Abstract
Methylenetetrahydrofolate reductase gene (MTHFR), transcobalaminII (TCN2) and ring finger protein 213 (RNF213) are related to homocysteine (Hcy) level and are of great significance for hypertension. We aimed to evaluate the associations of MTHFR (rs1801133, rs1801131, rs9651118), TCN2 (rs117353193) and RNF213 (rs9916351) with hypertension and blood pressure (BP). A total of 953 patients with hypertension and 1103 controls were enrolled. Genotyping was performed by Taqman. Logistic regression analysis indicated that A allele of TCN2 rs117353193 under the dominant model had a significantly protective effect (P=0.045) after adjustment, which showed that AA+GA genotype has a lower risk than GG. Additionally, the average diastolic BP (DBP) (P=0.044) and mean arterial pressure (MAP) (P=0.035) levels were significantly different between genotypes of RNF213 rs9916351. Further pairwise comparison showed that the average systolic BP (SBP) level of the TT genotype carriers were significantly higher than in CC (P=0.024), and the average DBP and MAP levels of the TT genotype carriers were higher than in CT (P=0.044, P=0.012, respectively) and CC (P=0.048, P=0.010, respectively). In the recessive model, the average SBP (P=0.043), DBP (P=0.018) and MAP (P=0.017) levels with the TT genotype carriers were significantly higher than in CT+CC. Multiple linear regression analysis suggested that RNF213 rs9916351 in the recessive model had significant effects on SBP (P=0.025), DBP (P=0.017) and MAP (P=0.010) as a risk factor. However, no associations were observed between MTHFR and hypertension. TCN2 rs117353193 might serve as a protective factor in hypertension, and RNF213 rs9916351 might be a risk factor that is linked to increase BP level in Northeast Chinese population.
Keywords: gene polymorphisms, Hypertension, MTHFR, RNF213, TCN2
Introduction
Hypertension is a multifactorial disease and is a major life-threatening health concern throughout the world. Approximately 2 million people in China die of diseases directly associated with hypertension each year, and its the prevalence rate is still on the rise. Especially in the middle-aged and old population, high blood pressure (BP) has become the main cause of coronary heart disease, stroke and many other cardiovascular diseases (CVDs). The results of an 8-year follow-up with 170000 people over 40 years old in China showed that coronary heart disease was the first cause factor of death, while hypertension was the first risk factor for total mortality, and the relative risk ratio (RR) was 1.48 [1]. The pathogenesis of hypertension is known as a result of the interaction of lifestyle exposures, such as high dietary sodium, overweight and excess alcohol consumption [2]. However, previous studies have shown that up to 60% of the variation in inducing increased hypertension risk could be due to genetic factors [3].
A number of previous studies have suggested that genetic alterations in the genes controlling homocysteine (Hcy) and folate metabolism were linked to onset of CVDs [4,5]. Hyperhomocysteinemia (HHcy), an important and independent risk factor, which also contributes to endothelial damage and oxidative stress [6], has been linked to hypertension as it induces arteriolar constriction, renal dysfunction and increase in sodium reabsorption [7]. The excessive increase in Hcy level was mainly caused by gene mutation of key enzymes in the metabolic pathways.
Methylenetetrahydrofolate reductase gene (MTHFR), which catalyzes the conversion of 5,10-methylenetetrahydrofolate into 5-methyltetrahydrofolate, is a crucial enzyme in the metabolism of Hcy and folate which both have associated with methylation of genomic DNA [8]. Two common single nucleotide polymorphisms (SNPs), C677T (rs1801133) and A1298C (rs1801131), are particularly reported to be associated with reduced enzyme activity and thermostability, resulting in a relative deficiency in the re-methylation process and interfering with the metabolic pathway. The C/T variant at site 677 is to replace the encoded alanine with valine, and the A/C variant at site 1298, is to convert its encoded glutamic acid into alanine, which leads to elevated plasma Hcy [9–14] and damages the integrity of blood vessels [15]. Furthermore, recent evidence has found that MTHFR rs9651118 was associated with the serum level of Hcy and also contributes to the development of vascular diseases [16], the same is true of transcobalaminII (TCN2) rs117353193 and RNF213 rs9916351. However, rs9651118 is an intron variant that does not cause amino acid changes.
Vitamin B12 is considered as a nutritional factor for regulating Hcy metabolism, the absorption and cellular delivery of which largely depend on the specific plasma transporter, TCN2 [17]. Vitamin B12 and TCN2 combine to form holotranscobalamin (holo-TC) complexus, which plays an important role in cells within target tissues [18]. The variant in its loci rs117353193 causes the encoded arginine to be converted into glutamine. Because the biological function of TCN2 is mainly regulated by its own genetic polymorphism, it is also one of the important genetic factors affecting the metabolism of Hcy.
Ring finger protein 213 (RNF213) was originally identified as a susceptibility gene for moyamoya disease (MMD) [19]. MMD is often accompanied by hypertension [20,21], and the incidence of hypertension in MMD patients is significantly higher than that of the general population, suggesting that there may be a common susceptibility gene between the two. Rs9916351, though, is also an intron variant that does not cause amino acid changes. However, little data were so far found concerning the link of hypertension.
The loci of MTHFR, TCN2 and RNF213 have been found to be related to Hcy level in the study of susceptibility genes to MMD, which provides an idea for our study to see if there is actually a susceptibility gene loci between the two diseases. Many genome-wide association studies have been done on hypertension in different ethnic and regional populations [22,23], and the identified novel loci differ, requiring further verification. The present study was conducted to examine the association of MTHFR rs1801133, rs1801131, rs9651118, TCN2 rs117353193 and RNF213 rs9916351 gene polymorphisms with the risk of hypertension and BP in Northeast Chinese population.
Materials and methods
Editorial policies and ethical considerations
Before data collection, all subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by Ethics Committee of China Medical University.
Study population
The sample size was determined using the software Power and Sample Size (PS) for calculation. The relevant parameters of the statistical efficiency calculation in the present study were as follows: (1) the lowest allele frequencies of each SNP in the Chinese population referred to the dbSNP website, where the frequency of MTHFR rs1801131 was set at 0.219, the frequency of rs1801133 was set at 0.333, the frequency of rs9651118 was set at 0.267, the frequency of TCN2 rs117353193 was set at 0.033, the frequency of RNF213 rs9916351 was set at 0.329; (2) their odds ratios (ORs) were estimated based on previous observations [16,24,25], in which the OR value of MTHFR rs1801131 was set to 1.56, the OR value of rs1801133 was set to 1.38, the OR value of rs9651118 was set to 0.65, the OR value of TCN2 rs117353193 was set to 1.87, and the OR value of RNF213 rs9916351 was set to 1.96. Among them, MTHFR rs9651118, TCN2 rs117353193 and RNF213 rs9916351 were recently found in the study of genome-wide association for MMD, there is no correlation of hypertension, so the OR value setting adopts their OR value in MMD study; (3) the ratio of sample size of control group to case group was set at 1:1; (4) test level α was set to 0.05. From the sample size calculation: 416 cases were needed for rs1801131 to be able to reject the null hypothesis reaching at least 80% power in the present study, 651 cases for rs1801133, 485 cases for rs9651118, 954 cases for rs117353193 and 145 cases for rs9916351. Meanwhile, the results of power calculation based on our sample size show that: 98.9% power for rs1801131, 92.4% power for rs1801133, 97.6% power for rs9651118, 80.0% power for rs117353193, 100% power for rs9916351.
