Endothelin-1 (ET-1) plays important roles in endothelial dysfunction, vascular physiology, inflammation, and atherosclerosis. Nonetheless, the role of ET-1 (EDN1) gene variants on coronary artery disease (CAD) risk remains poorly understood. The aim of the present study was to evaluate the role of EDN1 gene polymorphisms on individual susceptibility to CAD. We genotyped five tagSNPs (single-nucleotide polymorphisms) (rs6458155, rs4145451, rs9369217, rs3087459, and rs2070699) within EDN1 gene in 525 CAD patients and 675 control subjects. In a multivariate logistic regression analysis, we detected an association of rs6458155 in EDN1 gene with the CAD risk; compared with the TT homozygotes, the CT heterozygotes (odds ratio (OR) = 1.53, 95% confidence interval (CI) = 1.02–2.29, P=0.040) and the CC homozygotes (OR = 1.55, 95% CI = 1.01–2.36, P=0.043) were statistically significantly associated with the increased risk for CAD. A similar trend of the association was found in dominant model (OR = 1.53, 95% CI = 1.05–2.25, P=0.029). Consistently, the haplotype rs6458155C-rs4145451C containing rs6458155 C allele exhibited the increased CAD risk (OR = 1.22, 95% CI = 1.03–1.43, and P=0.018). In addition, CT genotype of rs6458155 conferred the increased plasma ET-1 levels compared with TT genotype (P<0.05). No association of the other four tagSNPs in EDN1 gene with CAD risk was observed. In conclusion, our study provides the first evidence that EDN1 tagSNP rs6458155 is associated with CAD risk in the Chinese Han population, which is probably due to the influence of the circulating ET-1 levels.

Introduction

Coronary artery disease (CAD) is the leading cause of morbidity and mortality in humans worldwide, despite advances in treatment and lifestyle modification. As a complex disease, both genetic and environmental factors contribute to the occurrence and development of CAD, as evident by its high heritability in twin and family studies [1]. Previous studies have reported that an assessment of genetic risk burden can identify individuals at increased risk for incident CAD in population-based epidemiological cohorts [2–4]. Recently, a number of investigations have demonstrated the effect of polymorphic variants in candidate genes on CAD susceptibility, supporting the critical roles of host genetic alterations on the pathogenesis of CAD [5–7].

Endothelin-1 (ET-1), encoded by EDN1 gene, is a potent and long-lasting vasoconstrictor [8]. Several studies have found that endothelial dysfunction was detectable in the early stage of CAD, which could decrease cell-to-cell communication and increase vascular permeability [9–11]. ET-1, primarily released from endothelial cells, plays a crucial role in maintaining vascular homeostasis [12]. ET-1 is found to reduce nitric oxide bioavailability and enhance reactive oxygen species formation [12,13]. In addition to be a potent regulator of vascular tone, ET-1 is a pro-inflammatory factor in the development of cardiovascular disease. For example, ET-1 induces not only the stimulation of adhesion molecules, but also the activation of pro-inflammatory transcription factor NF-κB and expression of several pro-inflammatory cytokines including TNF-α, IL-1, and IL-6 [14,15]. Vascular smooth muscle cell (VSMC) is the main cell type in vessel wall and its accumulation is a hallmark of atherosclerosis [16]. ET-1 promotes atherosclerotic plaque development through VSMC-mediated vasomotor constriction, remodeling, and proliferation [13,17,18]. Moreover, ET-1 is significantly increased in CAD patients than healthy volunteers [19]. Taking together, ET-1 may exert a crucial role in the pathophysiology of CAD.

Genome-wide association studies (GWASs) have mapped more than 65 genomic loci for CAD, which are mostly residing in non-coding sequence [20–22]. Populations of affected and unaffected individuals could be studied in association with CAD by genotyping common single-nucleotide polymorphisms (SNPs) within candidate genes and its regulatory sequences [23]. On the basis of the biological and pathologic significance of ET-1 in CAD, we postulated that genetic variations in the EDN1 gene contribute to the development of CAD. Therefore, we conducted a case–control study to elucidate the association of five EDN1 tagSNPs (rs6458155, rs4145451, rs9369217, rs3087459, and rs2070699) with the risk of CAD.

Materials and methods

Study subjects

In this case–control study, a total of 1200 Chinese Han subjects with 525 CAD patients and 675 control subjects were recruited from the First People’s Hospital of Foshan (Foshan, China) and the Affiliated Hospital of Guangdong Medical University (Zhanjiang, China) between March 2011 and October 2015. Inclusion and exclusion criteria, diagnosis, and evaluation as well as criteria for CAD and controls were described in our previous studies [24]. All subjects were genetically unrelated ethnic Han Chinese and a structured questionnaire was administered by them at the enrollment to collect information on demographic data and risk factors related to CAD. The study was approved by the Medical Ethics Committee of the First People’s Hospital of Foshan and the Affiliated Hospital of Guangdong Medical University, and written consent was obtained before the commencement of the study.

