https://orcid.org/0000-0003-4488-5579
Abstract
Although the genetic factors associated with hypertension remain unknown, genetic variations in genes related to ion channels, inflammation, and the cell cycle may affect susceptibility to hypertension. In the present study, the association between hypertension and 10 candidate single-nucleotide polymorphisms (SNPs) was evaluated among Chinese Dai people, who have a smaller gene pool than Han individuals.
A total of 1,193 samples from Dai people were collected, including 488 with hypertension and 705 with normal blood pressure. Based on the preliminary results of whole-genome sequencing among pools of individuals (Pool-seq), 10 candidate SNPs in 6 genes (FAM110D, ADD1, RAG1, CACNA1C, CACNA1A, and NLRP12) were genotyped in the case and control groups by multiplex PCR for SNP genotyping with next-generation sequencing (MultiPCR-NGS). The relationship between hypertension and each candidate SNP was evaluated using the χ 2 test and multiple logistic regression analysis.
The χ 2 test showed that the allele frequencies of rs3748856 in FAM110D, rs139118504 in CACNA1A, and rs34436714 in NLRP12 were significantly different between the case and control groups (P < 0.005). After adjusting for age, body mass index, total cholesterol, triglyceride, and low-density lipoprotein, logistic regression analyses revealed that the association between the 3 SNPs and hypertension among Dai people remained significant (P = 0.012, 2.71 × 10−4, and 0.017, respectively).
These findings indicate that there may be different molecular pathogeneses of hypertension among Dai people, which should be noted in future studies.
Hypertension is characterized by a continuous increase in systemic arterial systolic blood pressure and/or diastolic blood pressure, which can be divided into essential hypertension and secondary hypertension. Since essential hypertension accounts for the vast majority of hypertensive patients, essential hypertension is referred to as hypertension below. Long-term high blood pressure leads to changes in the structure and function of the heart and blood vessels, so it is an important risk factor for many diseases, including stroke, coronary heart disease, cardiac insufficiency, and kidney disease,1,2 and it imposes a heavy burden on individuals, families, and society. In 2010, an analysis of nearly 1 million people in 90 countries showed that 31.1% of the world’s population suffered from hypertension.3 In mainland China, hypertension affects 44.7% of adults aged 35–75. A total of 30.1% of patients have received treatment, and only 7.2% of patients have effective control of their blood pressure.4 Epidemiological studies have shown that genetic factors determine the susceptibility to hypertension, and its heritability has been shown to be 20%–60% in family studies5 and as high as 60% in twin studies.6 Therefore, molecular genetics research on the pathogenesis of hypertension is of great significance for improving the prevention and treatment of hypertension as well as improving the quality of life of patients.
Hundreds of hypertension susceptibility genes have been reported. However, due to the heterogeneity among different ethnic groups, the results of these gene studies are difficult to replicate in different populations. For example, the ADD1 polymorphism (rs4961) is a locus that can affect blood pressure in the Caucasian population in Madeira,7 but it does not contribute to the occurrence of hypertension in African Americans.8 Therefore, it is essential to carry out association analyses of hypertension susceptibility genes in various ethnic groups with different genetic backgrounds. Dai people are a unique ethnic group in Yunnan, China, who have lived in the valley area for generations and generally have endogamous marriages. This population may be less affected by genetic heterogeneity, and the pathogenic spectrum of hypertension may be relatively narrow,9 thus leading to specific molecular genetic mechanisms.
Previous studies have shown that genetic variations related to ion channels, inflammation, and the cell cycle might affect blood pressure. Many candidate genes responsible for susceptibility to hypertension are involved in these pathways.10–12 Based on the preliminary association results obtained by Pool-seq,13 we selected 10 candidate single-nucleotide polymorphisms (SNPs) in 6 genes (rs3748856 in FAM110D; rs4691 in ADD1; rs2227963 in RAG1; rs2239026, rs2239027, and rs7957163 in CACNA1C; rs12459602, rs139118504, and rs1019472 in CACNA1A; and rs34436714 in NLRP12) for genotyping. ADD1, CACNA1C, and CACNA1A are ion channel genes; NLRP12 and RAG1 are related to inflammation; and FAM110D is involved in the cell cycle. Subsequently, 10 selected SNPs were validated among 1,193 Dai individuals to identify the genetic association of hypertension.
SUBJECTS AND METHOD
Subject recruitment
The subjects were Dai people from Xishuangbanna and Dehong regions, Yunnan Province, China, in this study. Five administrative villages were randomly selected from each of the 2 regions, and adults who participated in the physical examination were used as the study subjects. Each participant belonged to the same ethnic group for 3 generations and had no blood relationship with other participants. Among individuals who did not use antihypertensive drugs, blood pressure was measured 3 times every 5 minutes using the standard mercury measurement method, and the average value was used for analysis. People with systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg were defined as the hypertensive group, while systolic blood pressure <140 mm Hg and diastolic blood pressure <90 mm Hg were defined as the healthy control group. Patients with other chronic diseases were excluded. The height, weight, age, gender, and family history of hypertension of all the subjects were collected, and blood biochemical indexes such as total cholesterol (TC), triglyceride (TG), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) levels were measured. A total of 1,543 subjects were recruited. Considering the influence of age on the prevalence of hypertension, we chose ≥35 years old as the study subject. Finally, 1,193 subjects were chosen, including 488 cases and 705 controls. This study was approved by the Ethics Committee of the Institute of Medical Biology Chinese Academy of Medical Sciences, and informed consent was obtained from all subjects before participating in the trial.