A total of 953 patients with hypertension and 1103 controls were enrolled from Fushun and Panjin City in Liaoning Province, China. The population proportions from the two cities were not significantly different in the case and control groups (P>0.05). There were 894 male (43.5%) and 1162 female (56.5%) in our study. In summary, participants with hypertension who met the following criteria were recruited: (1) systolic BP (SBP) of at least 140 mmHg or diastolic BP (DBP) of at least 90 mmHg were measured three times on different days in resting state; (2) people who had been treated with antihypertensive drugs. The control group was normotensive after medical measurement (SBP < 140 mmHg and DBP < 90 mmHg). Both groups were 18 years of age and older and excluded severe liver, kidney and acute or chronic infectious diseases, hyperthyroidism or hypothyroidism, systemic arteriopathy, various tumors and other cerebrovascular diseases and metabolic diseases.
Data collection and clinical evaluation
Clinical data including gender, age, height, weight, body mass index (BMI), waistline and smoking history were recorded in health datasheet. After sitting for 5 min, baseline BP was measured three times using a standardized mercury-gravity monometer with a 30-s interval between replicates, and the mean value of three measurements was taken. Pulse pressure (PP) was calculated as the difference of SBP and DBP. Mean arterial pressure (MAP) was calculated as the sum of one-third SBP and two-thirds DBP. Ten milliliters of peripheral blood of each fasting study individual was collected in EDTA vacutainer. Biochemical profiles, including fasting blood glucose (FBG), total cholesterol (TC) and triglyceride (TG) were done on automated biochemical analyzer (Murray, BS-820).
DNA isolation and genotyping
After receiving informed consent, 10 ml peripheral vein blood without centrifugation was extracted from available hypertensive patients and normal control subjects placed in EDTANa4 anticoagulant tubes, and stored in a freezer at −80°C until analysis. Genomic DNA was extracted from blood samples with a Blood Genetic DNA Mini Kit (CWBIO, Beijing, China). The concentration of the 2056 DNA was tested by NanoDrop 2000 (Thermo Fisher Scientific, Waltham, MA, U.S.A.), of which the purity is considered to be up to the requirements of subsequent tests, then stored at −80°C for future genotyping.
Genotyping of five SNPs in all participants was conducted using Taqman™ Probe (Taqman™ SNP Genotyping Assays; Applied Biosystems, Foster City, CA, U.S.A.) and a QuantStudio™ 6 Flex Real-Time PCR System (Applied Biosystems, Foster City, U.S.A.) in a single lab. The total system contained 5 μl, 2.0 μl purified genomic DNA, 2.5 μl of TaqPath™ ProAmp™ Master Mixes (Applied Biosystems, Foster City, CA, U.S.A.), 0.1 μl of 40× SNP Genotyping Assay and 0.4 μl deoxyribonuclase-free water. The appropriate PCR thermal cycling conditions were as follows: maintained for 5 min for initial denature/enzyme activation, 40 cycles of 5 s at 95°C for denaturation, and 1 min at 60°C for annealing and extension. After each PCR amplification, an end point plate read was conducted using QuantStudio™ 6Flex Real-Time PCR System. The genotype of each sample was confirmed based on the fluorescence signals. We sampled DNA and repeated genotyping, and the results were consistent with previous experiments.
Statistical analysis
The Epidata 3.1 software package was used for database design, data entry and data check, and SBP and DBP were respectively increased by 10 and 5 mmHg in patients with hypertension treated with drugs [26]. Statistical analysis was performed with SPSS 21.0 software. Quantitative variables were tested for normality and homoscedasticity. Those conforming to normal distributions were expressed as mean ± standard deviation, while skewed distributions were expressed as median (P25–P75). The comparison between the two groups was carried out by independent-samples t test or nonparametric test, and one-way analysis of variance (ANOVA) was used in the multi-group comparison. Pairwise comparison was performed by least significant difference (LSD) and student newman keuls (SNK-q) test. Qualitative variables were compared chi-square test or Fisher’s exact test and expressed as counts and proportions. Univariate logistic regression analysis was used to test the association between each SNP and hypertension under the genetic models. Binary regression analysis was used to test the association between environmental factors and hypertension. Furthermore, the multivariate logistic regression analysis was used to test the susceptibility of each SNP to hypertension after correcting the confounding factors. Multiple linear regression analysis was used to see the effects of other covariates and genetic component on BP. The SHEsis [27] software was used for Hardy–Weinberg balance test, allele and genotype correlation analysis with hypertension and haploid analysis. A value of P<0.05 was considered as statistically significant.
Results
Baseline characteristics
The clinical and demographic characteristics of 953 patients and 1103 controls are reported in Table 1. Compared with controls, the patients had significant differences in gender, age, weight, waistline, BMI, smoking frequency and higher levels of SBP, DBP, PP, MAP, FBG, TC and TG. All of these foregoing parameters were statistically higher in patients when compared with control subjects (P<0.05).However, no statistically significant differences were observed in height (P=0.864) between the two groups.
Table 1. Clinical and demographic characteristics of the study subjects.
| Characteristics | Patients (n=953) | Controls (n=1103) | P-value |
|---|---|---|---|
| Male:Female | 439:514 | 455:648 | 0.0291 |
| Age (years) | 66 (49–71) | 44 (32–66) | <0.0011 |
| Height (cm) | 162.44 ± 7.56 | 162.52 ± 6.93 | 0.864 |
| Weight (kg) | 63.15 ± 9.25 | 60.89 ± 8.57 | <0.0011 |
| Waistline (cm) | 82.83 ± 9.01 | 80.03 ± 6.76 | <0.0011 |
| BMI (kg/m2) | 23.92 ± 3.09 | 23.02 ± 2.84 | <0.0011 |
| SBP (mmHg) | 150.68 ± 15.20 | 121.62 ± 11.32 | <0.0011 |
| DBP (mmHg) | 91.05 ± 9.34 | 77.98 ± 6.80 | <0.0011 |
| PP (mmHg) | 59.63 ± 14.87 | 43.64 ± 9.25 | <0.0011 |
| MAP (mmHg) | 110.94 ± 9.28 | 92.51 ± 7.40 | <0.0011 |
| Smoking (%) | 16.80% | 7.75% | <0.0011 |
| FBG (mmol/l) | 5.60 ± 1.86 | 5.17 ± 1.20 | <0.0011 |
| TC (mmol/l) | 4.80 ± 1.99 | 4.45 ± 1.20 | <0.0011 |
| TG (mmol/l) | 1.78 ± 1.10 | 1.51 ± 1.20 | <0.0011 |
1Significant difference (P<0.05).
The distributions of genotypes, alleles and associations with hypertension
Genotypes and alleles frequencies of five SNPs in patients and controls are shown in Table 2. The observed genotype distributions of five SNPs among controls were in agreement with Hardy–Weinberg equilibrium (P=0.503 for rs1801131; P=0.151 for rs1801133; P=0.707 for rs9651118; P=0.555 for rs117353193; P=0.545 for rs9916351). However, the genotypes’ distributions and the alleles frequencies were not statistically different between the two groups (P>0.05).