DNA extraction

Genomic DNA was isolated from peripheral whole blood using TIANamp blood DNA extraction kit (TianGen Biotech, Beijing, China) according to the manufacturer’s instructions. All DNA samples were dissolved in water and stored at −20°C until use.

TagSNPs selection and genotyping

Five tagSNPs (rs6458155, rs4145451, rs9369217, rs3087459, and rs2070699) were selected from the HapMap database using the parameters of r2 > 0.8 threshold for clusters of linkage disequilibrium (LD) amongst polymorphisms, and minor allele frequency (MAF) > 5%. The 5 tagSNPs would capture a total of 12 common SNPs with an MAF > 0.05 in the Chinese Han population (Supplementary Table S1). Two LD blocks amongst the five tagSNPs in the present study were also estimated by the Haploview software version 4.2 [25]. Then the haplotype analysis was performed with the SHEsis platform [26].

Genomic DNA was genotyped by PCR-ligase detection reaction (PCR-LDR) method (Shanghai Biowing Applied Biotechnology Company) as described previously [27]. The sequences of primers and probes are listed in Supplementary Table S2. In addition, approximately 10% of the samples were randomly selected to perform the repeated assays and the results were 100% concordant.

Determination of ET-1 levels

The plasma ET-1 levels in 48 individuals were quantitated by means of the ELISA kit (ZCI Bio, China). ET-1 levels were calculated with a standard curve drawn using absorbance according to standards provided by the manufacturer.

Statistical analysis

The sample size was performed using PS program (Power and Sample size calculations, version 3.0.43). Our study provided a statistical power of 74.1% to detect the differences between 525 CAD cases and 675 control subjects with an OR of 1.53 at a significant level of 0.05 in the dominant model. Hardy–Weinberg equilibrium was tested by the use of a goodness-of-fit χ2 test in the controls. Data were presented as mean ± S.D. for the quantitative variables and percentages for the qualitative variables. The differences of the demographic and clinical characteristics between cases and controls were estimated using the Student’s t test (for continuous variables) and χ2 test (for categorical variables). To evaluate the associations between the EDN1 tagSNPs and CAD risk, odds ratio (OR) and 95% confidence interval (CI) were calculated by unconditional logistic regression analysis with adjustments for age, sex, body mass index (BMI), smoking, drinking, hypertension, diabetes, and hyperlipidemia. Analyses were performed using SPSS version 21.0. Statistical differences of ET-1 expression levels between different groups of samples in ELISA experiment were determined by Mann–Whitney U-test. P<0.05 was considered statistically significant for all tests.

Results

Characteristics of the study participants

Table 1 shows the demographic and clinical characteristics of the participants in the present study. There was a significant sex difference between cases and controls due to the high prevalence of males amongst CAD patients. The average BMI of the CAD cases were significantly higher than that of the controls. In addition, CAD patients had higher frequencies of smokers and alcohol consumers, and a higher fasting glucose level as compared with controls. Lipid profile data demonstrated significantly higher levels of triglyceride (TG), low-density lipoprotein cholesterol (LDLC) and lower levels of high-density lipoprotein cholesterol (HDLC) in CAD patients when compared with controls. Patients with CAD were more likely to be diabetic, hypertensive, and dyslipidemic than the control subjects. Systolic blood pressure and diastolic blood pressure were significantly higher in CAD groups when compared with controls. These data demonstrated that male gender, obesity, tobacco use, alcohol intake, hypertension, diabetes, and hyperlipidemia were the important risk factors for developing CAD in Chinese population.

Table 1
The characteristics of CAD cases and controls
Variable Controls (n=675) Cases (n=525) P1 
Age (years) 61.81 ± 12.35 63.82 ± 11.86 0.004 
Sex (male) (%) 405 (60.0) 361 (68.8) 0.002 
Smoking (%) 163 (24.1) 297 (56.6) <0.001 
Drinking (%) 93 (13.8) 135 (25.7) <0.001 
Hypertension (%) 240 (35.6) 335 (63.8) <0.001 
Diabetes (%) 111 (16.4) 249 (47.4) <0.001 
Hyperlipidemia (%) 254 (37.6) 383 (73.0) <0.001 
BMI (kg/m223.12 ± 1.83 23.37 ± 2.10 0.029 
Systolic BP (mmHg) 132.83 ± 19.12 142.02 ± 18.18 <0.001 
Diastolic BP (mmHg) 73.14 ± 10.62 76.94 ± 10.17 <0.001 
FPG (mM) 5.80 ± 1.88 6.66 ± 1.62 <0.001 
TG (mM) 1.51 ± 0.91 2.10 ± 1.01 <0.001 
TC (mM) 4.63 ± 1.12 4.74 ± 1.24 0.119 
HDLC (mM) 1.36 ± 0.39 1.20 ± 0.40 <0.001 
LDLC (mM) 2.64 ± 0.88 3.06 ± 0.93 <0.001 
Variable Controls (n=675) Cases (n=525) P1 
Age (years) 61.81 ± 12.35 63.82 ± 11.86 0.004 
Sex (male) (%) 405 (60.0) 361 (68.8) 0.002 
Smoking (%) 163 (24.1) 297 (56.6) <0.001 
Drinking (%) 93 (13.8) 135 (25.7) <0.001 
Hypertension (%) 240 (35.6) 335 (63.8) <0.001 
Diabetes (%) 111 (16.4) 249 (47.4) <0.001 
Hyperlipidemia (%) 254 (37.6) 383 (73.0) <0.001 
BMI (kg/m223.12 ± 1.83 23.37 ± 2.10 0.029 
Systolic BP (mmHg) 132.83 ± 19.12 142.02 ± 18.18 <0.001 
Diastolic BP (mmHg) 73.14 ± 10.62 76.94 ± 10.17 <0.001 
FPG (mM) 5.80 ± 1.88 6.66 ± 1.62 <0.001 
TG (mM) 1.51 ± 0.91 2.10 ± 1.01 <0.001 
TC (mM) 4.63 ± 1.12 4.74 ± 1.24 0.119 
HDLC (mM) 1.36 ± 0.39 1.20 ± 0.40 <0.001 
LDLC (mM) 2.64 ± 0.88 3.06 ± 0.93 <0.001 