Choosing SNPs and genotyping
This study prescreened candidate SNPs in the Dai people based on Pool-seq and gene functional candidate strategies. A total of 488 patients and 705 normal control DNA samples were composed of case pooling and control pooling for Pool-seq, respectively, to estimate the allele frequencies by the number of reads that carried different alleles of SNPs. The differences in the case and control pools were compared using the χ 2 test. SNPs with minor allele frequency > 0% and a P value <0.01 were selected. Subsequently, individual genotyping was performed on the hypertension and control groups to confirm the association with hypertension. Ten candidate SNPs were genotyped with MultiPCR-NGS.14
After the subject’s written informed consent was obtained, 3 ml of peripheral blood was collected. DNA preparation: genomic DNA was extracted from 300 l peripheral blood following the manufacturer’s instructions of the AxyPrep Blood Genomic DNA Mini Prep Kit (Axygen, Hangzhou City, China) and stored at −70 °C until use. PCR primer design: Primer3 (v0.4.0) was used to design specific multiple primers for specific amplification of candidate SNP sites. Amplification of specific PCR products: specific PCR products were obtained using 2 rounds of PCR amplification. The reaction system and cyclic reaction conditions followed the method previously established by the study group.15 After PCR products were ligated with the sequencing adapter and barcode sequences were added to each sample, PCR products were subjected to high-throughput sequencing. The Illumina HiSeq platform (Illumina, San Diego, CA) was used for sequencing PCR products at Novogene (Beijing, China). The original image data were converted into sequence reads through base recognition analysis, and final identification of SNPs was conducted using Samtools (version 0.1.19).16
Statistical analysis
Quantitative data are shown as the mean ± SD, and comparisons between the 2 groups were performed using Student’s t test. Qualitative data and allele frequency were compared using the χ 2 test in SPSS software (IBM Corporation, Armonk, NY). The association between the candidate SNPs and the risk of hypertension was calculated using genetic model analyses (dominant, recessive). Under the assumption of the best-fit model, logistic regression adjusted for age, gender, body mass index (BMI), total TG, LDL, and HDL was applied to further evaluate the associations between the positive SNPs and hypertension. The relative risk of disease was expressed by odds ratio (OR) and 95% confidence interval (95% CI). These statistical analyses were performed by plink 1.9.17 Bonferroni’s correction was applied for multiple tests. All statistical tests were 2-tailed, and P < 0.05 was defined as statistically significant.
RESULTS
Basic characteristics of the study subjects
The characteristics of the study subjects are given in Table 1. A total of 1,193 Dai individuals were enrolled in the study, including 488 individuals with hypertension and 785 healthy individuals. The mean blood pressure was significantly higher among hypertensive patients than among normal controls (160.80/92.80 vs. 119.75/73.81 mm Hg). No significant differences were found between the hypertension and control groups in terms of gender (P = 0.054) or HDL (P = 0.684). However, in terms of age, BMI, TC, TG, and LDL, significant differences were found between the hypertension and control groups (their P values were less than 0.001), suggesting that the occurrence of hypertension is affected by complex factors.
The characteristics of the study subjects
| Characteristics | Hypertension (N = 488) | Normal (N = 705) | P |
|---|---|---|---|
| Gender (male/female) | 207/281 | 260/445 | 0.054 |
| Age, mean ± SD, years | 59.16 ± 11.34 | 51.71 ± 11.52 | 4.27 × 10−27 |
| BMI, mean ± SD, kg/m2 | 24.00 ± 2.94 | 22.69 ± 2.70 | 5.01 × 10−15 |
| TC, mean ± SD, mmol/l | 4.65 ± 1.83 | 3.89 ± 1.69 | 3.20 × 10−13 |
| TG, mean ± SD, mmol/l | 2.25 ± 1.62 | 1.78 ± 1.44 | 2.07 × 10−7 |
| LDL, mean ± SD, mmol/l | 2.73 ± 0.80 | 2.50 ± 0.76 | 4.19 × 10−7 |
| HDL, mean ± SD, mmol/l | 1.42 ± 0.32 | 1.43 ± 0.31 | 0.684 |
| Characteristics | Hypertension (N = 488) | Normal (N = 705) | P |
|---|---|---|---|
| Gender (male/female) | 207/281 | 260/445 | 0.054 |
| Age, mean ± SD, years | 59.16 ± 11.34 | 51.71 ± 11.52 | 4.27 × 10−27 |
| BMI, mean ± SD, kg/m2 | 24.00 ± 2.94 | 22.69 ± 2.70 | 5.01 × 10−15 |
| TC, mean ± SD, mmol/l | 4.65 ± 1.83 | 3.89 ± 1.69 | 3.20 × 10−13 |
| TG, mean ± SD, mmol/l | 2.25 ± 1.62 | 1.78 ± 1.44 | 2.07 × 10−7 |
| LDL, mean ± SD, mmol/l | 2.73 ± 0.80 | 2.50 ± 0.76 | 4.19 × 10−7 |
| HDL, mean ± SD, mmol/l | 1.42 ± 0.32 | 1.43 ± 0.31 | 0.684 |
Data are presented as mean ± SD. P value in boldface indicates statistical significance (*P < 0.05).
