An update on genome-wide association studies of hypertension

Neurocardiac regulation: from cardiac mechanisms to novel therapeutic approaches

Cardiac sympathetic overactivity is a well-established contributor to the progression of neurogenic hypertension and heart failure, yet the underlying pathophysiology remains unclear. Recent studies have highlighted the importance of acutely regulated cyclic nucleotides and their effectors in the control of intracellular calcium and exocytosis. Emerging evidence now suggests that a significant component of sympathetic overactivity and enhanced transmission may arise from impaired cyclic nucleotide signalling, resulting from compromised phosphodiesterase activity, as well as alterations in receptor-coupled G-protein activation. In this review, we address some of the key cellular and molecular pathways that contribute to sympathetic overactivity in hypertension and discuss their potential for therapeutic targeting.

The genetic risk for hypertension is lower among the Hungarian Roma population compared to the general population

Estimating the prevalence of cardiovascular diseases (CVDs) and risk factors among the Roma population, the largest minority in Europe, and investigating the role of genetic or environmental/behavioral risk factors in CVD development are important issues in countries where they are significant minority. This study was designed to estimate the genetic susceptibility of the Hungarian Roma (HR) population to essential hypertension (EH) and compare it to that of the general (HG) population. Twenty EH associated SNPs (in AGT, FMO3, MTHFR-NPPB, NPPA, NPPA-AS1, AGTR1, ADD1, NPR3-C5orf23, NOS3, CACNB2, PLCE1, ATP2B1, GNB3, CYP1A1-ULK3, UMOD and GNAS-EDN3) were genotyped using DNA samples obtained from HR (N = 1176) and HG population (N = 1178) subjects assembled by cross-sectional studies. Allele frequencies and genetic risk scores (unweighted and weighted genetic risk scores (GRS and wGRS, respectively) were calculated for the study groups and compared to examine the joint effects of the SNPs. The susceptibility alleles were more frequent in the HG population, and both GRS and wGRS were found to be higher in the HG population than in the HR population (GRS: 18.98 ± 3.05 vs. 18.25 ± 2.97, p<0.001; wGRS: 1.52 [IQR: 0.99–2.00] vs. 1.4 [IQR: 0.93–1.89], p<0.01). Twenty-seven percent of subjects in the HR population were in the bottom fifth (GRS ≤ 16) of the risk allele count compared with 21% of those in the HG population. Thirteen percent of people in the HR group were in the top fifth (GRS ≥ 22) of the GRS compared with 21% of those in the HG population (p<0.001), i.e., the distribution of GRS was found to be left-shifted in the HR population compared to the HG population. The Roma population seems to be genetically less susceptible to EH than the general one. These results support preventive efforts to lower the risk of developing hypertension by encouraging a healthy lifestyle.

A system view and analysis of essential hypertension

OBJECTIVES

The goal of this study was to investigate genes associated with essential hypertension from a system perspective, making use of bioinformatic tools to gain insights that are not evident when focusing at a detail-based resolution.

METHODS

Using various databases (pathways, Genome Wide Association Studies, knockouts etc.), we compiled a set of about 200 genes that play a major role in hypertension and identified the interactions between them. This enabled us to create a protein-protein interaction network graph, from which we identified key elements, based on graph centrality analysis. Enriched gene regulatory elements (transcription factors and microRNAs) were extracted by motif finding techniques and knowledge-based tools.

RESULTS

We found that the network is composed of modules associated with functions such as water retention, endothelial vasoconstriction, sympathetic activity and others. We identified the transcription factor SP1 and the two microRNAs miR27 (a and b) and miR548c-3p that seem to play a major role in regulating the network as they exert their control over several modules and are not restricted to specific functions. We also noticed that genes involved in metabolic diseases (e.g. insulin) are central to the network.

CONCLUSION

We view the blood-pressure regulation mechanism as a system-of-systems, composed of several contributing subsystems and pathways rather than a single module. The system is regulated by distributed elements. Understanding this mode of action can lead to a more precise treatment and drug target discovery. Our analysis suggests that insulin plays a primary role in hypertension, highlighting the tight link between essential hypertension and diseases associated with the metabolic syndrome.