Abbreviations: BP, blood pressure; FPG, fasting plasma glucose; TC, total cholesterol.

1

Two-sided chi-square test or independent-samples ttest.

Multivariate associations of EDN1 tagSNPs with the risk of CAD

Five EDN1 tagSNPs (rs6458155, rs4145451, rs9369217, rs3087459, and rs2070699) were genotyped in 525 CAD patients and 675 control subjects. Primary information of tested five tagSNPs was summarized in Table 2. The MAF of these five tagSNPs amongst controls was similar to those from the HapMap Project Chinese Han data. Genotypic distribution of each tagSNP was not deviated from the Hardy–Weinberg equilibrium (P>0.05, Table 2).

Table 2
Primary information for the polymorphisms in EDN1 gene
Genotyped SNPs Chr Pos (Genome Build 108) Pos in EDN1 gene MAF for Chinese in HapMap1 MAF in our controls (n=675) P-value for HWE test in our controls2 
rs6458155 12261688 5′ UTR 0.415 0.427 0.985 
rs4145451 12264392 5′ UTR 0.463 0.464 0.198 
rs9369217 12283529 5′ UTR 0.167 0.164 0.558 
rs3087459 12289406 Promoter 0.171 0.236 0.907 
rs2070699 12292539 Intron 2 0.451 0.452 0.977 
Genotyped SNPs Chr Pos (Genome Build 108) Pos in EDN1 gene MAF for Chinese in HapMap1 MAF in our controls (n=675) P-value for HWE test in our controls2 
rs6458155 12261688 5′ UTR 0.415 0.427 0.985 
rs4145451 12264392 5′ UTR 0.463 0.464 0.198 
rs9369217 12283529 5′ UTR 0.167 0.164 0.558 
rs3087459 12289406 Promoter 0.171 0.236 0.907 
rs2070699 12292539 Intron 2 0.451 0.452 0.977 
1

MAF.

2

HWE, Hardy–Weinberg equilibrium.

Multivariate logistic regression analysis was performed to adjust for age, sex, BMI, smoking, drinking, hypertension, diabetes, and hyperlipidemia, known to affect risk of CAD. The multiple genetic models of EDN1 tagSNPs and their associations with CAD risk were summarized in Table 3. Presence of CT and CC genotypes (C carriers) of rs6458155 were associated with an increased risk of CAD compared with the TT genotype (OR = 1.53, 95% CI = 1.02–2.29, P=0.040; and OR = 1.55, 95% CI = 1.01–2.36, P=0.043, respectively), listed in genotype model. Moreover, analysis results of the dominant model suggested that the CC+CT genotype had a higher CAD risk compared with the rs6458155 TT genotype (OR = 1.53, 95% CI = 1.05–2.25, P=0.029). These data indicate that EDN1 rs6458155 polymorphism may be associated with risk of CAD and that individuals carrying C allele may have significantly increased CAD susceptibility. However, no association of other four tagSNPs with CAD risk was detected in the EDN1 gene.