Abbreviations: BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol; TG, triglycerides.
The characteristics of the study subjects
| Characteristics | Hypertension (N = 488) | Normal (N = 705) | P |
|---|---|---|---|
| Gender (male/female) | 207/281 | 260/445 | 0.054 |
| Age, mean ± SD, years | 59.16 ± 11.34 | 51.71 ± 11.52 | 4.27 × 10−27 |
| BMI, mean ± SD, kg/m2 | 24.00 ± 2.94 | 22.69 ± 2.70 | 5.01 × 10−15 |
| TC, mean ± SD, mmol/l | 4.65 ± 1.83 | 3.89 ± 1.69 | 3.20 × 10−13 |
| TG, mean ± SD, mmol/l | 2.25 ± 1.62 | 1.78 ± 1.44 | 2.07 × 10−7 |
| LDL, mean ± SD, mmol/l | 2.73 ± 0.80 | 2.50 ± 0.76 | 4.19 × 10−7 |
| HDL, mean ± SD, mmol/l | 1.42 ± 0.32 | 1.43 ± 0.31 | 0.684 |
| Characteristics | Hypertension (N = 488) | Normal (N = 705) | P |
|---|---|---|---|
| Gender (male/female) | 207/281 | 260/445 | 0.054 |
| Age, mean ± SD, years | 59.16 ± 11.34 | 51.71 ± 11.52 | 4.27 × 10−27 |
| BMI, mean ± SD, kg/m2 | 24.00 ± 2.94 | 22.69 ± 2.70 | 5.01 × 10−15 |
| TC, mean ± SD, mmol/l | 4.65 ± 1.83 | 3.89 ± 1.69 | 3.20 × 10−13 |
| TG, mean ± SD, mmol/l | 2.25 ± 1.62 | 1.78 ± 1.44 | 2.07 × 10−7 |
| LDL, mean ± SD, mmol/l | 2.73 ± 0.80 | 2.50 ± 0.76 | 4.19 × 10−7 |
| HDL, mean ± SD, mmol/l | 1.42 ± 0.32 | 1.43 ± 0.31 | 0.684 |
Data are presented as mean ± SD. P value in boldface indicates statistical significance (*P < 0.05).
Abbreviations: BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol; TG, triglycerides.
Typing and analyses of SNPs
All participants were genotyped with MultiPCR-NGS, and the call rate of genotyping was more than 95%. All 10 SNPs conformed to the Hardy–Weinberg proportions in the controls (P > 0.05; shown in Table 2), indicating that the selected sample comes from a random mating population. The allele frequencies of 10 SNPs on 6 genes in the case and control groups were compared using χ 2 tests. Multiple tests were corrected by Bonferroni’s correction. P < 0.005 (0.05/10) was defined as statistically significant, and the results are given in Table 2. SNP rs3748856 in FAM110D, SNP rs139118504 in CACNA1A, and SNP rs34436714 in NLRP12 showed significant differences between the hypertension and control groups.
Comparison of the gene frequencies of 10 SNPs in hypertensive and normal populations
| Gene | SNP | Minor/major | MAFa (%) | MAFb (%) | Allele, P value | HWE-P |
|---|---|---|---|---|---|---|
| FAM110D | rs3748856 | G/A | 25.5 | 32.7 | 8.98 × 10−4 | 0.839 |
| ADD1 | rs4961 | T/G | 43.6 | 41.4 | 0.304 | 0.099 |
| RAG1 | rs2227973 | A/G | 44.1 | 42.6 | 0.812 | 0.058 |
| CACNA1C | rs2239026 | T/C | 29.6 | 31.9 | 0.24 | 0.982 |
| rs2239027 | C/A | 29.5 | 31.9 | 0.227 | 0.982 | |
| rs7957163 | A/C | 9.1 | 8.6 | 0.669 | 0.87 | |
| CACNA1A | rs12459602 | G/A | 34.3 | 31.5 | 0.171 | 0.084 |
| rs139118504 | A/G | 30.7 | 25.1 | 0.003 | 0.888 | |
| rs1019472 | G/C | 35.1 | 36.1 | 0.629 | 0.099 | |
| NLRP12 | rs34436714 | A/C | 24.4 | 19.4 | 0.004 | 0.886 |
| Gene | SNP | Minor/major | MAFa (%) | MAFb (%) | Allele, P value | HWE-P |
|---|---|---|---|---|---|---|
| FAM110D | rs3748856 | G/A | 25.5 | 32.7 | 8.98 × 10−4 | 0.839 |
| ADD1 | rs4961 | T/G | 43.6 | 41.4 | 0.304 | 0.099 |
| RAG1 | rs2227973 | A/G | 44.1 | 42.6 | 0.812 | 0.058 |
| CACNA1C | rs2239026 | T/C | 29.6 | 31.9 | 0.24 | 0.982 |
| rs2239027 | C/A | 29.5 | 31.9 | 0.227 | 0.982 | |
| rs7957163 | A/C | 9.1 | 8.6 | 0.669 | 0.87 | |
| CACNA1A | rs12459602 | G/A | 34.3 | 31.5 | 0.171 | 0.084 |
| rs139118504 | A/G | 30.7 | 25.1 | 0.003 | 0.888 | |
| rs1019472 | G/C | 35.1 | 36.1 | 0.629 | 0.099 | |
| NLRP12 | rs34436714 | A/C | 24.4 | 19.4 | 0.004 | 0.886 |
P value in boldface indicates statistical significance after Bonferroni correction (0.05/10 = 0.005).