Table 2. Genotypes and alleles frequency of five SNPs in patients with hypertension and controls.
| Genotypes and alleles | Patients (%) | Controls (%) | OR (95% CI) | P-value |
|---|---|---|---|---|
| rs1801131 | ||||
| A | 1587 (85.3%) | 1852 (86.2%) | 0.93 (0.78–1.11) | 0.147 |
| C | 273 (14.7%) | 296 (13.8%) | ||
| AA | 679 (73.0%) | 801 (74.6%) | 0.720 | |
| AC | 229 (24.6%) | 250 (23.3%) | ||
| CC | 22 (2.4%) | 23 (2.1%) | ||
| rs1801133 | ||||
| C | 839 (44.9%) | 933 (43.4%) | 1.06 (0.94–1.20) | 0.333 |
| T | 1029 (55.1%) | 1217 (56.6%) | ||
| CC | 200 (21.4%) | 214 (19.9%) | 0.634 | |
| CT | 439 (47.0%) | 505 (47.0%) | ||
| TT | 295 (31.6%) | 356 (33.1%) | ||
| rs9651118 | ||||
| T | 1383 (74.2%) | 1564 (73.2%) | 0.95 (0.83–1.10) | 0.485 |
| C | 481 (25.8%) | 572 (26.8%) | ||
| TT | 517 (55.5%) | 575 (53.8%) | 0.764 | |
| TC | 349 (37.4%) | 414 (38.8%) | ||
| CC | 66 (7.1%) | 79 (7.4%) | ||
| rs117353193 | ||||
| G | 1774 (94.9%) | 2041 (94.8%) | 0.98 (0.74–1.29) | 0.873 |
| A | 96 (5.1%) | 113 (5.2%) | ||
| GG | 840 (89.8%) | 966 (89.7%) | 0.900 | |
| GA | 94 (10.1%) | 109 (10.1%) | ||
| AA | 1 (0.1%) | 2 (0.2%) | ||
| rs9916351 | ||||
| C | 1087 (58.3%) | 1195 (55.6%) | 1.12 (0.98–1.26) | 0.088 |
| T | 779 (41.7%) | 955 (44.4%) | ||
| CC | 319 (34.2%) | 337 (31.3%) | 0.236 | |
| CT | 449 (48.1%) | 521 (48.5%) | ||
| TT | 165 (17.7%) | 217 (20.2%) | ||
P is calculated for carriers of the polymorphism. Abbreviation: CI, confidence interval.
Logistic regression analysis of environmental and genetic factors
The results of binary regression analysis of environmental factors, including gender, age, weight, waistline, BMI, smoking frequency, FBG, TC and TG, are shown in Table 3. We found that waistline (P=0.006) and BMI (P=0.016) were risk factors associated with hypertension. The results of logistic regression analysis of genetic factors are shown in Table 4. Univariate logistic regression analysis showed that the five SNPs had no significant differences under the three genetic models. After adjusting for important confounding factors, including gender, age, waistline, BMI, smoking, FBG, TC and TG, the results showed that A allele carriers of TCN2 rs117353193 under the dominant genetic model (AA+GA vs GG) had a significantly protective effect compared with the risk of hypertension [OR = 0.56, 95% confidence interval (CI) (0.32–0.99); P=0.045], and the AA+GA genotype carriers had 0.56-times higher risk than the GG genotype carriers. Additionally, a borderline significant association was observed under the additive model (GA vs GG) after adjustment [OR = 0.59, 95% CI (0.33–1.04); P=0.069]. However, in the adjusted analysis, the effects of waistline and BMI still exist.
Table 3. Binary regression analysis of environmental factors.
| Environmental factors | OR (95% CI) | P-value |
|---|---|---|
| Gender (%) | 1.129 (0.747–1.705) | 0.565 |
| Age (years) | 1.007 (0.978–1.037) | 0.632 |
| Weight (kg) | 0.988 (0.953–1.025) | 0.528 |
| Waistline (cm) | 1.033 (1.010–1.058) | 0.0061 |
| BMI (kg/m2) | 1.139 (1.025–1.267) | 0.0161 |
| Smoking (%) | 1.556 (0.962–2.520) | 0.072 |
| FBG (mmol/l) | 1.058 (0.967–1.157) | 0.217 |
| TC (mmol/l) | 0.938 (0.833–1.057) | 0.294 |
| TG (mmol/l) | 0.964 (0.863–1.077) | 0.521 |
1Significant difference (P<0.05).
Table 4. Logistic regression analysis of five SNPs in three genetic models.
| SNP | Genetic model | Univariate | Adjusted1 | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | P-value | OR (95% CI) | P-value | |||
| rs1801131 | Additive | CC vs AA | 1.13 (0.62–2.04) | 0.690 | 1.32 (0.45–3.86) | 0.608 |
| AC vs AA | 1.08 (0.88–1.33) | 0.462 | 0.92 (0.64–1.33) | 0.667 | ||
| Dominant | CC+AC vs AA | 1.09 (0.89–1.32) | 0.425 | 0.95 (0.67–1.35) | 0.782 | |
| Recessive | CC vs AC+AA | 1.11 (0.61–2.00) | 0.736 | 1.35 (0.47–3.92) | 0.579 | |
| rs1801133 | Additive | TT vs CC | 0.89 (0.69–1.14) | 0.340 | 1.24 (0.81–1.92) | 0.324 |
| CT vs CC | 0.93 (0.74–1.17) | 0.540 | 1.38 (0.92–2.06) | 0.117 | ||
| Dominant | TT+CT vs CC | 0.91 (0.74–1.17) | 0.405 | 1.32 (0.91–1.93) | 0.143 | |
| Recessive | TT vs CT+CC | 0.93 (0.77–1.13) | 0.464 | 1.00 (0.71–1.40) | 0.999 | |
| rs9651118 | Additive | CC vs TT | 0.93 (0.66–1.32) | 0.679 | 0.77 (0.41–1.46) | 0.425 |
| TC vs TT | 0.94 (0.78–1.13) | 0.496 | 0.81 (0.58–1.12) | 0.196 | ||
| Dominant | CC+TC vs TT | 0.94 (0.79–1.12) | 0.464 | 0.80 (0.59–1.10) | 0.166 | |
| Recessive | CC vs TC+TT | 0.95 (0.68–1.34) | 0.786 | 0.85 (0.45–1.58) | 0.601 | |
| rs117353193 | Additive | AA vs GG | - | - | - | - |
| GA vs GG | 0.99 (0.74–1.33) | 0.955 | 0.59 (0.33–1.04) | 0.069 | ||
| Dominant | AA+GA vs GG | 0.98 (0.74–1.31) | 0.910 | 0.56 (0.32–0.99) | 0.0452 | |
| Recessive | AA vs GA+GG | - | - | - | - | |
| rs9916351 | Additive | TT vs CC | 1.25 (0.97–1.61) | 0.091 | 1.13 (0.68–1.89) | 0.644 |
| CT vs CC | 1.13 (0.89–1.44) | 0.304 | 0.86 (0.61–1.20) | 0.552 | ||
| Dominant | TT+CT vs CC | 0.88 (0.73–1.06) | 0.176 | 0.91 (0.66–1.25) | 0.552 | |
| Recessive | TT vs CT+CC | 0.85 (0.68–1.06) | 0.155 | 1.23 (0.76–1.99) | 0.398 | |
Adjusted for gender, age, waistline, BMI, smoking, FBG, TC and TG.
2Significant difference (P<0.05).
Haplotype distribution of three SNPs of MTHFR gene
Haplotype analysis of rs1801131, rs1801133 and rs9651118 polymorphisms of MTHFR gene are represented in Table 5. No significant differences were observed in any of the examined haplotypes (ACC, ACT, ATT, CCT) between hypertensive patients and controls (P>0.05). These findings suggest that the haplotypes of the MTHFR gene are not associated with susceptibility of hypertension in our subjects.
