Paraoxonase Activity, But Not Haplotype Utilizing the
Linkage Disequilibrium Structure, Predicts Vascular Disease
Gail P. Jarvik, Thomas S. Hatsukami, Chris Carlson, Rebecca J. Richter, Rachel Jampsa,
Victoria H. Brophy, Sadie Margolin, Mark Rieder, Deborah Nickerson, Gerard D. Schellenberg,
Patrick J. Heagerty, Clement E. Furlong
Downloaded from http://atvb.ahajournals.org/ by guest on June 14, 2017
Objective—The effects of paraoxonase (PON1) activity and of genetic variation in the PON1 promoter and coding region
on carotid artery disease (CAAD) were investigated.
Methods and Results—We identified functional promoter polymorphisms and examined their effects in a cohort with and
without CAAD. We used the full sequences in 23 white subjects to determine the linkage disequilibrium (LD) structure
of the PON1 region and to direct the grouping of haplotypes for disease association testing. There are several discrete
regions of the PON1 gene with strong local LD, but the useful levels of LD do not extend across the entire gene. Indeed,
PON1⫺162/⫺108/55/192 haplotype did not predict additional variation in PON1 activities compared with the 4 genotypes
separately. PON1 hydrolysis activity predicted CAAD status, but this was not attributable to the promoter or coding
region polymorphisms or haplotype or to the effects of smoking or statin use on PON1 activity.
Conclusions—PON1 does not have LD across the gene, and use of haplotypes in association studies should consider the
LD structure. PON1 activity predicts CAAD, yet 4 functional polymorphisms do not. Additional investigations of
genetic and environmental factors that influence PON1 activity as a risk factor for vascular disease are warranted.
(Arterioscler Thromb Vasc Biol. 2003;23:1465-1471.)
Key Words: carotid 䡲 haplotype 䡲 oxidation 䡲 paraoxonase
W
e have previously reported that paraoxonase (PON1)
activity for the hydrolysis of paraoxon (POase) or
diazoxon (DZOase) predicted carotid artery disease (CAAD)
status, even though PON1Q192R, the Gln(Q) to Arg(R) substitution at amino acid 192, and PON1L55M, the Leu(L) to
Met(M) substitution at amino acid 55, coding region polymorphisms were not marginally predictive of disease.1 This
result has recently been confirmed to be true for coronary
heart disease.2 The inhibition or reduction of atherogenic
LDL oxidation by HDL seems, at least in part, to be a
function of PON1, which is physically associated with the
HDL particle.3–7 Thus, the PON1-CVD association is expected to result from the role of PON1 in the metabolism of
bioactive lipid molecules and protection against damage
caused by oxidized low-density lipoprotein (LDL). PON1
knockout mice are devoid of PON1 hydrolysis activity in
plasma and liver 8 and are more susceptible to
atherosclerosis.9
only some finding an excess of the PON1192R or PON155L
alleles associated with cardiovascular disease.10 –25 Our previous work suggested that unmeasured factors that alter
PON1 hydrolysis activity were important in heart disease.1
We hypothesized that lowered protein concentration attributable to PON1 5⬘ regulatory region polymorphisms might
predict both the reduced activity and disease status. We
subsequently identified 5 promoter region polymorphisms26
and used in vitro luciferase expression studies to determine
that 2 polymorphisms, PON1⫺108 and PON1⫺162, influenced
PON1 expression levels.27 The PON1⫺108 polymorphism accounted for 23% of the variance in PON1 expression.27
Additionally, as detailed below, smoking may depress PON1
activity and statin use may elevate PON1 activity, so PON1
activity effects in vascular disease may also reflect these
environmental exposures.
The goal of this study was to further the understanding of
the role of PON1 activity in CAAD and determine if a
fraction of the reduced activity in cases was attributable to
differences in PON1 5⬘ region variations or to differences in
smoking or statin use. We also considered haplotype effects,
which offer the potential advantage of higher heterozygosi-
See page 1317
Association studies of the PON1192 or PON155 polymorphisms with vascular disease have had mixed results, with
Received February 12, 2003; revision accepted May 19, 2003.
From the Departments of Medicine (Division of Medical Genetics) (G.P.J., R.J.R., R.J., V.H.B., S.M., C.E.F.), Epidemiology (G.P.J.), Genome
Sciences (G.P.J., C.C., M.R., D.N., C.E.F.), Neurology (G.D.S.), and Biostatistics (P.J.H.), University of Washington, and the Puget Sound Veterans
Affairs Health Care System (T.S.H., G.D.S.), Seattle, Wash.
Correspondence to Gail Jarvik, MD, PhD, University of Washington Medical Center, Division of Medical Genetics, Box 357720, Seattle, WA
98195-7720. E-mail [email protected]
© 2003 American Heart Association, Inc.
Arterioscler Thromb Vasc Biol. is available at http://www.atvbaha.org
1465
DOI: 10.1161/01.ATV.0000081635.96290.D3
1466
Arterioscler Thromb Vasc Biol.
August 2003
Downloaded from http://atvb.ahajournals.org/ by guest on June 14, 2017
Figure 1. Associations between PON1
region SNPs with a minor allele frequency of ⱖ0.1 r2 is shown above the
diagonal and D⬘ below. Sixty SNPs with
a minor allele frequency greater than 5%
were detected in the 46 chromosome
SNP discovery panel. Each cell represents the observed LD value for a pair of
SNPs; r2 is shown above the diagonal
and D⬘ below the diagonal. Each cell is
shaded by value: values below 0.5 are
white, values between 0.5 and 0.75 are
light grey, values between 0.75 and 1.0
are dark grey, and values of 1.0 are
black. D⬘ is useful in detecting nonrecombinant regions; D⬘ is 1 for all pairwise comparisons within a nonrecombinant region, so these regions can be
seen as dark triangles below the diagonal, as in the region between 6807 and
12194.
ty28 and may capture the effects of even unsampled diseasecausing mutations.29 Gabriel et al30 identified sizable regions
with little recombination and with only a few common
haplotypes, sometimes referred to as haplotype blocks. These
authors estimated that most of the human genome is contained in large blocks and that within a block, 90% of all
chromosomes are captured by only 3 to 5 haplotypes. However, utility of haplotype depends on the LD structure of the
gene of interest. Therefore, this was investigated for PON1 in
23 white subjects. The LD structure then determined the
strategy for the testing of PON1 polymorphism effects in
vascular disease.