Abbreviations: HWE-P, P value of Hardy–Weinberg equilibrium; SNP, single-nucleotide polymorphism.
aMinor allele frequency in hypertensive patients.
bMinor allele frequency in normal controls.
Comparison of the gene frequencies of 10 SNPs in hypertensive and normal populations
| Gene | SNP | Minor/major | MAFa (%) | MAFb (%) | Allele, P value | HWE-P |
|---|---|---|---|---|---|---|
| FAM110D | rs3748856 | G/A | 25.5 | 32.7 | 8.98 × 10−4 | 0.839 |
| ADD1 | rs4961 | T/G | 43.6 | 41.4 | 0.304 | 0.099 |
| RAG1 | rs2227973 | A/G | 44.1 | 42.6 | 0.812 | 0.058 |
| CACNA1C | rs2239026 | T/C | 29.6 | 31.9 | 0.24 | 0.982 |
| rs2239027 | C/A | 29.5 | 31.9 | 0.227 | 0.982 | |
| rs7957163 | A/C | 9.1 | 8.6 | 0.669 | 0.87 | |
| CACNA1A | rs12459602 | G/A | 34.3 | 31.5 | 0.171 | 0.084 |
| rs139118504 | A/G | 30.7 | 25.1 | 0.003 | 0.888 | |
| rs1019472 | G/C | 35.1 | 36.1 | 0.629 | 0.099 | |
| NLRP12 | rs34436714 | A/C | 24.4 | 19.4 | 0.004 | 0.886 |
| Gene | SNP | Minor/major | MAFa (%) | MAFb (%) | Allele, P value | HWE-P |
|---|---|---|---|---|---|---|
| FAM110D | rs3748856 | G/A | 25.5 | 32.7 | 8.98 × 10−4 | 0.839 |
| ADD1 | rs4961 | T/G | 43.6 | 41.4 | 0.304 | 0.099 |
| RAG1 | rs2227973 | A/G | 44.1 | 42.6 | 0.812 | 0.058 |
| CACNA1C | rs2239026 | T/C | 29.6 | 31.9 | 0.24 | 0.982 |
| rs2239027 | C/A | 29.5 | 31.9 | 0.227 | 0.982 | |
| rs7957163 | A/C | 9.1 | 8.6 | 0.669 | 0.87 | |
| CACNA1A | rs12459602 | G/A | 34.3 | 31.5 | 0.171 | 0.084 |
| rs139118504 | A/G | 30.7 | 25.1 | 0.003 | 0.888 | |
| rs1019472 | G/C | 35.1 | 36.1 | 0.629 | 0.099 | |
| NLRP12 | rs34436714 | A/C | 24.4 | 19.4 | 0.004 | 0.886 |
P value in boldface indicates statistical significance after Bonferroni correction (0.05/10 = 0.005).
Abbreviations: HWE-P, P value of Hardy–Weinberg equilibrium; SNP, single-nucleotide polymorphism.
aMinor allele frequency in hypertensive patients.
bMinor allele frequency in normal controls.
Analyses of the genetic model of the correlation between 10 candidate SNPs and hypertension
Under the assumption of various genetic models with minor D (dominant model: DD + Dd vs. dd, recessive model: DD vs. Dd + dd), the association between the hereinabove 3 positive SNPs and the risk of hypertension was evaluated, and the results are given in Table 3. The results of the dominant model were consistent with the results of the χ 2 test of allele frequency. SNP rs3748856 in FAM110D (OR = 0.670, P = 0.001), SNP rs139118504 in CACNA1A (OR = 1.439, P = 0.003), and SNP rs34436714 in NLRP12 (OR = 1.448, P = 0.002) were still significantly associated with the risk of hypertension (the dominant model is the best-fit model).