Table 5. Haplotype distribution of three SNPs of MTHFR gene.
| Haplotype | Patients (%) | Controls (%) | OR (95% CI) | P-value |
|---|---|---|---|---|
| ACC | 462.58 (25.1%) | 548.54 (25.9%) | 0.96 (0.83–1.11) | 0.576 |
| ACT | 95.85 (5.2%) | 87.92 (4.1%) | 1.27 (0.94–1.71) | 0.115 |
| ATT | 1000.58 (54.4%) | 1178.35 (55.6%) | 0.95 (0.84–1.08) | 0.412 |
| CCT | 264.13 (14.4%) | 282.32 (13.3%) | 1.09 (0.91–1.31) | 0.355 |
All those P<0.05 will be ignored in analysis.
Comparison of BP levels of five SNPs in three genetic models
As shown in Table 6, we found that the average DBP (P=0.044) and MAP (P=0.035) levels of RNF213 rs9916351 were significantly different under the additive model, and the average SBP level had a borderline significant difference (P=0.077). Further pairwise comparison showed that the average SBP level with the homozygous TT genotype carriers were significantly higher than in CC genotype carriers (P=0.024), the average DBP and MAP levels with the homozygous TT genotype carriers were significantly higher than in CT (P=0.044 for DBP, P=0.012 for MAP) and CC (P=0.048 for DBP, P=0.010 for MAP) genotypes carriers. In the recessive model, the average SBP, DBP and MAP levels of RNF213 rs9916351 with the homozygous TT genotype carriers were significantly higher than in CT+CC genotype carriers (P=0.043 for SBP, P=0.018 for DBP, P=0.017 for MAP). However, there were no significant differences in BP levels of rs1801131, rs1801133, rs9651118 and rs117353193 under the three genetic models.
Table 6. Comparison of BP levels of five SNPs in three genetic models.
| SNP | Genetic model | n | SBP (Mean ± SD) | P-value | DBP (Mean ± SD) | P-value | PP (Mean ± SD) | P-value | MAP (Mean ± SD) | P-value | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| rs1801131 | Additive | CC | 24 | 138.58 ± 17.63 | 0.711 | 87.38 ± 11.77 | 0.572 | 51.21 ± 13.48 | 0.599 | 104.42 ± 12.50 | 0.689 |
| AC | 267 | 136.67 ± 18.22 | 85.01 ± 9.18 | 51.66 ± 14.73 | 102.24 ± 10.90 | ||||||
| AA | 754 | 137.77 ± 20.48 | 85.12 ± 10.95 | 52.65 ± 15.00 | 102.67 ± 13.05 | ||||||
| Dominant | CC+AC | 291 | 136.82 ± 18.15 | 0.466 | 85.21 ± 9.42 | 0.902 | 51.62 ± 14.61 | 0.316 | 102.42 ± 11.03 | 0.760 | |
| AA | 754 | 137.77 ± 20.48 | 85.12 ± 10.95 | 52.65 ± 15.00 | 102.67 ± 13.05 | ||||||
| Recessive | CC | 24 | 138.58 ± 17.63 | 0.789 | 87.38 ± 11.77 | 0.295 | 51.21 ± 13.48 | 0.701 | 104.42 ± 12.50 | 0.472 | |
| AC+AA | 1021 | 137.48 ± 19.91 | 85.09 ± 10.51 | 52.39 ± 14.93 | 102.56 ± 12.52 | ||||||
| rs1801133 | Additive | TT | 307 | 136.59 ± 19.20 | 0.655 | 84.48 ± 10.83 | 0.392 | 52.11 ± 14.41 | 0.914 | 101.86 ± 12.49 | 0.469 |
| CT | 520 | 137.78 ± 20.60 | 85.24 ± 10.46 | 52.54 ± 15.15 | 102.75 ± 12.80 | ||||||
| CC | 223 | 137.92 ± 18.92 | 85.70 ± 10.25 | 52.23 ± 14.98 | 103.11 ± 11.80 | ||||||
| Dominant | TT+CT | 827 | 137.34 ± 20.09 | 0.696 | 84.96 ± 10.60 | 0.353 | 52.38 ± 14.87 | 0.892 | 102.42 ± 12.69 | 0.464 | |
| CC | 223 | 137.92 ± 18.92 | 85.70 ± 10.25 | 52.23 ± 14.98 | 103.11 ± 11.80 | ||||||
| Recessive | TT | 307 | 136.59 ± 19.20 | 0.359 | 84.48 ± 10.83 | 0.209 | 52.11 ± 14.41 | 0.740 | 101.86 ± 12.49 | 0.239 | |
| CT+CC | 743 | 137.82 ± 20.10 | 85.38 ± 10.39 | 52.45 ± 15.09 | 102.86 ± 12.50 | ||||||
| rs9651118 | Additive | CC | 72 | 139.35 ± 20.29 | 0.649 | 86.90 ± 11.78 | 0.342 | 52.44 ± 14.67 | 0.808 | 102.86 ± 12.50 | 0.453 |
| TC | 419 | 137.67 ± 20.44 | 84.98 ±10.21 | 52.69 ±15.56 | 102.53 ± 12.46 | ||||||
| TT | 551 | 137.11 ± 19.32 | 85.05 ± 10.66 | 52.06 ± 14.38 | 102.41 ± 12.44 | ||||||
| Dominant | CC+TC | 491 | 137.14 ± 19.66 | 0.515 | 85.26 ± 10.47 | 0.749 | 52.65 ± 15.42 | 0.522 | 102.80± 12.62 | 0.610 | |
| TT | 551 | 137.11 ± 19.32 | 85.05 ± 10.66 | 52.06 ± 14.38 | 102.41 ± 12.44 | ||||||
| Recessive | CC | 72 | 139.35 ± 20.29 | 0.410 | 86.90 ± 11.78 | 0.144 | 52.44 ± 14.67 | 0.951 | 102.86 ± 12.50 | 0.211 | |
| TC+TT | 970 | 137.35 ± 19.80 | 85.02 ± 10.46 | 52.33 ± 14.90 | 102.46 ± 12.44 | ||||||
| rs117353193 | Additive | AA | 1 | 130 | 0.620 | 80 | 0.889 | 50 | 0.474 | 97 | 0.817 |
| GA | 96 | 135.71 ± 18.26 | 85.14 ± 10.39 | 50.57 ± 12.39 | 102.01 ± 12.33 | ||||||
| GG | 946 | 137.62 ± 19.98 | 85.12 ± 10.56 | 52.50 ± 15.10 | 102.62 ± 12.54 | ||||||
| Dominant | AA+GA | 97 | 135.65 ± 18.17 | 0.350 | 85.08 ± 10.35 | 0.973 | 50.57 ± 12.33 | 0.152 | 101.96 ± 12.18 | 0.620 | |
| GG | 946 | 137.62 ± 19.98 | 85.12 ± 10.56 | 52.50 ± 15.10 | 102.62 ± 12.54 | ||||||
| Recessive | AA | 1 | 130 | - | 80 | - | 50 | - | 97 | - | |
| GA+GG | 1042 | 137.45 ± 19.82 | 85.12 ± 10.54 | 52.32 ± 14.88 | 102.56 ± 12.50 | ||||||
| rs9916351 | Additive | TT | 123 | 140.91 ± 19.69 | 0.077 | 87.23 ± 11.58 | 0.0441 | 53.68 ± 13.59 | 0.432 | 105.11 ± 13.36 | 0.0351 |
| CT | 487 | 137.69 ±20.40 | 85.08 ± 10.69 | 52.61 ± 15.37 | 102.62 ± 12.76 | ||||||
| CC | 434 | 136.35 ± 19.23 | 84.54 ± 10.01 | 51.81 ± 14.86 | 101.81 ± 11.90 | ||||||
| Dominant | TT+CT | 610 | 138.34 ± 20.28 | 0.110 | 85.51 ± 10.90 | 0.140 | 52.83 ± 15.02 | 0.279 | 103.12 ± 12.91 | 0.093 | |
| CC | 434 | 136.35 ± 19.23 | 84.54 ± 10.01 | 51.81 ± 14.86 | 101.81 ± 11.90 | ||||||
| Recessive | TT | 123 | 140.91 ± 19.69 | 0.0431 | 87.23 ± 11.58 | 0.0181 | 53.68 ± 13.59 | 0.313 | 105.11 ± 13.36 | 0.0171 | |
| CT+CC | 921 | 137.06 ± 19.86 | 84.83 ± 10.37 | 52.23 ± 15.13 | 102.24 ± 12.36 |
Abbreviation: SD, standard deviation.
