Skip to main content
PMC home page
Scientific Reports logoLink to Scientific Reports
. 2015 Oct 21;5:15245. doi: 10.1038/srep15245

Sex-specific association of rs4746172 of VCL gene with hypertension in two Han populations from Southern China

Qin Yu 1, Hong-Peng Sun 2, Wan-Qun Chen 3, Xiao-Qiong Chen 1, Yong Xu 2,a, Yong-Han He 1,b, Qing-Peng Kong 1,c
PMCID: PMC4613695  PMID: 26487440

Abstract

Hypertension is the most common and lethal risk factor for cardiovascular disease (CVD). Numerous variants have been associated with hypertension, however, most of which failed to get replication due to ethnic differences. In this study, we analyzed associations of 10 newly reported single nucleotide polymorphisms (SNPs) in Europeans with hypertension in Chinese. A total of 1766 samples consisting of 880 subjects with hypertension and 886 controls were collected and the SNPs were genotyped using multiple assays based on the SNaPshot mini-sequencing approach. Our results revealed a significant genotypic association of rs4746172 of VCL with hypertension with a lower frequency of minor allele in male subjects (OR = 0.70, 95% CI: 0.54–0.92, p = 0.011) but not in females. To validate the result, we genotyped the SNPs in another Chinese population with 546 individuals, and got a consistent association for the rs4746172 (OR = 0.56, 95% CI: 0.38-0.82, p = 2.4 × 10−3) in males. The VCL-encoding protein was involved in cardiomyopathy that associated with hypertension, therefore our results suggest the rs4746172 of VCL may be a novel target for clinical interventions to reduce CVD risk by regulating blood pressure in male Chinese.

Hypertension (HT) is one of the most common diseases around the world with about 1 out of every 3 adults have HT in US1, and among Chinese adults, the prevalence of HT is 26.6% according to the 2007–2008 survey conducted by the China Diabetes and metabolic Disorders Study2. Moreover, HT is an important risk factor for cardiovascular diseases, such as heart failure (HF)3, stroke4 and myocardial infarction5. Much of the excess CVD risk led by HT can be ameliorated through blood pressure (BP)-lowering interventions. Accordingly, identifying novel genes and pathways involved in the regulation of BP may provide new ways of reducing BP and CVD risk. The causation of HT is complex, environmental (e.g. lifestyle) or genetic or combination of both could elevate BP6. Although unhealthy lifestyle, such as excess intake of salt and alcohol and lack of exercise, are shown to increase BP and the risk of developing HT7, a substantial contribution of genetic factors has been documented in many studies, in which numerous genes and single nucleotide polymorphisms (SNPs) have been shown involved in the regulation of BP or HT8,9,10.

With the development of the next generation sequence, the understanding of genetic component of BP or HT has made a big progress, especially in the genome-wide association studies (GWAS)11,12. However, many studies failed to get replication association in independent populations, which may be accounted by ethnic differences among populations13,14. Therefore, it’s a tendency and necessary to identify universal BP-associated loci using multiethnic samples15. Recently, novel associations between several SNPs and BP traits were identified in more than ten thousands of individuals of European ancestry16,17. Zhang and his colleagues used novel strategies to discover some novel BP loci, one of which was proved to be a new hypertension-susceptibility locus18. However, these candidate BP-associated loci have not been investigated in Asia populations. Accordingly, the study is designed to replicate the reported gene SNPs in Chinese cohorts, aiming to find candidate gene loci involved in BP regulation. As a result, we found that the VCL rs4746172 had a lower frequency of minor allele and associated with HT in male Chinese. Importantly, the result was well replicated in another smaller Chinese population. Therefore, the VCL rs4746172 may serve as a novel target for clinical interventions to lower BP and CVD risk in male Chinese.

Methods

Subjects

A total of 1766 subjects were recruited from Jiangsu province, China (hereafter referred to as Jiangsu population). Among them, there were 880 subjects with HT defined as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg according to the HT criterion19. Normal BP was defined as SBP <140 mmHg and DBP <90 mmHg. Clinical characteristics, including age, sex, SBP, DBP, height, body weight, triglyceride (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-c), low density lipoprotein cholesterol (LDL-c) and fasting blood glucose (GLU) were collected. Pulse pressure (PP) was calculated as SBP minus DBP, and median arterial pressure (MAP) was defined as 1/3 SBP plus 2/3 DBP. Another population containing 546 individuals with 269 HT patients and 277 controls from Guangdong province of China was collected for use of validation (hereafter referred to as Guangdong population). The study protocol was approved by the Ethics Committee at Kunming Institute of Zoology, Chinese Academy of Sciences. Written informed consent was obtained from each of the participants. All of the methods were carried out in “accordance” with the approved guidelines (http://www.nature.com/srep/policies/index.html#experimental-subjects).

SNP selection and genotyping

Ten recently reported SNPs associated with DBP, SBP, PP, MAP or HT in Europeans16,17,18 were selected for genotyping (Supplemental Table S1). Total genomic DNA was extracted from the whole blood using the AxyPrepTM Blood Genomic DNA Miniprep Kit (Axygen, USA) according to the manufacturer’s protocol, and quantified spectrophotometrically by OD260/OD280 ratio and stored at 4 °C for short-term. Primers were optimally designed using a web-based software provided by Beckman Coulter (available at www.autoprimer.com) and were listed in Supplemental Table S2 and Table S3. Genotyping was performed based on the SNaPshot mini-sequencing approach which can detect multiple polymorphisms in a single assay20,21. The resulting data were analyzed using GeneMarker (SoftGenetics, State College, PA).

Statistical analysis

Continuous variables were displayed as mean ± SD and differences between groups were assessed by student’s t-test or ANOVA analysis. The difference of categorical variables was assessed by the Chi-square test. Odds ratios (ORs) and 95% confidence intervals (CIs) of alleles were calculated by the R software using Fisher’s exact test (2-side). Odds ratios (ORs) and 95% confidence intervals (CIs) of genotypes were calculated using SNPStats adjusted by age and sex, under co-dominant, dominant, recessive, over-dominant and log-additive models22. For sex subgroup analysis, age was adjusted for the association of SNPs with HT.