Genetic model analyses of the 10 candidate SNPs in hypertensive and normal populations
| Gene | SNP | Dominant | Recessive | ||
|---|---|---|---|---|---|
| P | OR (95% CI) | P | OR (95% CI) | ||
| FAM110D | rs3748856 | 9.86 × 10−4 | 0.670 (0.528–0.851) | 0.008 | 0.532 (0.333–0.851) |
| ADD1 | rs4961 | 0.617 | 1.066 (0.829–1.372) | 0.192 | 1.232 (0.901–1.685) |
| RAG1 | rs2227973 | 0.504 | 1.093 (0.842–1.418) | 0.713 | 0.942 (0.685–1.296) |
| CACNA1C | rs2239026 | 0.558 | 0.933 (0.738–1.178) | 0.097 | 0.700 (0.458–1.069) |
| rs2239027 | 0.534 | 0.929 (0.735–1.173) | 0.095 | 0.698 (0.457–1.067) | |
| rs7957163 | — | 1.071 (0.787–1.456) | — | 1.084 (0.241–4.863) | |
| CACNA1A | rs12459602 | 0.334 | 1.128 (0.884–1.439) | 0.135 | 1.373 (0.905–2.082) |
| rs139118504 | 0.003 | 1.439 (1.134–1.827) | 0.17 | 1.380 (0.870–2.188) | |
| rs1019472 | 0.613 | 0.940 (0.738–1.196) | 0.789 | 0.948 (0.641–1.402) | |
| NLRP12 | rs34436714 | 0.002 | 1.448 (1.139–1.839) | 0.389 | 1.293 (0.720–2.321) |
| Gene | SNP | Dominant | Recessive | ||
|---|---|---|---|---|---|
| P | OR (95% CI) | P | OR (95% CI) | ||
| FAM110D | rs3748856 | 9.86 × 10−4 | 0.670 (0.528–0.851) | 0.008 | 0.532 (0.333–0.851) |
| ADD1 | rs4961 | 0.617 | 1.066 (0.829–1.372) | 0.192 | 1.232 (0.901–1.685) |
| RAG1 | rs2227973 | 0.504 | 1.093 (0.842–1.418) | 0.713 | 0.942 (0.685–1.296) |
| CACNA1C | rs2239026 | 0.558 | 0.933 (0.738–1.178) | 0.097 | 0.700 (0.458–1.069) |
| rs2239027 | 0.534 | 0.929 (0.735–1.173) | 0.095 | 0.698 (0.457–1.067) | |
| rs7957163 | — | 1.071 (0.787–1.456) | — | 1.084 (0.241–4.863) | |
| CACNA1A | rs12459602 | 0.334 | 1.128 (0.884–1.439) | 0.135 | 1.373 (0.905–2.082) |
| rs139118504 | 0.003 | 1.439 (1.134–1.827) | 0.17 | 1.380 (0.870–2.188) | |
| rs1019472 | 0.613 | 0.940 (0.738–1.196) | 0.789 | 0.948 (0.641–1.402) | |
| NLRP12 | rs34436714 | 0.002 | 1.448 (1.139–1.839) | 0.389 | 1.293 (0.720–2.321) |
P value in boldface indicates statistical significance after Bonferroni correction (0.05/10 = 0.005).
Abbreviations: CI, confidence interval; OR, odds ratio; SNP, single-nucleotide polymorphism.
Genetic model analyses of the 10 candidate SNPs in hypertensive and normal populations
| Gene | SNP | Dominant | Recessive | ||
|---|---|---|---|---|---|
| P | OR (95% CI) | P | OR (95% CI) | ||
| FAM110D | rs3748856 | 9.86 × 10−4 | 0.670 (0.528–0.851) | 0.008 | 0.532 (0.333–0.851) |
| ADD1 | rs4961 | 0.617 | 1.066 (0.829–1.372) | 0.192 | 1.232 (0.901–1.685) |
| RAG1 | rs2227973 | 0.504 | 1.093 (0.842–1.418) | 0.713 | 0.942 (0.685–1.296) |
| CACNA1C | rs2239026 | 0.558 | 0.933 (0.738–1.178) | 0.097 | 0.700 (0.458–1.069) |
| rs2239027 | 0.534 | 0.929 (0.735–1.173) | 0.095 | 0.698 (0.457–1.067) | |
| rs7957163 | — | 1.071 (0.787–1.456) | — | 1.084 (0.241–4.863) | |
| CACNA1A | rs12459602 | 0.334 | 1.128 (0.884–1.439) | 0.135 | 1.373 (0.905–2.082) |
| rs139118504 | 0.003 | 1.439 (1.134–1.827) | 0.17 | 1.380 (0.870–2.188) | |
| rs1019472 | 0.613 | 0.940 (0.738–1.196) | 0.789 | 0.948 (0.641–1.402) | |
| NLRP12 | rs34436714 | 0.002 | 1.448 (1.139–1.839) | 0.389 | 1.293 (0.720–2.321) |
| Gene | SNP | Dominant | Recessive | ||
|---|---|---|---|---|---|
| P | OR (95% CI) | P | OR (95% CI) | ||
| FAM110D | rs3748856 | 9.86 × 10−4 | 0.670 (0.528–0.851) | 0.008 | 0.532 (0.333–0.851) |
| ADD1 | rs4961 | 0.617 | 1.066 (0.829–1.372) | 0.192 | 1.232 (0.901–1.685) |
| RAG1 | rs2227973 | 0.504 | 1.093 (0.842–1.418) | 0.713 | 0.942 (0.685–1.296) |
| CACNA1C | rs2239026 | 0.558 | 0.933 (0.738–1.178) | 0.097 | 0.700 (0.458–1.069) |
| rs2239027 | 0.534 | 0.929 (0.735–1.173) | 0.095 | 0.698 (0.457–1.067) | |
| rs7957163 | — | 1.071 (0.787–1.456) | — | 1.084 (0.241–4.863) | |
| CACNA1A | rs12459602 | 0.334 | 1.128 (0.884–1.439) | 0.135 | 1.373 (0.905–2.082) |
| rs139118504 | 0.003 | 1.439 (1.134–1.827) | 0.17 | 1.380 (0.870–2.188) | |
| rs1019472 | 0.613 | 0.940 (0.738–1.196) | 0.789 | 0.948 (0.641–1.402) | |
| NLRP12 | rs34436714 | 0.002 | 1.448 (1.139–1.839) | 0.389 | 1.293 (0.720–2.321) |
P value in boldface indicates statistical significance after Bonferroni correction (0.05/10 = 0.005).
