this study population. Interestingly, four SNPs within these five face morphology-related loci showed discrepancies in allele frequencies among ethnic groups. CONCLUSIONS: We identified five novel face morphology loci that were associated with facial frontal contour, nose shape, and eye shape. Our findings provide useful genetic information for the determination of face morphology.

GAREM1 regulates the PR interval on electrocardiograms.

PR interval is the period from the onset of P wave to the start of the QRS complex on electrocardiograms. A recent genomewide association study (GWAS) suggested that GAREM1 was linked to the PR interval on electrocardiograms. This study was designed to validate this correlation using additional subjects and examined the function of Garem1 in a mouse model. We analyzed the association of rs17744182, a variant in the GAREM1 locus, with the PR interval in 5646 subjects who were recruited from 2 Korean replication sets, Yangpyeong (n = 2471) and Yonsei (n = 3175), and noted a significant genomewide association by meta-analysis (P = 2.39 × 10-8). To confirm the function of Garem1 in mice, Garem1 siRNA was injected into mouse tail veins to reduce the expression of Garem1. Garem1 transcript levels declined by 53% in the atrium of the heart (P = 0.029), and Garem1-siRNA injected mice experienced a significant decrease in PR interval (43.27 ms vs. 44.89 ms in control, P = 0.007). We analyzed the expression pattern of Garem1 in the heart by immunohistology and observed specific expression of Garem1 in intracardiac ganglia. Garem1 was expressed in most neurons of the ganglion, including cholinergic and adrenergic cells. We have provided evidence that GAREM1 is involved in the PR interval of ECGs. These findings increase our understanding of the regulatory signals of heart rhythm through intracardiac ganglia of the autonomic nervous system and can be used to guide the development of a therapeutic target for heart conditions, such as atrial fibrillation.

Csk Regulates Blood Pressure by Controlling the Synthetic Pathways of Aldosterone.

BACKGROUND: Blood pressure is regulated by a network of diverse physiological pathways. The C-terminal Src kinase (CSK) locus (15q24) is associated with blood pressure in various ethnic groups. It was recently reported thatCskinsufficiency increases blood pressure through Src. The mechanisms of hypertension inCsk+/-mice are examined further in this study.Methods and Results:To identify a causal component responsible for hypertension inCsk+/-, the heart rate was measured by electrocardiogram and plasma volume by Evans blue dilution. Plasma volume increased inCsk+/-compared with wild-types, while the heart rate did not change. Plasma sodium and aldosterone levels rose consistently inCsk+/-vs. wild-types, and spironolactone, a mineralocorticoid receptor antagonist, reduced blood pressure. The amounts of Sgk1 and Na+/K+-ATPase (NKA) increased in the kidney ofCsk+/-compared with wild-types. It was also found that Cyp11b2 (aldosterone synthase) was upregulated in the adrenal glands ofCsk+/-, and that Csk was enriched in the zona glomerulosa of adrenals, the major site of aldosterone production in the normal mouse. CONCLUSIONS: The results of the present study identify a physiological pathway by which blood pressure is regulated, in which the insufficiency ofCskinduces aldosterone production with zonal specificity in the adrenal glands, increasing sodium reabsorption and plasma volume and thus resulting in hypertension.

Identification of three novel genetic variations associated with electrocardiographic traits (QRS duration and PR interval) in East Asians.

The electrocardiogram has several advantages in detecting cardiac arrhythmia-it is readily available, noninvasive and cost-efficient. Recent genome-wide association studies have identified single-nucleotide polymorphisms that are associated with electrocardiogram measures. We performed a genome-wide association study using Korea Association Resource data for the discovery phase (Phase 1, n = 6805) and two consecutive replication studies in Japanese populations (Phase 2, n = 2285; Phase 3, n = 5010) for QRS duration and PR interval. Three novel loci were identified: rs2483280 (PRDM16 locus) and rs335206 (PRDM6 locus) were associated with QRS duration, and rs17026156 (SLC8A1 locus) correlated with PR interval. PRDM16 was recently identified as a causative gene of left ventricular non-compaction and dilated cardiomyopathy in 1p36 deletion syndrome, which is characterized by heart failure, arrhythmia and sudden cardiac death. Thus, our finding that a PRDM16 SNP is linked to QRS duration strongly implicates PRDM16 in cardiac function. In addition, C allele of rs17026156 increases PR interval (beta ± SE, 2.39 ± 0.40 ms) and exists far more frequently in East Asians (0.46) than in Europeans and Africans (0.05 and 0.08, respectively).

A common variant in SLC8A1 is associated with the duration of the electrocardiographic QT interval.