In addition to paraoxon and diazoxon, PON1 hydrolyzes
phenylacetate and the nerve agents soman and sarin. PON1
arylesterase (Aase) activity in the hydrolysis of phenylacetate
was known to be only slightly impacted by the PON1192
polymorphism27,31 and thus more reflective of the concentration of PON1 protein present. Because of our interest in the
changes in the amount of protein that are expected by the
functional 5⬘ region variants, we added Aase as a PON1
activity measure.
Methods
Subjects
The present sample of 302 white men used to evaluate the role of
PON1 in vascular disease was expanded from the 212 men previously reported to lack marginal effects of the PON1 coding regions
on CAAD prediction.1 All subjects were collected from Puget Sound
Veterans Affairs Health Care System (PSVAHCS). The cases had
greater than 80% internal carotid artery stenosis, unilaterally or
bilaterally, on angiography or had undergone a carotid endarterec-
tomy. All controls were drawn from patients without codes for
vascular disease who subsequently were shown to have had less than
15% internal carotid artery stenosis, bilaterally, on carotid duplex
ultrasound. Subjects with total serum cholesterol greater than 400
mg/dL or with known coagulopathies were excluded. Subjects were
matched by censored age (within 12 months). Race was ascertained
from both PSVAHCS record and self-report. Censored age matching
was based on the age at time of blood draw for controls and age at
diagnoses of carotid artery disease for cases. The mean duration of
documented vascular disease before sampling of cases was 2.6 years.
The study was approved by both the University of Washington and
the PSVAHCS human subject review processes. Subjects gave
written informed consent. These subjects had a mean censored age of
65.8 years (range, 41 to 84 years). Of the 302 white male subjects,
38% were taking lipid-lowering medications (from the patients’
pharmacy medication history) and 68% were current smokers or
former smokers (dichotomous traits, by self-report).
Twenty-three white individuals (46 chromosomes) from the Center d’Etude du Polymorphisme Humain (CEPH) genomics repository
were fully sequenced for the PON1 region. This separate sample was
used to generate the linkage disequilibrium data summarized in
Figure 1.
PON1 Genotype and Activity Phenotype Methods
DNA was prepared from buffy coat preparations by a modification of
the procedure of Miller et al32 using Puregene reagents (Gentra). All
genotyping was conducted by polymerase chain reaction amplification followed by polymorphism-specific restriction digestion and gel
electrophoresis. The PON1Q192R polymorphism was detected by Alw
I digestion and the PON1L55M polymorphism was detected by Nla III
digestion as published previously.33 The genotype of the PON1C-108T
polymorphism and the PON1A-162G polymorphism were determined
by Bst UI digestion as described26,27 Genotype distributions did not
significantly differ from Hardy-Weinberg equilibrium expectations.
PON1 paraoxon (POase activity) and diazoxon (DZOase activity)
hydrolysis rates were measured spectrophotometrically with lithium
Jarvik et al
PON1 5ⴕ Region, Linkage Disequilibrium, and Disease
heparin plasma, as described.34 All samples were run in duplicate;
the averaged value was used for analysis. PON1192 genotype can be
predicted with high accuracy from examination of the 2D plot of
paraoxon and diazoxon hydrolysis rates.34 When assignments did not
match, both genotyping and phenotyping studies were repeated.
Three hundred subjects had genotype-phenotype agreement. Two
subjects genotyped as PON1192QR and phenotyped as PON1192RR,
indicating a nonfunctional Q allele. Sequencing of the coding regions
confirmed the PON1192QR genotype but failed to detect a coding
region change that would account for the inactive allele. Arylesterase
activities were performed in triplicate as described.27
Lipid Measurements
Lipid measurements were performed on fasting whole plasma.
Standard enzymatic methods were used to determine levels of total
cholesterol, triglycerides, and HDL cholesterol on an Abbott Spectrum analyzer.35–37 LDL cholesterol was calculated.38 Apolipoprotein AI measurement methods were as previously reported.39
Statistical Methods
Downloaded from http://atvb.ahajournals.org/ by guest on June 14, 2017
Linkage disequilibrium across the 26-kb PON1 region was assessed
in the 23 white subjects from the CEPH sample that were fully
sequenced for this region by the SeattleSNPs program. These
sequences are publicly available at http://pga.mbt.washington.edu.
The linkage disequilibrium statistics D⬘ and r 2 were both computed
for each pair of single nucleotide polymorphisms (SNPs) with an
observed minor allele frequency (MAF) of greater than or equal to
10%. Given 2 SNPs (A and B) with 2 alleles each (A1/A2 and
B1/B2), the basic metric of LD is D, the difference between the
observed number of A1B1 haplotypes and the expected number of
A1B1 haplotypes if genotypes were independent at A and B
(D⫽pA1B1⫺pA1*pB1). The range of D depends on the allele
frequencies at A and B, so it is difficult to compare D between
marker pairs. D⬘ normalizes D to the maximum possible D given the
allele frequencies at A and B, so the range of D⬘ is (⫺1,1) for all
pairs of SNPs. |D⬘|⫽1 if only 3 of the 4 possible haplotypes are
observed, so it is a useful statistic for detecting recombination
between A and B. r 2 is the Pearson correlation coefficient for alleles
at A and B. The range of r 2 depends on how similar allele
frequencies are at A and B. If A and B have the same allele
frequency, then the range of r 2 is (0,1); otherwise the range is (0,⬍1).
r 2⫽1 if only 2 of the 4 possible haplotypes are observed, meaning
that genotype at A and B is perfectly correlated, so r 2 is a useful
statistic for determining whether an effect associated with B could be
detected by genotyping at A. Haplotypes were inferred for all sites
with MAF ⬎5% using the program PHASE.40 This allowed us to
examine how well haplotypes constructed from the 4 genotyped
polymorphisms capture the overall haplotype structure of the gene.