Abbreviations: CI, confidence interval; OR, odds ratio; SNP, single-nucleotide polymorphism.
Logistic regression analysis of the association between 3 positive SNPs and hypertension
Crude analyses of the χ 2 test and genetic model showed that rs3748856 in FAM110D, rs139118504 in CACNA1A and rs34436714 in NLRP12 were all significantly associated with a risk of hypertension. However, the occurrence of hypertension is affected by complex factors. It is essential to adjust the association between polymorphisms and hypertension using logistic regression. Since there was no significant difference in gender and HDL between hypertension and the control group, we just introduced covariables (age, BMI, TC, TG, and LDL) into logistic regression models. The results showed that FAM110D SNP rs3748856, CACNA1A SNP rs139118504, and NLRP12 SNP rs34436714 were still significantly associated with the risk of hypertension under the assumption of the dominant model (OR = 711, P = 0.012; OR = 1.632, P = 2.71 × 10−4; OR = 1.377, P = 0.017; respectively). The results are given in Table 4.
Risk estimates using a logistic regression model for the 3 positive SNPs
| SNP | Gene | Risks | SE(β) | OR (95% CI) | P |
|---|---|---|---|---|---|
| rs3748856 | FAM110D | 0.135 | 0.711 (0.545–0.927) | 0.012 | |
| Age | 0.006 | 1.064 (1.052–1.077) | 4.52 × 10−25 | ||
| BMI | 0.025 | 1.156 (1.100–1.215) | 1.05 × 10−8 | ||
| TC | 0.044 | 1.249 (1.415–1.362) | 5.68 × 10−7 | ||
| TG | 0.049 | 1.137 (1.033–1.251) | 0.009 | ||
| LDL | 0.097 | 1.077 (0.890–1.304) | 0.445 | ||
| rs139118504 | CACNA1A | 0.134 | 1.632 (1.254–2.124) | 2.71 × 10−4 | |
| Age | 0.006 | 1.062 (1.050–1.074) | 3.61 × 10−24 | ||
| BMI | 0.026 | 1.178 (1.121–1.239) | 1.40 × 10−10 | ||
| TC | 0.006 | 1.000 (0.988–1.011) | 0.947 | ||
| TG | 0.051 | 1.209 (1.094–1.336) | 1.89 × 10−4 | ||
| LDL | 0.092 | 1.268 (1.059–1.518) | 0.01 | ||
| rs34436714 | NLRP12 | 0.134 | 1.377 (1.059–1.789) | 0.017 | |
| Age | 0.006 | 1.059 (1.047–1.071) | 1.97 × 10−23 | ||
| BMI | 0.025 | 1.165 (1.109–1.224) | 1.40 × 10−9 | ||
| TC | 0.006 | 1.000 (0.988–1.011) | 0.933 | ||
| TG | 0.052 | 1.224 (1.106–1.354) | 8.92 × 10−5 | ||
| LDL | 0.088 | 1.268 (1.068–1.506) | 0.007 |
| SNP | Gene | Risks | SE(β) | OR (95% CI) | P |
|---|---|---|---|---|---|
| rs3748856 | FAM110D | 0.135 | 0.711 (0.545–0.927) | 0.012 | |
| Age | 0.006 | 1.064 (1.052–1.077) | 4.52 × 10−25 | ||
| BMI | 0.025 | 1.156 (1.100–1.215) | 1.05 × 10−8 | ||
| TC | 0.044 | 1.249 (1.415–1.362) | 5.68 × 10−7 | ||
| TG | 0.049 | 1.137 (1.033–1.251) | 0.009 | ||
| LDL | 0.097 | 1.077 (0.890–1.304) | 0.445 | ||
| rs139118504 | CACNA1A | 0.134 | 1.632 (1.254–2.124) | 2.71 × 10−4 | |
| Age | 0.006 | 1.062 (1.050–1.074) | 3.61 × 10−24 | ||
| BMI | 0.026 | 1.178 (1.121–1.239) | 1.40 × 10−10 | ||
| TC | 0.006 | 1.000 (0.988–1.011) | 0.947 | ||
| TG | 0.051 | 1.209 (1.094–1.336) | 1.89 × 10−4 | ||
| LDL | 0.092 | 1.268 (1.059–1.518) | 0.01 | ||
| rs34436714 | NLRP12 | 0.134 | 1.377 (1.059–1.789) | 0.017 | |
| Age | 0.006 | 1.059 (1.047–1.071) | 1.97 × 10−23 | ||
| BMI | 0.025 | 1.165 (1.109–1.224) | 1.40 × 10−9 | ||
| TC | 0.006 | 1.000 (0.988–1.011) | 0.933 | ||
| TG | 0.052 | 1.224 (1.106–1.354) | 8.92 × 10−5 | ||
| LDL | 0.088 | 1.268 (1.068–1.506) | 0.007 |
P value in boldface indicates statistical significance under the assumption of a dominant model (*P < 0.05).
