search for




 

A genome-wide association study of the association between single nucleotide polymorphisms and brachial-ankle pulse wave velocity in healthy Koreans
J Genet Med 2017;14:8-17
Published online June 30, 2017;  https://doi.org/10.5734/JGM.2017.14.1.8
© 2017 Korean Society of Medical Genetics.

EnShi Xu1, Jinho Shin1,*, Ji Eun Lim2, Mi Kyung Kim3,4, Bo Youl Choi3,4, Min-Ho Shin5, Dong Hoon Shin6, Young-Hoon Lee7, Byung-Yeol Chun8,9, Kyung-Won Hong10, and Joo-Yeon Hwang10

1Department of Internal Medicine, Hanyang University College of Medicine, Seoul, Korea,
2Department of Biochemistry and Molecular Biology, Kyung Hee University School of Medicine, Seoul, Korea,
3Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Korea,
4Institute for Health and Society, Hanyang University, Seoul, Korea,
5Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Korea,
6Department of Preventive Medicine, Keimyung University School of Medicine, Daegu, Korea,
7Department of Preventive Medicine & Institute of Wonkwang Medical Science, Wonkwang University School of Medicine, Iksan, Korea,
8Department of Preventive Medicine, Kyungpook National University School of Medicine, Daegu, Korea,
9Health Promotion Center, Kyungpook National University Hospital, Daegu, Korea,
10Center for Genome Science, National Institute of Health, Osong Health Technology Administration complex, Cheongju, Korea
Jinho Shin, M.D., Ph.D. Department of Internal Medicine, Hanyang University College of Medicine, 222 Wansimni-ro, Seongdong-gu, Seoul 04763, Korea. Tel: +82-2-2290-8308, Fax: +82-2-2299-0278, E-mail: jhs2003@hanyang.ac.kr
Received February 6, 2017; Revised March 15, 2017; Accepted March 31, 2017.
cc This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

Purpose:

Pulse wave velocity (PWV) is an indicator of arterial stiffness, and is considered a marker of vascular damage. However, a genome-wide association study analyzing single nucleotide polymorphisms (SNPs) associated with brachial-ankle PWV (baPWV) has not been conducted in healthy populations. We performed this study to identify SNPs associated with baPWV in healthy populations in Korea.

Materials and Methods:

Genomic SNPs data for 2,407 individuals from three sites were analyzed as part of the Korean Genomic Epidemiologic Study. Without replication samples, we performed multivariable analysis as a post hoc analysis to verify the findings in site adjusted analysis. Healthy subjects aged between 40 and 70 years without self-reported history or diagnosis of hypertension, diabetes, hyperlipidemia, heart disease, cerebrovascular disease and cancer were included. We excluded subjects with a creatinine level >1.4 mg/dL (men) and 1.2 mg/dL (women).

Results:

In the site-adjusted association analysis, significant associations (P<5×10–8) with baPWV were detected for only 5 SNPs with low minor allele frequency. In multivariable analysis adjusted by age, sex, height, body mass index, mean arterial pressure, site, smoking, alcohol, and exercise, 11 SNPs were found to be associated (P<5×10–8) with baPWV. The 5 SNPs (P<5×10–8) linked to three genes (OPCML, PRR35 and RAB40C) were common between site-adjusted analysis and multivariable analysis. However, meta-analysis of the result from three sites for the 11 SNPs showed no significant associations.

Conclusion:

Using the recent standard for genome-wide association study, we did not find any evidence of significant association signals with baPWV.

Keywords : Genome-wide association study, Association, Nucleotides, Pulse
Introduction

Cardiovascular disease is a substantial cause of morbidity and mortality worldwide and aging is an independent risk factor for cardiovascular disease. Aging of the arterial system is accompanied by structural changes including fragmentation and degeneration of elastin, an increase in collagen, thickening of the arterial walls, and progressive dilatation of arteries. These changes result in a gradual stiffening of the vasculature.

Arterial stiffness is a general term that refers to the elasticity of arteries, reflecting the state of vessel wall diastolic function and buffering capacity. Arterial stiffness occurs as a consequence of age and arteriosclerosis. Age-related stiffness occurs when elastic fibers within the arterial wall (elastin) begin to fray due to mechanical stress. The two leading causes of death in the developed world, myocardial infarction and stroke, are both a direct consequence of atherosclerosis. Atherosclerosis progression can be clinically evaluated by examinations that are able to identify its different aspects [1].

Increased arterial stiffness is associated with an increased risk of cardiovascular events. Pulse wave velocity (PWV) is an indicator of arterial stiffness [2,3], and a marker of vascular damage [46]. Thus, in the review of 2013 Korean Society of Hypertension guidelines for the management of hypertension, PWV was also advanced as a method of detecting subclinical organ damage [7]. PWV is the velocity of propagation of forward and backward pressure waves between two points on the arterial tree [8], generally between the carotid and femoral arteries, and represents the mean rigidity between the two points. Carotid-femoral PWV (cfPWV) measurement is one of the most popular techniques for estimating the level of arterial stiffness; however, accurate measurement of cfPWV levels requires specialized training, and the procedure can be cumbersome.

Brachial-ankle PWV (baPWV) measurement, which is easy to perform, has recently become available in clinical practice [9]. For example, the well-known study by Kawai et al. [10,11] used baPWV as a measure of arterial stiffness. Its validity and utility have been proven in various clinical settings [1217] and in meta-analyses [18,19], and many studies have investigated its clinical significance [9,20]. baPWV can easily be calculated using pressure cuffs on the brachial and tibial arteries. It reflects the stiffness of central to middle-sized arteries, and can be applied as a first line tool. Additionally, baPWV is closely correlated with cfPWV.