Table 3
Multivariate associations of five tagSNPs in EDN1 gene with the risk of CAD
Type Controls (n=675) Cases (n=525) OR (95% CI)1 P1 
  Number (%) Number (%)   
rs6458155 
Genotype TT 123 (18.2) 67 (12.8) 1.00 
 CT 330 (48.9) 263 (50.1) 1.53 (1.02–2.29) 0.040 
 CC 222 (32.9) 195 (37.1) 1.55 (1.01–2.36) 0.043 
Dominant TT 123 (18.2) 67 (12.8) 1.00 
 CC+CT 552 (81.8) 458 (87.2) 1.53 (1.05–2.25) 0.029 
Recessive TT+CT 453 (67.1) 330 (62.9) 1.00 
 CC 222 (32.9) 195 (37.1) 1.12 (0.84–1.49) 0.435 
rs4145451 
Genotype AA 153 (22.7) 89 (17.0) 1.00 
 AC 319 (47.3) 257 (49.0) 1.33 (0.92–1.92) 0.129 
 CC 203 (30.0) 179 (34.0) 1.33 (0.90–1.96) 0.158 
Dominant AA 153 (22.7) 89 (17.0) 1.00 
 CC+AC 522 (77.3) 436 (83.0) 1.33 (0.94–1.87) 0.107 
Recessive AA+AC 472 (70.0) 346 (66.0) 1.00 
 CC 203 (30.0) 179 (34.0) 1.09 (0.81–1.46) 0.583 
rs9369217 
Genotype CC 475 (70.3) 359 (68.4) 1.00 
 CT 180 (26.7) 156 (29.7) 1.25 (0.92–1.69) 0.156 
 TT 20 (3.0) 10 (1.9) 0.61 (0.24–1.57) 0.308 
Dominant CC 475 (70.3) 359 (68.4) 1.00 
 TT+CT 200 (29.7) 166 (31.6) 1.18 (0.88–1.59) 0.268 
Recessive TT 20 (3.0) 10 (1.9) 1.00 
 CC+CT 655 (97.0) 515 (98.1) 1.74 (0.68–4.45) 0.247 
rs3087459 
Genotype AA 395 (58.5) 316 (60.2) 1.00 
 AC 242 (35.9) 185 (35.2) 0.86 (0.64–1.15) 0.299 
 CC 38 (5.6) 24 (4.6) 0.71 (0.37–1.36) 0.297 
Dominant AA+AC 637 (94.4) 501 (95.4) 1.00 
 CC 38 (5.6) 24 (4.6) 0.75 (0.39–1.43) 0.378 
Recessive AA 395 (58.5) 316 (60.2) 1.00 
 CC+AC 280 (41.5) 209 (39.8) 0.84 (0.64–1.11) 0.216 
rs2070699 
Genotype TT 203 (30.1) 146 (27.8) 1.00 
 GT 334 (49.5) 267 (50.9) 1.17 (0.85–1.61) 0.342 
 GG 138 (20.4) 112 (21.3) 1.06 (0.71–1.58) 0.764 
Dominant TT 203 (30.1) 146 (27.8) 1.00 
 GG+GT 472 (69.9) 379 (72.2) 1.14 (0.84–1.54) 0.408 
Recessive GG 138 (20.4) 112 (21.3) 1.00 
 TT+GT 537 (79.6) 413 (78.7) 1.04 (0.74–1.46) 0.819 
Type Controls (n=675) Cases (n=525) OR (95% CI)1 P1 
  Number (%) Number (%)   
rs6458155 
Genotype TT 123 (18.2) 67 (12.8) 1.00 
 CT 330 (48.9) 263 (50.1) 1.53 (1.02–2.29) 0.040 
 CC 222 (32.9) 195 (37.1) 1.55 (1.01–2.36) 0.043 
Dominant TT 123 (18.2) 67 (12.8) 1.00 
 CC+CT 552 (81.8) 458 (87.2) 1.53 (1.05–2.25) 0.029 
Recessive TT+CT 453 (67.1) 330 (62.9) 1.00 
 CC 222 (32.9) 195 (37.1) 1.12 (0.84–1.49) 0.435 
rs4145451 
Genotype AA 153 (22.7) 89 (17.0) 1.00 
 AC 319 (47.3) 257 (49.0) 1.33 (0.92–1.92) 0.129 
 CC 203 (30.0) 179 (34.0) 1.33 (0.90–1.96) 0.158 
Dominant AA 153 (22.7) 89 (17.0) 1.00 
 CC+AC 522 (77.3) 436 (83.0) 1.33 (0.94–1.87) 0.107 
Recessive AA+AC 472 (70.0) 346 (66.0) 1.00 
 CC 203 (30.0) 179 (34.0) 1.09 (0.81–1.46) 0.583 
rs9369217 
Genotype CC 475 (70.3) 359 (68.4) 1.00 
 CT 180 (26.7) 156 (29.7) 1.25 (0.92–1.69) 0.156 
 TT 20 (3.0) 10 (1.9) 0.61 (0.24–1.57) 0.308 
Dominant CC 475 (70.3) 359 (68.4) 1.00 
 TT+CT 200 (29.7) 166 (31.6) 1.18 (0.88–1.59) 0.268 
Recessive TT 20 (3.0) 10 (1.9) 1.00 
 CC+CT 655 (97.0) 515 (98.1) 1.74 (0.68–4.45) 0.247 
rs3087459 
Genotype AA 395 (58.5) 316 (60.2) 1.00 
 AC 242 (35.9) 185 (35.2) 0.86 (0.64–1.15) 0.299 
 CC 38 (5.6) 24 (4.6) 0.71 (0.37–1.36) 0.297 
Dominant AA+AC 637 (94.4) 501 (95.4) 1.00 
 CC 38 (5.6) 24 (4.6) 0.75 (0.39–1.43) 0.378 
Recessive AA 395 (58.5) 316 (60.2) 1.00 
 CC+AC 280 (41.5) 209 (39.8) 0.84 (0.64–1.11) 0.216 
rs2070699 
Genotype TT 203 (30.1) 146 (27.8) 1.00 
 GT 334 (49.5) 267 (50.9) 1.17 (0.85–1.61) 0.342 
 GG 138 (20.4) 112 (21.3) 1.06 (0.71–1.58) 0.764 
Dominant TT 203 (30.1) 146 (27.8) 1.00 
 GG+GT 472 (69.9) 379 (72.2) 1.14 (0.84–1.54) 0.408 
Recessive GG 138 (20.4) 112 (21.3) 1.00 
 TT+GT 537 (79.6) 413 (78.7) 1.04 (0.74–1.46) 0.819 
1