Results

Clinical characteristics

Clinical characteristics of two populations used in this study were shown in Table 1. All subjects were the Han nationality. Age and sex distribution in Jiangsu population and age distribution in Guangdong population were different between the control and HT groups (p < 0.05). In Jiangsu population, SBP, DBP, PP, MAP, body weight, TG, TC, LDL-c and GLU were significantly higher in the HT group than that in control group (p < 0.05), however, there were not any differences in height and HDL-c between groups (p > 0.05). For Guangdong population, SBP, SBP, DBP, PP, MAP, weight, HDL-c and GLU were higher in the HT group in contrast to the controls (p < 0.05), with an increasing trend but no significance for height, TG, TC and LDL-c (p > 0.05).

Table 1. Clinical characteristics of control and HT groups in two Chinese populations.

Variables Jiangsu population (n = 1766)
 Guangdong population (n = 546)
Control (n = 886) HT (n = 880) p Control (n = 277) HT (n = 269) p
Age, years 70.46 ± 6.50 71.11 ± 6.06 0.030 42.60 ± 10.16 47.64 ± 11.75 1.34 × 10−7
Sex (M/F) 479/407 419/461 7.74× 10−3 237/40 234/35 0.718
SBP, mmHg 126.78 ± 9.15 163.18 ± 16.15 <2.20 × 10−16 117.12 ± 10.57 141.07 ± 18.11 <2.20 × 10−16
DBP, mmHg 76.80 ± 8.16 98.46 ± 7.82 <2.20 ×  × 10−16 76.44 ± 6.97 93.88 ± 8.84 <2.20 × 10−16
PP, mmHg 49.98 ± 9.73 64.72 ± 15.11 <2.20 × 10−16 40.68 ± 8.23 47.19 ± 16.87 6.32 × 10-6
MAP, mmHg 93.46 ± 7.16 120.03 ± 8.78 <2.20 × 10−16 90.00 ± 7.38 109.61 ± 9.91 <2.20 × 10−16
Height, cm 158.89 ± 8.62 158.33 ± 8.70 0.197 166.53 ± 6.61 167.08 ± 6.97 0.349
Weight, kg 56.70 ± 9.43 59.30 ± 10.41 2.37 × 10−7 68.11 ± 9.27 73.87 ± 12.51 2.48 × 10−9
TG, mmol/L 1.32 ± 0.68 1.46 ± 0.79 8.58 × 10−5 2.24 ± 1.28 2.36 ± 1.80 0.113
TC, mmol/L 5.05 ± 0.86 5.29 ± 0.97 2.78 × 10−8 5.58 ± 1.23 5.59 ± 0.99 0.908
HDL-c, mmol/L 1.55 ± 0.42 1.59 ± 0.42 0.107 1.21 ± 0.28 1.30 ± 0.30 4.08 × 10−4
LDL-c, mmol/L 2.75 ± 0.70 2.91 ± 0.78 6.34 × 10−6 3.24 ± 0.85 3.32 ± 0.70 0.264
GLU, mmol/L 5.77 ± 0.99 6.16 ± 1.23 7.87 × 10−16 5.40 ± 1.60 5.79 ± 1.81 7.05 × 10−4
Open in a new tab

M, male; F, female; SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; MAP, median arterial pressure; TG, triglyceride; TC, total cholesterol; HDL-c, high density lipoprotein cholesterol; LDL-c, low density lipoprotein cholesterol; GLU, fasting blood glucose; HT, hypertensive patient. PP was calculated as SBP minus DBP, and MAP was calculated as 1/3 SBP plus 2/3 DBP. All quantitative data were presented as mean ± SD. P values < 0.05 were marked in bold.

Allelic distributions and associations with HT

After genotyping, we compared the minor allele frequencies (MAFs) of the SNPs in Jiangsu and Guangdong populations to that of the Southern Han Chinese (CHS) from the 1000 Genomes Project. MAFs among 3 populations were quite similar (Supplemental Table S1). We then analyzed the association of the minor alleles of all SNPs with HT but found no significant associations in total Jiangsu population (p > 0.05, Table 2). However, the minor allele T of rs4746172 had a significant association with HT in male subjects of Jiangsu subjects (OR = 0.80, 95% CI = 0.65–0.97, p = 0.020) with a lower frequency (0.36) in HT group than that in the controls (0.41), however, no association was observed in females (OR = 1.11, 95% CI = 0.91–1.36, p = 0.298).

Table 2. Allelic ORs and 95% CI for HT in Jiangsu population.

