© 1999 American Heart Association, Inc.
Original Contributions |
Pleiotropy and Genotype by Diet Interaction in a Baboon Model for Atherosclerosis
A Multivariate Quantitative Genetic Analysis of HDL Subfractions in Two Dietary Environments
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Abstract |
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Abstract—We investigated dietary effects on pleiotropic relationships among 3 HDL cholesterol (C) subfractions (HDL1-C, HDL2-C, and HDL3-C; levels quantified by gradient gel electrophoresis) for 942 pedigreed baboons (Papio hamadryas) who were fed a basal (Chow) diet and a high cholesterol, saturated fat (HCSF) challenge diet. Using multivariate maximum likelihood methods we estimated heritabilities for all 6 traits, genetic and environmental correlations (
G and E) between them, and the additive
genetic variance of each subfraction's response to the diets. On the
Chow diet, genetic correlations between the 3 subfractions were
significant, and we observed complete pleiotropy between
HDL1-C and HDL3-C (G=-0.81). On
the HCSF diet, only the genetic correlation between HDL1-C
and HDL3-C (G=-0.61) was significant.
Genetic correlations between individual subfractions on the Chow and
HCSF diets did not differ significantly from 1.0, indicating that the
same additive genes influenced each subfraction's levels regardless of
diet. However, the additive genetic variance of response to the diets
was highly significant for HDL1-C and HDL2-C,
but not for HDL3-C. Similar sets of genes influence
variation in the 3 HDL subfractions on the Chow diet, and the same set
influences variation in each subfraction on the HCSF diet. However, the
expression of genes influencing HDL1-C and
HDL2-C is altered by the HCSF diet, disrupting the
pleiotropy observed between the 3 subfractions on the Chow diet.
Key Words: atherosclerosis risk factors • lipemic response • dietary challenge • animal models • statistical genetics • gradient gel electrophoresis
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Introduction |
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HDL particles are heterogeneous and comprise several distinct subfractions that differ in their composition of apolipoproteins and lipids.1 2 Although an inverse association between circulating concentrations of total HDL cholesterol (HDL-C) and risk of cardiovascular disease (CVD) in humans has gained wide acceptance during the past 2 to 3 decades,3 4 5 6 7 8 9 HDL-C subfractions also have been implicated as CVD risk factors. CVD risk is reported to decrease when HDL2b-C is increased relative to HDL3b-C and HDL3c-C.9 10 Compared with total HDL-C, the HDL2-C and HDL3-C subfractions are better predictors of atherosclerotic plaque at specific sites, and HDL3-C is suggested to provide specific protective effects at early stages of atherosclerosis.11
Associations between HDL-C subfractions and other CVD risk factors also have been reported.12 13 Reduced HDL2-C levels are correlated with a predominance of large LDL particles and increased CVD risk14 and with increased fibrinogen levels in nonsmokers without CVD or infection.15 Hypertriglyceridemia, hyperinsulinemia, and high hepatic lipase activity are reported to be associated independently with decreased HDL2b-C levels in men with or without coronary artery disease and non–insulin-dependent diabetes mellitus.16
Several studies report associations of circulating levels of HDL-C and its subfractions with dietary intake of fat or cholesterol in humans. One study of 28 healthy subjects reports a negative correlation between the magnitude of postprandial triglyceridemic response to a standard oral fat meal and both total HDL-C and HDL2-C.17 Increased total HDL-C and HDL2-C levels are reported in response to postprandial lipemia in both carriers and controls in a study of the effects of the apolipoprotein apoA1Milano polymorphism.18 In 56 normocholesterolemic and hypercholesterolemic subjects, Clifton et al19 observed increases in total HDL-C levels and, in a high-response subgroup, a 10% increase in the proportion of HDL2-C to total HDL-C in response to a high cholesterol dietary challenge. Katan and Beynen20 reported a positive correlation between responsiveness to dietary cholesterol and serum HDL2-C levels whereas Fisher et al21 observed the opposite relationship.
