Identification of a genetic variant at 2q12.1 associated with blood pressure in East Asians by genome-wide scan including gene-environment interactions

Genetics of Hypertension: From Monogenic Analysis to GETomics

Arterial hypertension is the most frequent cardiovascular risk factor all over the world, and it is one of the leading drivers of the risk of cardiovascular events and death. It is a complex trait influenced by heritable and environmental factors. To date, the World Health Organization estimates that 1.28 billion adults aged 30-79 years worldwide have arterial hypertension (defined by European guidelines as office systolic blood pressure ≥ 140 mmHg or office diastolic blood pressure ≥ 90 mmHg), and 7.1 million die from this disease. The molecular genetic basis of primary arterial hypertension is the subject of intense research and has recently yielded remarkable progress. In this review, we will discuss the genetics of arterial hypertension. Recent studies have identified over 900 independent loci associated with blood pressure regulation across the genome. Comprehending these mechanisms not only could shed light on the pathogenesis of the disease but also hold the potential for assessing the risk of developing arterial hypertension in the future. In addition, these findings may pave the way for novel drug development and personalized therapeutic strategies.

Development and validation of hypertension prediction models: The Korean Genome and Epidemiology Study_Cardiovascular Disease Association Study (KoGES_CAVAS)

This study aimed to develop and validate the hypertension risk prediction models of the CArdioVascular disease Association Study (CAVAS). Overall, 6,186 participants without hypertension at baseline were randomly divided into derivation and internal validation sets in a 6:4 ratio. We derived two prediction models: the first used the Framingham hypertension risk prediction factors (F-CAVAS-HTN); the second considered additional risk factors identified using stepwise Weibull regression analysis (CAVAS-HTN). These models were externally evaluated among Ansan and Ansung (A&A) participants, and the external validity of the Framingham and A&A prediction models (F-HTN and A&A-HTN) were assessed using the internal validation set of CAVAS. The discrimination, calibration, and net reclassification were determined. During the 4-year follow-up, 777 new cases of hypertension were diagnosed. All four models showed good discrimination (C-statistic ≥ 0.7). Internal calibrations were good for both the coefficient-based and the risk score-based F-CAVAS-HTN models, respectively (Hosmer-Lemeshow chi-square, H-L χ² < 20, P ≥ 0.05). However, the two CAVAS models (H-L χ² ≥ 20, P < 0.05, both) as well as the F-HTN and the A&A-HTN prediction models (H-L χ² = 155.39, P < 0.0001; H-L χ² = 209.72, P < 0.0001, respectively) were not externally calibrated. The F-CAVAS-HTN may be better than models with additional risk factors or derived for another population in the view of the findings of the internal validation in the present study, although future studies to improve the external validity of the F-CAVAS-HTN are needed.

First evidence for STING SNP R293Q being protective regarding obesity-associated cardiovascular disease in age-advanced subjects - a cohort study

Obesity is a risk factor for several aging-related diseases such as type 2 diabetes, cardiovascular disease, and cancer. Especially, cardiovascular disease is triggered by obesity by inducing vascular senescence and chronic low-grade systemic inflammation, also known as inflamm-aging. Released molecules from damaged cells and their recognition by the innate immune system is one of the mechanisms driving inflamm-aging. Obesity results in mitochondrial damage, leading to endothelial inflammation triggered by cytosolic mtDNA via the cGAS/STING pathway. Recently, we have shown STING SNP R293Q to be associated with a decreased risk for aging-related diseases in current smokers. Since current smoking triggers DNA damage that, similar to obesity, may result in the release of DNA into the cytoplasm, we hypothesized that the cGAS/STING pathway can modify the phenotype of aging also in obese subjects. Therefore, the objective of our study was to investigate whether STING R293Q is associated with aging-related diseases in obese individuals. We indeed show that STING 293Q is associated with protection from combined aging-related diseases (P = 0.014) and, in particular, cardiovascular disease in these subjects (P = 0.010). Therefore, we provide the first evidence that stratification for obesity may reveal new genetic loci determining the risk for aging-related diseases.