SNP Allele HWE All
 Female
 Male
Control n (freq) HT n (freq) OR (95% CI) p Control n (freq) HT n (freq) OR (95% CI) p Control n (freq) HT n (freq) OR (95% CI) p
rs16823124 G 1.00 1015 (0.57) 1008 (0.57) 1.00 (0.87–1.15) 1.000 470 (0.58) 536 (0.58) 0.98 (0.81–1.20) 0.884 545 (0.57) 472 (0.56) 1.02 (0.84–1.24) 0.812
A   757 (0.43) 752 (0.43) 344 (0.42) 386 (0.42) 413 (0.43) 366 (0.44)
rs4245739 A 0.49 1723 (0.97) 1720 (0.98) 0.82 (0.52–1.28) 0.391 790 (0.97) 905 (0.98) 0.62 (0.31–1.21) 0.154 933 (0.97) 815 (0.97) 1.05 (0.57–1.95) 0.884
C   49 (0.03) 40 (0.02) 24 (0.03) 17 (0.02) 25 (0.03) 23 (0.03)
rs2158394 C 0.06 1156 (0.65) 1128 (0.64) 1.05 (0.92–1.21) 0.459 518 (0.64) 577 (0.63) 1.05 (0.86–1.29) 0.617 638 (0.67) 551 (0.66) 1.04 (0.85–1.27) 0.726
G   612 (0.35) 630 (0.36) 292 (0.36) 343 (0.37) 320 (0.33) 287 (0.34)
rs1570105 A 0.73 1001 (0.56) 1011 (0.57) 0.96 (0.84–1.10) 0.587 458 (0.56) 532 (0.58) 0.94 (0.78–1.15) 0.560 543 (0.57) 479 (0.57) 0.98 (0.81–1.19) 0.849
G   771 (0.44) 749 (0.43) 356 (0.44) 390 (0.42) 415 (0.43) 359 (0.43)
rs2014408 C 0.63 1256 (0.71) 1270 (0.72) 0.94 (0.81–1.09) 0.412 585 (0.72) 676 (0.73) 0.93 (0.75–1.16) 0.518 671 (0.70) 594 (0.71) 0.96 (0.78–1.18) 0.717
T   516 (0.29) 490 (0.28) 229 (0.28) 246 (0.27) 287 (0.30) 244 (0.29)
rs33063 G 0.17 1657 (0.94) 1653 (0.94) 0.93 (0.70–1.24) 0.628 756 (0.93) 870 (0.94) 0.78 (0.52–1.17) 0.236 901 (0.94) 783 (0.93) 1.11 (0.74–1.66) 0.625
A   115 (0.06) 107 (0.06) 58 (0.07) 52 (0.06) 57 (0.06) 55 (0.07)
rs7297416 C 0.08 1091 (0.62) 1071 (0.61) 1.03 (0.9–1.18) 0.679 503 (0.62) 559 (0.61) 1.05 (0.86–1.28) 0.622 588 (0.61) 512 (0.61) 1.01 (0.83–1.23) 0.923
A   681 (0.38) 689 (0.39) 311 (0.38) 363 (0.39) 370 (0.39) 326 (0.39)
rs59251428 G 0.40 1279 (0.72) 1265 (0.72) 1.02 (0.87–1.18) 0.851 582 (0.71) 673 (0.73) 0.93 (0.75–1.15) 0.519 697 (0.73) 592 (0.71) 1.11 (0.90–1.37) 0.344
T   493 (0.28) 495 (0.28) 232 (0.29) 249 (0.27) 261 (0.27) 246 (0.29)
rs3858313 A 0.52 1100 (0.62) 1124 (0.64) 0.93 (0.81–1.06) 0.280 514 (0.63) 586 (0.64) 0.98 (0.80–1.20) 0.881 586 (0.61) 538 (0.64) 0.88 (0.72–1.07) 0.187
G   672 (0.38) 636 (0.36) 300 (0.37) 336 (0.36) 372 (0.39) 300 (0.36)
rs4746172 C 0.44 1077 (0.61) 1097 (0.62) 0.94 (0.82–1.08) 0.350 515 (0.63) 560 (0.61) 1.11 (0.91–1.36) 0.298 562 (0.59) 537 (0.64) 0.80 (0.65–0.97) 0.020
T   695 (0.39) 663 (0.38) 299 (0.37) 362 (0.39) 396 (0.41) 301 (0.36)
Open in a new tab

HWE, Hardy–Weinberg Equilibrium, p wwvalue for HWE in control was shown; HT, hypertensive patient; OR, Odds ratio; 95% CI, 95% confidence interval; freq means the frequencies of allele. P values < 0.05 were marked in bold.

Genotypic distributions and associations with HT

We next investigated the genotypic associations of all selected SNPs with HT in Jiangsu population. Genotypic distributions of all SNPs in the controls of Jiangsu population were in agreement with the Hardy–Weinberg Equilibrium (HWE) (all p > 0.05, Table 2). Odds ratios (ORs) were calculated using five genetic models (co-dominant, dominant, recessive, over-dominant and log-additive) adjusted by sex and age. As shown in Table 3, there were not any significant associations between the selected SNPs and HT in total Jiangsu samples (p > 0.05). Due to the known effect of sex on BP23, we divided the samples into male and female subgroups and then analyzed the associations of the SNPs with HT. Interestingly, we observed the rs59251428 was significantly associated with HT in the co-dominant and over-dominant models in males (OR = 1.37, 95% CI: 1.04–1.80, p = 0.035 and OR = 1.40, 95% CI: 1.08–1.83, p = 0.012, respectively). In addition, the rs4746172 were also found to associate with HT in either of co-dominant, dominant and additive models in males (OR = 0.71, 95% CI: 0.53–0.95, p = 0.039; OR = 0.70, 95% CI: 0.54–0.92, p = 0.011; and OR = 0.80, 95% CI: 0.66–0.97, p = 0.020, respectively), being consistent with its allelic associations described above.

Table 3. Genotypic associations of SNPs with HT in Jiangsu population.

