Heritability of blood pressure traits and the genetic contribution to blood pressure variance explained by four blood-pressure-related genes.

Abstract

To study the heritability of four blood pressure traits and the proportion of variance explained by four blood-pressure-related genes.
All participants are members of an extended pedigree from a Dutch genetically isolated population. Heritability and genetic correlations of systolic blood pressure, diastolic blood pressure, mean arterial pressure and pulse pressure were assessed using a variance components approach (SOLAR). Polymorphisms of the alpha-adducin (ADD1), angiotensinogen (AGT), angiotensin II type 1 receptor (AT1R) and G protein beta3 (GNB3) genes were typed.
Heritability estimates were significant for all four blood pressure traits, ranging between 0.24 and 0.37. Genetic correlations between systolic blood pressure, diastolic blood pressure and mean arterial pressure were high (0.93-0.98), and those between pulse pressure and diastolic blood pressure were low (0.05). The ADD1 polymorphism explained 0.3% of the variance of pulse pressure (P = 0.07), and the polymorphism of GNB3 explained 0.4% of the variance of systolic blood pressure (P = 0.02), 0.2% of mean arterial pressure (P = 0.05) and 0.3% of pulse pressure (P = 0.06).
Genetic factors contribute to a substantial proportion of blood pressure variance. In this study, the effect of polymorphisms of ADD1, AGT, AT1R and GNB3 explained a very small proportion of the heritability of blood pressure traits. As new genes associated with blood pressure are localized in the future, their effect on blood pressure variance should be calculated.

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Heritability of blood pressure traits and the genetic
contribution to blood pressure variance explained by four
blood-pressure-related genes
Marie Josee E. van Rijna,M, Anna F.C. Schuta,M, Yurii S. Aulchenkoa,
Jaap Deinumc, Fakhredin A. Sayed-Tabatabaeia, Mojgan Yazdanpanaha,
Aaron Isaacsa, Tatiana I. Axenovichd, Irina V. Zorkoltsevad,
M. Carola Zillikensa,b, Huib A.P. Polsa,b, Jacqueline C.M. Wittemana,
Ben A. Oostraaand Cornelia M. van Duijna
Objective To study the heritability of four blood pressure
traits and the proportion of variance explained by four
blood-pressure-related genes.
Methods All participants are members of an extended
pedigree from a Dutch genetically isolated population.
Heritability and genetic correlations of systolic blood
pressure, diastolic blood pressure, mean arterial pressure
and pulse pressure were assessed using a variance com-
ponentsapproach(SOLAR).Polymorphismsofthea-adducin
(ADD1),angiotensinogen(AGT),angiotensinIItype1receptor
(AT1R) and G protein b3 (GNB3) genes were typed.
Results Heritability estimates were significant for all four
blood pressure traits, ranging between 0.24 and 0.37.
Genetic correlations between systolic blood pressure,
diastolic blood pressure and mean arterial pressure were
high (0.93–0.98), and those between pulse pressure and
diastolic blood pressure were low (0.05). The ADD1
polymorphism explained 0.3% of the variance of pulse
pressure (PU0.07), and the polymorphism of GNB3
explained 0.4% of the variance of systolic blood pressure
(PU0.02), 0.2% of mean arterial pressure (PU0.05) and
0.3% of pulse pressure (PU0.06).
Conclusion Genetic factors contribute to a substantial
proportion of blood pressure variance. In this study, the
effect of polymorphisms of ADD1, AGT, AT1R and GNB3
explained a very small proportion of the heritability of blood
pressure traits. As new genes associated with blood
pressure are localized in the future, their effect on blood
pressure variance should be calculated. J Hypertens
25:565–570 Q 2007 Lippincott Williams & Wilkins.
Journal of Hypertension 2007, 25:565–570
Keywords: blood pressure, genes, heritability, variance
aGenetic Epidemiology Unit, Department of Epidemiology & Biostatistics and
Department of Clinical Genetics,bDepartment of Internal Medicine, Erasmus
Medical Center, Rotterdam,cDepartment of Internal Medicine, University Medical
Center, St Radboud, Nijmegen, The Netherlands anddInstitute of Cytology and
Genetics SD RAS, Novosibirsk, Russia
Correspondence and requests for reprints to Prof. C. M. van Duijn, Department of
Epidemiology & Biostatistics, Erasmus Medical Center, PO Box 1738, 3000 DR
Rotterdam, The Netherlands
Tel: +31 10 408 73 94; fax: +31 10 408 94 06; e-mail: c.vanduijn@erasmusmc.nl
Sponsorship: This research was supported by the Centre for Medical Systems
Biology (CMSB), the Netherlands Organisation for Scientific Research (NWO)
and the Netherlands Kidney Foundation.