HDL-C and its subfractions respond to variation in fat and cholesterol in the diet, but only a limited number of studies have addressed the genetics of this response. Although a study of monozygotic twins22 reports a significant intrapair resemblance in HDL-C response to a 22-day overfeeding challenge, nearly all other published studies focusing on HDL-C report attempts to detect associations between variation in lipemic response and mutations in specific candidate genes. Mata et al23 observed no differences in HDL-C levels when individuals of different apoA4 genotypes are challenged with high fat, high cholesterol diets. However, one study reports that the apoE 4/3 genotype is associated with a greater response of HDL-C to a high fat, high cholesterol diet than other apoE genotypes.24
There is substantial interspecies variability in responsiveness to dietary lipids and cholesterol.25 As is the case for studies of human subjects, nonhuman animal research has focused predominantly on the ß-lipoproteins and pre-ß-lipoproteins and associated components, ie, LDL-C, VLDL-C, apoB, apoE, and triglycerides, some of which have been posited as major factors in cholesterol transport.25 Only a limited number of groups have investigated the genetics of diet-induced changes in circulating HDL-C levels. In a laboratory marsupial (Monodelphis domestica), Rainwater and VandeBerg26 observed increased HDL-C levels in response to a cholesterol- and fat-enriched diet in animals that could be classified as both high and low responders on the basis of their VLDL+LDL-C responses to that same diet. In inbred mice the Ath-1 locus influences HDL-C response to dietary challenge and also influences atherogenesis in the aortic arch.27 Although these results suggest important insights into likely genetic influences on lipemic response in the studied species, salient differences in lipoprotein metabolism of these animal models may temper direct extrapolation to humans.
Their genetic proximity and metabolic similarities to humans increase the likelihood that studies using Old World monkeys to investigate genetic influences on lipoprotein metabolism and lipemic response will offer insights relevant to human physiology. Responses to dietary challenges have been reported for a number of nonhuman primate species, but those of baboons have proven to be more similar to humans in terms of cholesterol, lipoprotein, and lipid metabolism than those of other related species such as rhesus (Macaca mulatta), cynomolgus (Macaca fasicularis), or African green monkeys (Cercopithecus aethiops).25 28 McGill et al29 reported variable responses (from none to a 5-fold change) in both VLDL-C and HDL-C in 555 captive baboons (Papio hamadryas) challenged with a high fat, high cholesterol diet for 7 weeks. The HDL-C levels of the juvenile baboons, offspring of selectively bred high- and low-responding parents, were observed to diverge from birth through 2 years of age while they were fed identical diets.30 31 Although affecting HDL-C levels in these baboons, neither an unidentified major locus nor the LCAT structural locus exerted an effect on lipemic response32 33 ; however, another major locus for apoA1 was found to account for 33% of the variance in apoA1 response to diet.34
Studies in humans and nonhuman animals alike strongly support the notion that genes influence lipemic response of HDL-C or its subfractions. However the presence, nature, and extent of interactions between genes influencing quantitative variation in the concentrations of HDL-C subfractions and the amount of dietary cholesterol and fat have not been resolved. We have conducted a statistical genetic analysis in a baboon model for lipoprotein metabolism and atherosclerosis with the following objectives: (1) to detect and quantify the additive effects of genes on normal variation in serum levels of HDL1-C, HDL2-C, and HDL3-C in 2 dietary environments—a low cholesterol, low fat diet and a high cholesterol, high fat diet; (2) to detect and characterize pleiotropic interactions among the additive polygenes influencing normal variation in serum levels of HDL1-C, HDL2-C, and HDL3-C in each of the 2 dietary environments; and (3) to evaluate evidence for interaction between HDL-C subfraction genotype and diet.
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Methods |
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Animals and Blood Samples
We analyzed normal quantitative variation in HDL1-C, HDL2-C, and HDL3-C levels for 942 baboons (P h adryas sensu lato35 36 ) resident at the Southwest Foundation for Biomedical Research in San Antonio, Tex. The sample consisted mainly of Yellow baboons (Papio hamadryas cynocephalus), Olive baboons (P h anubis), and their hybrids: 352 males and 590 females, 27 months to 20 years of age, distributed among 17 pedigrees with 8 to 154 members each. Within these pedigrees there were 14 249 relative pairs. Kinship coefficients (
)
for these relative pairs were calculated by means of the Stevens-Boyce
algorithm.37 Within these pedigrees there were 2015
relative pairs with =0.25 (first-degree relatives), 21 with
=0.1875, 83 with =0.15625, 10 142 with =0.1250, 1955 with
=0.0625, and 33 with =0.03125. Baboons were maintained on a monkey chow diet low in cholesterol (0.03 mg/kcal) and fat (4% of calories, derived from vegetable oils; referred to below as "Chow" diet). Animals were fasted overnight and blood samples were drawn from the femoral vein after immobilization with ketamine (10 mg/kg body weight). The same animals were then fed a diet enriched in cholesterol (1.7 mg/kcal) and saturated fat (40% of total calories, derived from lard; HCSF diet). After eating this diet for 7 weeks, fasted animals were bled for the HCSF diet sample. All protocols were reviewed and approved by the institutional Animal Care and Use Committee.