Abbreviations: BMI, body mass index; CI, confidence interval; LDL, low-density lipoprotein; OR, odds ratio; TC, total cholesterol; TG, triglycerides; SE(β), standard error; SNP, single-nucleotide polymorphism.
Risk estimates using a logistic regression model for the 3 positive SNPs
| SNP | Gene | Risks | SE(β) | OR (95% CI) | P |
|---|---|---|---|---|---|
| rs3748856 | FAM110D | 0.135 | 0.711 (0.545–0.927) | 0.012 | |
| Age | 0.006 | 1.064 (1.052–1.077) | 4.52 × 10−25 | ||
| BMI | 0.025 | 1.156 (1.100–1.215) | 1.05 × 10−8 | ||
| TC | 0.044 | 1.249 (1.415–1.362) | 5.68 × 10−7 | ||
| TG | 0.049 | 1.137 (1.033–1.251) | 0.009 | ||
| LDL | 0.097 | 1.077 (0.890–1.304) | 0.445 | ||
| rs139118504 | CACNA1A | 0.134 | 1.632 (1.254–2.124) | 2.71 × 10−4 | |
| Age | 0.006 | 1.062 (1.050–1.074) | 3.61 × 10−24 | ||
| BMI | 0.026 | 1.178 (1.121–1.239) | 1.40 × 10−10 | ||
| TC | 0.006 | 1.000 (0.988–1.011) | 0.947 | ||
| TG | 0.051 | 1.209 (1.094–1.336) | 1.89 × 10−4 | ||
| LDL | 0.092 | 1.268 (1.059–1.518) | 0.01 | ||
| rs34436714 | NLRP12 | 0.134 | 1.377 (1.059–1.789) | 0.017 | |
| Age | 0.006 | 1.059 (1.047–1.071) | 1.97 × 10−23 | ||
| BMI | 0.025 | 1.165 (1.109–1.224) | 1.40 × 10−9 | ||
| TC | 0.006 | 1.000 (0.988–1.011) | 0.933 | ||
| TG | 0.052 | 1.224 (1.106–1.354) | 8.92 × 10−5 | ||
| LDL | 0.088 | 1.268 (1.068–1.506) | 0.007 |
| SNP | Gene | Risks | SE(β) | OR (95% CI) | P |
|---|---|---|---|---|---|
| rs3748856 | FAM110D | 0.135 | 0.711 (0.545–0.927) | 0.012 | |
| Age | 0.006 | 1.064 (1.052–1.077) | 4.52 × 10−25 | ||
| BMI | 0.025 | 1.156 (1.100–1.215) | 1.05 × 10−8 | ||
| TC | 0.044 | 1.249 (1.415–1.362) | 5.68 × 10−7 | ||
| TG | 0.049 | 1.137 (1.033–1.251) | 0.009 | ||
| LDL | 0.097 | 1.077 (0.890–1.304) | 0.445 | ||
| rs139118504 | CACNA1A | 0.134 | 1.632 (1.254–2.124) | 2.71 × 10−4 | |
| Age | 0.006 | 1.062 (1.050–1.074) | 3.61 × 10−24 | ||
| BMI | 0.026 | 1.178 (1.121–1.239) | 1.40 × 10−10 | ||
| TC | 0.006 | 1.000 (0.988–1.011) | 0.947 | ||
| TG | 0.051 | 1.209 (1.094–1.336) | 1.89 × 10−4 | ||
| LDL | 0.092 | 1.268 (1.059–1.518) | 0.01 | ||
| rs34436714 | NLRP12 | 0.134 | 1.377 (1.059–1.789) | 0.017 | |
| Age | 0.006 | 1.059 (1.047–1.071) | 1.97 × 10−23 | ||
| BMI | 0.025 | 1.165 (1.109–1.224) | 1.40 × 10−9 | ||
| TC | 0.006 | 1.000 (0.988–1.011) | 0.933 | ||
| TG | 0.052 | 1.224 (1.106–1.354) | 8.92 × 10−5 | ||
| LDL | 0.088 | 1.268 (1.068–1.506) | 0.007 |
P value in boldface indicates statistical significance under the assumption of a dominant model (*P < 0.05).
Abbreviations: BMI, body mass index; CI, confidence interval; LDL, low-density lipoprotein; OR, odds ratio; TC, total cholesterol; TG, triglycerides; SE(β), standard error; SNP, single-nucleotide polymorphism.