Conflicts of interest: none.
Received 14 July 2006 Revised 2 November 2006
Accepted 15 November 2006
See editorial commentary on page 505
Introduction
It has long been recognized that genetic factors play a
crucial role in blood pressure regulation. Twin, adoption
and nuclear family studies indicated that a substantial
proportion of systolic blood pressure (SBP) and diastolic
blood pressure (DBP) variance is due to the effect of
genes [1–8]. Heritability estimates, however, range
widely between different study populations, depending
heavily on the type of relative pairs used. Heritability
estimates for SBP and DBP vary around 60% in twin
studies and around 25% in nuclear family studies
[1,3,5,6,9]. Scarcer studies on the heritability of mean
arterialpressure(MAP)andpulsepressure(PP)produced
estimates that vary from 35 to 60% [10–12]. As these
estimates are based on blood pressure correlations
between first-degree relatives only, they are likely to
be confounded by the effects of a sharedfamilialenviron-
ment, which causes an overestimation of heritability.
Large family-based samples, including second-degree
and third-degree relatives, who do not usually share
the same household, may therefore generate more accu-
rate heritability estimates of blood pressure.
A number of genes may explain part of the heritability of
blood pressure. Genes involved in salt sensitivity and the
renin–angiotensin system are known to play a role in
Original article 565
?M.J.E.v.R. and A.F.C.S. contributed equally to this paper.
0263-6352 ? 2007 Lippincott Williams & Wilkins

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blood pressure variance [13]. Until now, no studies have
addressed the extent to which these genes explain the
heritability of blood pressure.
In the present study, we aimed to assess to what extent
genes influence blood pressure variance in 1006 inhabi-
tants of a genetically isolated community in the south-
west part of The Netherlands. The participants were all
related to each other in one extended pedigree, making it
lesslikelythatfamilialeffectsplay arole.Heritabilitywas
estimated for four quantitative blood pressure traits: SBP,
DBP, MAP and PP. We also estimated what proportion
of blood pressure variance could be explained by the
following polymorphisms: Gly460Trp of ADD1, M235T
of angiotensinogen (AGT), C573T of the angiotensin II
type 1 receptor (AT1R) and rs2301339G/A of GNB3.
Materials and methods
Setting
Analyses were performed on phenotypic data collected
from Dutch inhabitants of a genetically isolated com-
munity in the south-west part of the Netherlands, who
participated in the Erasmus Rucphen Family (ERF)
study. The ERF study is a family-based cohort study,
and is part of an ongoing research program called Genetic
Research in Isolated Populations. This program aims to
identify genetic risk factors in the development of com-
plex disorders [14]. The study was approved by the
Medical Ethics Committee of Erasmus Medical Center
Rotterdam. Written informed consent was obtained from
all participants.
Participants
Genealogical records demonstrated that almost all of the
inhabitants of this isolated population could be traced
back to about 150 individuals who founded this com-
munityaround1750.Foryears,minimalinwardmigration
and considerable population growth characterized this
population. About 20000 inhabitants are now scattered
over eight adjacent villages. Genealogical information on
this population was reconstructed using church and the
municipality records, and is currently available in the
form of a large pedigree database including over 63000
individuals.
FortheERFstudy,20couplesthathadatleastsixchildren
baptisedinthecommunitychurchbetween1880and1900
were identified with the help of genealogical records. All
living descendants of these couples and their spouses
were invited to participate in the study. With the use of
the pedigree database, these families could be linked to
one founder couple in a large, complex pedigree.
Data collection
Participants were invited for a series of clinical examin-
ations at our research center, located within the com-
munity. The blood pressure was measured twice in the
sitting positionin the right upper arm using an automated
device (OMRON 711, automatic IS; Omron Healthcare
Inc., Bannockburn, Illinois, USA). The average of these
two measurements was used for analysis. The MAP
(1/3SBPþ2/3DBP)andPP(SBP?DBP)werecalculated.