Serum was isolated by low-speed centrifugation and was stored at -80°C as individual aliquots in plastic tubing segments.38 This method of storage ensures that each sample is subjected to a single freeze-thaw cycle before analysis and is protected from oxidation and desiccation.
Measurement of Cholesterol Concentration in HDL
Subfractions
HDL-C concentrations were measured
enzymatically39 40 in the serum supernatant after
precipitation of apoB-containing lipoproteins by use of
heparin-Mn2+.41 42 HDL particles
were resolved on the basis of size by use of gradient gel
electrophoresis in nondenaturing gradient gels (PAA 2 to 16,
Pharmacia). Distributions of cholesterol were visualized by
prestaining with Sudan black B,33 43 which has been shown
to be a reliable indicator of lipoprotein cholesterol
concentrations.44 45 46 47 Gels were subjected to densitometry
at 610 nm with a Cliniscan Densitometer (Helena Laboratories). HDL
absorbance profiles were then cut into 3 fractions on the basis of
consistent features of a lyophilized baboon serum standard that
was run on each gel. The fractions (and their approximate size
intervals33 ) were HDL3 (8.2 to 10.2
nm), HDL2 (10.2 to 14.2 nm), and
HDL1 (14.2 to 19.3 nm). Absorbances within each
fraction were summed and expressed as a fraction of total HDL
absorbance and then multiplied by the HDL-C value to obtain
cholesterol concentrations within each HDL subfraction
(detailed description and validation are presented
elsewhere43 ). We have previously reported the
multivariate coefficient of variation for these
measures to be 8.2%.33
Statistical Genetic Analysis
We used the computer package PEDSYS48 for pedigree
and phenotype data management and preparation. Statistical
genetic analyses were conducted using maximum likelihood
methods to compute the likelihoods of genetic models on data from
pedigrees.49
In accordance with established quantitative genetic
theory,50 we partitioned the total phenotypic variance in
the traits (
2P) into
2G, the variance due to the
additive effects of genes, and
2E, the variance due to
nongenetic (or environmental) effects. We estimated heritability
(h2), the proportion of the phenotypic
variance due to the additive effects of genes, for each of the
subfractions as
2G/2P.
We modeled the multivariate phenotype of an
individual as a linear function of the measurements on the
individual's traits, the means of these traits in the population, and
the covariates and their regression coefficients, plus the additive
genetic values and unmeasured nongenetic deviations.51 52 53 54 55
We further partitioned the phenotypic variance-covariance
matrix into the additive genetic and environmental
variance-covariance matrices, given the relationships (kinship
coefficients) observed in the pedigree. From these 2 matrices, we
estimated the additive genetic correlation,
G, and the environmental correlation,
E, between trait pairs. Respectively, these
correlations are estimates of the additive effects of shared genes (ie,
pleiotropy) and shared unmeasured, nonhereditary (often referred to as
"random environmental") factors on the variance in a trait. Because
the contributions of the genetic and environmental components of the
phenotypic correlation matrix are additive,50 we used
the maximum likelihood estimates of these 2 correlations to obtain the
total phenotypic correlation, P, between 2
traits as
 | (1) |
), heritabilities (h2), and the
effects of sex, age-by-sex, age2-by-sex, nursery
status (a dichotomous trait: baboons reared until weaning in a nursery
were scored as "1" and those reared by mothers, "0"), and
percent Yellow baboon admixture (method of calculation is reported
elsewhere36 ) for all 6 traits, as well as the genetic
and environmental correlations between them. Before analysis,
we performed a loge (ln) transformation on the
HDL-C subfraction data to reduce skewness and to mitigate effects of
scale on parameter estimation. No other prior adjustments
to the data were made. Although the hexavariate model applied in this study is a polygenic one, it is possible, in fact likely, that individual loci contribute some proportion of the shared variation between the phenotypes in the different dietary environments. The maximum likelihood methods used rely on the assumption of multivariate normality as a "working model," but are robust to deviations from multivariate normality in the underlying distribution.57 Consequently, valid maximum likelihood estimates for the parameters of the genetic model can be obtained even if major loci, not modeled in this analysis, are involved.57
We assessed the significance of each of the estimated
parameters (excluding means, µ, and standard deviations,
) by likelihood ratio tests, wherein -2xln likelihood of a
restricted model, in which a parameter value is fixed at 0,
is compared with the similarly calculated value for the more general
hexavariate model, in which all parameter values are
estimated. The likelihood ratio test statistic,
[i] (where i indicates
degrees of freedom), is distributed approximately as a
2 variate with degrees of freedom equal to the
difference in the number of parameters in the 2 models
being compared.58 A hypothesis of pleiotropy is
supported when an additive genetic correlation is found by likelihood
ratio test to be significantly different from zero. Complete pleiotropy
would indicate that the same gene or set of genes was influencing
variation in HDL-C subfraction levels to the same degree in both diets.