Recent genetic studies have identified specific genes that contribute to arterial stiffening, and genome-wide linkage analyses have revealed genetic polymorphisms associated with arterial stiffness. However, PWV is affected by various factors other than genetic background including blood pressure, diabetes, hypercholesterolemia, and renal function. Genetic association studies with statistical adjustment for these covariates have not been reported. We performed this study to identify the single nucleotide polymorphisms (SNPs) associated with baPWV in three rural areas in Korea.

Materials and Methods

1. Study population

The study subjects were selected from the cohort of an ongoing population-based study designated as the Korean Genome and Epidemiology Study (KoGES). Participants were recruited from among residents aged 38-89 years from three rural areas: Yangpyeong in Gyeonggi-do province, Goryeong in Gyeongsangbuk-do province, and Namwon in Jeollabuk-do province. In total, 8,702 males and females were recruited for the baseline study between 2004 and 2008. The study was approved by the Institutional Review Board (IRB) of the Korea National Institute of Health and all study subjects gave written informed consent before participation (IRB No. HYUH IRB 2005-15, HYUH IRB 2006-32 and HYUH IRB 2007-04).

2. General characteristics and anthropometric measurements

All participants were interviewed by trained interviewers, and general information (such as age, sex, smoking status, alcohol consumption, past medical history) was collected using a structured questionnaire. The criterion for regular exercise was ≥3 times per week and ≥30 minutes per session. Smoking status was classified as non-smoker, past smoker, or current smoker. Alcohol consumption was classified as non-drinker, past drinker, or current drinker.

In addition, 10 mL samples of fasting venous blood were collected in plain tubes. Sera were isolated by centrifugation at 3,000 rpm for 10 minutes at 4°C and were stored in liquid nitrogen. Biochemical tests including fasting serum glucose, fasting serum insulin, total cholesterol, triglyceride (TG), high density lipoprotein-cholesterol (HDL-cholesterol), and others were conducted.

The anthropometric characteristics (such as height, weight, waist circumference, blood pressure) were measured according to standardized protocols. Waist circumference was measured half way between the lowest rib margin and the iliac crest. For measurement of blood pressure, subjects had been sitting for at least 5 minutes. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured with a standard mercury sphygmomanometer using the first and fifth Korotkoff sounds, to the nearest 2 mmHg. If two systolic or DBP readings were more than 5 mmHg apart, an additional measurement was performed, and the mean of the last two measurements was used for subsequent analyses.

From the total of 8,702 subjects that were identified, we excluded subjects that had self-reported disease histories and were receiving drug-treatment for hypertension, type 2 diabetes mellitus, hyperlipidemia, heart disease, cerebrovascular diseases, and cancer. We also excluded subjects who were diagnosed with hypertension by supine position blood pressure measurements (SBP ≥140 mmHg and/or DBP ≥90 mmHg), and type 2 diabetes by fasting plasma glucose level (Glu ≥126 mg/dL). Finally, 4,034 healthy subjects that did not have a history or characteristics of any of the six diseases were selected for SNP genotyping, and 3,667 of these subjects fulfilled the quality control specifications included in the study.

The following participants were excluded: participants who were ≤40 or ≥70 years of age; those with missing height or weight values; with total cholesterol ≥250 mg/dL or creatinine >1.4 mg/dL for men and creatinine >1.2 mg/dL for women; subjects with missing alcohol consumption or smoking histories, or unmeasured right brachial SBP or pulse rate >100 beats/min during arterial stiffness measurement; and those with missing exercise variable values in the structured questionnaire. Based on these exclusion criteria, 2,407 of the 3,667 individuals were included in the study.

3. Genotyping and quality control

In total, 4,034 KoGES Cardiovascular Disease Association study samples were genotyped from the three rural areas using HumanOmni1 Quad v1.0 (Illumina, San Diego, CA, USA). Genotypes were called using Bead Studio (Illumina, San Diego, CA, USA). Samples that exhibited the following properties were excluded: low genotyping calls <98% (n=185), excessive heterozygosity (n=61), sex inconsistency (n=18), and cryptic relatedness (n=4). Markers with high missing gene call rates (>5%), rare minor allele frequency (MAF; <0.01), or significant deviation from the Hardy-Weinberg equilibrium (P<1×10–6) were also excluded. To detect the population substructure, we performed multidimensional scaling and principal component analyses using PLINK software (PLINK v1.07; Purcell Lab, New York, NY, USA) and R programming language (R Foundation for Statistical Computing, Vienna, Austria). Dimensions were calculated on the basis of identity-by-descent pair-wise distances between all individuals. The degree of relation was determined in individuals (n=99) whose estimated identity-by-state values were high (>0.80). After these quality control steps, 3,667 samples were selected. Markers with high missing gene call rates (>5%), rare MAF (<0.01), and significant deviations from the Hardy-Weinberg equilibrium (P<1×10–6) were excluded, leaving a total of 723,056 markers for examination in 3,667 individuals.

4. Measurement of brachial-ankle pulse wave velocity and right brachial systolic pressure

Arterial tonometry was conducted to assess arterial stiffness in all the cohort participants during their examinations. Two primary tonometry phenotypes were analyzed: left and right PWV. PWV was measured using the VP-2000. Tonometry was performed in a supine position after 5 minutes of rest. Arterial tonometry was obtained with simultaneous ECG recording.