A Systematically Assembled Signature of Genes to be Deep-Sequenced for Their Associations with the Blood Pressure Response to Exercise

: Background: Exercise is one of the best nonpharmacologic therapies to treat hypertension. The blood pressure (BP) response to exercise is heritable. Yet, the genetic basis for the antihypertensive effects of exercise remains elusive. Methods: To assemble a prioritized gene signature, we performed a systematic review with a series of Boolean searches in PubMed (including Medline) from earliest coverage. The inclusion criteria were human genes in major BP regulatory pathways reported to be associated with: (1) the BP response to exercise; (2) hypertension in genome-wide association studies (GWAS); (3) the BP response to pharmacotherapy; (4a) physical activity and/or obesity in GWAS; and (4b) BP, physical activity, and/or obesity in non-GWAS. Included GWAS reports disclosed the statistically significant thresholds used for multiple testing. Results: The search yielded 1422 reports. Of these, 57 trials qualified from which we extracted 11 genes under criteria 1, 18 genes under criteria 2, 28 genes under criteria 3, 27 genes under criteria 4a, and 29 genes under criteria 4b. We also included 41 genes identified from our previous work. Conclusions: Deep-sequencing the exons of this systematically assembled signature of genes represents a cost and time efficient approach to investigate the genomic basis for the antihypertensive effects of exercise.

Generality of genomic findings on blood pressure traits and its usefulness in precision medicine in diverse populations: A systematic review

Remarkable findings from genome-wide association studies (GWAS) on blood pressure (BP) traits have made new insights for developing precision medicine toward more effective screening measures. However, generality of GWAS findings in diverse populations is hampered by some technical limitations. There is no comprehensive study to evaluate source(s) of the non-generality of GWAS results on BP traits, so to fill the gap, this systematic review study was carried out. Using MeSH terms, 1545 records were detected through searching in five databases and 49 relevant full-text articles were included in our review. Overall, 749 unique variants were reported, of those, majority of variants have been detected in Europeans and were associated to systolic and diastolic BP traits. Frequency of genetic variants with same position was low in European and non-European populations (n = 38). However, more than 200 (>25%) single nucleotide polymorphisms were found on same loci or linkage disequilibrium blocks (r² ≥ 80%). Investigating for locus position and linkage disequilibrium of infrequent unique variants showed modest to high reproducibility of findings in Europeans that in some extent was generalizable in other populations. Beyond theoretical limitations, our study addressed other possible sources of non-generality of GWAS findings for BP traits in the same and different origins.

Hypertension genomics and cardiovascular prevention

Hypertension continues to be a major risk factor for global mortality, and recent genome-wide association studies (GWAS) have expanded in size, leading to the identification of further genetic loci influencing blood pressure. In light of the new knowledge from the largest cardiovascular GWAS to date, we review the potential impact of genomics on discovering potential drug targets, risk stratification with genetic risk scores, drug selection with pharmacogenetics, and exploring insights provided by gene-environment interactions.

Imbalanced Nutrient Intake in Cancer Survivors from the Examination from the Nationwide Health Examination Center-Based Cohort

This study was conducted to examine the nutrient intake status of cancer survivors. A total of 5224 cancer survivors, 19,926 non-cancer individuals without comorbidities (non-cancer I), and 20,622 non-cancer individuals with comorbidities, matched by age, gender, and recruitment center location were included in the analysis. Generally, the proportion of total energy from carbohydrates was higher and the proportion from fat was lower in cancer survivors. The odds ratios (ORs) for total energy (OR = 0.92, 95% confidence interval (CI) = 0.86–0.99), proportion of total energy from fat (OR = 0.54, 95% CI = 0.35–0.83), and protein (OR = 0.85, 95% CI = 0.79–0.90) were significantly lower, and the OR for the proportion of total energy from carbohydrates was higher (OR = 1.21, 95% CI = 1.10–1.33) in the cancer survivors than in non-cancer I. Additionally, the cancer survivors’ protein, vitamin B₁, vitamin B₂, niacin, and phosphorus intakes were lower, whereas their vitamin C intake was higher. When divided by cancer type, the ORs for the carbohydrate percentages were significantly higher in the colon and breast cancer survivors, whereas protein intake was lower in gastric, breast, and cervical cancer survivors. The nutrient intake patterns in Asian cancer survivors are poor, with higher carbohydrate and lower fat and protein intakes.