As a test for complete pleiotropy, we compared the likelihood of an
unrestricted model in which the genetic correlation is estimated with
that of a restricted model in which it is fixed at 1.0.
Detecting Genotype by Diet Interactions
We have implemented an analytical approach that enables us to
quantify the proportion of the variance in response to these 2 dietary
environments that is attributable to the additive effects of genes. We
consider the 2 dietary regimens as different environments.
Genotype by environment interaction occurs when there is a
significant genetic component to the variance in response to the
environment.59 The additive genetic variance in an HDL-C
subfraction's response is a function of both that subfraction's
additive genetic variance in the 2 diets and the additive genetic
correlation between levels of that subfraction in the 2 diets. An
absence of additive genetic variance in response to the diet implies
that there is no additive polygenotype by diet interaction.
This occurs when the proportions of the variances due to the additive
effects of genes are equal in both diets (when
2G1=2G2)
and when the proportion of the correlation between 1 subfraction in the
2 diets that is due to the additive effects of shared genes is complete
(ie, positive pleiotropy,
G=1).59 These 2 conditions
serve as null hypotheses. Although the rejection of either is
indicative of polygenotype by diet interaction, the more
salient of the two is the test of equal genetic variances for the same
subfraction in 2 diets.
To test the first of these hypotheses for each of the i
subfractions, we estimated the additive genetic variance of the
response to the 2 diets as
 | (2) |
Pi is the maximum likelihood
estimate of the 2 residual phenotypic standard deviations (one from
each of the i=2 environments), and
h2 is the heritability estimate. First-order Taylor series approximations of the standard errors of these estimates were obtained from the variance-covariance matrix of the genetic correlations with respect to the genetic variances. A Wald test with 1 degree of freedom60 was used to determine the significance of the genetic variance of the response for each HDL-C subfraction to the 2 diets.
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Results |
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Univariate descriptive statistics for the raw measures of the 3 subfractions measured in samples obtained from the 2 diets are presented for each sex in Table 1
. On both diets, mean concentrations of
both HDL1-C and HDL2-C are
slightly lower in females than in males whereas the opposite is true
for HDL3-C subfraction. In both sexes, the HCSF
diet is associated with higher mean and median serum levels for all 3
HDL-C subfractions. Although the data in this table are from related
individuals and tests of significance that do not account for this fact
would be inappropriate, the magnitudes of the observed differences are
suggestive of greater lipemic responses for the
HDL1-C and HDL2-C
subfractions than for HDL3-C.
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Maximum likelihood estimates of h2 as
well as of the phenotypic means, standard deviations, covariate
effects, and their standard errors are found in Table 2 (note that because the 95% confidence
intervals about the maximum likelihood parameter estimates
are asymmetrical, the common practice of adding ±2 times the standard
error to the estimate will not reliably indicate significance).
Likelihood ratio tests disclosed significant additive genetic effects,
ie, significant h2 estimates, on all 3
HDL-C subfractions measured in both diets. Significant sex effects were
found only for HDL2-C measured on the HCSF diet.
For both sexes, age contributed significantly to variation in
HDL1-C and HDL3-C levels on
the Chow diet, but on the HCSF diet, age influenced variation in
HDL1-C and HDL2-C only in
females and in HDL3-C only in males. Nursery and
percent P h cynocephalus admixture (ßsubspecies) effects
were significant only for HDL3-C measured on the
HCSF diet.