1Significant difference (P<0.05).
Multiple linear regression analysis of five SNPs in genetic models
The results of multiple linear regression analysis of baseline covariates and genetic component on BP are shown in Table 7. In the recessive model, we found that RNF213 rs9916351 had significant effects on SBP (P=0.025), DBP (P=0.017) and MAP (P=0.010) as a risk factor. However, there were no significant associations of rs1801131, rs1801133, rs9651118 and rs117353193 on BP.
Table 7. Multiple linear regression analysis of five SNPs in three genetic models.
| SNP | Genetic models | SBP | DBP | PP | MAP | ||||
|---|---|---|---|---|---|---|---|---|---|
| β | P-value | β | P-value | β | P-value | β | P-value | ||
| rs1801131 | Additive | 1.238 | 0.347 | 0.079 | 0.913 | 1.159 | 0.275 | 0.468 | 0.570 |
| Dominant | 1.763 | 0.241 | 0.344 | 0.678 | 1.420 | 0.241 | 0.807 | 0.392 | |
| Recessive | −1.213 | 0.778 | −1.955 | 0.407 | 0.742 | 0.830 | −1.600 | 0.551 | |
| rs1801133 | Additive | −0.627 | 0.503 | −0.295 | 0.565 | −0.332 | 0.660 | −0.424 | 0.469 |
| Dominant | −1.954 | 0.230 | −1.009 | 0.258 | −0.945 | 0.471 | −1.338 | 0.188 | |
| Recessive | 0.045 | 0.975 | 0.093 | 0.907 | −0.048 | 0.968 | 0.045 | 0.961 | |
| rs9651118 | Additive | 0.806 | 0.457 | 0.492 | 0.412 | 0.314 | 0.719 | 0.617 | 0.365 |
| Dominant | 0.854 | 0.524 | 0.714 | 0.335 | 0.139 | 0.897 | 0.785 | 0.351 | |
| Recessive | 1.522 | 0.571 | 0.151 | 0.919 | 1.370 | 0.527 | 0.634 | 0.707 | |
| rs117353193 | Additive | 2.698 | 0.266 | 0.227 | 0.865 | 2.471 | 0.207 | 1.026 | 0.500 |
| Dominant | 2.666 | 0.285 | 0.084 | 0.951 | 2.582 | 0.199 | 0.924 | 0.554 | |
| Recessive | 9.356 | 0.604 | 8.155 | 0.412 | 1.202 | 0.934 | 8.279 | 0.464 | |
| rs9916351 | Additive | 1.137 | 0.258 | 0.851 | 0.124 | 0.287 | 0.724 | 0.939 | 0.135 |
| Dominant | 0.049 | 0.972 | 0.370 | 0.624 | 0.321 | 0.772 | 0.258 | 0.764 | |
| Recessive | 4.547 | 0.0251 | 2.669 | 0.0171 | 1.878 | 0.253 | 3.279 | 0.0101 | |
Abbreviation: β, partial regression coefficient.
1Significant difference (P<0.05).
Discussion
Hypertension is a complex disease that comes about as a result of the interaction between genetic and environmental factors. Recently, numerous gene polymorphisms have been found to be associated with hypertension. The present study was designed to investigate the association of MTHFR (rs1801133, rs1801131, rs9651118), TCN2 (rs117353193) and RNF213 (rs9916351) gene variants with the susceptibility of hypertension and BP among the population of northeast in China.
In our study, we found that three MTHFR gene polymorphisms were not significantly associated with hypertension. Many genetic studies have shown that genetic variants in the MTHFR gene have been linked to CVDs, such as coronary heart disease [28,29], type 2 diabetes [30,31], ischemic stroke [32,33] and hypertension [34,35], but the clear mechanisms need to be further investigated. Markan et al. [36] reported that MTHFR rs1801133 and rs1801131 alleles and the co-occurrence of rs1801133 CT/rs1801131 CC genotypes were linked to increased risk of hypertension in the Indian population. Koupepidou et al. [37] also suggested that rs1801133 TT/CT and rs1801131 CC genotypes may be the risk factors for hypertensive renal sclerosis and chronic renal damage in hypertensive patients. Furthermore, a meta-analysis combining 5207 patients and 5383 control subjects indicated a significant association between the rs1801133 gene polymorphism and hypertension, which suggested that carriers of the T allele and TT genotype were more susceptible [38], but no significant association with rs1801131 was found. Additionally, more studies have reported that the MTHFR rs1801133 is an independent factor for hypertension in different ethnic groups [39–41]. A meta-analysis from 114 studies with 15411 cases and 21970 controls shown that the rs1801133 polymorphism was significantly associated with hypertension, and stratified analysis by ethnicity revealed a significant association among East Asians and Caucasians, but not among Latinos, Black Africans, and Indians and Sri Lankans. This shows the effect of ethnicity on the results, the differences in environmental exposures and genetic background among different populations might suggest potentially different pathways of BP regulation. However, for the rs1801131 polymorphism, no significant association was observed either in overall or subgroup analysis under all genetic models [24]. A number of studies have revealed that Hcy may be involved in the pathogenesis of hypertension, including that plasma Hcy could induce arteriolar constriction, renal dysfunction and increased sodium reabsorption [7]. Increased plasma Hcy levels contribute to vascular endothelium damage and promote oxidative stress, which lead to endothelial dysfunction and an imbalance of the antioxidant status [42–44]. Our study did not evaluate the level of Hcy. Inversely, Ravera et al. [45] found no association between rs1801133 and BP level among hypertensive patients. It was also reported that there is no significant association between rs1801133 and hypertension in Algerians [46]. In addition, no studies had reported the significant association between rs1801131 and hypertension. These results were consistent with ours. Meanwhile, we found that the statistical efficiency of these loci was above 80% in our power calculations, the results showed that: 98.9% power for rs1801131, 92.4% power for rs1801133, 97.6% power for rs9651118, 80.0% power for rs117353193, 100% power for rs9916351, this excluded the reason of insufficient sample size for the lack of association. We think the difference is more likely due to difference in the degree of ethnic heterogeneity. Regarding rs9651118, little studies on hypertension have been conducted up to date. Similarly vitamin B12 and folate are also involved in the metabolism of Hcy as nutritional factors. The former acts as a coenzyme in the catalytic synthesis of methionine with Hcy. TCN2 is recognized as a specific plasma transporter to facilitate the cellular uptake of vitamin B12 by its receptor-mediated endocytosis [47]. Thus, TCN2 gene polymorphisms have been considered as another genetic trait which may affect Hcy metabolism by modulating the bioavailability of vitamin B12 [48]. A genome-wide association study for MMD found that TCN2 rs117353193 genotype frequency was significant difference between patients with normal Hcy levels and hyperhomocysteine [16], and this applies to MTHFR rs9651118 as well. In our study, we found that the risk of GG genotype carriers of rs117353193 is lower than that of GA+AA genotypes carriers after adjusting for confounding factors, which suggested that it may be a protective factor for hypertension. However, waistline and BMI also play important roles, so our results may be more influenced by environmental and genetic interactions. In addition, the mutant AA genotype carries are too few to compare with other genotypes carries. The relevant studies are so few and there is need for it to be further explored.