Adjusted for age, sex, BMI, smoking, drinking, hypertension, diabetes, and hyperlipidemia.

Stratification analyses of EDN1 rs6458155 polymorphism and risk of CAD

We further analyzed the associations of the rs6458155 polymorphism with the risk of CAD stratified by age, gender, status of smoking and drinking. When stratification either by age or gender was performed, no more significant association between rs6458155 and CAD risk was found (Supplementary Table S3). However, we found that the association of rs6458155 genotypes with CAD risk in multiple models was more pronounced in the subgroups (male 50 years old, female ≥ 60 years old), which might be the interaction with age and gender (Table 4). In addition, the increased risk associated with rs6458155 genotypes was more notable amongst non-drinkers and smokers (Table 4).

Table 4
Multivariate associations of the rs6458155 in EDN1 gene with the risk of CAD by further stratification
Type Controls Cases OR (95% CI) P 
  Number (%) CAD (%)   
Male ≥ 50, female ≥ 601  n=479 n=429   
Genotype TT 90 (18.8) 53 (12.4) 1.00 
 CT 221 (46.1) 209 (48.7) 1.80 (1.14–2.84) 0.012 
 CC 168 (35.1) 167 (38.9) 1.67 (1.04–2.67) 0.033 
Dominant TT 90 (18.8) 53 (12.4) 1.00 
 CC+CT 389 (81.2) 376 (87.6) 1.74 (1.13–2.67) 0.012 
Recessive TT+CT 311 (64.9) 262 (61.1) 1.00 
 CC 168 (35.1) 167 (38.9) 1.08 (0.78–1.49) 0.635 
Non-drinkers2  n=582 n=390   
Genotype TT 111 (19.1) 52 (13.3) 1.00 
 CT 283 (48.6) 189 (48.5) 1.58 (1.02–2.46) 0.043 
 CC 188 (32.3) 149 (38.2) 1.76 (1.1–2.79) 0.017 
Dominant TT 111 (19.1) 52 (13.3) 1.00 
 CC+CT 471 (80.9) 338 (86.7) 1.65 (1.09–2.52) 0.019 
Recessive TT+CT 394 (67.7) 241 (61.8) 1.00 
 CC 188 (32.3) 149 (38.2) 1.25 (0.91–1.71) 0.174 
Smokers3  n=163 n=297   
Genotype TT 26 (16.0) 33 (11.1) 1.00 
 CT 82 (50.3) 153 (51.5) 2.37 (1.17–4.81) 0.016 
 CC 55 (33.7) 111 (37.4) 2.04 (0.98–4.25) 0.056 
Dominant TT 26 (16.0) 33 (11.1) 1.00 
 CC+CT 137 (84.0) 264 (88.9) 2.23 (1.14–4.35) 0.019 
Recessive TT+CT 108 (66.3) 186 (62.6) 1.00 
 CC 55 (33.7) 111 (37.4) 1.04 (0.64–1.69) 0.878 
Type Controls Cases OR (95% CI) P 
  Number (%) CAD (%)   
Male ≥ 50, female ≥ 601  n=479 n=429   
Genotype TT 90 (18.8) 53 (12.4) 1.00 
 CT 221 (46.1) 209 (48.7) 1.80 (1.14–2.84) 0.012 
 CC 168 (35.1) 167 (38.9) 1.67 (1.04–2.67) 0.033 
Dominant TT 90 (18.8) 53 (12.4) 1.00 
 CC+CT 389 (81.2) 376 (87.6) 1.74 (1.13–2.67) 0.012 
Recessive TT+CT 311 (64.9) 262 (61.1) 1.00 
 CC 168 (35.1) 167 (38.9) 1.08 (0.78–1.49) 0.635 
Non-drinkers2  n=582 n=390   
Genotype TT 111 (19.1) 52 (13.3) 1.00 
 CT 283 (48.6) 189 (48.5) 1.58 (1.02–2.46) 0.043 
 CC 188 (32.3) 149 (38.2) 1.76 (1.1–2.79) 0.017 
Dominant TT 111 (19.1) 52 (13.3) 1.00 
 CC+CT 471 (80.9) 338 (86.7) 1.65 (1.09–2.52) 0.019 
Recessive TT+CT 394 (67.7) 241 (61.8) 1.00 
 CC 188 (32.3) 149 (38.2) 1.25 (0.91–1.71) 0.174 
Smokers3  n=163 n=297   
Genotype TT 26 (16.0) 33 (11.1) 1.00 
 CT 82 (50.3) 153 (51.5) 2.37 (1.17–4.81) 0.016 
 CC 55 (33.7) 111 (37.4) 2.04 (0.98–4.25) 0.056 
Dominant TT 26 (16.0) 33 (11.1) 1.00 
 CC+CT 137 (84.0) 264 (88.9) 2.23 (1.14–4.35) 0.019 
Recessive TT+CT 108 (66.3) 186 (62.6) 1.00 
 CC 55 (33.7) 111 (37.4) 1.04 (0.64–1.69) 0.878 
1