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The hypothesis of different means in the 2 dietary environments for
each subfraction was also addressed. Likelihood ratio tests rejected
the hypothesis
µCHOW=µHCSF for
HDL1-C
(2[1]=63.48,
P<0.000001) and HDL2-C
(2[1]=224.47, P<<0.000001)
but not for HDL3-C
(2[1]=0.04,
P=0.81).
Table 3 presents maximum likelihood
estimates of the additive genetic and environmental correlations and
their standard errors plus the phenotypic correlations calculated using
equation 1
. In data from animals on the Chow diet, we observed
significant genetic correlations between all 3 subfractions
[P(G=0)<0.001] and could not
reject complete pleiotropy
[P(||G||=1.0)>0.1] between
HDL1-C and HDL3-C
(G=-0.82). Within the Chow dietary
environment, shared genes account for 38% to 67% of the genetic
variance in the 3 subfractions. In data obtained from animals after the
HCSF dietary challenge, only the genetic correlation between
HDL1-C and HDL3-C
(G=-0.62) was significant
(P<0.01). Within the HCSF dietary environment, shared genes
account for about 38% of the genetic variance in these 2 subfractions.
Likelihood ratio tests revealed significant additive genetic
correlations, indicative of pleiotropy, between the same HDL-C
subfractions measured in both the Chow diet and HCSF diets. Up to 100%
of the genetic variance in levels of HDL1-C in
the 2 dietary environments is attributable to the shared additive
effects of the same genes. The same is true for the
HDL3-C subfraction. For
HDL2-C, more than 72% of the genetic variance in
levels of this subfraction measured in the 2 diets is because of the
additive effects of shared genes.
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The product of the squared genetic correlation and the heritability
for 1 of the 2 correlated phenotypes (ie,
2I,jxh2
yields an estimate of the proportion of the residual (ie, after
partitioning out the effects of measured covariates) phenotypic
variance for that phenotype that is attributable to the effects
of shared genes (Table 4). Shared genes
account for a greater proportion of the residual phenotypic variance in
all subfractions measured in the Chow diet than in those measured in
the HCSF environment. In the Chow diet, genes shared by
HDL1-C and the other 2 subfractions account for a
greater proportion of both their genetic and residual phenotypic
variances than do genes shared by HDL2-C and
HDL3-C. Within the HCSF dietary environment, this
relationship does not obtain. Only genes shared by
HDL1-C and HDL3-C account
for more than 5% of the residual phenotypic variance in subfraction
pairs measured in animals on the HCSF diet (18.5% and 14.3%,
respectively).
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Because maximum likelihood estimates of genetic correlations between the same subfraction measured on the 2 diets were either 1.0 or, in the case of HDL2-C, not significantly different from 1.0, the proportions of the residual phenotypic variance attributable to shared genes approximated the diet-specific heritability estimates of the subfractions. Compared with the Chow diet estimates, the proportion of the residual phenotypic variance due to shared gene effects on the HCSF diet was increased for HDL1-C, but reduced by more than half for HDL2-C and only slightly for HDL3-C.
The environmental correlations between the 3 HDL-C subfractions exhibit
very similar patterns in the 2 diets (Table 3). On both Chow and
HCSF diets, the correlations between HDL1-C and
HDL3-C are of greatest magnitude. Approximately
25% of the variance due to unmeasured, nongenetic factors in these 2
subfractions is accounted for by factors that are shared in both the
Chow and HCSF environments. This proportion does not exceed 3.9% for
any of the other subfraction pairs within each diet. There is less
difference between the environmental correlations obtained when
comparing each of the same subfractions across the 2 diets. For
example, at E=0.30, the environmental
correlation between the 2 HDL2-C measures is
about 50% higher than those for the other 2 subfractions. On average,
shared unmeasured, nongenetic factors account for slightly more than
3% (HDL3-C) to 9%
(HDL2-C) of the environmental variance in
subfraction concentration between diets.
The product of the squared environmental correlation
(2E) and the quantity
1-h2 provides an estimate of the
proportion of the residual phenotypic variance for a phenotype
that is attributable to the shared effects of unmeasured nongenetic
factors (Table 5). Shared unmeasured,
nongenetic effects account for more than 10% of the residual
phenotypic variance only for HDL1-C and
HDL3-C levels measured within each of the 2
diets. Unmeasured, nongenetic effects shared by the same fraction
assayed in 2 different diets account for no more than 7.2% of the
residual phenotypic variance.
