Genetic Insights into the Therapeutic Targets for Essential Hypertension: Mendelian Randomization and Colocalization Analysis

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Genetic Insights into the Therapeutic Targets for Essential Hypertension: Mendelian Randomization and Colocalization Analysis

https://doi.org/10.21203/rs.3.rs-3808465/v1

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The prevalence of Essential Hypertension (EH) is increasing globally, and the effectiveness of pharmacological treatments remains far from ideal. Combining Mendelian Randomization (MR) to identify potential drug targets may be key to reducing the disease burden and developing potential treatments. We utilized the UK Biobank cohort (ncase = 54358, ncontrol = 408652) to extract summary statistics for EH and further validated in the FinnGen cohort (ncase = 92462, ncontrol = 265626). Cis-expression quantitative trait loci (cis-eQTL) from available druggable genes were retrieved and used as genetic instrumental variables. Two-sample MR analysis and colocalization analysis were conducted to examine whether identified genes and EH share variants, further consolidating MR results. Ten drug targets (FES, SLC22A4, PTK2B, BLK, ITPR1, NEGR1, GRK4, ADM, MAPK3, MAST3) showed significant MR results in two independent datasets, with no reverse causation observed. Colocalization analysis indicated that FES (PP.H4 = 0.99) and SLC22A4 (PP.H4 = 0.82) shared the same variants with EH, providing strong evidence. Additionally, FES showed significant associations with reduced risk of coronary artery disease, systolic blood pressure, and diastolic blood pressure, while SLC22A4 was significantly associated with increased diastolic blood pressure. Our results suggest that targeting FES and SLC22A4 might treat or cause EH, potentially revealing new pathophysiological pathways and treatment targets for EH.

Bioinformatics

Essential hypertension

Genetics

Drug

Mendelian randomization

Hypertension is one of the most common cardiovascular diseases, affecting 1.13 billion people globally (1) and causing 9.4 million deaths annually (2). By 2025, the global number of individuals diagnosed with hypertension is estimated to reach 1.5 billion. Approximately 95% of all hypertensive patients are diagnosed with Essential Hypertension (EH) (3). Despite extensive research on hypertension, little is known about the mechanisms of blood pressure regulation and hypertension pathophysiology, and patients vary in their response and adherence to current pharmacological treatments, which are far from satisfactory (3). Effective and safe medications remain a primary concern for clinicians. Therefore, identifying effective drugs that can prevent EH is crucial to alleviating the global burden of hypertension.

Mendelian Randomization (MR) is a method used to investigate the causal relationship between exposures and outcomes (4). In drug target gene MR analysis, cis-expression quantitative trait loci (cis-eQTL) located in the genomic region of drug target genes are often considered proxies, functioning as regulatory factors influencing gene expression (5). A recent study used the principles of MR to investigate the association between GWAS results, blood transcriptome, and heart failure to identify candidate genetic loci (6). Additionally, MR analysis of the relationship between antihypertensive drugs and Alzheimer's disease has provided new insights into the evaluation of antihypertensive drug applications (7). This analysis can often avoid residual confounding and reverse causation in traditional observational studies (8).

Current pharmacological treatments for EH are far from ideal. Hence, combining MR to find attractive drug targets for potential treatment is necessary to prevent or slow the progression of EH. In this study, we conducted MR analysis by combining eQTLs with two independent EH datasets from GWAS. We investigated the associations between medicinal genes represented by genetic agents and the risk of EH, as well as the associations of these genes with systolic blood pressure, diastolic blood pressure, and eight common cardiovascular diseases.

Identification of Cis-eQTL Data Related to Druggable Genes

Four thousand three hundred two druggable autosome genes with HGNC names were identified (9). These included 1,375 clinically developed protein therapeutic targets, 2927 proteins associated with drug targets. Considering that cis-eQTLs in drug development research are closer to the genes of interest, we obtained statistically significant cis-eQTLs (false discovery rate < 0.05, per probe 1 Mb) from the eQTLGen Consortium and a meta-analysis of blood eQTLs from 31,684 individuals (10). To obtaining genetic tools for the 4,302 drug targets, we selected cis-eQTLs within 100 kb of each gene's genomic location, resulting in eQTLs for 2,664 druggable targets.