Adjusted for BMI, smoking, drinking, hypertension, diabetes, and hyperlipidemia.

2

Adjusted for age, sex, BMI, smoking, hypertension, diabetes, and hyperlipidemia.

3

Adjusted for age, sex, BMI, drinking, hypertension, diabetes, and hyperlipidemia.

Haplotype analysis of EDN1 tagSNPs with the risk of CAD

As shown in Figure 1, LD analysis showed that there were two blocks in EDN1 gene. rs6458155 and rs4145451 were located in block 1; rs9369217, rs3087459, and rs2070699 were situated in block 2. Frequencies of derived common haplotypes (>3%) and their risk prediction for CAD are summarized in Table 5. In block 1, the haplotype rs6458155C-rs4145451C carrying C allele of rs6458155 was found to be associated with increased risk (OR = 1.22, 95% CI: 1.03–1.43, P=0.018), while rs6458155T-rs4145451A were associated with decreased risk of CAD (OR = 0.81, 95% CI: 0.69–0.96, P=0.014). For further stratified analysis, rs6458155C-rs4145451C appeared to a higher risk of CAD in male and non-drinkers, while rs6458155T-rs4145451A had more significant protection from CAD (Table 6).

Schematic of EDN1 gene structure and pairwise LD between EDN1 variants

Figure 1
Schematic of EDN1 gene structure and pairwise LD between EDN1 variants

EDN1 gene is composed of five exons which are represented as boxes. Arrows indicated the locations of SNP. Two blocks in this plot were generated by the Haploview program. D′ values are plotted as a graph to show LD between these variants.

Figure 1
Schematic of EDN1 gene structure and pairwise LD between EDN1 variants

EDN1 gene is composed of five exons which are represented as boxes. Arrows indicated the locations of SNP. Two blocks in this plot were generated by the Haploview program. D′ values are plotted as a graph to show LD between these variants.

Table 5
Haplotype analysis between cases and controls
Haplotype1 Controls Cases OR (95% CI) P 
 n=675 n=525   
Block 1 
rs6458155C-rs4145451 A 60.50 (4.5) 48.51 (4.6) 1.03 (0.70–1.52) 0.865 
rs6458155C-rs4145451C 713.50 (52.9) 604.49 (57.6) 1.22 (1.03–1.43) 0.018 
rs6458155T-rs4145451A 564.50 (41.8) 386.49 (36.8) 0.81 (0.69–0.96) 0.014 
Block 2 
rs9369217C-rs3087459A-rs2070699G 315.76 (23.4) 270.28 (25.7) 1.17 (0.97–1.41) 0.104 
rs9369217C-rs3087459A-rs2070699T 708.68 (52.5) 519.23 (49.5) 0.92 (0.78–1.09) 0.324 
rs9369217C-rs3087459C-rs2070699G 100.23 (7.4) 78.24 (7.5) 1.03 (0.76–1.40) 0.867 
rs9369217T-rs3087459C-rs2070699G 193.41 (14.3) 136.51 (13.0) 0.92 (0.72–1.16) 0.460 
Haplotype1 Controls Cases OR (95% CI) P 
 n=675 n=525   
Block 1 
rs6458155C-rs4145451 A 60.50 (4.5) 48.51 (4.6) 1.03 (0.70–1.52) 0.865 
rs6458155C-rs4145451C 713.50 (52.9) 604.49 (57.6) 1.22 (1.03–1.43) 0.018 
rs6458155T-rs4145451A 564.50 (41.8) 386.49 (36.8) 0.81 (0.69–0.96) 0.014 
Block 2 
rs9369217C-rs3087459A-rs2070699G 315.76 (23.4) 270.28 (25.7) 1.17 (0.97–1.41) 0.104 
rs9369217C-rs3087459A-rs2070699T 708.68 (52.5) 519.23 (49.5) 0.92 (0.78–1.09) 0.324 
rs9369217C-rs3087459C-rs2070699G 100.23 (7.4) 78.24 (7.5) 1.03 (0.76–1.40) 0.867 
rs9369217T-rs3087459C-rs2070699G 193.41 (14.3) 136.51 (13.0) 0.92 (0.72–1.16) 0.460 
1

Haplotype with frequency less than 3% was excluded.