SNP Sex OR (95% CI)
Co-dominant p Dominant p Recessive p Over-dominant p Log-additive p
rs16823124 all 0.97 (0.79–1.20) 1.02 (0.77–1.33) 0.940 0.98 (0.81–1.20) 0.880 1.03 (0.81–1.31) 0.800 0.97 (0.80–1.17) 0.730 1.00 (0.88–1.15) 0.970
female 0.85 (0.63–1.15) 1.01 (0.68–1.51) 0.490 0.89 (0.67–1.18) 0.420 1.11 (0.78–1.59) 0.560 0.85 (0.65–1.11) 0.230 0.98 (0.80–1.19) 0.810
male 1.10 (0.82–1.48) 1.03 (0.71–1.49) 0.810 1.08 (0.81–1.42) 0.610 0.97 (0.70–1.35) 0.860 1.09 (0.84–1.42) 0.530 1.02 (0.85–1.23) 0.810
rs4245739 all 0.85 (0.55–1.31) 0.00 (0.00–NA) 0.410 0.83 (0.54–1.28) 0.400 0.00 (0.00–NA) 0.270 0.85 (0.55–1.31) 0.460 0.82 (0.54–1.25) 0.360
female 0.61 (0.32–1.16) NA 0.130                
male 1.15 (0.63–2.08) 0.00 (0.00–NA) 0.480 1.10 (0.61–1.98) 0.750 0.00 (0.00–NA) 0.260 1.15 (0.63–2.08) 0.640 1.05 (0.59–1.86) 0.860
rs2158394 all 1.08 (0.88-1.31) 1.08 (0.79–1.50) 0.750 1.08 (0.89–1.30) 0.440 1.04 (0.77–1.41) 0.790 1.06 (0.88–1.28) 0.560 1.05 (0.91–1.22) 0.480
female 1.12 (0.84–1.49) 1.11 (0.71–1.73) 0.750 1.11 (0.84–1.47) 0.450 1.04 (0.69–1.58) 0.850 1.09 (0.83–1.42) 0.530 1.07 (0.87–1.31) 0.510
male 1.05 (0.79–1.38) 1.08 (0.68–1.71) 0.920 1.05 (0.81–1.37) 0.700 1.05 (0.68–1.63) 0.830 1.03 (0.80–1.34) 0.800 1.04 (0.85–1.28) 0.700
rs1570105 all 0.87 (0.71–1.08) 0.93 (0.71–1.23) 0.450 0.89 (0.73–1.09) 0.250 1.01 (0.80–1.29) 0.910 0.89 (0.74–1.08) 0.250 0.95 (0.83–1.09) 0.480
female 0.92 (0.68–1.25) 0.86 (0.59–1.28) 0.750 0.91 (0.68–1.21) 0.510 0.91 (0.64–1.28) 0.580 0.97 (0.75–1.27) 0.850 0.93 (0.77–1.12) 0.450
male 0.83 (0.62–1.12) 1.01 (0.69–1.47) 0.360 0.88 (0.66–1.16) 0.360 1.13 (0.81–1.58) 0.480 0.83 (0.64–1.08) 0.160 0.98 (0.81–1.18) 0.830
rs2014408 all 1.04 (0.85–1.26) 0.76 (0.53–1.10) 0.270 0.99 (0.82–1.19) 0.890 0.75 (0.53–1.07) 0.110 1.07 (0.89–1.30) 0.470 0.94 (0.81–1.09) 0.430
female 1.03 (0.78–1.36) 0.74 (0.44–1.24) 0.460 0.98 (0.75–1.28) 0.860 0.73 (0.44–1.21) 0.220 1.07 (0.81–1.41) 0.630 0.93 (0.75–1.15) 0.520
male 1.05 (0.80–1.38) 0.80 (0.48–1.32) 0.570 1.00 (0.77–1.31) 0.970 0.78 (0.48–1.27) 0.320 1.08 (0.83–1.41) 0.560 0.96 (0.78–1.18) 0.690
rs33063 all 0.89 (0.67–1.19) 2.16 (0.19–23.92) 0.600 0.91 (0.68–1.20) 0.490 2.19 (0.20–24.24) 0.510 0.89 (0.67–1.19) 0.440 0.92 (0.70–1.21) 0.550
female 0.74 (0.49–1.10) NA 0.140                
male 1.07 (0.71–1.61) 2.30 (0.21–25.47) 0.740 1.09 (0.73–1.63) 0.660 2.28 (0.21–25.25) 0.490 1.07 (0.71–1.60) 0.750 1.11 (0.76–1.63) 0.590
rs7297416 all 1.07 (0.88–1.32) 1.04 (0.77–1.40) 0.790 1.07 (0.88–1.30) 0.520 0.99 (0.76–1.31) 0.970 1.06 (0.88–1.28) 0.520 1.03 (0.90–1.19) 0.660
female 1.12 (0.83–1.49) 1.07 (0.70–1.61) 0.770 1.10 (0.84–1.46) 0.490 1.00 (0.68–1.46) 0.990 1.10 (0.84–1.43) 0.500 1.05 (0.86–1.28) 0.620
male 1.04 (0.78-1.38) 1.01 (0.65–1.56) 0.970 1.03 (0.78–1.36) 0.830 0.99 (0.66–1.47) 0.950 1.03 (0.80–1.35) 0.800 1.01 (0.83–1.24) 0.900
rs59251428 all 1.06 (0.87–1.29) 0.98 (0.67–1.43) 0.800 1.05 (0.87–1.27) 0.600 0.95 (0.66–1.38) 0.800 1.07 (0.88–1.29) 0.510 1.02 (0.88–1.19) 0.750
female 0.81 (0.61–1.08) 1.15 (0.67–1.95) 0.240 0.86 (0.65–1.12) 0.260 1.26 (0.75–2.11) 0.380 0.80 (0.61–1.05) 0.110 0.94 (0.76–1.17) 0.590
male 1.37 (1.04–1.80) 0.83 (0.48–1.44) 0.035 1.28 (0.99–1.67) 0.061 0.71 (0.42–1.23) 0.220 1.40 (1.08–1.83) 0.012 1.12 (0.90–1.38) 0.300
rs3858313 all 0.92 (0.75–1.13) 0.86 (0.64–1.15) 0.540 0.91 (0.75–1.10) 0.320 0.90 (0.69–1.17) 0.430 0.96 (0.80–1.16) 0.670 0.93 (0.81–1.06) 0.270
female 1.04 (0.78–1.39) 0.92 (0.61–1.39) 0.820 1.01 (0.77–1.33) 0.930 0.90 (0.61–1.32) 0.580 1.07 (0.82–1.40) 0.640 0.98 (0.81–1.19) 0.830
male 0.82 (0.61–1.08) 0.81 (0.54–1.22) 0.330 0.82 (0.62–1.07) 0.140 0.91 (0.62–1.32) 0.610 0.86 (0.66–1.12) 0.270 0.88 (0.73–1.06) 0.190
rs4746172 all 0.96 (0.79–1.18) 0.86 (0.65–1.15) 0.590 0.94 (0.77–1.14) 0.510 0.88 (0.68–1.14) 0.340 1.00 (0.83–1.21) 0.960 0.94 (0.82–1.07) 0.340
female 1.31 (0.98–1.75) 1.11 (0.74–1.67) 0.190 1.26 (0.95–1.65) 0.100 0.96 (0.66–1.40) 0.830 1.27 (0.97–1.66) 0.081 1.11 (0.91–1.35) 0.300
male 0.71 (0.53–0.95) 0.68 (0.46–1.01) 0.039 0.70 (0.54–0.92) 0.011 0.82 (0.57–1.18) 0.280 0.80 (0.61–1.04) 0.091 0.80 (0.66–0.97) 0.020
Open in a new tab

OR, Odds ratio; 95% CI, 95% confidence interval. For total samples, p values were adjusted by sex and age, and for sex subgroup analyses, p values were adjusted by age. P values < 0.05 were marked in bold.