5. Statistical analysis

General characteristics were analyzed using SAS 9.2. (SAS Institute, Cary, NC, USA) Measurement data were expressed as means±standard deviations, and count data as percentages. Genetic analysis was conducted using PLINK software (PLINK v1.07; Purcell Lab, New York, NY, USA). In the genetic analysis, standardized multivariable-adjusted PWV and tonometry residuals were generated as described above. Mean arterial pressure (MAP) was calculated from the systolic and DBP. The dependent variable, mean baPWV, was transformed to a normal distribution using reciprocal transformation methods, and the covariates adjusted were age, sex, height, body mass index (BMI), MAP, site information, smoking status, alcohol consumption, and exercise. The numerical covariates were also transformed to form as normal a distribution as possible. A P-value <5×10–8 was regarded as statistically significant.

Results

1. General characteristics

The mean age of the 2,407 individuals was 56.6 years; 36.1% of the participants were male and the mean BMI was 24.0±3.0 kg/m2. Subjects that smoked accounted for 16.2% of the total study population. Alcohol consumption was noted in 47.3% of the study subjects. Of the total study population, 30.2% exercised regularly. Casual SBP was 115.1±11.1 mmHg, casual MAP was 88.6±7.9 mmHg and casual DBP was 75.4±7.5 mmHg. Blood sample analysis indicated that the mean uric acid level was 4.7±1.3 mg/dL, total cholesterol was 190.6±29.4 mg/dL, HDL-cholesterol was 45.0±10.7 mg/dL, TGs were 133.7±79.1 mg/dL, glucose was 93.8±9.6 mg/dL, and creatinine was 0.9±0.1 mg/dL. Right brachial pulse rates were 64.8±9.3 beats/min. In the baPWV, the right baPWV was 1,390.8±228.5 cm/s, the left was 1,403.0±227.2 cm/s, and the mean baPWV was 1,396.9±224.4 cm/s. Table 1 also shows the characteristics of these variables at each study site, including the observation that mean baPWV differed across the study sites (Yangpyeong: 1,387.0±220.5 cm/s, Namwon: 1,421.2±232.3 cm/s, and Goryeong: 1,388.8±221.2 cm/s; P=0.0049).

2. Site information adjusted association

The arterial stiffness phenotype, mean baPWV, was used as an indicator for the site information adjusted association analysis. After applying the exclusion criteria, 723,056 SNPs were included in the analysis. The separate analysis adjusted by site information is shown in Table 2 and Fig 1. Nine SNPs had P-values <10–6 for the association with baPWV. Of these, five SNPs (rs476840, rs11607110, rs1010761, rs11643412, and rs11648607) had P-values <5×10–8 and were linked to three genes (OPCML, PRR35, RAB40C). However, the MAFs were all low, i.e., between 0.01 and 0.05.

3. Multivariable adjusted association

The variations in age, sex, height, BMI, MAP, site information, smoking status, alcohol consumption, and exercise variables were adjusted for the arterial stiffness phenotype analysis. Covariate analysis included adjustment factors such as age, sex, and multiple variables. After adjusting for multiple confounding factors, it appeared that 18 SNPs had P-values <10–6 for the association with baPWV. Of these, 11 SNPs (rs11993165, rs7121368, rs476840, rs11607110, rs10152599, rs2279496, rs7172171, rs16955052, rs1010761, rs11643412, and rs11648607) had P-values <5×10–8 and were linked to five genes (PDGFRL, OPCML, APBA2, PRR35 and RAB40C) as shown in Table 3 and Figs. 2 and 3.

From the above results, five SNPs were common between site information adjusted association and multivariable adjusted association, with a P-value <5×10–8, and were linked to three genes (OPCML, PRR35, and RAB40C).

OPCML, the opioid-binding protein/cell adhesion molecule, is a protein that is encoded by the OPCML gene in humans [21,22]. This gene encodes a member of the IgLON subfamily in the immunoglobulin superfamily of proteins. The encoded pre-protein is proteolytically processed to generate the mature protein. This protein is localized in the plasma membrane and may have an accessory role in opioid receptor function. This gene has an ortholog in rats and bovines. The opioid binding-cell adhesion molecule encoded by the rat gene binds opioid alkaloids in the presence of acidic lipids, exhibits selectivity for mu ligands, and acts as a glucose-6-phosphate isomerase-anchored protein. Since the encoded protein is highly conserved across species during evolution, it may have a fundamental role in mammalian systems. Two transcript variants encoding different isoforms have been found for this gene [22]. Alternative splicing results in multiple transcript variants, at least one of which encodes an isoform that is proteolytically processed (provided by RefSeq, Jan 2016). PRR35 or proline rich 35, is a protein that is encoded by the PRR35 gene in humans [23]. RAB40C is a member of the renin–angiotensin system oncogene family, but its specific role and mechanism has not been elucidated in the current study.

4. Multivariable adjusted association analyses for three study sites

For the 11 SNPs, multivariable analyses were preformed separately for three study sites as shown in Table 4, and the effect sizes for all 11 SNPs were extremely large only in Yangpyeong.

5. Meta-analysis

For the 11 SNPs, the results from the three study sites were meta-analyzed. Among the 11 SNPs, none was associated with baPWV with P-values <5×10–6 as shown in Table 5.