Prolongation of the electrocardiographic QT interval, a measure of cardiac repolarization, predisposes one to ventricular arrhythmias and sudden cardiac death. Since NOS1AP, a regulator of neuronal nitric oxide synthase, was discovered in a genome-wide association study (GWAS) as a novel target that modulates cardiac repolarization, several loci have been linked to the QT interval in studies (QTGEN and QTSCD) of European descendents. However, there has been no GWAS of the QT interval in Asian populations. We conducted a GWAS with regard to the QT interval in Korea Association Resource (KARE [n = 6,805]) cohorts. Replication studies in independent populations of Korean (n = 4,686) and Japanese (n = 2,687) groups validated the association between a SNP, rs13017846, which maps to near SLC8A1 (sodium/calcium exchanger 1 precursor, overall p = 8.0 × 10(-14)), and the QT interval. The minor allele frequency (MAF) of rs13017846 varies widely between ethnicities-0.053 in Europeans (HapMap CEU [Utah residents with ancestry from northern and western Europe from the Centre d'Étude du Polymorphisme Humain collection] samples) versus 0.080 in Africans (HapMap YRI [Yoruba in Ibadan, Nigeria] samples)-whereas a MAF of 0.500 has been reported in Asians (HapMap HCB [Han Chinese in Beijing, China] and JPT [Japanese in Tokyo, Japan] samples). This might explain why this locus has not been identified in Europeans in previous studies.

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. We constructed 112 RFPRSs and analyzed the association of RFPRSs with diseases to identify disease-related RFPRSs in 700 diseases, using the UK Biobank dataset. We uncovered 6157 statistically significant associations between 247 diseases and 109 RFPRSs. We estimated the disease PRSs of 70 diseases that exhibited statistically significant heritability, to generate RFDiseasemetaPRS-a combined PRS integrating RFPRSs and disease PRS-and compare the prediction performance metrics between RFDiseasemetaPRS and disease PRS. RFDiseasemetaPRS showed better performance for Nagelkerke's pseudo-R2, odds ratio (OR) per 1 SD, net reclassification improvement (NRI) values and difference of R2 considered by variance of R2 in 31 out of 70 diseases. Additionally, we assessed risk classification between two models by examining OR between the top 10% and remaining 90% individuals for the 31 diseases; RFDiseasemetaPRS exhibited better R2, NRI and OR than disease PRS. These findings highlight the importance of utilizing RFDiseasemetaPRS, which can provide personalized healthcare and tailored prevention strategies.

Association between the SPRY1 gene polymorphism and obesity-related traits and osteoporosis in Korean women.

BACKGROUND: Emerging evidence has revealed a close relationship between obesity and osteoporosis. It was reported recently that conditional knockout of the Spry1 gene in mice adipocytes causes an increase in body fat and a decrease in bone mass, and that these phenotypes are rescued by Spry1 overexpression in adipose tissue. In this study, we investigated whether genetic variation in the human SPRY1 gene is associated with obesity-related phenotypes and/or osteoporosis in humans. METHODS: We performed a candidate gene association analysis between the four single nucleotide polymorphisms (SNPs) and 14 imputed SNPs in the SPRY1 gene and obesity-related traits and osteoporosis in a Korean women cohort (3013 subjects). RESULTS: All four SPRY1 gene SNPs were significantly associated with either obesity-related traits or osteoporosis. The TGCC haplotype in the SRPY1 gene showed simultaneous association with an increased risk for obesity-related traits, percentage body fat (p=0.0087) and percentage abdominal fat (p=0.047), and osteoporosis (odds ratio=1.50; p=0.025) in the recessive genetic model. CONCLUSIONS: Our results support a previous finding in conditional Spry1 gene knockout mice and suggest that the SPRY1 gene is an important genetic factor for determining the risk of both obesity and osteoporosis in humans.

Common variants in RYR1 are associated with left ventricular hypertrophy assessed by electrocardiogram.

AIMS: To identify the genetic risk factors that influence the development of electrocardiographic (ECG) left ventricular hypertrophy (LVH), a major risk factor for cardiovascular (CV) morbidity and mortality. METHODS AND RESULTS: We performed a genomewide association study (GWAS) of ECG-LVH, in which the community-based Korea Association REsource (KARE) study (8432 controls and 398 cases) was analysed by Affymetrix SNP array 5.0. The GWAS results were validated in hospital-based samples (597 controls and 207 cases). Fourteen single-nucleotide polymorphisms (SNPs) in eight genetic loci (5q35.1, 6p22.3-22.1, 8q24.2, 11p15, 11q21-22.1, 14q12, 17q11.2, and 19q13.1) were associated with ECG-LVH in the original GWAS study (P < 1 × 10(-5)). Of these SNPs, 12 were genotyped in the hospital sample. There was consistent association with the 19q13.1 region which contains RYR1 gene. The most significant SNP in the region was rs10500279, which had genomewide significance in the combined GWAS/replication sample [odds ratio = 1.58 (confidence interval: 1.35-1.85), P = 1.0 × 10(-8)]. Mutations in RYR1, which encodes a major Ca(2+) channel in the skeletal muscle, have been reported to correlate with CV diseases. CONCLUSION: We performed the first GWAS for ECG-LVH, implicating the skeletal muscle Ca(2+) channel protein RYR1 as a genetic risk factor. These results might increase our understanding of the development of ECG-LVH.

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.

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.

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.

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.

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.

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.