Table 6
Haplotype block 1 analysis between cases and controls by further stratification for gender and drinking status
Haplotype1 Controls Cases OR (95% CI) P 
 No. (%) No. (%)   
Male n=405 n=361   
rs6458155C-rs4145451 A 32.24 (4.0) 30.42 (4.2) 1.07 (0.64–1.77) 0.801 
rs6458155C-rs4145451C 422.76 (52.2) 418.58 (58.0) 1.28 (1.05–1.57) 0.016 
rs6458155T-rs4145451A 348.76 (43.1) 263.58 (36.5) 0.77 (0.62–0.94) 0.011 
Non-drinkers n=582 n=390   
rs6458155C-rs4145451A 49.39 (4.2) 36.45 (4.7) 1.11 (0.72–1.72) 0.637 
rs6458155C-rs4145451C 609.61 (52.4) 450.55 (57.8) 1.26 (1.05–1.51) 0.014 
rs6458155T- rs4145451A 495.61 (42.6) 283.55 (36.4) 0.77 (0.64–0.93) 0.007 
Haplotype1 Controls Cases OR (95% CI) P 
 No. (%) No. (%)   
Male n=405 n=361   
rs6458155C-rs4145451 A 32.24 (4.0) 30.42 (4.2) 1.07 (0.64–1.77) 0.801 
rs6458155C-rs4145451C 422.76 (52.2) 418.58 (58.0) 1.28 (1.05–1.57) 0.016 
rs6458155T-rs4145451A 348.76 (43.1) 263.58 (36.5) 0.77 (0.62–0.94) 0.011 
Non-drinkers n=582 n=390   
rs6458155C-rs4145451A 49.39 (4.2) 36.45 (4.7) 1.11 (0.72–1.72) 0.637 
rs6458155C-rs4145451C 609.61 (52.4) 450.55 (57.8) 1.26 (1.05–1.51) 0.014 
rs6458155T- rs4145451A 495.61 (42.6) 283.55 (36.4) 0.77 (0.64–0.93) 0.007 
1

Haplotype with frequency less than 3% was excluded.

Association between tagSNP rs6458155 and plasma ET-1 levels

To further investigate the functional relevance of the EDN1 rs6458155 polymorphism, we conducted a correlation analysis between the genotypes and plasma ET-1 levels. In Figure 2, our results showed that the CT genotype in EDN1 gene was associated with significantly higher plasma ET-1 levels compared with the TT genotype (P=0.042, Figure 2). Similarly, a marginal significant association between the combined CT/CC genotypes and higher levels of ET-1 was observed (P=0.057, Figure 2).

Association between tagSNP rs6458155 and plasma ET-1 levels.

Figure 2
Association between tagSNP rs6458155 and plasma ET-1 levels.

Analysis of ET-1 expression levels in 48 individuals carrying TT compared with CT compared with CC genotypes (A) or the combined CC+CT genotypes compared with TT genotype (B).

Figure 2
Association between tagSNP rs6458155 and plasma ET-1 levels.

Analysis of ET-1 expression levels in 48 individuals carrying TT compared with CT compared with CC genotypes (A) or the combined CC+CT genotypes compared with TT genotype (B).

Discussion

Genetic association studies have provided tremendous insight into the diversity of genetic factors contributing to the risk of CAD [20,22,28]. Studies have reported that ET-1 was implicated in a broad range of cardiovascular diseases, such as CAD, myocardial infarction, and hypertension [29–31]. In the present study, we performed a genetic association analysis on five EDN1 tagSNPs (rs6458155, rs4145451, rs9369217, rs3087459, and rs2070699) in a Chinese Han population. Multivariate methods based on logistic regression analysis was performed to test individual tagSNP association, which was adjusted by multiple risk factors, including age, sex, BMI, smoking, drinking, hypertension, diabetes, and hyperlipidemia. As a result, we found that rs6458155 polymorphism was associated with the risk of CAD in multiple genetic models; the haplotype rs6458155C-rs4145451C containing rs6458155 C allele conferred the increased susceptibility to CAD. Furthermore, carriers of the C allele (CT/CC genotypes) had higher plasma ET-1 levels compared with non-C carriers (TT genotype).