Hypertension was defined as a DBP of 90mmHg or
higher and/or a SBP of 140mmHg or higher and/or use
of antihypertensive medication indicated for the treat-
ment of hypertension [15,16]. Height and weight were
measured with the participant dressed in light under-
clothing and the body mass index (BMI) was calculated
(kg/m2). Finally, a research physician obtained infor-
mation on medical history, medication use, smoking
and alcohol use in a personal interview.
At the start of the clinical examinations, fasting blood
samples were drawn for the extraction of DNA and the
measurement of lipids, glucose, plasma creatinine, and
plasma albumin levels according to a standardized pro-
cedure [17,18]. Serum samples were obtained from whole
blood after clotting; plasma samples were obtained from
whole blood collected in disodium ethylenediamine
tetraacetic acid.
Hyperlipidemia was defined as the use of lipid-lowering
medication or total cholesterol levels between 6.5 and
9.0mmol/l and a total cholesterol to high-density lipo-
protein (HDL) cholesterol ratio above 5.0, or total cho-
lesterol below 6.5mmol/l and a ratio above 8.0, or total
cholesterol above 9.0mmol/l, independent of the ratio, or
triglycerides above 4.0mmol/l. These criteria are in
accordance with those of the Dutch college of general
practitioners. Diabetes mellitus was defined as the use of
blood glucose-lowering medication or a fasting serum
glucose level above 7.0mmol/l [19].
Data collection started in June 2002 and was finished in
February 2005. In the present study, we focused on the
first 1006 participants for whom complete phenotypic,
genotypic and genealogical information was available.
Genotyping
We genotyped the following polymorphisms: ADD1
Gly460Trp, AGT M235T, AT1R C573T and GNB3
rs2301339G/A.GenotypingwasperformedusingTaqMan
allelic discrimination Assays-By-Design (Applied Biosys-
tems, Foster City, California, USA) [20]. For ADD1, the
forward primer sequence was 50-GAGAAGACAAGATG
GCTGAACTCT-30and the reverse primer sequence
50-GTCTTCGACTTGGGACTGCTT-30. The minor
groove binding probes were 50-VIC-CATTCTGCCCT
TCCTC-NFQ-30
and50-FAM-ATTCTGCCATTCC
TC-NFQ-30. We used the reverse strand design for this
polymorphism. Forward primer sequences were 50-GGT
TTGCCTTACCTTGGAAGTG-30and 50-TGTGCTT
TCCATTATGAGTCCCAAA-30for AGT and AT1R,
respectively. Reverse primer sequences were 50-GCTGT
566
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2007, Vol 25 No 3

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GACAGGATGGAAGACT-30and 50-CAGAAAAGGAA
ACAGGAAACCCAGTATA-30for AGT and AT1R, res-
pectively. The minor groove binding probes were 50-VIC-
TGGCTCCCATCAGG-NFQ-30and 50-FAM-CTGGC
TCCCGTCAGG-NFQ-30for AGT. The minor groove
binding probes were 50-VIC-CTATCGGGAGGGTTG-
NFQ-30and 50-FAM-CTATCGGAAGGGTTG-NFQ-30
for AT1R. We used the reverse strand design for this
polymorphism. For GNB3, the forward primer sequence
was 50-GGCAGGGCTGCTTCTCA-30and the reverse
primer sequence was 50-GCAAGCCGCTGCTCTCA-30.
The minor groove binding probes were 50-VIC-AAAC
CAAGGAAGGGACA-NFQ-30and 50-FAM-ACCAAGG
GAGGGACA-NFQ-30. The assays utilized 5ng genomic
DNA and 5ml reaction volumes. The amplification and
extension protocol was as follows: an initial activation step
of 10min at 958C preceded 40 cycles of denaturation at
958Cfor15s,andannealingandextensionat508Cfor60s.
Allele-specific fluorescence was then analyzed on an ABI
Prism 7900HT Sequence Detection System with SDS
version 2.1 (Applied Biosystems). Based on the analysis
of blind duplicates, there was a 98% concordance in
genotyping of ADD, 99.4% for AGT and 100% for AT1R
and GNB3.
Statistical analysis
Baseline characteristics were compared using univariate
analysis of variance or chi-squared statistics. Univariate
analyses of variance were used to assess the relation
between the ADD1 Gly460Trp, the AGT M235T, the
AT1R C573T and the GNB3 rs2301339G/A polymorph-
isms and blood pressure traits (SPSS 11.0; SPSS Inc.,
Chicago, Illinois, USA). A variance component maximum
likelihood approach, implemented in the SOLAR soft-
ware package, was used to estimate heritability and
genetic correlations for SBP, DBP, MAP and PP [21].