Association validation of SNPs with HT in another independent population

To confirm above associations of SNPs with HT in Jiangsu population, we further genotyped the SNPs in Guangdong samples (Supplemental Table S4). As shown in Table 4, we did not found any associations for the rs59251428 with HT in any of the five models. However, we did observe that the rs4746172 was associated with HT in Guangdong population (co-dominant, OR = 0.58, 95% CI: 0.40–0.84, p = 2.9 × 10−3; dominant, OR = 0.67, 95% CI: 0.47–0.95, p = 0.024; and over-dominant, OR = 0.55, 95% CI: 0.39–0.79, p = 9.0 × 10−4). Consistently, subgroup analysis by sex revealed the association of rs4746172 with HT were only in male subjects (co-dominant, OR = 0.58, 95% CI: 0.39–0.86, p = 8.6 × 10−3; dominant, OR = 0.66, 95% CI: 0.45–0.96, p = 0.028; and over-dominant, OR = 0.56, 95% CI: 0.38–0.82, p = 2.4 × 10−3, Table 4), but not in the females (co-dominant, OR = 0.63, 95% CI: 0.21–1.87, p = 0.290; dominant, OR = 0.83, 95% CI: 0.30–2.28, p = 0.720; and over-dominant, OR = 0.54, 95% CI: 0.19–1.53, p = 0.240, Table 4). In this population, a marginally significant association of rs2014408 with HT was observed (over-dominant, OR = 0.68, 95% CI: 0.48–0.98, p = 0.036, Table 4). However, the association disappeared in stratification analysis by sex (Table 4). There were not any associations for the rest SNPs with HT in Guangdong population.

Table 4. Genotypic association of SNPs with HT in Guangdong population.