In the stratified analysis, our data revealed that the increased risk of EDN1 rs6458155 C allele in CAD was more remarkable amongst older subjects (male 50 years old, female ≥ 60 years old), suggesting an age-related mechanism is involved. These results are in agreement with other studies reporting the differential effects of age on the association of gene polymorphisms with cardiovascular disease [32–34]. Previous studies have reported the associations between alcohol intake and CAD [35–37]. In the present study, the association between rs6458155 polymorphism and CAD risk was more pronounced in non-drinking individuals. The effect of ethanol on the cardiovascular system is dose-dependent and the differences observed for alcohol drinking may mask the influence of individual variants of this polymorphism in the present study population [38]. Previous studies have indicated that ET-1 is increased in cigarette smoke exposed rats, and ET-1 receptors are also up-regulated in the rat coronary and cerebral arteries after cigarette smoke exposure [39–41]. In this study, we found a more significant association between the EDN1 rs6458155 polymorphism and CAD risk in cigarette smokers, suggesting there is a gene–environment interaction between rs6458155 polymorphism and tobacco exposure. Further studies are required to confirm these findings.

It is important to note that haplotype rs6458155C-rs4145451C containing rs6458155 C allele in a strong LD block 1 was associated with a significantly higher risk for developing CAD. There is a possibility that the effect on gene expression may be dependent on the interaction between two or more SNPs, indicating a co-operative influence on transcriptional regulation [42]. Besides, haplotype can mark unique chromosomal segments which contained risk alleles [27]. May be it is a causal variant, by regulating the gene expression of EDN1, and subsequently contribute to the CAD risk. Thus, it was reasonable to speculate that the association of the rs6458155 polymorphism with the risk of CAD may be due to a direct causative effect of this SNP, or because it is in LD with other functional variants located in or near the EDN1 gene and is associated with CAD risk. Further extensive analyses for this locus, dense LD mapping or further confirmation studies are also required to link the EDN1 locus to the genetic susceptibility of CAD as a whole.

Growing evidences have suggested that ET-1 plays an important role during various phases of CAD pathophysiology, contributing in early stages to endothelial dysfunction, inflammation, and atherosclerotic plaque formation [13,15,43]. ET-1 levels were increased in plasma of patients with CAD [19]. Seveal reports have found the association between EDN1 genotypes (Ala288Ser, Lys198Asn, rs9658631, rs9658634, rs7159323) and plasma ET-1 levels [44–46]. Previous studies showed that polymorphism in 5′ UTR region may alter the transcription and expression of the corresponding gene and thereby influence the individual susceptibility to human diseases [47,48]. In this study, the plasma ET-1 levels of the individuals carrying rs6458155 CT/CC genotypes were higher than those of the TT genotype carriers. Considering the important role of rs6458155 on plasma ET-1 levels, we speculated that rs6458155 polymorphism in the 5′ UTR of EDN1 gene may influence its transcriptional activity and alter the circulating ET-1 concentration, thereby conferring the individual’s susceptibility to CAD.

Several limitations in the present study need to be addressed. First, the subjects in our study were recruited from hospital which might result in potential selection bias. Nonetheless, the genotype distribution amongst control subjects complied with Hardy–Weinberg equilibrium. Second, the strategy of screening candidate common polymorphisms depended on the prediction from HapMap database, which was not rigorous enough to discover all possibly functional SNPs including rare variants. Finally, the results in our study were not replicated, further studies in different hospitals will be of help to confirm the significant association of these five tagSNPs with CAD risk.

In summary, our finding provides the first evidence that EDN1 tagSNP rs6458155 and the haplotype rs6458155C-rs4145451C are associated with the risk of CAD in the Chinese Han population, suggesting that EDN1 gene polymorphisms may play an important role in the pathogenesis of CAD, although further studies with larger sample size are needed to validate our results.

Funding

This work was supported by the National Natural Science Foundation of China [grant number 81370456]; the Natural Science Foundation of Guangdong Province [grant number 2014A030311015]; the Yangfan Training Program of Guangdong Province [grant number 4YF16006G]; the Science and Technology Planning Project of Dongguan City [grant numbers 2015108101015, 2013108101057]; and the Foundation for Science and Technology Innovation (Climbing Program) in College Students of Guangdong Province [grant numbers pdjh2017a0218, pdjh2017b0224, pdjha0217].

Competing interests

The authors declare that there are no competing interests associated with the manuscript.

Author contribution

L.-l.L., L.C., and J.C. carried out the statistical analysis. L.-l.L. drafted the manuscript. L.-l.L., L.C., M.-y.Z., M.-y.C., J.C., Y.C., S.-k.Y., L.-b.C., and Z.-b.T. helped to collect the study subjects. X.-l.Y., C.C., and X.L. contributed reagents and materials. X.-d.X. participated in the design of the study and helped to revise the manuscript. All authors read and approved the final manuscript.

Abbreviations

     
  • BMI

    body mass index

  •  
  • CAD

    coronary artery disease

  •  
  • CI

    confidence interval

  •  
  • ET-1

    endothelin-1

  •  
  • LD

    linkage disequilibrium

  •  
  • MAF

    minor allele frequency

  •  
  • OR

    odds ratio

  •  
  • SNP

    single-nucleotide polymorphism

  •  
  • VSMC

    vascular smooth muscle cell

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Author notes

*

These authors contributed equally to this work.

This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

Supplementary data