Heritability (h2) was estimated as the ratio of the variance
of the trait explained by additive polygenic effects to the
total phenotypic variance of the trait. We identified
significant (environmental) covariates for each blood
pressure trait in order to estimate the contribution of
environmental factors to blood pressure variance. Signifi-
cant effects of each covariate were tested using a like-
lihood ratio test with one degree of freedom (SPSS 11.0).
Covariates that were included in the final model were
significant at the 0.10 level. These were age, sex, total
cholesterol, HDL cholesterol, glucose levels, alcohol
intake, antihypertensive medication, and BMI. In order
to satisfy distributional assumptions, the SBP, DBP,
MAP and PP were natural log-transformed to ensure
normally distributedresiduals. Heritability
included age, sex, antihypertensive medication, BMI,
total cholesterol, HDL cholesterol, alcohol intake and
glucose levels as covariates.
models
Bivariate analysis was performed to estimate the genetic
and environmental correlations between the four blood
pressure traits [22,23]. The phenotypic correlations
between the blood pressure traits were then calculated
by the following formula [24,25]: rP¼Hh12Hh22rGþ
H(1?h12)H(1?h22)rE, where h12and h22are the herit-
ability estimates of the two blood pressure traits, for
which the phenotypic correlation is calculated, and rG
and rEare the genetic and environmental correlations
between these two traits (as estimated in the bivariate
analyses). Significance of the phenotypic, additive
genetic and environmental correlations was determined
using a likelihood ratio test. To test whether a given
correlation between two blood pressure traits was signifi-
cantly different from zero, the likelihood of a model in
which this correlation was constrained to zero was com-
pared with a model in which the same correlation was
estimated.Twicethedifferenceinln-likelihoodsofthese
models yields a test statistic that is asymptotically dis-
tributedasachi-squaredstatisticwithdegreesoffreedom
equal to the difference in number of parameters esti-
mated in the two models. All bivariate analyses were
adjusted for age and sex.
Next, we assessed the proportion of variance explained
by the following polymorphisms: Gly460Trp of ADD1,
M235T of AGT, C573T of AT1R and rs2301339G/A of
GNB3. The significance of these polymorphisms was
tested using the likelihood ratio test, where the like-
lihood of a model including the polymorphism is esti-
mated, then compared with the likelihood of a model
without the polymorphism. Twice the difference in the
natural ln-likelihoods values of these models yields a test
statistic that is asymptotically distributed as a chi-squared
statistic with degrees of freedom equal to the difference
in number of estimated parameters in the two models
beingcompared[26].Intheoutput files,theproportionof
variance explained by all covariates is presented. The
absolute increase in this proportion by adding the poly-
morphism to the model equals the proportion of variance
explained by the polymorphism.
Results
In total, 1006 participants were available for analysis,
including 907 first-degree relative pairs, 659 second-
degree relative pairs and 2370 third-degree relative pairs.
Table 1 presents the general descriptives of the total
study population stratified by sex. The mean age was
56years for men and 54years for women, but as partici-
pants were ascertained from three generations the age
range was very broad (18–92years). Most characteristics
were higher in men, with the exception of HDL choles-
terol and smoking.
The heritability estimates for SBP, DBP, MAP and PP
are presented in Table 2. Heritability estimates for SBP,
DBP, MAP and PP were significant (P<0.001), and
ranged from 0.24 for PP to 0.37 for DBP.
Heritability of blood pressure traits van Rijn et al.
567

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Table 3 presents the results of the bivariate analyses. All
correlations were positive and significant, with the excep-
tions of the phenotypic and genetic correlations between
DBP and PP. The phenotypic correlations ranged
between 0.15 for DBP–PP (not significant) and 0.90
for SBP–MAP and DBP–MAP. Genetic correlations
between SBP, DBP and MAP were significant and very
high(0.93–0.98),whereas
between PP and DBP was not significant and was low
(0.05). Environmental correlations ranged widely, from
0.19 for DBP–PP to 0.89 for SBP–MAP.
thegeneticcorrelation
Figure 1 shows that SBP was significantly higher in AG
carriers of GNB3 compared with AA carriers (P¼0.003).
SBP was also higher in GG carriers, but this was not
significant (P¼0.06). MAP was also significantly higher
in AG carriers (P¼0.02) and PP was higher in both AG
(P¼0.002) and GG carriers (P¼0.05, data not shown).