Genetics of Human Primary Hypertension: Focus on Hormonal Mechanisms

Increasingly, primary hypertension is being considered a syndrome and not a disease, with the individual causes (diseases) having a common sign-an elevated blood pressure. To determine these causes, genetic tools are increasingly employed. This review identified 62 proposed genes. However, only 21 of them met our inclusion criteria: (i) primary hypertension, (ii) two or more supporting cohorts from different publications or within a single publication or one supporting cohort with a confirmatory genetically modified animal study, and (iii) 600 or more subjects in the primary cohort; when including our exclusion criteria: (i) meta-analyses or reviews, (ii) secondary and monogenic hypertension, (iii) only hypertensive complications, (iv) genes related to blood pressure but not hypertension per se, (v) nonsupporting studies more common than supporting ones, and (vi) studies that did not perform a Bonferroni or similar multiassessment correction. These 21 genes were organized in a four-tiered structure: distant phenotype (hypertension); intermediate phenotype [salt-sensitive (18) or salt-resistant (0)]; subintermediate phenotypes under salt-sensitive hypertension [normal renin (4), low renin (8), and unclassified renin (6)]; and proximate phenotypes (specific genetically driven hypertensive subgroup). Many proximate hypertensive phenotypes had a substantial endocrine component. In conclusion, primary hypertension is a syndrome; many proposed genes are likely to be false positives; and deep phenotyping will be required to determine the utility of genetics in the treatment of hypertension. However, to date, the positive genes are associated with nearly 50% of primary hypertensives, suggesting that in the near term precise, mechanistically driven treatment and prevention strategies for the specific primary hypertension subgroups are feasible.

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.

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.

Mendelian randomisation in cardiovascular research: an introduction for clinicians

Understanding the causal role of biomarkers in cardiovascular and other diseases is crucial in order to find effective approaches (including pharmacological therapies) for disease treatment and prevention. Classical observational studies provide naïve estimates of the likely role of biomarkers in disease development; however, such studies are prone to bias. This has direct relevance for drug development as if drug targets track to non-causal biomarkers, this can lead to expensive failure of these drugs in phase III randomised controlled trials. In an effort to provide a more reliable indication of the likely causal role of a biomarker in the development of disease, Mendelian randomisation studies are increasingly used, and this is facilitated by the availability of large-scale genetic data. We conducted a narrative review in order to provide a description of the utility of Mendelian randomisation for clinicians engaged in cardiovascular research. We describe the rationale and provide a basic description of the methods and potential limitations of Mendelian randomisation. We give examples from the literature where Mendelian randomisation has provided pivotal information for drug discovery including predicting efficacy, informing on target-mediated adverse effects and providing potential new evidence for drug repurposing. The variety of the examples presented illustrates the importance of Mendelian randomisation in order to prioritise drug targets for cardiovascular research.

Robustness of meta-analyses in finding gene × environment interactions

Meta-analyses that synthesize statistical evidence across studies have become important analytical tools for genetic studies. Inspired by the success of genome-wide association studies of the genetic main effect, researchers are searching for gene × environment interactions. Confounders are routinely included in the genome-wide gene × environment interaction analysis as covariates; however, this does not control for any confounding effects on the results if covariate × environment interactions are present. We carried out simulation studies to evaluate the robustness to the covariate × environment confounder for meta-regression and joint meta-analysis, which are two commonly used meta-analysis methods for testing the gene × environment interaction or the genetic main effect and interaction jointly. Here we show that meta-regression is robust to the covariate × environment confounder while joint meta-analysis is subject to the confounding effect with inflated type I error rates. Given vast sample sizes employed in genome-wide gene × environment interaction studies, non-significant covariate × environment interactions at the study level could substantially elevate the type I error rate at the consortium level. When covariate × environment confounders are present, type I errors can be controlled in joint meta-analysis by including the covariate × environment terms in the analysis at the study level. Alternatively, meta-regression can be applied, which is robust to potential covariate × environment confounders.

Genetic Programming of Hypertension

The heritability of hypertension (HTN) is widely recognized and as a result, extensive studies ranging from genetic linkage analyses to genome-wide association studies are actively ongoing to elucidate the etiology of both monogenic and polygenic forms of HTN. Due to the complex nature of essential HTN, however, single genes affecting blood pressure (BP) variability remain difficult to isolate and identify and have rendered the development of single-gene targeted therapies challenging. The roles of other causative factors in modulating BP, such as gene-environment interactions and epigenetic factors, are increasingly being brought to the forefront. In this review, we discuss the various monogenic HTN syndromes and corresponding pathophysiologic mechanisms, the different methodologies employed in genetic studies of essential HTN, the mechanisms for epigenetic modulation of essential HTN, pharmacogenomics and HTN, and finally, recent advances in genetic studies of essential HTN in the pediatric population.

Systematic Characterization and Prediction of Human Hypertension Genes

Hypertension is a major cardiovascular risk factor and accounts for a large part of cardiovascular mortality. In this work, we analyzed the properties of hypertension genes and found that when compared with genes not yet known to be involved in hypertension regulation, known hypertension genes display distinguishing features: (1) hypertension genes tend to be located at network center; (2) hypertension genes tend to interact with each other; and (3) hypertension genes tend to enrich in certain biological processes and show certain phenotypes. Based on these features, we developed a machine-learning algorithm to predict new hypertension genes. One hundred and seventy-seven candidates were predicted with a posterior probability >0.9. Evidence supporting 17 of the predictions has been found.