SNP Sex OR (95% CI)
Co-dominant p Dominant p Recessive p Over-dominant p Log-additive p
rs16823124 all 0.71 (0.48–1.04) 0.66 (0.39–1.11) 0.140 0.69 (0.48–1.00) 0.048 0.81 (0.51–1.29) 0.370 0.80 (0.57–1.14) 0.220 0.79 (0.62–1.02) 0.066
female 0.54 (0.17–1.66) 0.31 (0.06–1.68) 0.330 0.48 (0.16–1.43) 0.180 0.46 (0.10–2.11) 0.310 0.75 (0.27–2.08) 0.590 0.55 (0.25–1.22) 0.140
male 0.74 (0.49–1.11) 0.72 (0.42–1.24) 0.290 0.73 (0.50–1.08) 0.120 0.86 (0.53–1.40) 0.540 0.82 (0.57–1.19) 0.300 0.83 (0.64–1.08) 0.160
rs2158394 all 1.08 (0.74–1.57) 1.49 (0.87–2.56) 0.350 1.17 (0.82–1.66) 0.400 1.43 (0.86–2.37) 0.160 0.98 (0.69–1.38) 0.890 1.18 (0.92–1.52) 0.190
female 1.57 (0.49–5.07) 4.10 (0.81–20.83) 0.220 1.98 (0.66–5.97) 0.220 3.15 (0.72–13.70) 0.110 0.99 (0.35–2.79) 0.990 1.93 (0.89–4.18) 0.091
male 1.02 (0.68–1.52) 1.29 (0.73–2.31) 0.660 1.08 (0.74–1.57) 0.700 1.28 (0.75–2.20) 0.360 0.95 (0.66–1.39) 0.810 1.11 (0.85–1.45) 0.460
rs1570105 all 0.91 (0.61–1.35) 1.36 (0.82–2.26) 0.240 1.01 (0.70–1.47) 0.940 1.44 (0.93–2.25) 0.100 0.81 (0.57–1.14) 0.230 1.13 (0.88–1.45) 0.340
female 0.42 (0.13–1.42) 0.55 (0.12–2.53) 0.360 0.46 (0.15–1.42) 0.170 0.90 (0.24–3.40) 0.870 0.52 (0.18–1.54) 0.230 0.68 (0.32–1.44) 0.310
male 0.97 (0.64–1.48) 1.52 (0.88–2.61) 0.200 1.10 (0.74–1.64) 0.640 1.54 (0.96–2.48) 0.072 0.83 (0.57–1.20) 0.330 1.20 (0.92–1.56) 0.190
rs2014408 all 0.70 (0.49–1.01) 1.38 (0.64–3.02) 0.078 0.77 (0.54–1.09) 0.130 1.60 (0.75–3.44) 0.220 0.68 (0.48–0.98) 0.036 0.89 (0.67–1.19) 0.440
female 0.73 (0.26–2.06) NA (0.00–NA) 0.200 0.85 (0.31–2.34) 0.760 NA (0.00–NA) 0.089 0.65 (0.23–1.80) 0.410 1.09 (0.44–2.68) 0.850
male 0.71 (0.48–1.04) 1.22 (0.55–2.70) 0.150 0.76 (0.52–1.10) 0.150 1.40 (0.64–3.06) 0.390 0.69 (0.47–1.02) 0.059 0.88 (0.65–1.19) 0.400
rs33063 all 0.66 (0.38–1.13) 0.22 (0.02–2.18) 0.120 0.62 (0.36–1.05) 0.073 0.23 (0.02–2.30) 0.170 0.67 (0.39–1.15) 0.140 0.62 (0.38–1.00) 0.049
female 1.20 (0.27–5.36) 0.00 (0.00–NA) 0.420 0.91 (0.22–3.77) 0.900 0.00 (0.00–NA) 0.190 1.25 (0.28–5.56) 0.770 0.75 (0.22–2.56) 0.640
male 0.60 (0.34–1.08) 0.32 (0.03–3.77) 0.160 0.58 (0.33–1.03) 0.062 0.35 (0.03–4.02) 0.380 0.61 (0.34–1.09) 0.091 0.60 (0.35–1.02) 0.054
rs7297416 all 1.10 (0.75–1.61) 1.18 (0.69–2.03) 0.810 1.12 (0.78–1.60) 0.560 1.12 (0.68–1.83) 0.650 1.04 (0.74–1.48) 0.810 1.09 (0.84–1.41) 0.520
female 0.89 (0.30–2.63) 0.81 (0.16–4.06) 0.960 0.87 (0.32–2.42) 0.800 0.86 (0.19–3.91) 0.850 0.94 (0.34–2.59) 0.900 0.90 (0.43–1.88) 0.780
male 1.14 (0.76–1.72) 1.24 (0.70–2.21) 0.720 1.16 (0.79–1.72) 0.450 1.15 (0.68–1.93) 0.610 1.07 (0.74–1.55) 0.720 1.12 (0.85–1.47) 0.420
rs59251428 all 0.95 (0.66–1.37) 1.13 (0.63–2.01) 0.840 0.98 (0.70–1.39) 0.930 1.16 (0.67–2.01) 0.600 0.93 (0.65–1.32) 0.680 1.02 (0.79–1.32) 0.860
female 0.45 (0.15–1.40) 0.26 (0.05–1.37) 0.180 0.40 (0.14–1.16) 0.085 0.39 (0.08–1.82) 0.230 0.63 (0.22–1.75) 0.370 0.49 (0.23–1.07) 0.066
male 1.04 (0.71–1.54) 1.36 (0.73–2.55) 0.620 1.10 (0.76–1.60) 0.810 1.33 (0.73–2.43) 0.340 0.99 (0.68–1.43) 0.940 1.12 (0.85–1.48) 0.410
rs3858313 all 1.29 (0.88–1.89) 1.06 (0.64–1.77) 0.400 1.22 (0.86–1.74) 0.260 0.93 (0.58–1.49) 0.760 1.27 (0.89–1.80) 0.180 1.08 (0.85–1.38) 0.540
female 1.34 (0.45–3.99) 1.36 (0.26–7.13) 0.850 1.35 (0.48–3.75) 0.570 1.18 (0.24–5.69) 0.840 1.26 (0.45–3.55) 0.660 1.22 (0.58–2.57) 0.610
male 1.29 (0.86–1.94) 1.05 (0.61–1.79) 0.440 1.22 (0.83–1.77) 0.310 0.92 (0.56–1.50) 0.730 1.27 (0.88–1.85) 0.210 1.07 (0.83–1.38) 0.610
rs4746172 all 0.58 (0.40–0.84) 1.30 (0.70–2.43) 2.9 × 10−3 0.67 (0.47–0.95) 0.024 1.72 (0.95–3.12) 0.070 0.55 (0.39–0.79) 9.0 × 10−4 0.89 (0.68–1.16) 0.380
female 0.63 (0.21–1.87) 2.70 (0.41–17.73) 0.290 0.83 (0.30–2.28) 0.720 3.34 (0.54–20.70) 0.180 0.54 (0.19–1.53) 0.240 1.14 (0.53–2.46) 0.740
male 0.58 (0.39–0.86) 1.19 (0.61–2.33) 8.6 × 10−3 0.66 (0.45–0.96) 0.028 1.59 (0.85–3.00) 0.150 0.56 (0.38–0.82) 2.4 × 10−3 0.86 (0.65–1.15) 0.310
Open in a new tab

OR, Odds ratio; 95% CI, 95% confidence interval. For total samples, p values were adjusted by sex and age, and for sex subgroup analyses, p values were adjusted by age. P values < 0.05 were marked in bold.

Association of SNPs with blood pressure parameters

Since there was an association between some of the selected SNPs and HT, we next wondered whether they were associated with BP parameters, including SBP, DBP, PP and MAP. For the rs4746172, we did see a decreased trend for SBP, DBP and MAP in CT or TT carriers compared to the CC subjects in male subject despite of no significance in Jiangsu samples (Supplemental Fig. S1). There were not any associations for the rest SNPs with BP parameters (Supplemental Table S5 and supplemental Table S6). In Guangdong samples, the association of rs4746172 with DBP were significant among 3 genotype carriers (p = 0.020, Supplemental Table S7). The results further supported the association of the rs4746172 with HT in male Chinese, which may be mediated by its association with DBP.

Discussion

In this study, we investigated the associations of 10 newly reported BP-associated SNPs in Europeans with BP or HT in Chinese populations, and finally identified a lower allelic frequency of rs4746172 in HT subjects in male Chinese. This result was well replicated in another independent Chinese population. To our knowledge, this the first report on the male-specific association of rs4746172 with HT in two Chinese populations.

In Jiangsu samples, we just found two male-specific hypertension-related loci, rs59251428 and rs4746172, were associated with HT, while the remaining 8 loci were not. The failed replication of association for the rest 8 SNPs with HT compared to the European population was most likely due to the ethnic differences between both populations. For the rs59251428, we failed to replicate its association with HT between two Chinese populations, which may result from several reasons. Firstly, the sample size in the validation population might be relatively small to test the significance. Secondly, geographic and social difference may affect the results of replication. The third is the average age between two populations. Guangdong subjects are younger than Jiangsu population consisting of old subjects which are prone to develop HT as documented in other study24. Even though, the association of rs4746172 with HT in Jiangsu samples was validated in Guangdong population with smaller sample size. Of notice is that the minor allelic frequency was lower in HT subjects than in the controls. Although it was reported to be a risk locus for BP, its lower allelic frequency and OR value indicate a likely protective role in developing HT. The strong association with BP in Europeans and association with HT in Chinese suggest its great potential of being a influential factor for HT in both populations.