Discussion

1. Main findings

The main finding of the present study, which was performed in a healthy and normal general population, was that 5 SNPs were common between site information adjusted association and the multivariable adjusted association analyses with regard to baPWV. The multiple adjusted model was provided as a post hoc analysis to verify the findings of the site information adjusted association analysis. However, such associations with low MAF seemed to be noise signals because the multivariable analyses performed for the three individual study sites and the corresponding meta-analyses showed no significant SNPs associated with baPWV. From the statistical viewpoint, these were considered as negative findings.

2. Previous genome-wide association studies on arterial stiffness

Previous investigations of the association between arterial stiffness and SNPs using genome-wide association studies (GWAS) involved pulse pressure and PWV. The genes identified in the various studies using pulse pressure, which included the Framingham Heart Study, did not yield consistent findings [2426]. PWV is currently considered an optimal measure, as recently described in the consensus statement on arterial stiffness [27]. cfPWV is the gold-standard index of arterial stiffness; however, the complexity of measuring cfPWV [28] prevents its use in routine clinical practice. Measurement of baPWV is simple, noninvasive, and more practical for population surveys. However, in the Framingham Offspring Study, as in studies using carotid-brachial PWV, rs10514688, rs770189, and rs7042864 had significant associations after adjustment for age, sex, age2, height, and weight (P<10–5) [29]. However, these SNPs were not associated with baPWV in our study. These results of our study may be related to differences in ethnicity, study subjects, and other factors. In addition, recent studies also showed that common genetic variants associated with baPWV could not be identified in cardiovascular patients [30]. Compared with our study, this study was based on cardiovascular patients as subjects and factors such as age, sex, SBP, and DBP were adjusted in the model.

3. Study population

Previously arterial stiffness and GWAS studies did not exclude subjects with chronic diseases, but the present study was undertaken in a healthy population to exclude the effect of acquired or environmental influences. McEniery et al. [31] and Giannattasio et al. [32] showed that arterial stiffening is associated with aging, hypertension, diabetes mellitus, and chronic kidney disease. We performed a GWAS for arterial stiffness in Korean individuals aged 40 to 70 years. The study subjects included three rural populations, and the analysis focused on distinct SNPs that regulate a specific, pathophysiologically relevant pathway that can alter downstream arterial properties. Moreover, there is increasing evidence that arterial stiffness and cardiovascular events are closely parallel. To the best of our knowledge, this study is the first GWAS to investigate the association between SNPs and baPWV levels in a healthy and normal general population.

4. Biological consideration: explanation for the genes and SNPs

Recently, GWAS have shown associations between measures of arterial stiffness and the loci on different chromosomes (chromosomes 2, 7, 13, and 15). However, the pathophysiological links between the genetic and physiological components remain largely unknown [26,33,34]. In addition, some studies have reported that SNPs can affect arterial stiffness [8]; examples include SNPs in genes such as those encoding the renin-angiotensin-aldosterone system, matrix protein/metalloproteinases, the nitric oxide pathway, β-adrenergic/endothelin receptors, the inflammatory cascade, and others [3539]. Compared with the current research, this study did not identify specific and relevant SNPs after accounting for various factors. The specific role and mechanism of the 5 SNPs and 3 genes has not been elucidated in the current research, but may be related to arterial stiffness and needs to be studied further.

5. Limitations of the present study

The present study has several limitations. First, with regard to age, the study subjects were a middle-aged cohort from three rural populations; therefore, a study needs to be conducted using a young population cohort. Secondly, the study involved a relatively small range of geographical areas and future studies should be conducted in the broader context of national and international populations. Third, in our study, we found 5 SNPs for 3 genes with a P-value less than 5×10–8 and MAFs about 0.02. With the current knowledge, the implication of an association by these low or rare MAFs cannot be interpreted properly. Because MAFs below 0.05 are usually excluded in standard GWAS, such SNPs with low MAFs shown in our study should not be regarded as an evidence of significant association and further studies for these low or rare MAFs are needed to properly interpret our results [40].

In conclusion, employing the recent standard applied to GWAS and due to the lack of replication analysis in the present study, we found no evidence of a significant association signal for baPWV.

Figures
Fig. 1.

Q-Q plot showing the genome-wide P-values for site information adjusted associations. Lambda GC=0.996. The plot shows P-values for all the single nucleotide polymorphisms that were analyzed. Note that the circles in the right half of the plot are significantly above the line. A QQ-plot of these P-values was used to see if the very low P-values differed from the expected distribution (a uniform distribution). GC, genomic control; Q-Q, quantile-quantile.


Fig. 2.

Manhattan plot showing genome-wide P-values of multiple associations. The minimum P-values under the genetic models were obtained by linear regression analysis with adjustment for age, sex, height, body mass index, mean arterial pressure, site information, smoking status, alcohol consumption, and exercise. The y axis shows the −log10P-values of 723,056 single nucleotide polymorphisms, and the x axis shows their chromosomal positions. The horizontal red and blue lines represent the thresholds of P=3.47×10−8 for Bonferroni’s significance and P=1×10−5 for selecting the SNPs for replication, respectively.


Fig. 3.

Q-Q plot showing genome-wide P-values in the multiple associations. Lambda GC=0.995. The plot shows P-values for all the single nucleotide polymorphisms that were analyzed. Note that the circles in the right half of the plot are significantly above the line. A QQ-plot of these P-values was used to see if the very low P-values differed from the expected distribution (a uniform distribution).