Regarding RNF213, it has been proved to be a susceptible gene to MMD [49], but it has also been reported to be involved in other vascular disorders such as coronary heart disease, hypertension, aneurysm and heterogeneous intracerebral vasculopathy [50–54]. One study showed a significant association of RNF213 polymorphisms with SBP [53]. Our present study demonstrated that the homozygous TT genotype carriers of rs9916351 had significantly higher SBP, DBP and MAP levels than the CT or CC genotypes carriers, that is to say TT genotype might be a risk factor that is linked to increase the level of BP. Similarly, the results of multiple linear regression analysis also show that rs9916351 had significant effects on SBP, DBP and MAP as a risk factor, this further supports our conclusion. MTHFR rs9651118, TCN2 rs117353193 and RNF213 rs9916351 were recently revealed as the novel susceptibility loci for MMD by a genome-wide association study, no studies were conducted concerning the link to hypertension, and more evidences are needed to support our results.
Some limitations of our study should be mentioned. First, we did not get some of the relevant data in subjects, such as serum Hcy, vitamin B12 and folate levels. Second, the differences in results in different studies of different countries may also attribute to the ethnic differences. Furthermore, hypertension is a complex disease and is affected by both environmental and genetic factors. Some important characteristics were significantly different between the patients and the controls in our study, such as BMI, smoking, FBG and so on. Smoking is particularly known to exacerbate hypertension [55], we speculate that the higher proportion of female in the controls may have led to a higher incidence of smoking in the cases than in controls, which also had an impact on our results. Hypertension is a disease that seriously affects the health of all mankind, but at the same time, hypertension is also a controllable disease. While preventing its various risk factors, gene polymorphism also provides an important direction for us to study its pathogenesis and disease progress. However, our study mainly aimed to determine the association of gene polymorphisms on hypertension, the differences of basic data between the two groups had no effect on genotypes. Our study needs to be further studied on account of the consideration of above limitations.
Conclusion
In summary, our study suggests that the TCN2 rs117353193 gene polymorphism might serve as protective factor in hypertension, and the RNF213 rs9916351 gene polymorphism might be an important risk factor that is linked to increase the level of BP among the population of northeast in China. Considering our relatively small sample size and narrow coverage, further studies are needed to confirm our results in the future.
Acknowledgments
The authors are thankful to all participants in the present study.
Abbreviations
- BMI
body mass index
- BP
blood pressure
- CVD
cardiovascular disease
- DBP
diastolic BP
- FBG
fasting blood glucose
- Hcy
homocysteine
- LSD
least significant difference
- MAP
mean arterial pressure
- MMD
moyamoya disease
- MTHFR
methylenetetrahydrofolate reductase gene
- OR
odds ratio
- PP
pulse pressure
- RNF213
ring finger protein 213
- RR
relative risk
- SBP
systolic BP
- SNK-q
student newman keuls
- SNP
single nucleotide polymorphism
- TC
total cholesterol
- TCN2
transcobalaminII
- TG
triglyceride
Author Contribution
All the authors participated in the whole work. However, S.L. designed the study and wrote the manuscript. M.L., Q.L., Y.W., M.M. and X.L. collected and analyzed the data. K.Z., L.Y. and Q.Z. assisted and involved in the experiment and in the revision of manuscript. M.S., F.Z. and F.N. contributed to select the samples, and the discussion. W.L. is the corresponding author and supervisor of the entire project.
Competing Interests
The authors declare that there are no competing interests associated with the manuscript.
Funding
This work was supported by the National Natural Science Foundation of China [grant number 81573240]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
References
- 1.He J., Gu D., Wu X., Reynolds K., Duan X., Yao C. et al. (2005) Major causes of death among men and women in China. N. Engl. J. Med. 353, 1124–1134 10.1056/NEJMsa050467 [DOI] [PubMed] [Google Scholar]
- 2.Snieder H., Harshfield G.A. and Treiber F.A. (2003) Heritability of blood pressure and hemodynamics in African- and European-American youth. Hypertension 41, 1196–1201 10.1161/01.HYP.0000072269.19820.0D [DOI] [PubMed] [Google Scholar]
- 3.Singh M., Mensah G.A. and Bakris G. (2010) Pathogenesis and clinical physiology of hypertension. Cardiol. Clin. 28, 545–559 10.1016/j.ccl.2010.07.001 [DOI] [PubMed] [Google Scholar]
- 4.Blom H.J. and Smulders Y. (2011) Overview of homocysteine and folate metabolism. With special references to cardiovascular disease and neural tube defects. J. Inherit. Metab. Dis. 34, 75–81 10.1007/s10545-010-9177-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Singh P.R. and Lele S.S. (2012) Folate gene polymorphisms MTR A2756G, MTRR A66G, and BHMT G742A and risk for coronary artery disease: a meta-analysis. Genet. Test Mol. Biomark. 16, 471–475 10.1089/gtmb.2011.0237 [DOI] [PubMed] [Google Scholar]
- 6.Schroecksnadel K., Frick B., Winkler C., Wirleitner B., Weiss G. and Fuchs D. (2005) Atorvastatin suppresses homocysteine formation in stimulated human peripheral blood mononuclear cells. Clin. Chem. Lab. Med. 43, 1373–1376 [DOI] [PubMed] [Google Scholar]
- 7.Fridman O., Porcile R., Morales A.V., Gariglio L.O., Potenzoni M.A. and Turk Noceto P.C. (2013) Association of methylenetetrahydrofolate reductase gene 677C>T polymorphism with hypertension in older women in a population of Buenos Aires city. Clin. Exp. Hypertens. 35, 159–166 [DOI] [PubMed] [Google Scholar]
- 8.Yang B., Fan S., Zhi X., Wang D., Li Y., Wang Y. et al. (2014) Associations of MTHFR C677T and MTRR A66G gene polymorphisms with metabolic syndrome: a case-control study in Northern China. Int. J. Mol. Sci. 15, 21687–21702 10.3390/ijms151221687 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Frosst P., Blom H.J., Milos R., Goyette P., Sheppard C.A., Matthews R.G. et al. (1995) A candidate genetic risk factor for vascular disease: a common mutation in methylenetetrahydrofolate reductase. Nat. Genet. 10, 111–113 10.1038/ng0595-111 [DOI] [PubMed] [Google Scholar]