Result Data

GWAS database for EH were obtained from UK Biobank, adjusted for covariates: 10 principal components including age, sex, age*sex, including 54,358 cases and 463,010 controls (Table S1). The FinnGen cohort included 92,462 EH patients and 265,626 controls, with GWAS, also adjusted for ten principal components and genotyping batches (11). Systolic blood pressure, diastolic blood pressure, and eight cardiac diseases were also included in the analysis.

Statistics

MR analysis was conducted using the TwoSampleMR package. Some settings were used to filter inadequacy instruments. Firstly, we excluded SNPs (F-statistic < 10). Then, SNPs without linkage imbalance (r² < 0.1, based on the European reference panel from the 1000 Genomes project) were selected as instrumental variables. We also used Steiger filtering to remove genes with greater variance in the EH trait than in the exposure (Table S3). Inverse Variance Weighted (IVW) as the main analysis. MR-Egger regression was used for genetic instruments with more than two variants to interpret potential pleiotropy between the exposure and outcome. Bonferroni correction was applied to establish the significance threshold for multiple testing adjustments in sensitivity analysis. In the study using UK Biobank data, a p-value less than 1.90E-5 (p = 0.05/2644) was found to be significant and then replicated significant targets in the FinnGen cohort. In the replication analysis, correlations with a p-value below 0.0017(p = 0.05/29) were considered significant. For significant MR results in two independent cohorts, colocalization analysis of EH risk was conducted using the coloc R package with default priors. For the eQTL dataset, significant colocalization was set as PPH4 > 0.80, and genes with strong colocalization with EH were considered potential target molecules. For potential indications, the relationship between target molecules and blood pressure (12), and eight cardiovascular diseases (11, 13–15) was explored through MR analysis. Finally, in order to evaluate the drug efficacy of candidate target genes, we systematically searched the DrugBank database to obtain approved or currently being studied drugs targeting molecules. Ethical approval was not required since our research is based on publicly available data.

Study overview

Our study aimed to identify therapeutic targets related to EH. The research flowchart is shown in Fig. 1, and data sources are available in Tables S1 and S2. Initially, we categorized 4,302 genes as druggable or potentially druggable. Then, we selected cis-eQTL closely related to concentrations and used the two-sample MR method to study significance of therapeutic target to EH risk. For the result of multiple adjustments, we performed colocalization to check whether different causal variants influenced MR results in linkage disequilibrium with each other.

Replication Analysis

In the public cohort, including 54,358 patients and 408,652 controls from the UK Biobank, we used IVW meta-analysis to summarize effects estimates from each genetic instrument. After multiple corrections, 29 genes were found to be associated with EH risk (p < 1.90E-5) (Table S4 and Table S5). We then attempted to replicate the effect estimates of the initial 29 genes using data from the FinnGen cohort (N = 358088). Ultimately, ten drug targets (FES, SLC22A4, PTK2B, BLK, ITPR1, NEGR1, GRK4, ADM, MAPK3, MAST3) were identified with no reverse causation found under the strict Bonferroni threshold (p < 0.0017, 0.05/29 genes) (Table 1 and Table S6 and Table S7).

Table 1

Mendelian randomization results.
Genes	UK Biobank cohort					FINNGEN cohort
	SNPs	OR (95% CI)	IVW p-value	MR-Egger intercept	Egger intercept p-value	SNPs	OR (95% CI)	IVW p-value	MR-Egger intercept	Egger intercept p-value
FES	34	0.990 (0.988, 0.992)	4.32e-18	0.001	0.16	33	0.882 (0.860,0.904)	4.89e-23	0.003	0.64
SLC22A4	20	1.008 (1.005, 1.010)	7.35e-10	1.74e-04	0.87	21	1.045 (1.020,1.071)	3.29e-04	0.019	0.10
PTK2B	40	0.994 (0.992, 0.996)	1.30 e-09	-0.001	0.09	39	0.951 (0.933,0.970)	3.87e-07	-0.006	0.17
BLK	47	0.993 (0.990, 0.996)	7.96e-07	-0.001	0.06	46	0.903 (0.878,0.929)	1.52e-12	-0.015	0.01
ITPR1	26	1.008 (1.005, 1.012)	1.18e-06	4.95e-05	0.88	26	1.076 (1.036,1.117)	1.40e-04	0.006	0.12
NEGR1	24	1.010 (1.006, 1.014)	2.09e-06	-1.83e-04	0.74	24	1.055 (1.022,1.090)	1.00e-03	0.003	0.51
GRK4	18	1.008 (1.005, 1.011)	2.59e-06	3.11e-04	0.64	18	1.057 (1.023,1.092)	9.07e-04	0.007	0.31
ADM	18	1.007 (1.004, 1.010)	3.32e-06	-1.77e-05	0.98	17	1.089 (1.040,1.140)	2.47e-04	-0.019	0.08
MAPK3	15	0.995 (0.992, 0.997)	3.33e-06	-0.003	0.21	15	0.951 (0.934,0.968)	4.34e-08	0.009	0.61
MAST3	18	0.992 (0.988, 0.995)	1.10 e-05	-0.001	0.22	18	0.925 (0.891,0.960)	3.64e-05	-0.001	0.86

Colocalization analysis

We further conducted a colocalization analysis to determine the probability of EH- and eQTL-related SNPs sharing causal genetic variants, identifying two potential druggable genes (Table S8). Results suggest that FES (PP.H4 = 0.97, Fig. 2A) and SLC22A4 (PP.H4 = 0.82, Fig. 2B) may share causal genetic variants with EH.

FES and SLC22A4

In the UK Biobank cohort, a correlation was found between increased expression of FES and reduced risk of EH (OR = 0.990, 95% CI: 0.988–0.992), and increased expression of SLC22A4 was correlated with a higher risk of EH (OR = 1.008, 95% CI: 1.005–1.010). Additionally, in the FinnGen cohort, increased expression of FES and SLC22A4 was correlated with EH risk (OR = 0.882, 95% CI: 0.860–0.904) and (OR = 1.045, 95% CI: 1.020–1.071) respectively. Thus, FES activators and SLC22A4 inhibitors may be new strategies for reducing EH risk, but their hypotensive effects and suitability for other cardiovascular diseases must be considered. Consequently, we assessed the causal relationship of genetic proxies of FES and SLC22A4 with systolic blood pressure, diastolic blood pressure, and eight other cardiovascular diseases. No significant correlations were observed between the FES gene and a range of cardiovascular diseases (p > 0.005, 0.05/10). However, predicted FES elevation was significantly negatively correlated with coronary artery disease (OR = 0.88, 95% CI: 0.86–0.90, p = 2.97E-29), systolic blood pressure (OR = 0.31, 95% CI: 0.23–0.41, p = 2.19E-15), and diastolic blood pressure (OR = 0.52, 95% CI: 0.44–0.62, p = 6.81E-14) (Fig. 3A). For SLC22A4, we found that predicted SLC22A4 elevation was significantly positively correlated only with diastolic blood pressure (OR = 1.41, 95% CI: 1.22–1.63, p = 2.76E-6) (Fig. 3B).

Additionally, in clinical settings, molecular compounds targeting FES are predominantly in the investigative stage, with most targeting tumor diseases and very few specifically for hypertension treatment. Currently, verapamil and Clonidine, inhibitors targeting SLC22A4, have been used in patients with hypertension (Table S9).

To explore the causal relationship of potentially druggable genes with EH, we conducted large-scale MR and colocalization analyses integrating GWAS datasets, pharmacogenomics, and gene expression data (eQTL). We found strong evidence that higher genetic proxies of FES expression are inversely correlated with EH risk, while higher SLC22A4 proxies are positively correlated. Associations with systolic and diastolic blood pressure also support these findings. Additionally, FES showed a significant correlation with reduced risk of coronary artery disease.

FES is a member of a distinct subfamily of cytoplasmic protein-tyrosine kinases, primarily involved in hematopoiesis and signal transduction of cytokine receptor superfamily members (16). The FES proto-oncogene product represents a new participant in regulating angiogenesis, with previous studies indicating its central role in movement, proliferation, differentiation, and survival of cells and inflammation (17–19). FES is also associated with atherosclerosis, cancer, and infections (20–22). For example, VEGF induces endothelial sprouting in blood vessels, partially activating PI3 kinase through c-Fes and enhancing capillary morphogenesis through an unknown signaling pathway (23). Angiopoietin-1 (Ang1) and Angiopoietin-2 (Ang2) play antagonistic roles in vascular stability and instability, respectively (24). Additionally, there is a delicate balance between vasodilators and vasoconstrictors (25, 26). Disruption of this balance can lead to endothelial dysfunction and excessive release of vasoconstrictive substances (27). These changes may lead to structural and functional alterations in macro-and micro-vasculature(28), as observed in pregnant women, where the release of bioactive factors into maternal circulation causes an imbalance in tyrosine kinases and cytokines in vessels, leading to increased vasoconstrictors and hypertension (29). Another study found FES expressed in immune cells (monocytes, macrophages, neutrophils) but enriched in endothelial cells (30). Upregulation of FES can induce endothelial dysfunction (31), suggesting its regulatory role in blood vessels. Fostamatinib, a Tyrosine-protein kinase Fes/Fps inhibitor, has a side effect of increased blood pressure (32). Previous studies considered VEGF signaling to play a significant role(33), and given its upstream action, FES's potential impact cannot be ruled out. A recent study has also shown FES's protective role in atherosclerosis, with PRDM1 expression in human atherosclerotic lesions potentially promoting atherosclerosis by reducing FES expression, as evidenced by increased smooth muscle cells in atherosclerotic lesions of FES knockout mice (22). Our new analysis also indicates FES's relevance to blood pressure (BP), further suggesting its role in regulating BP and CAD risk. However, no direct evidence currently shows FES's involvement in the treatment and progression of EH.