The rs4746172 is located in the gene VCL (vinculin) encoding a cytoskeletal protein that is associated with the maintenance of cell-to-cell and cell to matric junctions and plays a crucial role in normal embryonic development and cardiac function25. Mutation, altered expression and location of VCL have been associated with cardiomyopathy26,27,28,29, which emphasize the importance of VCL in human heart. Moreover, it has been suggested that the subjects harbored mutations of usually suffer from increased heart workload25, which may promote the development of HT30. Indeed, several studies have reported that the patients with dilated cardiomyopathy had a higher prevalence of HT than the general population31. So it was plausible that the rs4746172 led to increased heart workload, and indirectly caused HT. Until now, there has been no direct evidence on the relationship between VCL gene and HT. Consistent with the case that the SNP rs4746172 was associated with DBP in Europeans17, we also observed its association with DBP in Guangdong individuals. That the decreased trend for BP parameters in CT or TT carriers than the wild genotype (CC) carriers but with no significance is most likely due to the protective role of lower allelic frequency in HT patients, which, however, did not affect the association of rs4746172 with HT. It was worthy pointing out that the rs4746172-HT association was sex-specific, existed only in male but not in female Chinese. In fact, the sex differences in the regulation of BP had been well studied in the past decades. Epidemiological studies indicate that men have higher BP than age-matched, premenopausal women23,32. There were also some studies reporting sex differences in association between genetic factors and HT33,34. However, the mechanism of sex on BP is not fully understood. One possible mechanism may be the role of sex hormones. Solid evidence has demonstrated that increases in androgens or losses of estrogens, or even increased ratio of androgens/estrogens can promote higher BP35. In addition, the sex-specific difference in genotype-phenotype associations may be due to sex-specific genetic architecture. The autosomal genome is shared by both the male and female, but the gene expression is sexually dimorphic36,37. Furthermore, sex is considered as an “environmental” variable and gene expression may be different via interaction with sex, and the genotype-sex interaction effects on many human diseases and traits are common, such as HT and BP38.

The association of rs4746172 with HT in this study was convincingly supported by the replication in two independent populations as well as the reported result in Europeans. However, several limitations should be acknowledged. One is that the sample sizes of both populations are not equivalent, especially for Guangdong population. Another is the population characteristics with bias, such as the ratio of the male and female, and age of samples. Third, despite its association with blood pressure in Caucasians and Chinese, the rs4746172 is a tag SNP and therefore its roles in the regulation of blood pressure deserve further studies in the future. In addition, the causation of HT is the complex multifactorial interplay of genetic and environmental factor, but here we focused on genetic factors without taking much account into some environmental factors associated with HT except for sex, such as salt and alcohol intake, and physical activities.

In conclusion, our results suggest the rs4746172 of VCL was associated with HT in male subjects with a lower frequency of risk allele, which was validated in another Chinese population. The lower frequency of rs4746172 may be a male-specific protective factor for cardiovascular diseases, which may serve as a novel target and provide strategies for clinical interventions to reduce CVD risk in male Chinese.

Additional Information

How to cite this article: Yu, Q. et al. Sex-specific association of rs4746172 of VCL gene with hypertension in two Han populations from Southern China. Sci. Rep. 5, 15245; doi: 10.1038/srep15245 (2015).

Supplementary Material

Supplementary Information
srep15245-s1.pdf (771.6KB, pdf)

Acknowledgments

The study was supported by grants from National Basic Research Program of China (2013CB530802), Yunnan Province (2013FB069), Natural Science Foundation of China (31322029, 31460290, 81402761), Kunming Institute of Zoology, Chinese Academy of Sciences (GREKF12-11) and the Foundation Research Project of Jiangsu Province (BK20140361).

Footnotes

Author Contributions Designed the experiments: Y.X., Y.H.H. and Q.P.K. Collected the blood samples: H.P.S., W.Q.C. and Y.H.H. Performed the experiments: Q.Y. and X.Q.C. Analyzed the data: Q.Y. Wrote the manuscript: Q.Y. and Y.H.H.