No significant differences were found between the other
genotype groups and SBP, DBP, MAP or PP. From Fig. 1
we chose the following models for the analyses presented
in Table 4; ADD1 dominant model, AGT recessive
model, AT1R dominant model and GNB3 dominant
model.
Table 4 presents the proportion of variance in SBP, DBP,
MAP and PP explained by all covariates and by the
polymorphisms in ADD1, AGT, AT1R and GNB3. Age
and sex explained 25.8% of SBP, 7.4% of DBP, 18.4% of
MAP and 24.0% of PP (data not shown). Adding anti-
hypertensive medication, BMI, total cholesterol, HDL
cholesterol, alcohol intake and glucose levels to the
model only slightly increased the proportion of variance
explained by all covariates (with a maximum of 4%),
suggesting that age and sex explain most of the blood
pressure variance. The Gly460Trp polymorphism of
ADD1 explained 0.3% of the variance of PP (P¼0.07).
The rs2301339G/A polymorphism of GNB3, explained
0.4% of the variance of SBP (P¼0.02), 0.2% of MAP
(P¼0.05) and 0.3% of PP (P¼0.06). The other two
polymorphisms also explained less than 1% of the
variance of the blood pressure traits, but none were
significant.
Discussion
In the present study heritability estimates for blood
pressure traits were highly significant and varied between
24and40%.Highphenotypic,geneticandenvironmental
correlations between SBP, DBP, MAP and PP were
also found. The proportion of variance explained by
568
Journal of Hypertension
2007, Vol 25 No 3
Table 1
General characteristics of the study population
Men Women
P value
Number
Age (years)
Systolic blood pressure (mmHg)
Diastolic blood pressure (mmHg)
Mean arterial pressure (mmHg)
Pulse pressure (mmHg)
Antihypertensive medication (%)
Total cholesterol (mmol/l)
High-density lipoprotein cholesterol
(mmol/l)
Low-density lipoprotein cholesterol
(mmol/l)
Triglycerides (mmol/l)
Glucose (mmol/l)
Diabetes mellitus (%)
Hyperlipidemia (%)
Body mass index (kg/m2)
Current smokers (%)
Alcohol use (units/week)
407 597
55.7?14.4
145.7?20.3
82.6?10.4
103.6?12.4
63.2?16.0
29.1
5.5?1.1
1.1?0.3
53.5?15.6
139.5?22.4
78.7?9.6
98.6?12.5
59.8?18.0
22.4
5.6?1.2
1.4?0.4
0.02
<0.001
<0.001
<0.001
<0.01
0.02
0.11
<0.001
3.7?1.03.7?1.00.94
1.6?1.0
5.0?1.1
7.3
35.7
27.7?4.2
33.2
8.4?12.8
1.3?0.6
4.6?1.1
6.4
27.5
26.8?4.8
48.2
1.7?4.0
<0.001
<0.001
0.61
<0.01
<0.01
<0.001
<0.001
Data are unadjusted and presented as the percentage or the mean?SD.
Table 2
blood pressure, mean arterial pressure and pulse pressure
Heritability estimates of systolic blood pressure, diastolic
Phenotype
n
Heritability, H2?standard error
P value
Systolic blood pressure
Diastolic blood pressure
Mean arterial pressure
Pulse pressure
1006
1006
1006
1006
0.34?0.08
0.37?0.09
0.40?0.08
0.24?0.08
<0.001
<0.001
<0.001
<0.001
Heritability analyses are adjusted for age, sex, antihypertensive medication, body
mass index, total cholesterol, high-density lipoprotein cholesterol, alcohol intake
and glucose levels.
Table 3
between systolic blood pressure (SBP), diastolic blood pressure
(DBP), mean arterial pressure (MAP) and pulse pressure (PP)
Phenotypic, genetic and environmental correlations
Phenotypic
correlation, rP
Genetic
correlation, rG
Environmental
correlation, rE
SBP–DBP
SBP–MAP
SBP–PP
DBP–MAP
DBP–PP
MAP–PP
0.68?0.03
0.90?0.01
0.80?0.02
0.90?0.01
0.15?0.05M
0.52?0.03
0.93?0.13
0.98?0.04
0.70?0.11
0.93?0.03
0.05?0.22M
0.64?0.21
0.60?0.05
0.89?0.02
0.84?0.03
0.88?0.02
0.19?0.09
0.50?0.06
All correlations are significant at P<0.001, except for rPand rGbetween DBP
and PP.