TABLES

Table 1

Characteristics of the general study population

Yangpyeong (n=957)Namwon (n=654)Goryeong (n=796)Total (n=2407)
Age (yr)55.0±8.756.3±8.157.9±7.656.6±8.3
Male/female (n)347/610 (36.3/63.7)245/409 (37.5/62.5)276/520 (34.7/65.3)868/1,539 (36.1/63.9)
BMI (kg/m2)24.3±2.923.7±2.824.0±3.024.0±3.0
Smoking (n)143 (14.9)127 (19.4)121 (15.2)391 (16.2)
Alcohol (n)485 (50.7)290 (44.3)364 (45.7)1139 (47.3)
Exercise (n)314 (32.8)196 (30.0)217 (27.3)727 (30.2)
SBP (mmHg)114.4±10.6115.7±11.1115.4±11.7115.1±11.1
MAP (mmHg)88.8±7.687.8±8.289.0±8.088.6±7.9
DBP (mmHg)76.0±7.273.8±7.975.9±7.375.4±7.5
Uric acid (mg/dL)4.7±1.24.9±1.34.6±1.34.7±1.3
T-cholesterol (mg/dL)189.4±29.8194.4±28.4188.9±29.3190.6±29.4
HDL-cholesterol (mg/dL)45.2±10.843.8±10.145.7±10.845.0±10.7
TG (mg/dL)134.5±78.9130.3±77.5135.5±80.7133.7±79.1
FBG (mg/dL)95.4±9.391.2±9.293.9±9.793.8±9.6
Creatinine (mg/dL)0.9±0.10.9±0.10.9±0.10.9±0.1
Right brachial pulse rate (beats/min)64.4±9.566.3±9.564.1±8.764.8±9.3
Right baPWV (cm/sec)1,381.0±225.71,416.3±234.51,381.6±225.61,390.8±228.5
Left baPWV (cm/sec)1,393.0±221.01,426.2±235.91,396.0±226.11,403.0±227.2
Mean baPWV (cm/sec)1,387.0±220.51,421.2±232.31,388.8±221.21,396.9±224.4

Values are presented as mean±standard deviation or number (%).

BMI, body mass index; SBP, systolic blood pressure; MAP, mean arterial pressure; DBP, diastolic blood pressure; T-cholesterol, total-cholesterol; HDL-cholesterol, high-density lipoprotein-cholesterol; TG, Triglyceride; FBG, fasting blood glucose; baPWV, brachial ankle pulse wave velocity.


Table 2

Description of SNPs in the association study with adjustment for site information (P<10–6)

  SNPChromosomePosition Locus GeneLocationMinor alleleMAFRAFβSEPa
rs264529011192734311p11.1TBX15IntronA0.15860.159−0.17140.034627.82E-07
rs176678711192542811p11.1TBX15IntronC0.15880.159−0.17140.034637.9E-07
rs21481501119373580---A0.15210.152−0.1670.033818.45E-07
rs4768401113256853711q25OPCMLIntronC0.021680.02170.61690.10111.21E-09
rs1160711011132576733---A0.021540.02150.6140.10061.22E-09
rs2073350142361408814q11.2CPNE6IntronA0.41380.4140.10660.020893.57E-07
rs10107611655482216p13.3PRR35IntronG0.02510.02510.70080.11481.19E-09
rs116434121660532416p13.3RAB40CIntronG0.032180.03220.50310.091223.86E-08
rs116486071661124616p13.3RAB40CIntronA0.031780.03180.48920.088814.02E-08

a

Site information was adjusted.

SNP, single nucleotide polymorphism; MAF, minor allele frequency; RAF, Risk allele frequency; β, regression coefficient; SE, standard error.


Table 3

Description of SNPs in the multivariable adjusted association study (P<10–6)

  SNPChromosome Position Locus GeneLocationMinor alleleMAFRAFβSEPa
rs20465902186604548---A0.06290.06290.23660.04613.06E-07
rs103858721866140352q32.1LINC01473IntronA0.06210.06210.27270.05549.26E-07
rs119931658175273988p22-p21.3PDGFRLIntronA0.04050.04050.30150.05382.26E-08
rs7844026893622913---A0.04090.04090.40110.07772.67E-07
rs5109601113256548111q25OPCMLIntronG0.03070.03070.35240.06812.51E-07
rs71213681113256761811q25OPCMLIntronA0.0420.0420.47120.07842.12E-09
rs4768401113256853711q25OPCMLIntronC0.02170.02170.66780.07979.31E-17
rs1160711011132576733---A0.02150.02150.66460.07949.37E-17
rs29002641212357203---A0.09420.09420.17490.03464.61E-07
rs10152599152714950615q11-q12APBA2IntronA0.02960.02960.38640.06811.54E-08
rs2279496152715560815q11-q12APBA2IntronG0.02960.02960.38660.06811.50E-08
rs7172171152715747015q11-q12APBA2IntronA0.02960.02960.38650.0681.51E-08
rs16955052152716741515q11-q12APBA2IntronG0.030.030.38880.06851.52E-08
rs10107611655482216p13.3PRR35IntronG0.02510.02510.75830.09069.30E-17
rs116434121660532416p13.3RAB40CIntronG0.03220.03220.52980.07233.11E-13
rs116486071661124616p13.3RAB40CIntronA0.03180.03180.51530.07033.22E-13
rs1672228184718276118q21.2LOC100287225IntronA0.17630.1760.12970.02533.06E-07
rs821195184719058018q21.2LOC100287225IntronA0.16680.1670.13140.0264.46E-07

a

Age, sex, height, body mass index, mean arterial pressure, site information, smoking status, alcohol consumption and exercise variables were adjusted.