Discussion
FAM110D is a member of the FAM110 family (family with sequence similarity of 110), and its function has not yet been described. A functional study has shown that members of the FAM110 family are highly similar in structure (have centrosome-related protein domains),18 which provides important insights for understanding the function of FAM110D. Some genetic association studies have suggested that genetic variants of FAM110A and FAM110B may be associated with blood pressure phenotypes.10,19 The 2-stage genome-wide gene expression association study of young-onset hypertension in the Chinese Han population of Taiwan found that the differential expression of the FAM110A gene in 126 cases and 149 controls, and identified an expression regulation SNP rs552367 (intron variant).10 Based on the Gensalt study conducted in the Han Chinese population, FAM110B SNP rs10504249 (intron variant) was also identified as a novel blood pressure locus with suggestive genome-wide significance (P = 1.23 × 10−7).19 Therefore, we presumed that the FAM110D gene may also be related to the BP phenotype. To our knowledge, the current study is the first candidate gene study to investigate the association between FAM110 and hypertension. SNP rs3748856 (His53Arg) in FAM110D was found to be significantly associated with the susceptibility to hypertension in Dai people. Logistic regression analysis showed that carriers of the G minor allele on this SNP were associated with a decreased risk (OR = 0.711, P = 0.012). The polymorphism rs3748856 is a nonsynonymous variant located on the N-terminal domain that interacts with the centrosome, so it may affect the interaction between FAM110D and centrosome. However, whether it may participate in the regulation of blood pressure changes through the cell cycle remains to be studied.
The α-1A encoded by the CACNA1A gene is an isoform of the pore-forming subunit α-1. The expression of different α-1 isoforms can influence the distinctive properties of voltage-dependent calcium channel types. The α-1 subunit interacts with other auxiliary subunits (β, α-2/δ, and γ) to regulate the activity of calcium channels. Voltage-dependent calcium channels mediate the entry of calcium ions into excitable cells and are also involved in the contraction of vascular smooth muscle, the release of hormones or neurotransmitters, and the expression of genes. Therefore, we speculated that CACNA1A may play a key role in regulating blood pressure by affecting vascular smooth muscle.20–23 In the present study, we observed that rs139118504 (intron variant) was significantly associated with an increased risk of hypertension (OR = 1.632, P = 2.71 × 10−4), even after adjusting for age, BMI, TC, TG, and LDL. Compared with rs10425859 (intron variant) described previously, the association between rs139118504 and high blood pressure in the Dai population is more significant.15 In addition, the Gensalt study conducted in the Han Chinese population showed that rs8182538 (intron variant) was significantly correlated with diastolic blood pressure changes over time.24 Accumulated evidence indicates that CACNA1A plays an important role in the regulation of BP.
NLRP12 encodes an NLRP12 inflammasome, which can inhibit NF-κB signaling through classical and nonclassical pathways, thereby playing a regulatory role in inflammation and immune responses and ultimately limiting or inhibiting proinflammatory responses.25,26 Some studies have reported that variations in NLRP12 are related to cardiovascular diseases with inflammatory symptoms such as coronary artery disease, stroke, and Keshan disease.27–29 Therefore, we speculate that variations in NLRP12 may also be related to hypertension with low-grade inflammation. There have been no reports of an association between NLRP12 and hypertension. In our study, “AA” and “AC” of rs34436714 in NLRP12 were associated with an increased risk of hypertension under the dominant model (OR = 1.377, P = 0.017).
Hypertension is a complex polygenetic disease, and the identification of pathogenic genes is always difficult, usually requiring the use of genetic association to screen for the risk genetic variations. Due to the relatively complex genetic background of hypertension, the pathogenesis and mechanisms of hypertension may be different among different ethnic groups and regions. Genes or loci that can affect blood pressure in 1 population may not contribute to the pathogenesis of hypertension in another population. Therefore, it is meaningful to conduct an association analysis of hypertension susceptibility in ethnic groups with different genetic backgrounds. The Dai people are a unique ethnic group in the Yunnan region. They have unique cultural habits and endogamous marriages. Therefore, compared with the Han Chinese population with complex genetic heterogeneity, the genetic heterogeneity, and hypertension disease spectrum of this population are relatively small,9 which may provide the possibility for us to discover the specific molecular genetic mechanism of hypertension in this population.
FAM110D SNP rs3748856, CACNA1A SNP rs139118504, and NLRP12 SNP rs34436714 were found to be significantly associated with the occurrence of hypertension, suggesting that there is a specific molecular pathogenesis of hypertension among Dai people. These findings should be verified through functional studies.
FUNDING
This study was supported by some research grants including Yunnan Basic Research Program Key Project (2018FA010), Yunnan Science and Technology talents and platform Project, Special Funds of Advanced Scientific and Technological Talents and Innovation Teams (202005AC160011), Yunnan Training Program for the advanced Talents of Health and Medical Technology (D-2018013), and Chinese National Natural Science Foundation (31571304 and 31371265).
DISCLOSURE
The authors declared no conflict of interest.
REFERENCES
Author notes
Co-first author