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Tests of the hypothesis
2G1=2G2
reveal significant differences between the additive genetic variances
of the 2 dietary environments for HDL1-C (0.396
versus 0.539 on Chow and HCSF diets, respectively;
2=30.0, P<0.000001) and
HDL2-C (0.205 versus 0.122;
2=31.4, P<0.000001), but not for
the HDL3-C subfraction (0.214 versus 0.209;
2=0.05, P=0.92). In the cases of
HDL1-C and HDL2-C, this
indicates that the additive genetic variance of the response to the
dietary environments was significantly different from
zero.59 When compared with that estimated for the
Chow diet, the additive genetic variance in the HCSF diet was increased
by 36% for HDL1-C and decreased by 40.5% for
HDL2-C, remaining essentially unchanged for
HDL3-C.
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Discussion |
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The utility of estimating genetic correlations between ostensibly correlated phenotypes is well established in quantitative genetics.50 For the most part, such estimates have provided means for assessing the causes of observed phenotypic associations and the likelihoods of correlated responses of trait pairs to selective breeding in experimental and agricultural populations. In this study, we have used both established and novel multivariate extensions to basic quantitative genetics theory and methods to detect and characterize the genetic contributions to phenotypic associations between serum levels of 3 different but interrelated lipoprotein subfractions measured in 2 dietary environments. And although the baboon differs from humans with respect to characteristic HDL subfraction profile (in baboons, HDL1 and HDL2 predominate, whereas HDL2 and HDL3 are the major components of human HDL), the relevance of these analyses to human atherosclerosis is that they address the genetic influences on quantitative variation in HDL subfraction profiles and, by extension, on the metabolic pathways that may produce this variation.
Using a maximum likelihood-based hexavariate quantitative genetic analysis strategy applied to data from 942 pedigreed captive baboons, we have assessed the relative contributions of genes, shared genes, and shared nongenetic factors to normal phenotypic variation in serum levels of 3 HDL-C subfractions measured in 2 dietary environments. Further, we have tested these data for evidence of interaction between the 2 diets and the detected genes. We interpret the results of our analyses to indicate that variation in dietary cholesterol and fat intake influences quantitative variation in serum concentrations of the 3 HDL-C subfractions through effects on the expression of genes responsible for normal variation in levels of these 3 subfractions. In addition to demonstrating the existence of these genotype by diet interactions, the results of our analyses also provide indications of their magnitude and nature.
Our observations of significant increases in mean levels of
HDL1-C and HDL2-C, but not
of HDL3-C, in response to the HCSF diet are
consistent with those reported by others, including
observations of increased HDL2b and no change in
HDL3 in response to dietary supplementation with
n-3 fatty acids in humans61 ; an association between
increased formation of HDL particles and n-6 
-linolenic
acid–enriched evening primrose oil in rabbits62 ; and
increased HDL levels mediated by the effects of dietary fat on the
fractional catabolic rate of HDL cholesterol ester in the
human apoA1 mouse.63 We note, however, that significant
changes in phenotypic means are not necessary preconditions for the
subsequent detection of a genotype by diet interaction. In
fact, genotype by diet interaction can occur in the absence of
significant changes in phenotype means. This would be the case,
for example, if responses of specific genotypes were in
opposite directions with respect to the original mean and their changes
effectively canceled the effect of one another on the population
mean.
As noted above, the influence of genes on quantitative variation in levels of HDL-C and its subfractions is well accepted. The mean of the 6 heritability estimates in this study, 0.32, is within the range of heritability estimates reported for total HDL-C levels in numerous human twin and family studies, between 0.16 and 0.79 (reviewed previously55 ).