POase (sqrt-POase), DZOase (sqrt-DZOase), and Arylesterase
(sqrt-Aase) were square root transformed to reduce positive skewness. Preliminary analyses included the estimation of the variance in
sqrt-POase, sqrt-DZOase, and sqrt-Aase attributable to PON1192,
PON155, PON1⫺108, or PON1⫺162 genotypes and haplotypes using the
r 2 from linear regression. This was done separately for cases and
controls. The additional variance in PON1 activity measures explained by current smoking or pack years smoked, statin use, and
levels of HDL, HDL2, and HDL3 were sequentially evaluated.
Matched logistic regression was used to test for effects in the
prediction of CAAD cases (coded as 1) versus controls (coded as 0).
The matched analyses were done using the Cox regression function
in SPSS with CAAD status as the outcome, the pair ID as the strata,
and a constant survival time of 1 for all subjects and included the
covariates to be tested. Separate regressions were tested for a
PON1⫺108 or PON1⫺162 genotype effect or a POase or DZOase effect
on the prediction of CAAD status. Another logistic regression tested
whether the addition of genotype information for all 4 polymorphisms altered the significance of sqrt-POase, sqrt-DZOase, or
sqrt-Aase in the prediction of CAAD status. All regression analyses
used SPSS 8.0 for Windows.41
1467
Results
Patterns of Linkage Disequilibrium Between
Common Polymorphic Sites in the PON1
Gene Region
The linkage disequilibrium structure of the PON1 gene region
is shown graphically in Figure 1. The LD statistic D⬘ (shown
below the diagonal) is useful in detecting historical recombination events. A D⬘ value less than 1 for a pair of SNPs means
that all 4 possible 2 SNP haplotypes exist, suggesting either
recombination between the SNPs, recurrent mutation, or gene
conversion. As shown, the PON1 gene is not characterized by
a single large nonrecombinant haplotype block but rather by
several smaller relatively nonrecombinant regions. Relatively
nonrecombinant blocks span the regions of the promoter
(SNPs 63 to 4754, positions reported relative to Genbank
record AF539592), the amino acid 55 substitution (SNPs
6807 to 12194), the amino acid 192 substitution (SNPs 12826
to 25203), and the 3⬘ untranslated region (SNPs 26625 to
29021), but significant numbers of recombination events have
occurred at the boundaries between these regions.
Where D⬘ is useful in detecting recombination, r 2 describes
how closely correlated the genotype is between a pair of
SNPs, which is critical for association studies. Considering an
r2 above 0.5 as potentially useful for detecting untyped sites,42
it is clear that typing a single SNP in any given region yields
information about some nearby sites, but power to detect
untyped sites in other regions of PON1 is dramatically lower.
For example, D⬘⫽0.775 between PON155Leu and PON1192Arg,
suggesting that a modest number of recombinant chromosomes exist. However, the correlation between PON155Leu and
PON1192Arg is weak (r 2⫽0.087) because the rare allele at each
SNP is associated with the common allele at the other SNP,
and therefore typing either SNP alone would provide little
power to detect risks associated with the other polymorphism.
The observed patterns of D⬘ and r 2 suggest that a modest
number of sites need to be typed within each nonrecombinant
region, consistent with a small number of haplotypes across
each region. However, recombination between these relatively tractable regions has generated dramatic haplotype
diversity across PON1. Indeed, using only 2 promoter and 2
coding region polymorphisms, the PHASE program inferred
that 12 of 16 possible haplotypes were observed, with 8
haplotypes occurring at a frequency of ⬎0.3% (Table 1). In
regions with low levels of recombination, cladistic analysis
would allow for grouping of haplotypes based on evolutionary relationships. Given the high frequency of recombinant
haplotypes across PON1, we treated the 2 promoter area
SNPs (PON1⫺162/⫺108, D⬘⫽1) as a 2-site haplotype and analyzed the PON155 and PON1192 sites separately.
Finally, we were able to estimate the amount of common
genetic (MAF ⬎0.1) variation in the PON1. Sixty SNPs were
identified at greater than 10% MAF in the white CEPH
population. In the present study, 4 of these sites were typed
and 56 were unassayed. Of those 56, 44 (78.6%) exceed
r 2⫽0.25 with 1 or more of the 4 assayed SNPs; 29 (51.8%)
exceed r 2⫽0.5; 16 (28.6%) exceed r 2⫽0.75; and 12 (21.4%)
are perfectly associated (r 2⫽1) with 1 or more of the assayed
SNPs.
1468
Arterioscler Thromb Vasc Biol.
August 2003
TABLE 1. PON1ⴚ162/ⴚ108/55/192 Haplotypes Inferred in 302 White
Men (604 Alleles)
Haplotype
No.
Cases
Controls
GTMQ
177
83
94
ACLQ
116
56
60
GCLR
86
41
45
GTLQ
61
28
33
GTLR
54
30
24
GCLQ
48
31
17
GCMQ
24
14
10
ACLR
22
12
10
GCMR
6
3
3
ACMQ
5
1
4
ATMQ
4
2
2
ATLR
1
1
0
Downloaded from http://atvb.ahajournals.org/ by guest on June 14, 2017
Table 2 summarizes the portion of variances in each PON1
activity measure attributable to current age, all 4 PON1
genotypes measured, statin drug use, smoking behavior, and
levels of HDL, HDL2, and HDL3. Smoking behavior was
evaluated both as square root of pack-years smoked and
current smoking. Accurately reported, current smoking is
expected to impact PON1 activity more than smoking history.43 Cases had a larger total variance and larger portion of
unexplained variance relative to controls, except for sqrtPOase, which was largely explained by PON1192 genotype in
both cases and controls (Table 2). When jointly considered,
all 4 genotypes had statistically significant effects on DZOase
in controls at the P⬍0.05 level, and only the PON1⫺162
polymorphism did not significantly add to the prediction of
DZOase in CAAD cases. When jointly considered, the
PON1192 and PON1⫺108 polymorphisms predicted variation in
POase, as expected, whereas PON1⫺162 and PON155 did not in
either cases or controls. The fact that PON1 haplotypes
constructed using all 4 sites did not explain a greater fraction
of variance in the phenotypes than an analysis including all 4
sites separately seems to confirm that the haplotype construction did not provide additional information about unassayed
functional sites in the PON1 gene.
PON1⫺162 and PON1⫺108 polymorphisms are identified by the base change,
whereas the PON155 and PON1192 polymorphisms are identified by the amino
acid substitutions (italics).
Relationship Between PON1 Genotypes
and Phenotypes
As expected, the bivariate Pearson correlation between sqrtPOase and sqrt-DZOase was ⫺0.01 (NS), although the
correlation was high within PON1192 genotypes. The correlation between sqrt-POase and sqrt-Aase was 0.22 (P⬍0.01),
and the correlation between sqrt-DZOase and sqrt-Aase was
0.70 (P⬍0.01). Pack-years smoked was not significantly
correlated with sqrt-DZOase (correlation⫽⫺0.052, P⫽0.42),
sqrt-Aase (⫺0.067, P⫽0.30), or POase (⫺0.019, P⫽0.77).
Current smokers had significantly lower PON1 sqrt-POase
(P⫽0.019) but not sqrt-DZOase or sqrt-Aase (P⫽0.43) activity levels, although all activities tended to be lower in
smokers.
TABLE 2.
Phenotype and Genotype Effects on Vascular
Disease Prediction
As a single predictor, sqrt-DZOase (P⫽0.016) was a significant predictor of CAAD status using matched logistic regression, with cases having lowered activity, as expected (Table
3). Sqrt-POase (P⫽0.355) and sqrt-Aase (0.200) were not
significant for the prediction of CAAD status, although
sqrt-POase became highly predictive when genotypes were
jointly considered (Table 3, model 3). All 3 PON1 activities
were lower in cases than controls within the PON1192QQ or
PON1192QR genotypes, although this was not seen in the small
Sources of Variance (r2) in PON1 Activity Measures
Sqrt-Aase
Total variance
Sqrt-DZOase
Sqrt-POase
Control
Case
Control
Case
Control
Case
5.55
5.12
247.5
291.0
67.3
73.2
2.5
PON1-162 genotype, %
28.8
14.1
31.6
12.1
0.4
PON1-108 genotype, %
30.3
18.2
23.3
20.3
19.6
9.4
PON155 genotype, %
10.5
8.0
4.8
5.6
28.1
23.5
3.3
1.7
22.1
14.7
81.2
74.3
44.4
25.3
64.8
42.5
88.4
82.1
PON1192 genotype, %
All 4 PON1 genotypes (⫺162, ⫺108, 55, 192), %
PON1⫺162/⫺108 haplotype, %
37.7
19.7
36.8
17.2
30.5
13.8
PON155/192 haplotype, %
17.6
10.3
41.3
27.0
80.8
69.1
PON1⫺162/⫺108/55/192 haplotypes, %
42.9
22.7
61.5
34.6
84.1
74.6
4 genotypes, age, %
45.3
28.5
66.9
44.2
88.7
83.1
4 genotypes, age, statin, sqrt-packyear smoked, %
40.7
28.5
64.9
47.1
91.0
84.1
4 genotypes, age, statin, current smoker, %
42.1
32.4
66.2
47.1
89.4
83.3
4 genotypes, age, statin, current smoker, HDL,
HDL2, HDL3, %
47.0
35.8
70.1
51.6
89.8
84.9
Proportion of variance (r2 from regression) explained for each PON1 activity measure, separately for cases and
controls. Each row represents a different regression model. Variance in cases versus controls can be compared for
each model, and variance explained can be compared across models. Neither columns nor rows are additive.
Jarvik et al
PON1 5ⴕ Region, Linkage Disequilibrium, and Disease
TABLE 3. Predictors of Carotid Artery Disease Case Status
Using Logistic Regression
Exp(␤)
P
Value
Sqrt -DZOase
0.981
0.016*
Sqrt-POase
0.987
0.355
Sqrt -Aase
0.935
0.200
1.215, 0.867
0.473
0.735, 1.033
0.784
0.856, 0.736, 1.106
0.660
Predictors
Single predictors
PON1⫺108 (CC, CT)
PON1⫺162 (AA, AG)
PON1⫺108/⫺162 (AC, GT, AT)
Multiple predictors
0.802, 0.737, 1.029
0.749
PON155 (LL, LM)
1.300, 1.154
0.867
PON1192 (QQ, QR)
0.914, 0.788
0.819
Downloaded from http://atvb.ahajournals.org/ by guest on June 14, 2017
Model 2
PON1⫺108/⫺162 (AC, GT, AT)
1.276, 0.775, 1.074
0.379
PON155 (LL, LM)
1.844, 1.330
0.471
PON1192 (QQ, QR)
2.419, 1.429
0.223
0.964
0.001*
Sqrt -DZOase
Model 3
PON1⫺108/⫺162 (AC, GT, AT)
1.066, 0.631, 1.050
0.278
PON155 (LL, LM)
1.585, 1.229
0.631
PON1192 (QQ, QR)
0.068, 0.305
0.020*
0.882
0.001*
Sqrt-POase
types and PON155 and PON1192 genotypes were jointly
considered, none showed marginal significance for the prediction of CAAD status (Table 3, model 1).
PON1⫺162/⫺108, PON155, and PON1192 did not become significant (all P⬎0.2) when sqrt-DZOase was considered along
with the 4 genotypes in the regression model (Table 3, model
2). However, sqrt-POase (P⫽0.001) and PON1192 genotype
(P⫽0.02, degrees of fraction⫽2) did become significant in
the prediction of CAAD but the PON1⫺162/⫺108 (P⬎0.2) and
PON155 (P⬎0.2) did not when sqrt-POase was considered
along with the 4 genotypes in the regression model (Table 3,
model 3).
Discussion
Model 1
PON1⫺108/⫺162 (AC, GT, AT)
1469
For genotypes and haplotypes, dummy variables were used. PON1⫺108G/⫺162C,
PON155MM, and PON1192RR were the referent types. The exponentiated logistic
regression coefficients, Exp(␤)⫽e␤, where ␤ is the logistic regression coefficient, are listed for each variable in the regression in the order given under
predictors. Values less than 1 are seen for all activity measures, consistent with
lower activity in cases.
number of RR subjects. Separate matched analysis rejected
significant marginal effects of PON1⫺108 (P⫽0.30) or
PON1⫺162 (P⫽0.49) genotype, as well as PON1⫺108/⫺162 haplotype (P⫽0.54) in the prediction of CAAD status (Table 3).
Figure 2 demonstrates the genotype or haplotype frequencies
for both cases and controls. When all PON1⫺108/⫺162 haplo-
Figure 2. PON1 5⬘ and coding polymorphisms do not predict
CAAD case/control status when PON1 activity is not jointly considered. Note that PON1⫺162/⫺108 frequencies are haplotype frequencies and the PON1192 and PON155 frequencies are genotype frequencies. Actual percentages from left to right are 51.6,
45.1; 41.0, 42.2; 70.2, 76.2; 1.3, 1.3; 41.1, 35.8; 49.7, 53.6; 9.3,
10.6; 49.7, 51.0; 43.0, 43.7; and 7.3, 5.3.
Evidence of historical recombination events at several positions within the PON1 gene was observed, which restricts LD
to multiple small areas within and surrounding the gene.
Within each small, nonrecombinant region, a limited number
of local haplotypes was observed, but across the entire gene,
recombination between regions has generated a large number
of haplotypes. Although the recombinant structure of the
PON1 locus is most obvious from the sequencing of the full
gene in a group of individuals, it can also be inferred from the
large number of haplotypes predicted from just 4 genotyped
sites. Thus, cladistic analysis of the entire gene is not
appropriate, because significant numbers of recombinant
haplotypes exist but might be useful in smaller nonrecombinant regions if multiple SNPs were assayed within each
region. However, whole-gene haplotypes might be useful for
the prediction of disease if functional variants existed in
multiple nonrecombinant regions, if the effects of these
variants were not simply additive, and if appropriate groupings of haplotypes could be made to allow for reasonable
power.
The finding of lowered PON1 activity in vascular disease
without evident PON1 coding region genotype effects1 has
been recently confirmed in a cohort with coronary heart
disease2 and had previously been reported in myocardial
infarction survivors.44 We previously hypothesized that the
lower PON1 POase and DZOase activity found in CAAD
cases versus controls, not attributable to PON1 coding polymorphisms, may be due to lowered PON1 serum concentrations.1 One speculation was that frequent PON1 promoter
region polymorphisms might, at least in part, account for that
result. We subsequently identified functional PON1⫺162 and
PON1⫺108 polymorphisms26,27 and apparently nonfunctional
PON1⫺909, PON1-832, and PON1⫺126 polymorphisms. These
polymorphisms do not account for reduced PON1 activity in
CAAD versus controls, despite their large marginal effects on
PON1 activity, nor do they improve disease prediction in this
cohort. Leviev et al45 reported that PON1⫺909GG subjects had a
marginally lower risk of myocardial infarction, particularly in
subjects ⬍50 years old. They detected no effect in subjects
older than 60 years, similar to the result in our older subjects.
Additionally, PON1 arylesterase activity, which is more
closely related to protein level than POase or DZOase
activity, performed more poorly as a predictor of CAAD
status than DZOase activity. These data suggest that PON1
substrate specific activity, not simply quantity, may be
1470
Arterioscler Thromb Vasc Biol.
August 2003
Downloaded from http://atvb.ahajournals.org/ by guest on June 14, 2017
important in the determination of vascular disease risk. These
data continue to support the importance of PON1 activity
measurement, rather than only PON1 genotype, for studies of
cardiovascular risk. Indeed, we have recently shown that
PON1 phenotype-genotype discordance can be used to detect
novel PON1 nonsense and missense mutations.46
What factors may account for the lower PON1 activity in
the cases remains unclear. Cases have a higher variance in
activity than controls. It is noteworthy that 65% of the
variance in PON1 DZOase activity in controls is accounted
for by the 4 genotypes measured here, but only 43% of that in
the cases is explained along these lines (Table 2). The LD
data show substantial sequence variation in the PON1 region
that is not captured by disequilibrium to typed sites. Considering the unassayed sites with MAF greater than 0.1, 48% had
r 2⬍0.5, with 1 of the 4 sites assayed in this cohort. The extent
to which such unmeasured genetic variation contributes to the
difference in PON1 activity between cases and controls
remains to be determined. It will also be important to identify
other genes that influence PON1 levels. Intervention and
animal model studies will be of use in understanding the role
of environmental factors in PON1 expression.
Addition of smoking and statin use and HDL level–related
factors did not explain the difference in explained PON1
activity between cases and controls. This still leaves a large
gap in our understanding of the sources of variation in cases
versus controls, which requires additional investigation.
PON1 activity may be altered by environmental factors,
including tobacco smoking. Tobacco smoke depressed PON1
activity in ex vivo assays,47 and current tobacco use has been
reported to lower POase activity.43 In a Costa Rican sample,
PON1192 genotype predicted myocardial infarctions only in
nonsmokers,48 suggesting the importance of jointly considering environmental factors that modify PON1 activity. Additionally, elevation of PON1 activity in statin drug users was
consistent with the reported effects of Simvastatin on PON1
activity.49 PON1 POase activity has been found to increase
with pharmacological therapy with Simvastatin, possibly by
reducing oxidative stress.49 Dietary fat may also affect PON1
activity,50 because the concentration of PON1 mRNA has
been shown to increase or decrease in a strain-dependent
fashion when mice are given an atherogenic diet.51 Lipid,
lipoprotein, or apolipoprotein levels have been weakly associated with PON1 activity or genotype in some20,52–54 but not
all12,55 studies.
In conclusion, neither the common PON1 5⬘ functional
regulatory variants PON1⫺162 and PON1⫺108 nor the environmental impact of smoking and statin use explain the relationship of PON1 activity to disease status or the large unexplained PON1 activity variance in cases. The PON1 gene is
not contained in a single haplotype block, which suggests that
promoter and coding region variants should be treated as
independent factors and not grouped by haplotype for disease
association studies. However, consideration of PON1 haplotype effects did not improve CAAD case-control prediction.
This work continues to support the importance of measuring
expression of genes and activity of enzymes in addition to
genetic polymorphisms within and regulating a given gene.
Acknowledgments
This work was funded by the National Institutes of Health (NIH)
grant HL67406 and the Veteran Affairs Epidemiology Research and
Information Center Program (No. CSP 701S), with additional funding from an American Heart Association Physician Scientist Award
(G.P.J.) and NIH awards T32AG00057, P30ESO7033, ES09883,
HL66682, and HL66642. The authors would like to thank the
subjects for their participation and thank the following people for
their technical assistance: Laura McKinstry, Jeff Rodenbaugh, Andy
Louie, and Tianji Yu.
References
1. Jarvik G, Rozek LS, Brophy VH, Hatsukami TS, Richter RJ,
Schellenberg GD, Furlong CE. Paraoxonase (PON1) phenotype is a better
predictor of vascular disease than is PON1(192) or PON1(55) genotype.
Arterioscler Thromb Vasc Biol. 2000;20:2441–2447.
2. Mackness B, Gershan KD, Turkie W, Lee E, Roberts DH, Hill E, Roberts
C, Durrington P, Mackness M. Paraoxonase status in coronary heart
disease: are activity and concentration more important than genotype?
Arterioscler Thromb Vasc Biol. 2001;21:1451–1457.
3. Mackness M, Arrol S, Abbott C, Durrington PN. Protection of lowdensity lipoprotein against oxidative modification by high-density
lipoprotein associated paraoxonase. Atherosclerosis. 1993;104:129 –135.
4. Mackness M. The human paraoxonase polymorphism and atherosclerosis.
In: Mackness MI, Cleric M, eds. Esterases, Lipases, and Phospholipases.
New York: Plenum Press; 1994.
5. Watson A, Berliner JA, Hama SY, La Du BN, Fuall KF, Fogelman AM,
Navab M. Protective effect of high density lipoprotein associated paraoxonase: inhibition of the biological activity of minimally oxidized low
density lipoprotein. J Clin Invest. 1995;96:2882–2891.
6. Graham A, Hassall DG, Rafique S, Owen JS. Evidence for a
paraoxonase-independent inhibition of low-density lipoprotein oxidation
by high-density lipoprotein. Atherosclerosis. 1997;135:193–204.
7. Aviram M, Rosenblat M, Billecke S, Erogul J, Sorenson R, Bisgaier CL,
Newton RS, La Du B. Human serum paraoxonase (PON 1) is inactivated
by oxidized low density lipoprotein and preserved by antioxidants. Free
Radic Biol Med. 1999;26:892–904.
8. Furlong C, Li WF, Brophy VH, Jarvik GP, Richter RJ, Shih DM, Lusis
AJ, Costa LG. The PON1 gene and detoxication. Neurotoxicology. 2000;
21:581–587.
9. Shih D, Gu L, Xia YR, Navab M, Li WF, Hama S, Castellani LW,
Furlong CE, Costa LG, Fogelman AM, Lusis AJ. Mice lacking serum
paraoxonase are susceptible to organophosphate toxicity and atherosclerosis. Nature. 1998;394:284 –287.
10. Ruiz J, Blanche H, James RW, Garin MC, Vaisse C, Charpentier G,
Cohen N, Morabia A, Passa P, Froguel P. Gln-Arg192 polymorphism of
paraoxonase and coronary heart disease in type 2 diabetes. Lancet. 1995;
346:869 – 872.
11. Serrato M, Marian AJ. A variant of human paraoxonase/arylesterase
(HUMPONA) gene is a risk factor for coronary artery disease. J Clin
Invest. 1995;96:3005–3008.
12. Zama T, Murata M, Matsubara Y, Kawano K, Aoki N, Yoshino H,
Watanabe G, Ishikawa K, Ikeda Y. A 192Arg variant of the human
paraoxonase (HUMPONA) gene polymorphism is associated with an
increased risk for coronary artery disease in the Japanese. Arterioscler
Thromb Vasc Biol. 1997;17:3565–3569.
13. Sanghera D, Saha N, Aston CE, Kamboh MI. Genetic polymorphism of
paraoxonase and the risk of coronary heart disease. Arterioscler Thromb
Vasc Biol. 1997;17:1067–1073.
14. Sanghera D, Aston CE, Saha N, Kamboh MI. DNA polymorphisms in
two paraoxonase genes (PON1 and PON2) are associated with the risk of
coronary heart disease. Am J Hum Genet. 1998;62:36 – 44.
15. Pati N, Pati U. Paraoxonase gene polymorphism and coronary artery
disease in Indian subjects. Int J Cardiol. 1998;66:165–168.
16. Pfohl M, Koch M, Enderle MD, Kuhn R, Fullhase J, Karsch KR, Haring
HU. Paraoxonase 192 Gln/Arg gene polymorphism, coronary artery
disease, and myocardial infarction in type 2 diabetes. Diabetes. 1999;48:
623– 627.