Fig. 1
Mean systolic blood pressure levels in four different genotype groups.
?P<0.05 compared with the reference group AA, adjusted for age and
sex.

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covariates was mostly determined by age and sex. In the
single gene analysis, a small but significant proportion of
the variance of SBP and MAP was determined by the
rs2301339G/A polymorphism of GNB3.
The strength of our study lies within its population-based
nature, embedded in a family-based study design. The
members of our extended pedigree therefore represent a
random sample of our study population and were not
ascertainedthroughpersons
pressure values.
with extremeblood
All of our heritability estimates were significant and
ranged between 0.25 for SBP and 0.37 for DBP. They
are within the range of those reported in other family-
based studies, which range from 0.15 to 0.40 [5,7,8,27–
29]. Covariates accounted for about 16% of DBP variance
and up to one-third of SBP and PP variance. Other than
age and sex, the BMI, fasting glucose levels and alcohol
intake were the most important covariates influencing
blood pressure in this population.
We observed high genetic correlations between SBP,
DBP and MAP, indicating that these traits may share a
common genetic background. In other words, the genes
thatinfluenceSBPvariance also influence DBP and MAP
variance. In contrast, the genetic correlation between PP
and DBP was absent. This suggests the existence of an
independent set of genes influencing PP and DBP var-
iance. This is concordant with a previous finding
suggesting that DBP and PP map to different loci [30].
The PP is an important measure of arterial stiffness
and a strong predictor of cardiovascular morbidity and
mortality, independent of blood pressure [31]. Significant
heritability estimates for PP and the results of our bivari-
ate analyses therefore make this trait a suitable candidate
for future genetic analyses aimed at identifying genes
involved in arterial stiffness.
Previous studies found associations between polymorph-
isms in ADD1, AGT, AT1R, GNB3 and blood pressure,
hypertension and salt sensitivity [13,32–34]. None of
these studies, however, addressed the proportion of
blood pressure variance that these genes explain. In this
study, only the rs2301339G/A polymorphism of GNB3
significantly explained a portion of the variance of SBP
and MAP. This proportion, however, was less than 1%.
Tocalculatetheassociationbetweenthefourpolymorph-
isms and blood pressure traits, we used the measured
genotype approach, comparing the likelihood of a model
including the polymorphism with the likelihood of a
model without the polymorphism, using the SOLAR
software package. This approach was chosen because
of the complex nature of our pedigree, which contains
numerous loops. The SOLAR software package was able
to analyze the complete pedigree, without breaking any
of the loops.
In conclusion, our study shows that polymorphisms of
ADD1, AGT, AT1R and GNB3 explain a very small part
of the heritability of blood pressure traits. Given the
high heritability estimates, many genes remain to be
identified.
Acknowledgements
The authors would like to thank all assistants of the ERF
research center for their help in data collection, and
Petra Veraart, Hilda Kornman and Ed Boeren for their
contribution to genealogical research. They would also
like to thank all participants of the ERF study for their
cooperation.
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Heritability of blood pressure traits van Rijn et al.
569
Table 4
all covariates and four blood pressure genes
Proportion of variance of systolic blood pressure, diastolic blood pressure, mean arterial pressure and pulse pressure explained by
Phenotype
Proportion of variance explained by
All covariates (%)
ADD1 (%)
P value
AGT (%)
P value
AT1R (%)
P value
GNB3 (%)
P value
Systolic blood pressure
Diastolic blood pressure
Mean arterial pressure
Pulse pressure
27.8
11.0
21.5
24.5
0.2
0.1
0.16
0.78
0.54
0.07
<0.1
0.2
0.1
<0.1
0.45
0.84
0.73
0.09
<0.1
0.1
0.1
<0.1
0.93
0.95
0.99
0.15
0.4
0.3
0.2
0.3
0.02
0.24
0.05
0.06
<0.1
0.3
ADD1, a-adducin Gly460Trp; AGT, angiotensinogen M235T; AT1R, angiotensin II type 1 receptor C573T; GNB3, G-protein rs2301339G/A polymorphism. All analyses
are adjusted for age, sex, antihypertensive medication, body mass index, total cholesterol, high-density lipoprotein cholesterol, alcohol intake, glucose levels and pedigree
structure.

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