SLC22A4 encodes the Organic Cation/Carnitine Transporter 1 (OCTN1), a 551-amino acid protein belonging to the solute carrier family 22, functioning as a plasma membrane transporter for various substrates (34). The chromosome 5q31 region containing SLC22A4 also houses many genes involved in immune and inflammatory systems. Some inflammatory mediators play significant roles in the pathophysiology of hypertension, such as Angiotensin II stimulating peripheral blood monocytes to secrete tumor necrosis factor-alpha (TNF-α) and interleukin-1, which can stimulate fibroblasts to secrete VEGF, affecting endothelial function (35). OCTN1 is a pH-dependent transporter, and verapamil and Clonidine can inhibit OCTN1-mediated uptake of tetraethylammonium (TEA) (36). Additionally, studies indicate that the effects of NO on blood vessels are mediated by the activation of a tetraethylammonium-sensitive population of K + channels, which regulate blood pressure (37).

In our study, of the 29 drug targets identified in the UK Biobank cohort, only ten were successfully replicated in the FinnGen cohort. Although they share a common European ancestry genome and are considered homogeneous, their replicability was poor (38). Continuing this ancestral bias in genetics could exacerbate health disparities caused by race. Future research should focus more on the effects across different subgroups.

This study has several strengths. Firstly, to ensure the credibility of the results, we applied Bonferroni correction for multiple tests and pleiotropy-robust MR methods to reduce the possibility of bias due to pleiotropy rigorously, and colocalization analysis reinforced the credibility of our results. Secondly, we focused our research on druggable genes to enhance the efficacy of EH treatment and the success rate of drug development, identifying two drug targets (FES and SLC22A4) related to EH. Although there is no clear evidence yet that these targets are directly involved in EH treatment, but they at least provide ideas for drug development. Lastly, we conducted extensive MR analysis to identify more alternative indications, which is important.

The study has several limitations. Firstly, it is challenging to rule out the potential pleiotropy completely. Secondly, this study only examined eight cardiovascular diseases and did not analyze other diseases or adverse reactions. Thirdly, epigenetics may play a key role in BP regulation for EH, and the interaction with environmental factors needs to be considered in future research. Fourthly, biological and clinical studies are needed to verify their effectiveness in the treatment of EH. Finally, the research population is limited to Europeans and may not necessarily be suitable for other races.

Our analysis of two large European cohorts in genome-wide association studies indicates ten genetic loci have a causal relationship with EH risk. These genetic loci could become therapeutic targets for the treatment of EH, particularly FES and SLC22A4. The roles of these candidate loci warrant further investigation.

Acknowledgments

Data collection, analysis, and manuscript writing were all assisted by BH. The analytical procedures were confirmed by LLH and YGF, who also read the text. JF edited the text for key information. CML and JLS gathered some data. The final manuscript was reviewed and approved by all writers.

Funding

No funding.

Availability of data and materials

Corresponding author will provide the datasets upon reasonable request.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflict of Interest

The authors declare no conflict of interest.

Acknowledgments

We thank the participants and researchers in the eQTLGen Consortium, UK Biobank and FinnGen research.

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The authors declare no competing interests.

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Genetic Insights into the Therapeutic Targets for Essential Hypertension: Mendelian Randomization and Colocalization Analysis

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Abstract

Figures

Introduction

Methods

Identification of Cis-eQTL Data Related to Druggable Genes

Result Data

Statistics

Results

Study overview

Replication Analysis

Colocalization analysis

FES and SLC22A4

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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