17. Herrmann S, Blanc H, Poirier O, Arveiler D, Luc G, Evans A,
Marques-Vidal P, Bard JM, Cambien F. The Gln/Arg polymorphism of
human paraoxonase (PON 192) is not related to myocardial infarction in
the ECTIM Study. Atherosclerosis. 1996;126:299 –303.
18. Antikainen M, Murtomaki S, Syvanne M, Pahlman R, Tahvanainen E,
Jauhiainen M, Frick MH, Ehnholm C. The Gln-Arg191 polymorphism of
Jarvik et al
19.
20.
21.
22.
23.
24.
Downloaded from http://atvb.ahajournals.org/ by guest on June 14, 2017
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
PON1 5ⴕ Region, Linkage Disequilibrium, and Disease
the human paraoxonase gene (HUMPONA) is not associated with the risk
of coronary artery disease in Finns. J Clin Invest. 1996;98:833– 835.
Suehiro T, Nakauchi Y, Yamamoto M, Arii K, Itoh H, Hamashige N,
Hashimoto K. Paraoxonase gene polymorphism in Japanese subjects with
coronary heart disease. Int J Cardiol. 1996;57:69 –73.
Rice G, Ossei-Gerning N, Stickland MH, Grant PJ. The paraoxonase
Gln-Arg 192 polymorphism in subjects with ischaemic heart disease.
Coron Artery Dis. 1997;8:677– 682.
Schmidt H, Schmidt R, Niederkorn K, Gradert A, Schumacher M,
Watzinger N, Hartung HP, Kostner GM. Paraoxonase PON1 polymorphism leu-met54 is associated with carotid atherosclerosis: results of
the Austrian Stroke Prevention Study. Stroke. 1998;29:2043–2048.
Ombres D, Pannitteri G, Montali A, Candeloro A, Seccareccia F,
Campagna F, Cantini R, Campa PP, Ricci G, Arca M. The Gln-Arg192
polymorphism of human paraoxonase gene is not associated with coronary artery disease in Italian patients. Arterioscler Thromb Vasc Biol.
1998;18:1611–1616.
Blatter Garin M, James RW, Dussoix P, Blanche H, Passa P, Froguel P,
Ruiz J. Paraoxonase polymorphism Met-Leu54 is associated with
modified serum concentrations of the enzyme. J Clin Invest. 1997;99:
62– 66.
Sanghera D, Saha N, Kamboh MI. The codon 55 polymorphism in the
paraoxonase 1 gene is not associated with the risk of coronary heart
disease in Asian Indians and Chinese. Atherosclerosis. 1998;136:
217–223.
Chen Q, Reis SE, Kammerer CM, McNamara DM, Holubkov R, Sharaf
BL, Sopko G, Pauly DF, Bairey Merz CN, Kamboh MI for the WISE
Study Group. Association between the severity of angiographic coronary
artery disease and paraoxonase gene polymorphisms in the National
Heart, Lung, and Blood Institute: Sponsored Women’s Ischemia
Syndrome Evaluation (WISE) Study. Am J Hum Genet. 2003;72:13–22.
Brophy V, Hastings MD, Clendenning JB, Richter R, Jarvik GP, Furlong
CE. Polymorphisms in the human paraoxonase (PON1) promoter. Pharmacogenetics. 2001;11:77– 84.
Brophy V, Jampsa RL, Clendenning JB, McKinstry LA, Jarvik GP,
Furlong CE. Promoter polymorphism effects on paraoxonase (PON1)
expression. Am J Hum Genet. 2001;68:1428 –1436.
Stephens J, Schneider JA, Tanguay DA, Choi J, Acharya T, Stanley SE,
Jiang R, Messer CJ, Chew A, Han JH, Duan J, Carr JL, Lee MS, Koshy
B, Kumar AM, Zhang G, Newell WR, Windemuth A, Xu C, Kalbfleisch
TS, Shaner L, Arnold K, Schulz V, Drysdale CM, Nandabalan K, Judson
RS, Ruano G, Vovis GF. Haplotype variation and linkage disequilibrium
in 313 human genes. Science. 2001;293:489 – 493.
Judson R, Stephens JC, Windemuth A. The predictive power of haplotypes in clinical response. Pharmacogenomics. 2000;1:15–26.
Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B,
Higgins J, DeFelice M, Lochner A, Faggart M, Liu-Cordero SN, Rotimi
C, Adeyemo A, Cooper R, Ward R, Lander ES, Daly MJ, Altshuler D.
The structure of haplotype blocks in the human genome. Science. 2002;
296:2225–2229.
Eckerson H, Wyte CM, La Du BN. The human serum paraoxonase/
arylesterase polymorphism. Am J Hum Genet. 1983;35:1126 –1138.
Miller S, Dykes DD, Polesky HF. A simple salting out procedure for
extracting DNA from human nucleated cells. Nucl Acids Res. 1988;
16:1215.
Humbert R, Adler DA, Disteche CM, Hassett C, Omiecinski CJ, Furlong
CE. The molecular basis of the human serum paraoxonase activity polymorphism. Nat Genet. 1993;3:73–76.
Richter R, Furlong CE. Determination of paraoxonase (PON1) status
requires more than genotyping. Pharmacogenetics. 1999;9:745–753.
Warnick G, Benderson J, Albers JJ. Dextran sulfate-Mg2⫹ precipitation
procedure for quantitation of high-density-lipoprotein cholesterol. Clin
Chem. 1982;28:1379 –1388.
1471
36. Warnick G. Enzymatic methods for quantification of lipoprotein lipids.
Methods Enzymol. 1986;129:101–123.
37. Bachorik P, Albers JJ. Precipitation methods for quantification of
lipoproteins. Methods Enzymol. 1986;129:78 –100.