Gene-by-Psychosocial Factor Interactions Influence Diastolic Blood Pressure in European and African Ancestry Populations: Meta-Analysis of Four Cohort Studies

Inter-individual variability in blood pressure (BP) is influenced by both genetic and non-genetic factors including socioeconomic and psychosocial stressors. A deeper understanding of the gene-by-socioeconomic/psychosocial factor interactions on BP may help to identify individuals that are genetically susceptible to high BP in specific social contexts. In this study, we used a genomic region-based method for longitudinal analysis, Longitudinal Gene-Environment-Wide Interaction Studies (LGEWIS), to evaluate the effects of interactions between known socioeconomic/psychosocial and genetic risk factors on systolic and diastolic BP in four large epidemiologic cohorts of European and/or African ancestry. After correction for multiple testing, two interactions were significantly associated with diastolic BP. In European ancestry participants, outward/trait anger score had a significant interaction with the C10orf107 genomic region (p = 0.0019). In African ancestry participants, depressive symptom score had a significant interaction with the HFE genomic region (p = 0.0048). This study provides a foundation for using genomic region-based longitudinal analysis to identify subgroups of the population that may be at greater risk of elevated BP due to the combined influence of genetic and socioeconomic/psychosocial risk factors.

A Review of the Genetics of Hypertension with a Focus on Gene-Environment Interactions

PURPOSE OF REVIEW

Here, we discuss the interpretation and modeling of gene-environment interactions in hypertension-related phenotypes, with a focus on the necessary assumptions and possible challenges.

RECENT FINDINGS

Recently, small cohort studies have discovered several novel genetic variants associated with hypertension-related phenotypes through modeling gene-environment interactions. Several consortia-based meta-analytic efforts have uncovered many novel genetic variants in hypertension without modeling interaction terms, giving promise to future meta-analytic efforts that incorporate gene-environment interactions. Heritability studies and genome-wide association studies have established that hypertension, a prevalent cardiovascular disease, has a genetic component that may be modulated by the environment (such as lifestyle factors). This review includes a discussion of known genetic associations for hypertension/blood pressure, including those resulting from the incorporation of gene-environmental interaction modeling.

Joint genetic analysis using variant sets reveals polygenic gene-context interactions

Joint genetic models for multiple traits have helped to enhance association analyses. Most existing multi-trait models have been designed to increase power for detecting associations, whereas the analysis of interactions has received considerably less attention. Here, we propose iSet, a method based on linear mixed models to test for interactions between sets of variants and environmental states or other contexts. Our model generalizes previous interaction tests and in particular provides a test for local differences in the genetic architecture between contexts. We first use simulations to validate iSet before applying the model to the analysis of genotype-environment interactions in an eQTL study. Our model retrieves a larger number of interactions than alternative methods and reveals that up to 20% of cases show context-specific configurations of causal variants. Finally, we apply iSet to test for sub-group specific genetic effects in human lipid levels in a large human cohort, where we identify a gene-sex interaction for C-reactive protein that is missed by alternative methods.

Genetic effects on phenotypes can depend on external contexts, including environment. Statistical tests for identifying such interactions are important to understand how individual genetic variants may act in different contexts. Interaction effects can either be studied using measurements of a given phenotype in different contexts, under the same genetic backgrounds, or by stratifying a population into subgroups. Here, we derive a method based on linear mixed models that can be applied to both of these designs. iSet enables testing for interactions between context and sets of variants, and accounts for polygenic effects. We validate our model using simulations, before applying it to the genetic analysis of gene expression studies and genome-wide association studies of human blood lipid levels. We find that modeling interactions with variant sets offers increased power, thereby uncovering interactions that cannot be detected by alternative methods.