Observation of pleiotropy between the different HDL-C subfraction levels within the basal Chow diet is not entirely unexpected. A few studies have reported observations consistent with pleiotropic relationships between the HDL subclasses. One study64 of data from 116 human probands undergoing coronary arteriography at an early age, plus 676 relatives, detected possible pleiotropic effects of a locus, which also accounts for 33% of the variation in HDL3-C levels, on total HDL concentration and size. A multivariate segregation analysis of data from the Donner Laboratory Family Study detected evidence for a major locus for apoA1 serum levels that exerts pleiotropic effects on the relative distribution of HDL subfractions.65 Also, a study of 717 individuals from 26 families in the San Antonio Family Heart Study66 reported significant shared additive genetic effects on the distributional patterns of apoA1 among HDL subclasses. However, detecting significant pleiotropic interactions between the 3 subfractions and determining that the magnitudes of these interactions are diet-specific are novel outcomes of this study. In this nonhuman primate model for dietary and genotypic interactions with atherosclerosis risk factors, shared genes account for a greater proportion of the genetic variance in the 3 subfractions under a low cholesterol, low fat diet than under an HCSF diet. On the Chow diet, much of the genetic variance and a moderate amount of the residual phenotypic variance in the 3 different subfractions is attributable to the effects of shared genes. In contrast, on the HCSF diet, only HDL1-C and HDL3-C exhibit significant shared genetic effects. We interpret these differences as evidence for HCSF diet-induced alterations of the pleiotropic relationships among the 3 subfractions.
Comparison of the shared genetic effects on each of the 3 HDL-C subfractions measured in 2 different dietary environments provides additional information regarding this possible genotype by diet effect. Between dietary environments, shared genes account for nearly all of the genetic variance and moderate amounts of the phenotypic variance in each of the 3 subfractions. That is, for the most part, the detected significant changes in mean levels of 2 of the HDL-C subfractions are not caused by the additive effects of different genes in different dietary environments. Rather, we suggest that these differences are attributable to changes in expression of the shared genes in response to the HCSF diet.
The significant genetic contribution to the response to diet for HDL1-C and HDL2-C, the 2 subfractions that also exhibit significantly different mean levels under the 2 diets, provides further support for this suggestion. Specifically, the high cholesterol, high saturated fat levels of the HCSF diet are associated with a change in gene expression such that the genetic variance for the HDL1-C subfraction is increased whereas that for the HDL2-C subfraction is decreased. We conclude that some genes within the suite of genes influencing phenotypic variation in total HDL-C may exert dissimilar effects on different HDL subfractions in response to diet.
Shared, unmeasured environmental factors account for only moderate proportions of the residual environmental and phenotypic variances in HDL1-C and HDL3-C within dietary environments, and small to negligible proportions between dietary environments. Because these "environmental" correlations reflect shared effects of unmeasured, noninherited factors, it is unlikely that our inferences regarding additive genetic pleiotropy are biased upward or would be diminished by the subsequent elucidation, and inclusion in our models, of one or more of these factors. It is more likely that such additions to our analyses would only serve to increase the signal to noise ratio of the detected shared additive genetic effects.
We expect to follow up on these observations to obtain additional indications of the genetic, environmental, and metabolic contributions to the changes in the pleiotropic relationships among the 3 subfractions under the HCSF diet. A study in which many of these same animals have been challenged with a third diet that is low in cholesterol but high in saturated fats has begun to yield insights regarding the differential effects of cholesterol and saturated fat on the genes influencing lipemic response in LDL-C levels.67 Additionally, we have shown elsewhere68 69 70 that knowledge of both the pleiotropic interactions and shared effects of unmeasured nongenetic factors on pairs of phenotypes can be exploited to detect and localize specific genes influencing quantitative variation in those traits. Using the pleiotropy detected between HDL-C subfractions in these pedigreed baboons on 2 diets, we are initiating multivariate, multipoint variance component whole genome linkage screens71 to identify chromosomal regions harboring genes influencing lipemic response to a high cholesterol, high fat diet.
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Acknowledgments |
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The research reported in this manuscript was conducted as part of a Program Project, "Diet and Genotype in Primate Atherosclerosis," supported by grant number P01 HL28972 from the National Institutes of Health. Many of the statistical genetic methods employed in the research reported in this manuscript were developed with support from NIH grant number R01 DK44297. The authors acknowledge the scientific and technical contributions of the following persons: Dr K.D. Carey, T.D. Dyer, Dr B. Dyke, A. Ford, Dr C.M. Kammerer, Dr W.F. Lowe, Dr H.C. McGill, Jr, P.H. Moore, Jr, Dr K.S. Rice, W.R. Shelledy, A. Smith, M. Sparks, J.F. VandeBerg, and E. Windhorst.
Received February 4, 1997; accepted October 29, 1998.
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