SNP, single nucleotide polymorphism; MAF, minor allele frequency; RAF, Risk allele frequency; β, regression coefficient; SE, standard error.


Table 4

Description of SNPs in the multivariable adjusted association at each study site

Chromosome  SNP PositionA1βSEL95U95STATPa
In Yangpyeong
8rs1199316517527398A0.6080.076850.45740.75867.9127.06E-15
11rs7121368132567618A0.48160.079320.32610.63716.0721.83E-09
11rs476840132568537C0.71490.084240.54980.888.4878.13E-17
11rs11607110132576733A0.71490.084240.54980.888.4878.13E-17
15rs1015259927149506A0.38730.067870.25430.52035.7071.54E-08
15rs227949627155608G0.38730.06790.25420.52045.7041.57E-08
15rs717217127157470A0.38730.067870.25430.52035.7071.54E-08
15rs1695505227167415G0.38730.067870.25430.52035.7071.54E-08
16rs1010761554822G0.74140.087370.57010.91268.4868.21E-17
16rs11643412605324G0.50930.067380.37720.64147.5599.59E-14
16rs11648607611246A0.5020.066360.37190.6327.5659.21E-14
In Namwon
8rs1199316517527398A−0.012740.0324−0.076240.05077−0.39316.94E-01
11rs7121368132567618A0.0096720.03205−0.053150.072490.30187.63E-01
11rs476840132568537C−0.0048280.03207−0.067680.05802−0.15068.80E-01
11rs11607110132576733A−0.0048280.03207−0.067680.05802−0.15068.80E-01
15rs1015259927149506A−0.0035360.03217−0.066580.05951−0.10999.13E-01
15rs227949627155608G−0.0034910.03215−0.066510.05952−0.10869.14E-01
15rs717217127157470A−0.0034910.03215−0.066510.05952−0.10869.14E-01
15rs1695505227167415G−0.0034910.03215−0.066510.05952−0.10869.14E-01
16rs1010761554822G0.031570.03205−0.031250.094390.98493.25E-01
16rs11643412605324G0.020530.0323−0.042770.083840.63575.25E-01
16rs11648607611246A−0.011150.03223−0.074330.05202−0.3467.30E-01
In Goryeong
8rs1199316517527398A−0.057610.02744−0.1114-0.00382−2.0993.61E-02
11rs7121368132567618A−0.01590.02746−0.069710.03791−0.57925.63E-01
11rs476840132568537C0.0042070.02744−0.049580.0580.15338.78E-01
11rs11607110132576733A−0.00093640.02745−0.054740.05287−0.034119.73E-01
15rs1015259927149506A−0.015070.02743−0.068830.0387−0.54925.83E-01
15rs227949627155608G−0.014880.02741−0.06860.03883−0.5435.87E-01
15rs717217127157470A−0.015230.02742−0.068960.03851−0.55535.79E-01
15rs1695505227167415G−0.021660.0274−0.075360.03204−0.79044.30E-01
16rs1010761554822G−0.0096590.02751−0.063590.04427−0.3517.26E-01
16rs11643412605324G0.0052390.02761−0.048880.059350.18978.50E-01
16rs11648607611246A−0.0020220.02765−0.056220.05217−0.073139.42E-01

a

Age, sex, height, body mass index, mean arterial pressure, smoking status, alcohol consumption and exercise variables were adjusted.

SNP, single nucleotide polymorphism; A1, first allele code; β, regression coefficient; SE, standard error; L95, lower bound of 95% confidence interval; U95, upper bound of 95% confidence interval; STAT, coefficient t-statistic.


Table 5

Meta-analysis results of three study sites

ChromosomePositionSNPA1A2NP-valueaP-valuebββ(R)QI2
817527398rs11993165AG30.77270.2030.00580.16550.000097.02
11132567618rs7121368AG30.19070.14680.02640.13860.000094.38
11132568537rs476840CA30.039690.096310.04160.22010.000097.05
11132576733rs11607110AC30.054970.10020.03880.21840.000097.07
1527149506rs10152599AG30.22640.20930.02410.10930.000093.63
1527155608rs2279496GA30.22520.20870.02420.10930.000093.62
1527157470rs7172171AG30.2280.20940.0240.10920.000093.64
1527167415rs16955052GA30.30130.22280.02060.10720.000093.78
16554822rs1010761GA30.019450.077790.04740.23470.000097.04
16605324rs11643412GA30.0054390.12580.05570.16760.000095.99
16611246rs11648607AG30.044090.17490.04030.15290.000096.25

a

Fixed-effects meta-analysis,

b

random-effects meta-analysis.

SNP, single nucleotide polymorphism; A1, first allele code; A2, second allele code; N, number of valid studies for this SNP; P, fixed-effects meta-analysis P-value; β, fixed-effects β estimate; β(R), random-effects β estimate; Q, P-value for Cochrane’s Q statistic; I, I2 heterogeneity index (0-100).


References
  1. Takahara M, Katakami N, Osonoi T, Saitou M, Sakamoto F, and Matsuoka TA et al. Different impacts of cardiovascular risk factors on arterial stiffness versus arterial wall thickness in Japanese patients with type 2 Diabetes Mellitus. J Atheroscler Thromb 2015;22:971-80.
    Pubmed CrossRef
  2. Lehmann ED. Clinical value of aortic pulse-wave velocity measurement. Lancet 1999;354:528-9.
    CrossRef
  3. Asmar R, Benetos A, Topouchian J, Laurent P, Pannier B, and Brisac AM et al. Assessment of arterial distensibility by automatic pulse wave velocity measurement. Validation and clinical application studies. Hypertension 1995;26:485-90.
    Pubmed CrossRef
  4. Cohn JN. Vascular wall function as a risk marker for cardiovascular disease. J Hypertens Suppl 1999;17:S41-4.
  5. van Popele NM, Grobbee DE, Bots ML, Asmar R, Topouchian J, and Reneman RS et al. Association between arterial stiffness and atherosclerosis:the Rotterdam Study. Stroke 2001;32:454-60.
    Pubmed CrossRef
  6. Lee CJ, Wang JH, Chen ML, Yang CF, Chen YC, and Hsu BG. Serum osteoprotegerin is associated with arterial stiffness assessed according to the cardio-ankle vascular index in hypertensive patients. J Atheroscler Thromb 2015;22:304-12.
    Pubmed CrossRef
  7. Shin J, Park JB, Kim KI, Kim JH, Yang DH, and Pyun WB et al. 2013 Korean Society of Hypertension guidelines for the management of hypertension:part III-hypertension in special situations. Clin Hypertens 2015;21:3.
    CrossRef
  8. Lacolley P, Challande P, Osborne-Pellegrin M, and Regnault V. Genetics and pathophysiology of arterial stiffness. Cardiovasc Res 2009;81:637-48.
    Pubmed CrossRef
  9. Yamashina A, Tomiyama H, Takeda K, Tsuda H, Arai T, and Hirose K et al. Validity, reproducibility, and clinical significance of noninvasive brachial-ankle pulse wave velocity measurement. Hypertens Res 2002;25:359-64.
    Pubmed CrossRef
  10. Kawai T, Ohishi M, Takeya Y, Onishi M, Ito N, and Yamamoto K et al. Adiponectin single nucleotide polymorphism is a genetic risk factor for stroke through high pulse wave pressure:a cohort study. J Atheroscler Thromb 2013;20:152-60.
    Pubmed CrossRef
  11. Kawai T, Ohishi M, Onishi M, Ito N, Takeya Y, and Maekawa Y et al. Cut-off value of brachial-ankle pulse wave velocity to predict cardiovascular disease in hypertensive patients:a cohort study. J Atheroscler Thromb 2013;20:391-400.
    Pubmed CrossRef
  12. Nakamura M, Yamashita T, Yajima J, Oikawa Y, Sagara K, and Koike A et al. Brachial-ankle pulse wave velocity as a risk stratification index for the short-term prognosis of type 2 diabetic patients with coronary artery disease. Hypertens Res 2010;33:1018-24.
    Pubmed CrossRef
  13. Xiong Z, Zhu C, Zheng Z, Wang M, Wu Z, and Chen L et al. Relationship between arterial stiffness assessed by brachial-ankle pulse wave velocity and coronary artery disease severity assessed by the SYNTAX score. J Atheroscler Thromb 2012;19:970-6.
    Pubmed CrossRef
  14. Yamashina A, Tomiyama H, Arai T, Hirose K, Koji Y, and Hirayama Y et al. Brachial-ankle pulse wave velocity as a marker of atherosclerotic vascular damage and cardiovascular risk. Hypertens Res 2003;26:615-22.
    Pubmed CrossRef
  15. Youn JC, Kim JY, Park S, Kwon J, Lee HS, and Shin DH et al. Comparison of arterial stiffness indices measured by the Colins and SphygmoCor systems. Hypertens Res 2012;35:1180-4.
    Pubmed CrossRef
  16. Igarashi Y, Chikamori T, Tomiyama H, Usui Y, Hida S, and Tanaka H et al. Diagnostic value of simultaneous brachial and ankle blood pressure measurements for the extent and severity of coronary artery disease as assessed by myocardial perfusion imaging. Circ J 2005;69:237-42.
    Pubmed CrossRef
  17. Tomiyama H, Koji Y, Yambe M, Shiina K, Motobe K, and Yamada J et al. Brachial-ankle pulse wave velocity is a simple and independent predictor of prognosis in patients with acute coronary syndrome. Circ J 2005;69:815-22.
    Pubmed CrossRef
  18. Vlachopoulos C, Aznaouridis K, and Stefanadis C. Prediction of cardiovascular events and all-cause mortality with arterial stiffness:a systematic review and meta-analysis. J Am Coll Cardiol 2010;55:1318-27.
    Pubmed CrossRef
  19. Vlachopoulos C, Aznaouridis K, Terentes-Printzios D, Ioakeimidis N, and Stefanadis C. Prediction of cardiovascular events and all-cause mortality with brachial-ankle elasticity index:a systematic review and meta-analysis. Hypertension 2012;60:556-62.
    Pubmed CrossRef
  20. Cortez-Cooper MY, Supak JA, and Tanaka H. A new device for automatic measurements of arterial stiffness and ankle-brachial index. Am J Cardiol 2003;91:1519-22, A9.
  21. Smith MW, Clark SP, Hutchinson JS, Wei YH, Churukian AC, and Daniels LB et al. A sequence-tagged site map of human chromosome 11. Genomics 1993;17:699-725.
    Pubmed CrossRef
  22. Entrez Gene:OPCML opioid binding protein/cell adhesion molecule-like [Internet]. http://www.genenames.org/cgi-bin/gene_symbol_report?hgnc_id=HGNC.8143 cited 2017 Apr 22
  23. Entrez Gene:Proline rich 35 [Internet]. https://www.ncbi.nlm.nih.gov/gene/146325 cited 2017 Apr 22
  24. Atwood LD, Samollow PB, Hixson JE, Stern MP, and MacCluer JW. Genome-wide linkage analysis of pulse pressure in Mexican Americans. Hypertension 2001;37:425-8.
    Pubmed CrossRef
  25. Camp NJ, Hopkins PN, Hasstedt SJ, Coon H, Malhotra A, and Cawthon RM et al. Genome-wide multipoint parametric linkage analysis of pulse pressure in large, extended utah pedigrees. Hypertension 2003;42:322-8.
    Pubmed CrossRef
  26. DeStefano AL, Larson MG, Mitchell GF, Benjamin EJ, Vasan RS, and Li J et al. Genome-wide scan for pulse pressure in the National Heart, Lung and Blood Institute’s Framingham Heart Study. Hypertension 2004;44:152-5.
    Pubmed CrossRef
  27. Sugawara J, Hayashi K, Yokoi T, Cortez-Cooper MY, DeVan AE, and Anton MA et al. Brachial-ankle pulse wave velocity:an index of central arterial stiffness?. J Hum Hypertens 2005;19:401-6.
    Pubmed CrossRef
  28. Oliver JJ, and Webb DJ. Noninvasive assessment of arterial stiffness and risk of atherosclerotic events. Arterioscler Thromb Vasc Biol 2003;23:554-66.
    Pubmed CrossRef
  29. Levy D, Larson MG, Benjamin EJ, Newton-Cheh C, Wang TJ, and Hwang SJ et al. Framingham Heart Study 100K Project:genome-wide associations for blood pressure and arterial stiffness. BMC Med Genet 2007;8:S3.
    CrossRef
  30. Park S, Lee JY, Kim BK, Lee SH, Chang HJ, and Choi D et al. Lack of association between arterial stiffness and genetic variants by genome-wide association scan. Blood Press 2015;24:258-61.
    Pubmed CrossRef
  31. McEniery CM, Yasmin Hall IR, Qasem A, Wilkinson IB, and Cockcroft JR, ACCT Investigators. Normal vascular aging:differential effects on wave reflection and aortic pulse wave velocity:the Anglo-Cardiff Collaborative Trial (ACCT). J Am Coll Cardiol 2005;46:1753-60.
    Pubmed CrossRef
  32. Giannattasio C, Failla M, Capra A, Scanziani E, Amigoni M, and Boffi L et al. Increased arterial stiffness in normoglycemic normotensive offspring of type 2 diabetic parents. Hypertension 2008;51:182-7.
    Pubmed CrossRef
  33. Tarasov KV, Sanna S, Scuteri A, Strait JB, Orrù M, and Parsa A et al. COL4A1 is associated with arterial stiffness by genome-wide association scan. Circ Cardiovasc Genet 2009;2:151-8.
    Pubmed KoreaMed CrossRef
  34. Björck HM, Länne T, Alehagen U, Persson K, Rundkvist L, and Hamsten A et al. Association of genetic variation on chromosome 9p21.3 and arterial stiffness. J Intern Med 2009;265:373-81.
    Pubmed CrossRef
  35. Sie MP, Yazdanpanah M, Mattace-Raso FU, Uitterlinden AG, Hofman A, and Hoeks AP et al. Genetic variation in the renin-angiotensin system and arterial stiffness. The Rotterdam Study. Clin Exp Hypertens 2009;31:389-99.
    Pubmed CrossRef
  36. Mayer O, Filipovský J, Pesta M, Cífková R, Dolejsová M, and Simon J. The interaction of endothelial nitric oxide synthase polymorphism and current smoking in terms of increased arterial stiffness. Physiol Res 2010;59:529-36.
  37. Yasmin McEniery CM, O’Shaughnessy KM, Harnett P, Arshad A, and Wallace S et al. Variation in the human matrix metalloproteinase-9 gene is associated with arterial stiffness in healthy individuals. Arterioscler Thromb Vasc Biol 2006;26:1799-805.
    Pubmed CrossRef
  38. Schnabel R, Larson MG, Dupuis J, Lunetta KL, Lipinska I, and Meigs JB et al. Relations of inflammatory biomarkers and common genetic variants with arterial stiffness and wave reflection. Hypertension 2008;51:1651-7.
    Pubmed KoreaMed CrossRef
  39. Chen W, Srinivasan SR, Boerwinkle E, and Berenson GS. Beta-adrenergic receptor genes are associated with arterial stiffness in black and white adults:the Bogalusa Heart Study. Am J Hypertens 2007;20:1251-7.
    Pubmed CrossRef
  40. Wang K, Dickson SP, Stolle CA, Krantz ID, Goldstein DB, and Hakonarson H. Interpretation of association signals and identification of causal variants from genome-wide association studies. Am J Hum Genet 2010;86:730-42.
    Pubmed KoreaMed CrossRef


June 2017, 14 (1)
Full Text(PDF) Free

Social Network Service
Services

Cited By Articles
  • CrossRef (0)