- 10.Ma J., Stampfer M.J., Hennekens C.H., Frosst P., Selhub J., Horsford J. et al. (1996) Methylenetetrahydrofolate reductase polymorphism, plasma folate, homocysteine, and risk of myocardial infarction in US physicians. Circulation 94, 2410–2416 10.1161/01.CIR.94.10.2410 [DOI] [PubMed] [Google Scholar]
- 11.Kluijtmans L.A., van den Heuvel L.P., Boers G.H., Frosst P., Stevens E.M., van Oost B.A. et al. (1996) Molecular genetic analysis in mild hyperhomocysteinemia: a common mutation in the methylenetetrahydrofolate reductase gene is a genetic risk factor for cardiovascular disease. Am. J. Hum. Genet. 58, 35–41 [PMC free article] [PubMed] [Google Scholar]
- 12.Brattstrom L., Wilcken D.E., Ohrvik J. and Brudin L. (1998) Common methylenetetrahydrofolate reductase gene mutation leads to hyperhomocysteinemia but not to vascular disease: the result of a meta-analysis. Circulation 98, 2520–2526 10.1161/01.CIR.98.23.2520 [DOI] [PubMed] [Google Scholar]
- 13.Pereira A.C., Schettert I.T., Morandini Filho A.A., Guerra-Shinohara E.M. and Krieger J.E. (2004) Methylenetetrahydrofolate reductase (MTHFR) c677t gene variant modulates the homocysteine folate correlation in a mild folate-deficient population. Clin. Chim. Acta 340, 99–105 10.1016/j.cccn.2003.09.016 [DOI] [PubMed] [Google Scholar]
- 14.Dankner R., Chetrit A., Murad H., Sela B.A., Frystyk J., Raz I. et al. (2010) Serum adiponectin is associated with homocysteine in elderly men and women, and with 5,10-methylenetetrahydrofolate reductase (MTHFR) in a sex-dependent manner. Metabolism 59, 1767–1774, 10.1016/j.metabol.2010.05.001 [DOI] [PubMed] [Google Scholar]
- 15.Deshmukh A., Rodrigue K.M., Kennedy K.M., Land S., Jacobs B.S. and Raz N. (2009) Synergistic effects of the MTHFR C677T polymorphism and hypertension on spatial navigation. Biol. Psychol. 80, 240–245 10.1016/j.biopsycho.2008.10.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Duan L., Wei L., Tian Y., Zhang Z., Hu P., Wei Q. et al. (2018) Novel susceptibility loci for Moyamoya disease revealed by a genome-wide association study. Stroke 49, 11–18, 10.1161/STROKEAHA.117.017430 [DOI] [PubMed] [Google Scholar]
- 17.Oberley M.J. and Yang D.T. (2013) Laboratory testing for cobalamin deficiency in megaloblastic anemia. Am. J. Hematol. 88, 522–526 10.1002/ajh.23421 [DOI] [PubMed] [Google Scholar]
- 18.Riedel B.M., Molloy A.M., Meyer K., Fredriksen A., Ulvik A., Schneede J. et al. (2011) Transcobalamin polymorphism 67A->G, but not 776C->G, affects serum holotranscobalamin in a cohort of healthy middle-aged men and women. J. Nutr. 141, 1784–1790 [DOI] [PubMed] [Google Scholar]
- 19.Fujimura M., Sonobe S., Nishijima Y., Niizuma K., Sakata H., Kure S. et al. (2014) Genetics and biomarkers of Moyamoya disease: significance of RNF213 as a susceptibility gene. J. Stroke 16, 65–72 10.5853/jos.2014.16.2.65 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Yamada I., Himeno Y., Matsushima Y. and Shibuya H. (2000) Renal artery lesions in patients with moyamoya disease: angiographic findings. Stroke 31, 733–737 10.1161/01.STR.31.3.733 [DOI] [PubMed] [Google Scholar]
- 21.Togao O., Mihara F., Yoshiura T., Tanaka A., Kuwabara Y., Morioka T. et al. (2004) Prevalence of stenoocclusive lesions in the renal and abdominal arteries in moyamoya disease. AJR Am. J. Roentgenol. 183, 119–122 10.2214/ajr.183.1.1830119 [DOI] [PubMed] [Google Scholar]
- 22.Lu X., Wang L., Lin X., Huang J., Charles Gu C., He M. et al. (2015) Genome-wide association study in Chinese identifies novel loci for blood pressure and hypertension. Hum. Mol. Genet. 24, 865–874 10.1093/hmg/ddu478 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Levy D., Ehret G.B., Rice K., Verwoert G.C., Launer L.J., Dehghan A. et al. (2009) Genome-wide association study of blood pressure and hypertension. Nat. Genet. 41, 677–687 10.1038/ng.384 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Yang B., Fan S., Zhi X., Li Y., Liu Y., Wang D. et al. (2014) Associations of MTHFR gene polymorphisms with hypertension and hypertension in pregnancy: a meta-analysis from 114 studies with 15411 cases and 21970 controls. PLoS ONE 9, e87497 10.1371/journal.pone.0087497 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Yang J., Liu J., Liu J., Li W., Li X., He Y. et al. (2014) Genetic association study with metabolic syndrome and metabolic-related traits in a cross-sectional sample and a 10-year longitudinal sample of chinese elderly population. PLoS ONE 9, e100548 10.1371/journal.pone.0100548 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Cui J.S., Hopper J.L. and Harrap S.B. (2003) Antihypertensive treatments obscure familial contributions to blood pressure variation. Hypertension 41, 207–210 10.1161/01.HYP.0000044938.94050.E3 [DOI] [PubMed] [Google Scholar]
- 27.Shi Y.Y. and He L. (2005) SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci. Cell Res. 15, 97–98 10.1038/sj.cr.7290272 [DOI] [PubMed] [Google Scholar]
- 28.Klerk M., Verhoef P., Clarke R., Blom H.J., Kok F.J. and Schouten E.G. (2002) MTHFR 677C–>T polymorphism and risk of coronary heart disease: a meta-analysis. JAMA 288, 2023–2031 [DOI] [PubMed] [Google Scholar]
- 29.Sun J., Xu Y., Xue J., Zhu Y. and Lu H. (2005) Methylenetetrahydrofolate reductase polymorphism associated with susceptibility to coronary heart disease in Chinese type 2 diabetic patients. Mol. Cell. Endocrinol. 229, 95–101 10.1016/j.mce.2004.09.003 [DOI] [PubMed] [Google Scholar]
- 30.Mtiraoui N., Ezzidi I., Chaieb M., Marmouche H., Aouni Z., Chaieb A. et al. (2007) MTHFR C677T and A1298C gene polymorphisms and hyperhomocysteinemia as risk factors of diabetic nephropathy in type 2 diabetes patients. Diabetes Res. Clin. Pract. 75, 99–106 10.1016/j.diabres.2006.05.018 [DOI] [PubMed] [Google Scholar]
- 31.Benrahma H., Abidi O., Melouk L., Ajjemami M., Rouba H., Chadli A. et al. (2012) Association of the C677T polymorphism in the human methylenetetrahydrofolate reductase (MTHFR) gene with the genetic predisposition for type 2 diabetes mellitus in a Moroccan population. Genet. Test Mol. Biomark. 16, 383–387 10.1089/gtmb.2011.0179 [DOI] [PubMed] [Google Scholar]