38. Friedewald W, Levy RI, Fredrickson DS. Estimation of the concentration
of low-density lipoprotein cholesterol in plasma, without use of the
preparative ultracentrifuge. Clin Chem. 1972;18:499 –502.
39. Marcovina S, Albers JJ, Henderson LO, Hannon WH. International Federation of Clinical Chemistry standardization project for measurements of
apolipoproteins A-I and B, III: comparability of apolipoprotein A-I values
by use of international reference material. Clin Chem. 1993;39:773–781.
40. Stephens M, Smith NJ, Donnelly PA. New statistical method for haplotype reconstruction from population data. Am J Hum Genet. 2001;68:
978 –989.
41. SPSS. SPSS Statistical Algorithims. Chicago, IL: SPSS, Inc; 1991.
42. Pritchard J, Przeworski M. Linkage disequilibrium in humans: models
and data. Am J Hum Genet. 2001;69:1–14.
43. James R, Leviev I, Righetti A. Smoking is associated with reduced serum
paraoxonase activity and concentration in patients with coronary heart
disease. Circulation. 2000;101:2252–2257.
44. Ayub A, Mackness MI, Arrol S, Mackness B, Patel J, Durrington PN.
Serum paraoxonase after myocardial infarction. Arterioscler Thromb
Vasc Biol. 1999;19:330 –335.
45. Leviev I, Poirier O, Nicaud V, Evans A, Kee F, Arveiler D, Morrisson C,
Cambien F, James RW. High expressor paraoxonase PON1 gene
promoter polymorphisms are associated with reduced risk of vascular
disease in younger coronary patients. Atherosclerosis. 2002;161:
463– 467.
46. Jarvik G, Jampsa R, Richter RJ, Carlson CS, Rieder MJ, Nickerson DA,
Furlong CE. Novel paraoxonase (PON1) nonsense and missense
mutations predicted by functional genomic assay of PON1 status. Pharmacogenetics. 2003;13:291–295.
47. Nishio E, Watanabe Y. Cigarette smoke extract inhibits plasma paraoxonase activity by modification of the enzyme’s free thiols. Biochem
Biophys Res Commun. 1997;236:289 –293.
48. Sen-Banerjee S, Siles X, Campos H. Tobacco smoking modifies association between Gln-Arg192 polymorphism of human paraoxonase gene
and risk of myocardial infarction. Arterioscler Thromb Vasc Biol. 2000;
20:2120 –2126.
49. Tomas M, Senti M, Garcia-Faria F, Vila J, Torrents A, Covas M,
Marrugat J. Effect of simvastatin therapy on paraoxonase activity and
related properties in familial hypercholesterolemic patients. Arterioscler
Thromb Vasc Biol. 2000;20:2113–2119.
50. Sutherland W, Walker RJ, de Jong SA, van Rij AM, Phillips V,
Walker HL. Reduced postprandial serum paraoxonase activity after a
meal rich in used cooking fat. Arterioscler Thromb Vasc Biol. 1999;
19:1340 –1347.
51. Shih D, Gu L, Hama S, Xia YR, Navab M, Fogelman AM, Lusis
AJ. Genetic-dietary regulation of serum paraoxonase expression and its
role in atherogenesis in a mouse model. J Clin Invest. 1996;97:
1630 –1639.
52. Hegele R, Brunt JH, Connelly PW. Multiple genetic determinants of
variation of plasma lipoproteins in Alberta Hutterites. Arterioscler
Thromb Vasc Biol. 1995;15:861– 871.
53. Nevin D, Zambon A, Furlong CE, Richter R, Humbert R, Hokanson JE,
Brunzell JD. Paraoxonase genotypes, lipoprotein lipase activity and HDL.
Arterioscler Thromb Vasc Biol. 1996;16:1243–1249.
54. Boright A, Connelly PW, Brunt JH, Scherer SW, Tsui LC, Hegele RA.
Genetic variation in paraoxonase-1 and paraoxonase-2 is associated with
variation in plasma lipoproteins in Alberta Hutterites. Atherosclerosis.
1998;139:131–136.
55. Mackness B, Mackness MI, Arrol S, Turkie W, Durrington PN. Effect of
the molecular polymorphisms of human paraoxonase (PON1) on the rate
of hydrolysis of paraoxon. Br J Pharmacol. 1997;122:265–268.
Downloaded from http://atvb.ahajournals.org/ by guest on June 14, 2017
Paraoxonase Activity, But Not Haplotype Utilizing the Linkage Disequilibrium Structure,
Predicts Vascular Disease
Gail P. Jarvik, Thomas S. Hatsukami, Chris Carlson, Rebecca J. Richter, Rachel Jampsa,
Victoria H. Brophy, Sadie Margolin, Mark Rieder, Deborah Nickerson, Gerard D. Schellenberg,
Patrick J. Heagerty and Clement E. Furlong
Arterioscler Thromb Vasc Biol. 2003;23:1465-1471; originally published online June 12, 2003;
doi: 10.1161/01.ATV.0000081635.96290.D3
Arteriosclerosis, Thrombosis, and Vascular Biology is published by the American Heart Association, 7272
Greenville Avenue, Dallas, TX 75231
Copyright © 2003 American Heart Association, Inc. All rights reserved.
Print ISSN: 1079-5642. Online ISSN: 1524-4636
The online version of this article, along with updated information and services, is located on the
World Wide Web at:
http://atvb.ahajournals.org/content/23/8/1465
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published
in Arteriosclerosis, Thrombosis, and Vascular Biology can be obtained via RightsLink, a service of the
Copyright Clearance Center, not the Editorial Office. Once the online version of the published article for
which permission is being requested is located, click Request Permissions in the middle column of the Web
page under Services. Further information about this process is available in the Permissions and Rights
Question and Answer document.
Reprints: Information about reprints can be found online at:
http://www.lww.com/reprints
Subscriptions: Information about subscribing to Arteriosclerosis, Thrombosis, and Vascular Biology is online
at:
http://atvb.ahajournals.org//subscriptions/