Genome-wide study of resistant hypertension identified from electronic health records

Resistant hypertension is defined as high blood pressure that remains above treatment goals in spite of the concurrent use of three antihypertensive agents from different classes. Despite the important health consequences of resistant hypertension, few studies of resistant hypertension have been conducted. To perform a genome-wide association study for resistant hypertension, we defined and identified cases of resistant hypertension and hypertensives with treated, controlled hypertension among >47,500 adults residing in the US linked to electronic health records (EHRs) and genotyped as part of the electronic MEdical Records & GEnomics (eMERGE) Network. Electronic selection logic using billing codes, laboratory values, text queries, and medication records was used to identify resistant hypertension cases and controls at each site, and a total of 3,006 cases of resistant hypertension and 876 controlled hypertensives were identified among eMERGE Phase I and II sites. After imputation and quality control, a total of 2,530,150 SNPs were tested for an association among 2,830 multi-ethnic cases of resistant hypertension and 876 controlled hypertensives. No test of association was genome-wide significant in the full dataset or in the dataset limited to European American cases (n = 1,719) and controls (n = 708). The most significant finding was CLNK rs13144136 at p = 1.00x10^-6 (odds ratio = 0.68; 95% CI = 0.58–0.80) in the full dataset with similar results in the European American only dataset. We also examined whether SNPs known to influence blood pressure or hypertension also influenced resistant hypertension. None was significant after correction for multiple testing. These data highlight both the difficulties and the potential utility of EHR-linked genomic data to study clinically-relevant traits such as resistant hypertension.

Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits

The etiology of complex traits likely involves the effects of genetic and environmental factors, along with complicated interaction effects between them. Consequently, there has been interest in applying genetic association tests of complex traits that account for potential modification of the genetic effect in the presence of an environmental factor. One can perform such an analysis using a joint test of gene and gene-environment interaction. An optimal joint test would be one that remains powerful under a variety of models ranging from those of strong gene-environment interaction effect to those of little or no gene-environment interaction effect. To fill this demand, we have extended a kernel machine based approach for association mapping of multiple SNPs to consider joint tests of gene and gene-environment interaction. The kernel-based approach for joint testing is promising, because it incorporates linkage disequilibrium information from multiple SNPs simultaneously in analysis and permits flexible modeling of interaction effects. Using simulated data, we show that our kernel machine approach typically outperforms the traditional joint test under strong gene-environment interaction models and further outperforms the traditional main-effect association test under models of weak or no gene-environment interaction effects. We illustrate our test using genome-wide association data from the Grady Trauma Project, a cohort of highly traumatized, at-risk individuals, which has previously been investigated for interaction effects.

Genetic variant coding for iron regulatory protein HFE contributes to hypertension, the TAMRISK study

Iron is essential for body homeostasis, but iron overload may lead to metabolic abnormalities and thus increase the risk for atherosclerosis and many other diseases. Major histocompatibility complex class I-like transmembrane protein (HFE) is involved in body iron metabolism. The gene coding for HFE has 3 well-known polymorphic sites of which H63D (rs1799945, C > G) has recently been associated with hypertension in a genome-wide association study (GWAS) study. In the present study, we wanted to clarify whether the genetic variant associates with hypertension in a Finnish cohort consisting of 50-year-old men and women. The study included 399 hypertensive cases and 751 controls from the Tampere adult population cardiovascular risk study (TAMRISK) cohort. Genotyping of polymorphisms was done by polymerase chain reaction using DNAs extracted from buccal swabs. We found that individuals with the mutated form of the H63D polymorphic site (G-allele) had a 1.4-fold risk (P = 0.037, 95% confidence interval [CI] 1.02-1.89) for hypertension at the age of 50 years compared with the CC genotype carriers. When obese subjects (body mass index > 30 kg/m²) were analyzed in their own group, the risk for hypertension was even stronger (odds ratio 4.15, P < 0.001, 95% CI 1.98-8.68). We also noticed that the blood pressure (BP) readings were higher in those with the minor G-allele when compared to ones having a normal genotype already at the age of 35 years. Means of systolic/diastolic BPs were 127/81 mm Hg for CC and 131/83 mm Hg for CG + GG groups (P < 0.001 for systolic and P = 0.005 for diastolic pressure). In conclusion, HFE genetic variant H63D was associated with essential hypertension in Finnish subjects from the TAMRISK cohort confirming a previous GWAS study. The effect of this SNP on BP was also confirmed from a longitudinal study.