- 32.Hamzi K., Tazzite A. and Nadifi S. (2011) Large-scale meta-analysis of genetic studies in ischemic stroke: five genes involving 152,797 individuals. Indian J. Hum. Genet 17, 212–217 10.4103/0971-6866.92105 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.They-They T.P., Battas O. and Nadifi S. (2013) Synergistic effect of MTHFR C677T and F2 G20210A polymorphisms on ischemic stroke. Neurosci. Bull. 29, 725–730 10.1007/s12264-013-1381-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Cai W., Yin L., Yang F., Zhang L. and Cheng J. (2014) Association between Hcy levels and the CBS844ins68 and MTHFR C677T polymorphisms with essential hypertension. Biomed. Rep. 2, 861–868 10.3892/br.2014.357 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Nassereddine S., Kassogue Y., Korchi F., Habbal R. and Nadifi S. (2015) Association of methylenetetrahydrofolate reductase gene (C677T) with the risk of hypertension in Morocco. BMC Res. Notes 8, 775 10.1186/s13104-015-1772-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Markan S., Sachdeva M., Sehrawat B.S., Kumari S., Jain S. and Khullar M. (2007) MTHFR 677 CT/MTHFR 1298 CC genotypes are associated with increased risk of hypertension in Indians. Mol. Cell. Biochem. 302, 125–131 10.1007/s11010-007-9434-5 [DOI] [PubMed] [Google Scholar]
- 37.Koupepidou P., Deltas C., Christofides T.C., Athanasiou Y., Zouvani I. and Pierides A. (2005) The MTHFR 677TT and 677CT/1298AC genotypes in Cypriot patients may be predisposing to hypertensive nephrosclerosis and chronic renal failure. Int. Angiol. 24, 287–294 [PubMed] [Google Scholar]
- 38.Wu Y.L., Hu C.Y., Lu S.S., Gong F.F., Feng F., Qian Z.Z. et al. (2014) Association between methylenetetrahydrofolate reductase (MTHFR) C677T/A1298C polymorphisms and essential hypertension: a systematic review and meta-analysis. Metabolism 63, 1503–1511 10.1016/j.metabol.2014.10.001 [DOI] [PubMed] [Google Scholar]
- 39.Ilhan N., Kucuksu M., Kaman D., Ilhan N. and Ozbay Y. (2008) The 677 C/T MTHFR polymorphism is associated with essential hypertension, coronary artery disease, and higher homocysteine levels. Arch. Med. Res. 39, 125–130 10.1016/j.arcmed.2007.07.009 [DOI] [PubMed] [Google Scholar]
- 40.Heux S., Morin F., Lea R.A., Ovcaric M., Tajouri L. and Griffiths L.R. (2004) The methylentetrahydrofolate reductase gene variant (C677T) as a risk factor for essential hypertension in Caucasians. Hypertens. Res. 27, 663–667 10.1291/hypres.27.663 [DOI] [PubMed] [Google Scholar]
- 41.Qian X., Lu Z., Tan M., Liu H. and Lu D. (2007) A meta-analysis of association between C677T polymorphism in the methylenetetrahydrofolate reductase gene and hypertension. Eur. J. Hum. Genet. 15, 1239–1245 10.1038/sj.ejhg.5201914 [DOI] [PubMed] [Google Scholar]
- 42.Tawakol A., Omland T., Gerhard M., Wu J.T. and Creager M.A. (1997) Hyperhomocyst(e)inemia is associated with impaired endothelium-dependent vasodilation in humans. Circulation 95, 1119–1121 10.1161/01.CIR.95.5.1119 [DOI] [PubMed] [Google Scholar]
- 43.Au-Yeung K.K., Woo C.W., Sung F.L., Yip J.C., Siow Y.L. and O K. (2004) Hyperhomocysteinemia activates nuclear factor-kappaB in endothelial cells via oxidative stress. Circ. Res. 94, 28–36 10.1161/01.RES.0000108264.67601.2C [DOI] [PubMed] [Google Scholar]
- 44.McRae M.P. (2009) High-dose folic acid supplementation effects on endothelial function and blood pressure in hypertensive patients: a meta-analysis of randomized controlled clinical trials. J. Chiropr. Med. 8, 15–24 10.1016/j.jcm.2008.09.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Ravera M., Viazzi F., Berruti V., Leoncini G., Zagami P., Bezante G.P. et al. (2001) 5,10-Methylenetetrahydrofolate reductase polymorphism and early organ damage in primary hypertension. Am. J. Hypertens. 14, 371–376 10.1016/S0895-7061(00)01296-6 [DOI] [PubMed] [Google Scholar]
- 46.Amrani-Midoun A., Kiando S.R., Treard C., Jeunemaitre X. and Bouatia-Naji N. (2016) The relationship between MTHFR C677T gene polymorphism and essential hypertension in a sample of an Algerian population of Oran city. Int. J. Cardiol. 225, 408–411 10.1016/j.ijcard.2016.10.027 [DOI] [PubMed] [Google Scholar]
- 47.Lee Y.H. and Song G.G. (2012) Pathway analysis of a genome-wide association study of ileal Crohn’s disease. DNA Cell Biol. 31, 1549–1554 10.1089/dna.2012.1605 [DOI] [PubMed] [Google Scholar]
- 48.Zheng S., Wu H., Ye F., Xia X., Xia S., Lin X. et al. (2017) Association of transcobalamine II gene polymorphisms and serum homocysteine, vitamin B12 and folate levels with ulcerative colitis among Chinese patients. Zhong. Yi Xue Yi Chuan Xue Za Zhi 34, 731–736 [DOI] [PubMed] [Google Scholar]
- 49.Liu W., Morito D., Takashima S., Mineharu Y., Kobayashi H., Hitomi T. et al. (2011) Identification of RNF213 as a susceptibility gene for moyamoya disease and its possible role in vascular development. PLoS ONE 6, e22542 10.1371/journal.pone.0022542 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Miyawaki S., Imai H., Takayanagi S., Mukasa A., Nakatomi H. and Saito N. (2012) Identification of a genetic variant common to moyamoya disease and intracranial major artery stenosis/occlusion. Stroke 43, 3371–3374 10.1161/STROKEAHA.112.663864 [DOI] [PubMed] [Google Scholar]
- 51.Morimoto T., Mineharu Y., Ono K., Nakatochi M., Ichihara S., Kabata R. et al. (2017) Significant association of RNF213 p.R4810K, a moyamoya susceptibility variant, with coronary artery disease. PLoS ONE 12, e0175649 10.1371/journal.pone.0175649 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Zhou S., Ambalavanan A., Rochefort D., Xie P., Bourassa C.V., Hince P. et al. (2016) RNF213 is associated with intracranial aneurysms in the French-Canadian population. Am. J. Hum. Genet. 99, 1072–1085, 10.1016/j.ajhg.2016.09.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Koizumi A., Kobayashi H., Liu W., Fujii Y., Senevirathna S.T., Nanayakkara S. et al. (2013) P.R4810K, a polymorphism of RNF213, the susceptibility gene for moyamoya disease, is associated with blood pressure. Environ. Health Prev. Med. 18, 121–129 10.1007/s12199-012-0299-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Bang O.Y., Fujimura M. and Kim S.K. (2016) The pathophysiology of Moyamoya disease: an update. J. Stroke 18, 12–20 10.5853/jos.2015.01760 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Sung Y.J., Winkler T.W., de Las Fuentes L., Bentley A.R., Brown M.R., Kraja A.T. et al. (2018) A large-scale multi-ancestry genome-wide study accounting for smoking behavior identifies multiple significant loci for blood pressure. Am. J. Hum. Genet. 102, 375–400 10.1016/j.ajhg.2018.01.015 [DOI] [PMC free article] [PubMed] [Google Scholar]