References

  1. Go A. S. et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation. 127, e6–e245 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Gao Y. et al. Prevalence of Hypertension in China: A Cross-Sectional Study. PLoS ONE. 8, e65938 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Butler J. et al. Systolic blood pressure and incident heart failure in the elderly. The Cardiovascular Health Study and the Health, Ageing and Body Composition Study. Heart. 97, 1304–1311 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Tikhonoff V., Zhang H., Richart T. & Staessen J. A. Blood pressure as a prognostic factor after acute stroke. Lancet Neurol. 8, 938–948 (2009). [DOI] [PubMed] [Google Scholar]
  5. Dunn F. G. Hypertension and Myocardial Infarction. J Am Coll Cardiol. 1, 528–532 (1983). [DOI] [PubMed] [Google Scholar]
  6. Padmanabhan S. et al. Hypertension and genome-wide association studies combining high fidelity phenotyping and hypercontrols. J Hypertens. 10.1097/HJH.0b013e3282ff634f, 1275–1281 (2008). [DOI] [PubMed] [Google Scholar]
  7. Svetkey L. P. et al. Effect of lifestyle modifications on blood pressure by race, sex, hypertension status, and age. J Hum Hypertens. 19, 21–31 (2005). [DOI] [PubMed] [Google Scholar]
  8. Levy D. et al. Genome-wide association study of blood pressure and hypertension. Nat Genet. 41, 677–687 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Zhu Q. et al. Haplotype analysis of PPARgamma C681G and intron CT variants. Positive association with essential hypertension. Herz. 39, 264–270 (2014). [DOI] [PubMed] [Google Scholar]
  10. Padmanabhan S. et al. Genome-wide association study of blood pressure extremes identifies variant near UMOD associated with hypertension. PLoS Genet. 6, e1001177 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Padmanabhan S., Newton-Cheh C. & Dominiczak A. F. Genetic basis of blood pressure and hypertension. Trends Genet. 28, 397–408 (2012). [DOI] [PubMed] [Google Scholar]
  12. Cabrera C. P. et al. Exploring hypertension genome-wide association studies findings and impact on pathophysiology, pathways, and pharmacogenetics. WIREs Syst Biol Med. 7, 73–90 (2015). [DOI] [PubMed] [Google Scholar]
  13. Kidambi S. et al. Non-replication study of a genome-wide association study for hypertension and blood pressure in African Americans. BMC Med Genet. 13, 27 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Takeuchi F. et al. Blood pressure and hypertension are associated with 7 loci in the Japanese population. Circulation. 121, 2302–2309 (2010). [DOI] [PubMed] [Google Scholar]
  15. Franceschini N. et al. Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations. Am J Hum Genet. 93, 545–554 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Ganesh S. K. et al. Loci influencing blood pressure identified using a cardiovascular gene-centric array. Hum Mol Genet. 22, 1663–1678 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Tragante V. et al. Gene-centric meta-analysis in 87,736 individuals of European ancestry identifies multiple blood-pressure-related loci. Am J Hum Genet. 94, 349–360 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Zhang K. et al. Genetic implication of a novel thiamine transporter in human hypertension. J Am Coll Cardiol. 63, 1542–1555 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Chobanian A. V. et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension. 42, 1206–1252 (2003). [DOI] [PubMed] [Google Scholar]
  20. Esteves L. M., Bulhões S. M., Brilhante M. J. & Mota-Vieira L. Three multiplex snapshot assays for SNP genotyping in candidate innate immune genes. BMC Res Notes. 6, 54 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Fanis P., Kousiappa I., Phylactides M. & Kleanthous M. Genotyping of BCL11A and HBS1L-MYB SNPs associated with fetal haemoglobin levels: a SNaPshot minisequencing approach. BMC Genomics. 15, 108 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Sole X., Guino E., Valls J., Iniesta R. & Moreno V. SNPStats: a web tool for the analysis of association studies. Bioinformatics. 22, 1928–1929 (2006). [DOI] [PubMed] [Google Scholar]
  23. Sandberg K. & Ji H. Sex differences in primary hypertension. Biol Sex Differ. 3, 7 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Simino J. et al. Gene-age interactions in blood pressure regulation: a large-scale investigation with the CHARGE, Global BPgen, and ICBP Consortia. Am J Hum Genet. 95, 24–38 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Zemljic-Harpf A., Manso A. M. & Ross R. S. Vinculin and talin: focus on the myocardium. J Investig Med. 57, 849–855 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Zemljic-Harpf A. E. et al. Heterozygous Inactivation of the Vinculin Gene Predisposes to Stress Induced Cardiomyopathy. Am J Pathol. 165, 1033–1044 (2004). [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Vasile V. C., Edwards W. D., Ommen S. R. & Ackerman M. J. Obstructive hypertrophic cardiomyopathy is associated with reduced expression of vinculin in the intercalated disc. Biochem Biophys Res Commun. 349, 709–715 (2006). [DOI] [PubMed] [Google Scholar]
  28. Vasile V. C., Ommen S. R., Edwards W. D. & Ackerman M. J. A missense mutation in a ubiquitously expressed protein, vinculin, confers susceptibility to hypertrophic cardiomyopathy. Biochem Biophys Res Commun. 345, 998–1003 (2006). [DOI] [PubMed] [Google Scholar]
  29. Vasile V. C. et al. Identification of a metavinculin missense mutation, R975W, associated with both hypertrophic and dilated cardiomyopathy. Mol Genet Metab. 87, 169–174 (2006). [DOI] [PubMed] [Google Scholar]
  30. Wells Q. S. et al. Familial dilated cardiomyopathy associated with congenital defects in the setting of a novel VCL mutation (Lys815Arg) in conjunction with a known MYPBC3 variant. Cardiogenetics. 1 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Zheng X., Chen S., Wang J., Yang T. & Chen Y. Dilated cardiomyopathy with hypertension: prevalence and response to high-dose beta1-adrenoceptor antagonist therapy. Clin Exp Pharmacol Physiol. 36, 945–949 (2009). [DOI] [PubMed] [Google Scholar]
  32. Reckelhoff J. F. Gender Differences in the Regulation of Blood Pressure. Hypertension. 37, 1199–1208 (2001). [DOI] [PubMed] [Google Scholar]
  33. Dhanachandra Singh K., Jajodia A., Kaur H., Kukreti R. & Karthikeyan M. Gender specific association of RAS gene polymorphism with essential hypertension: a case-control study. Biomed Res Int. 2014, 538053 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Wu L. et al. A sex-specific effect of the CYP17A1 SNP rs11191548 on blood pressure in Chinese children. J Hum Hypertens. 26, 731–736 (2012). [DOI] [PubMed] [Google Scholar]
  35. Maric C. Sex differences in cardiovascular disease and hypertension: involvement of the renin-angiotensin system. Hypertension. 46, 475–476 (2005). [DOI] [PubMed] [Google Scholar]
  36. Rinn J. L. & Snyder M. Sexual dimorphism in mammalian gene expression. Trends Genet. 21, 298–305 (2005). [DOI] [PubMed] [Google Scholar]
  37. Yang X. et al. Tissue-specific expression and regulation of sexually dimorphic genes in mice. Genome Res. 16, 995–1004 (2006). [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Ober C., Loisel D. A. & Gilad Y. Sex-specific genetic architecture of human disease. Nat Rev Genet. 9, 911–922 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information
srep15245-s1.pdf (771.6KB, pdf)

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES