© 1998 American Heart Association, Inc.
Original Contributions |
Two Major Loci Control Variation in ß-Lipoprotein Cholesterol and Response to Dietary Fat and Cholesterol in Baboons
From the Departments of Genetics (D.L.R., C.M.K., J.E.H., B.D., J.F.V., S.H.S., L.D.A., J.L.V.) and of Physiology and Medicine (K.D.C., K.S.R., H.C.M.), Southwest Foundation for Biomedical Research, San Antonio, Tex.
Correspondence to David L. Rainwater, PhD, Department of Genetics, Southwest Foundation for Biomedical Research, PO Box 760549, San Antonio, TX 78245-0549. E-mail david{at}darwin.sfbr.org
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Abstract |
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Abstract—We explored the genetic control of cholesterolemic responses to dietary cholesterol and fat in 575 pedigreed baboons. We measured cholesterol in ß-lipoproteins (low density lipoprotein cholesterol [LDLC]) in blood drawn from baboons while they were consuming a baseline (low in cholesterol and fat) diet, a high–saturated fat (lard) diet, and a high-cholesterol, high–saturated fat diet. In addition to baseline levels (LDLCBase), we analyzed two variables for diet response: LDLCRF, which represents the LDLC response to increasing dietary fat (ie, high-fat diet minus baseline), and LDLCRC, which represents the LDLC response to increasing dietary cholesterol level (ie, high-cholesterol, high-fat diet minus high-fat diet). Heritabilities (h2) of the 3 traits were 0.59 for LDLCBase, 0.14 for LDLCRF, and 0.59 for LDLCRC. In addition, LDLCBase and LDLCRC had a significant genetic correlation (ie,
G=0.54), suggesting that 1 or more genes exert
pleiotropic effects on the 2 traits. Segregation analyses
detected a single major locus that accounted for nearly all genetic
variation in LDLCRC and some genetic variation in
LDLCBase and LDLCRF and confirmed the presence
of a different major locus that influences LDLCBase alone.
Preliminary linkage analyses indicated that neither locus was
linked to the LDL receptor gene, a likely candidate locus for LDLC.
Detection of these major loci with large effects on the LDLC response
to dietary cholesterol in a nonhuman primate offers hope of
detecting and ultimately identifying similar loci that determine LDLC
variation in human populations.
Key Words: LDL • diet • genetics • baboons
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Introduction |
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The role of plasma LDLC in predicting risk of coronary heart disease is well established,1 and dietary fat and cholesterol are recognized as the major environmental determinants of LDLC concentration.2 However, cholesterolemic responses to dietary lipids vary among individuals, in both humans and experimental animals, and much recent effort has been devoted to explaining this variability by physiological and, ultimately, genetic mechanisms.3
We began to search for genes controlling responsiveness to dietary lipids in baboons having high and low plasma cholesterol and lipoprotein levels after a 7-week challenge diet enriched in saturated fat and cholesterol. Both the basal and challenge levels of LDL and HDL were highly heritable.4 Subsequent studies focused on detecting and identifying the responsible genes by molecular5 6 7 8 and statistical9 10 11 genetic strategies. A previous study suggested that the response to dietary cholesterol and to dietary saturated fat might be controlled by separate genes in baboons.12 In the present study, we subjected baboons to a dietary challenge protocol that enabled us to analyze the separate responses to dietary cholesterol and saturated fat.
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Methods |
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Animals
Baboons (Papio hamadryas sensu lato13 14 ) were maintained at the Southwest Foundation for Biomedical Research, a facility certified by the American Association for the Accreditation of Laboratory Animal Care, under conditions approved by the institutional animal care and use committee.
We studied the effects of dietary fat and cholesterol in 575 pedigreed baboons, primarily olive (P h anubis) and yellow (P h cynocephalus), and their hybrids. Of these baboons, 47 (8%) had been reared in the nursery on artificial human infant formula, which has been shown to have a significant effect on cholesterol and apoAI concentrations.10 15 Average age (in years) of the baboons at the outset of the experiment was 9.0 (SD, 6.8; range, 2.2 to 28.5).
The pedigreed baboons represented the genetic diversity of 187 founders not known to be related. There were 209 males and 366 females in 28 sire families, with a total of 191 dams and their offspring. These families provided a large number of pairwise relationships for genetic analyses, including 1129 first-degree, 6099 second-degree, 2114 third-degree, and 633 fourth-degree relative pairs.
Diet Protocol
All baboons were subjected to the same 4-step diet challenge
protocol16 as follows: (1) Animals were fed a
baseline monkey diet (Wayne Teklad), low in fat (
4% of calories)
and cholesterol (0.03 mg/kcal), for at least 2 months
before the baseline blood sample was drawn. (2) They were then fed a
diet high in saturated fat (40% of calories from lard) and
cholesterol (1.7 mg/kcal) for 7 weeks before the
high-cholesterol, high-fat–diet blood sample was drawn.
(3) They were then fed the baseline diet again for 7 weeks to provide a
"washout" period between challenge diets. (4) After the washout,
the animals were fed a high fat–only diet (40% of calories from lard,
0.03 mg cholesterol per kcal) for 7 weeks before the high
fat–diet blood sample was taken. We previously
reported16 results of a pilot study with 60
animals, which indicated that LDLC values had returned to the initial
baseline levels by the end of the baseline diet washout period.
For each of the diet treatments, blood samples were drawn from the femoral vein after baboons were fasted overnight and immobilized with ketamine (10 mg/kg). The blood was allowed to clot and the serum was obtained by low-speed centrifugation. Serum samples were refrigerated until assay (within the week). At the time of blood drawing, body weight (in kilograms) was recorded.
Measurements of LDLC
Cholesterol concentrations were measured
enzymatically17 18 with a reagent supplied by
Boehringer Mannheim Diagnostics and a Ciba-Corning
Express Plus clinical chemistry analyzer.
Cholesterol in HDL was determined in the supernatant after
precipitation of apoB-containing lipoproteins by use of
heparin-Mn2+.19
Concentrations of cholesterol in apoB-containing
lipoproteins (LDLC) were estimated as the difference between total
serum and HDL cholesterol levels. Cholesterol
in baboon apoB-containing lipoproteins resides primarily in LDL
particles; other relatively minor contributors of
cholesterol to this LDLC value include VLDL, IDL, and
Lp(a). Average coefficients of variation for control products in
these assays were 2.2% and 4.6% for total cholesterol and
HDL cholesterol, respectively.
Three LDLC concentration variables were derived from these data and
used in the genetic analyses: (1)
LDLCBase, the LDLC level in the baseline blood
sample; (2) LDLCRF, a variable
representing LDLC response to dietary fat and calculated as
the difference between the high-fat and baseline blood sample values;
and (3) LDLCRC, a variable
representing LDLC response to dietary
cholesterol in the high-fat environment and calculated as
the difference between the high-cholesterol, high-fat–diet
and the high-fat–diet blood sample values. Figure 1
gives frequency histograms for these 3
variables. LDLCRC was not normally
distributed, so this variable was logarithmically (natural)
transformed before analysis (after first adding a constant
value to make all variables positive).
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Statistical Genetic Analyses
Quantitative Genetic Analyses
We used univariate quantitative genetic
analysis to assess heritability and to evaluate covariate
effects on the LDL traits.20 Effects of potential
covariates (eg, age, sex, weight, etc) were simultaneously
estimated. Significant covariates were determined by comparing a series
of submodels, in which the covariate effect was removed, to the most
general model in which it was included. We retained all covariates for
which the 
2 associated with the likelihood
ratio test was significant at the 0.10 level.
We used multivariate quantitative genetic
analysis to calculate genetic correlations
(G) among the LDLC phenotypes and to
estimate the magnitude of pleiotropic effects of underlying
genes.21 Large genetic correlations between
traits imply that the same genes influence both traits. Hypotheses
regarding the extent of pleiotropic effects (ie,
G=0 for no pleiotropy or
G=1.0 for complete pleiotropy) were evaluated
using likelihood ratio tests. The total phenotypic correlation
(P) was estimated as
P=
h12
· h22 ·
G+(1-h12)
· (1-h22) ·
E, where
h12 and
h22 are
heritabilities for traits 1 and 2, respectively, and
E is the environmental correlation.
Segregation Analyses
To detect and estimate the contribution of individual genes to
LDLC levels, we employed complex segregation
analysis22 by using the computer program
PAP.23 24 We compared selected
submodels that represented different transmission
hypotheses with an unrestricted general model that permitted a mixture
of as many as 3 normal phenotypic distributions. The mixture of
distributions can be interpreted to reflect genotypes, or
types,25 that result from 2 discrete factors.
Relative frequencies (under the assumption of Hardy-Weinberg
equilibrium) and means for each type were estimated, and a common SD
for the residual phenotypic distributions was assumed. Residual
nonindependence among relatives due to kinship was estimated by the
polygenic heritability (h2). The
transmission probabilities for 1 factor by individuals of different
genotypes were estimated in the general model.
We tested against the most general model several classes of submodels,
including a single-distribution (polygenic) model, a
multiple-distribution (environmental) model, and a mendelian model
(transmission probabilities, 
, fixed at their mendelian
expectations: AA=1,
Aa=0.5, and
aa=0).26 Each submodel
was compared with the unrestricted general model by using likelihood
ratio test statistics obtained as twice the difference between the
natural-log likelihoods of the 2 models. These test statistics
approximate a 2 distribution with degrees of
freedom equal to the difference in the numbers of
parameters between the 2 models. The best model is one that
has the fewest estimated parameters and is not
significantly worse than the most general
model.26
We have developed automated methods that search the likelihood surface for various models of inheritance.27 This approach enables us to find with high probability all maxima on the likelihood surface and to select the global maximum. In addition, simulation studies28 29 have shown that local maxima could contain information regarding additional loci affecting a trait.
Major Locus Pleiotropy
We employed bivariate segregation
analysis10 30 to determine whether the
major gene for 1 trait had any effect on phenotypic variation for a
second trait. In brief, this model considers the effect of a single
trait locus on 2 traits simultaneously. The hypothesis that
the major gene influences a second trait was tested by comparing the
likelihood of a model in which genotypic means were estimated for both
phenotypes (unrestricted model) to a model in which genotypic
means were estimated for the first trait and a single mean was
estimated for the second trait (restricted model). Major gene
pleiotropy is indicated if the likelihood of the former model is
significantly higher than that of the latter.
To further evaluate the pleiotropic actions of both genes
simultaneously and to estimate the proportions of
phenotypic variance explained by each locus on each trait, we also
performed a 2-locus, bivariate segregation
analysis.10 In this model, the effects of
2 loci were estimated on each of 2 traits simultaneously.
Under this more general framework, we tested several models, including
the following: (1) 1 locus affects both traits, (2) 2 loci are
present and 1 locus affects the first trait and the other locus
affects the second trait, and (3) the most general model, in which both
loci have effects on both traits. We also tested whether the 2 major
genes were linked by estimating the likelihood of a model in which
recombination frequency (
) was <0.5 (ie, the loci are linked)
versus a model in which was fixed at 0.5 (the loci are not
linked).10 As described previously, the models
were compared using the likelihood ratio test, and the best model was
the one with the fewest parameters estimated that was not
significantly different from the most general model.
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Results |
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Univariate Quantitative Genetic Analyses
Table 1
gives the
parameter estimates for each of the traits resulting from
univariate quantitative genetic analyses. We tested
for the effects of sex, age, and the square of age in males and
females; weight; nursery rearing; and subspecies admixture; only those
covariates giving a P value <0.10 were retained in the
final model for each trait. Two potential covariates found in other
studies, breast fed versus formula fed10 15 and
subspecies admixture,14 did not satisfy the
requirement and were not retained in subsequent models for any of the
traits.
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LDLCBase values averaged 0.91 mmol/L in these animals. Nearly 60% of residual phenotypic variation in LDLCBase was additive genetic (ie, h2=0.59, P<0.000001), and significant covariates included sex, linear and quadratic effects of age, and body weight. Increasing fat in the diet caused a 39% increase of LDLC to 1.26 mmol/L, but only a small portion of this increase was due to the additive effects of genes (h2=0.14 for LDLCRF, P=0.001). Increasing cholesterol level in the high-fat diet caused a near doubling of LDLC (from 1.26 to 2.27 mmol/L). This increase was strongly genetic (h2=0.59 for LDLCRC, P<0.00001), and weight was a significant covariate.
Bivariate Quantitative Genetic Analyses
There was a positive phenotypic correlation between
LDLCBase and LDLCRC (Table 2). This phenotypic correlation was
caused by a strong genetic correlation
(G=0.54), which explained 29% of the
covariance (ie, G2=0.29) in
the 2 traits. The phenotypic correlations of
LDLCRF with LDLCBase and
with LDLCRC were small or negative due to strong
negative environmental correlations.
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Segregation Analysis of LDLC Response to Dietary
Cholesterol
Segregation analysis (Table 3) indicated that a major gene, herein
labeled the R locus, affected LDLC response to dietary
cholesterol (ie, LDLCRC). The
single-distribution polygenic model was strongly rejected when compared
with the multiple-distribution unrestricted general model
(P<0.00001). Likewise, a multiple-distribution model that
allowed for polygenic effects (the environmental model) was also
rejected (P<0.000001). The multiple-distribution model in
which the underlying distributions reflected genotypes (the
mendelian model) described the data as well as the unrestricted model
(P=0.58). Estimates of the transmission
parameters in the unrestricted model (1, 0.43, and 0.12)
are similar to those expected under mendelian transmission (1,
1/2, and 0, respectively). Furthermore, the residual
heritability for the mendelian model was low
(h2=0.15±0.09), suggesting that genetic
variation at the R locus accounted for most of the genetic
control of LDLCRC. Figure 2 presents a frequency histogram for
LDLCRC together with the 3 underlying
distributions for each of the major gene genotypes as estimated
from segregation analysis.
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Analyses of Pleiotropic Effects
Previously, we detected a major gene for
LDLCBase, herein termed the B locus,
by using a different set of baboons.11 We wished
to determine whether the R gene for
LDLCRC concentrations was the same as the
B gene previously detected for
LDLCBase. First, we performed segregation
analysis to confirm the presence of a major gene affecting
LDLCBase in this group of animals and to estimate
the major gene parameters. The environmental and polygenic
models were rejected (data not shown), but not the mendelian model, and
the parameter estimates were similar to those previously
reported.11
Next, we conducted a 1-locus, bivariate segregation analysis to
test the hypothesis that the B locus for
LDLCBase exerts pleiotropic effects on
LDLCRC. Because the R locus for
LDLCRC explains virtually all of the genetic
variation in the trait, results of this analysis would indicate
whether only one major gene exerts pleiotropic effects on both
LDLCBase and LDLCRC. Table 4 presents the results of this
analysis. The B locus for
LDLCBase does not explain a significant
proportion of variation in LDLCRC
(P=0.37). We also tested for significant pleiotropic effects
of the LDLCRC major locus (R) on
LDLCBase. The R locus for
LDLCRC accounted for a small but significant
proportion of variation in LDLCBase
(P=0.0003) (Table 4). In addition, allowing for major locus
pleiotropy on the 2 traits reduces the residual heritability for
LDLCBase (from 0.59 to 0.45) as well as the
residual genetic correlation (from 0.42±0.29 to 0.09±0.30).
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On the basis of these results, we tested by 2-locus, bivariate
segregation analyses whether only 1 locus was affecting both
traits, 2 loci were affecting both traits, or there was some
intermediate effect (Table 5). In
comparison with the general model (No. 1) in which both loci affect
both traits, the 1-locus models (No. 5 and 6), in which 1 of the loci
(either B or R) affects both traits, were
strongly rejected. Likewise, the 2-locus model (No. 4), in which the
B locus affects only LDLCBase and the
R locus affects only LDLCRC, was
rejected. However, the model (No. 2) in which the B locus
affects LDLCBase only but the R locus
affects both LDLCBase and
LDLCRC was not significantly different from the
most general model. This model was the one suggested by the results of
the 1-locus, bivariate analyses. Furthermore, by comparing the
best 2-locus, bivariate model, in which recombination was estimated to
a model in which recombination was fixed at 1/2, we found no
evidence for linkage between the B and R loci
(P=0.21, results not shown). Using the best 2-locus,
bivariate model, we estimated the mean genotypic effects on
LDLCBase for the 9 possible genotype
combinations of the two loci. On average, bbrr individuals
had a 2.3-fold higher LDLC than did BBRR individuals (Table 6).
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Although the heritability of LDLCRF was low
(h2=0.14), we also tested whether the
B or R locus had pleiotropic effects on
LDLCRF. We found no strong consistent
effects of the B locus on LDLCRF (data
not shown), but we did find a significant (P=0.004)
pleiotropic effect of the R locus on
LDLCRF (Table 4
). The R locus
accounted for much of the additive genetic variance in
LDLCRF, as evidenced by the decrease in the
estimated residual heritability from 0.12±0.05 to 0.03±0.05. In
addition, the genetic correlation between the 2 traits dropped from
0.69±0.39 (Table 2, no major gene was included) to -0.34±0.67 (when
the R locus was included). This result implies that much of
the genetic correlation between the 2 traits was attributable to the
R major gene.
Components of Variance for 3 LDLC Traits
For each trait we estimated the proportion of total phenotypic
variance attributable to various factors (Table 7). For the trait
LDLCBase, the major gene for
LDLCBase (B locus) accounted for
27% of total phenotypic variance. Because this gene behaves as a
dominant-recessive locus, some of its effects are not detected as
additive (ie, not included in the heritability estimate). Approximately
3% of total phenotypic variance is accounted for by the major locus
for LDLCRC (the R locus), which exerts
a pleiotropic effect on LDLCBase. Residual
polygenic effects account for 38% of total phenotypic variance,
covariates explained 10% of total phenotypic variance, and 22%
(residual error) was not explained by the model for
LDLCBase.
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Approximately 47% of total phenotypic variation in
LDLCRC was explained by effects of the
R locus. After inclusion of the major locus effects, only a
small portion (4% of total phenotypic variation) of residual variation
was due to the additive effects of genes; covariates explained 7% of
the total phenotypic variance, and 42% was unexplained in the model
for LDLCRC.
Only 9% of the phenotypic variation in LDLCRF
could be explained by genes: the R locus accounted for 6%
and residual polygenes accounted for the remaining 3%. Ninety-one
percent of total phenotypic variation in LDLCRF
was not accounted for in the model.
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Discussion |
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Summary of the Results
The LDLC response to dietary cholesterol was strongly heritable (h2=0.59), whereas response to dietary fat was only weakly heritable (h2=0.14). A major gene (R locus) explained about half the phenotypic variation in LDLCRC. In addition, the R locus exerted pleiotropic effects on 2 other related traits: R explained
3% of the variation in LDLCBase and
6% of the phenotypic variation (and almost all of the additive
genetic variation) in LDLCRF. The previously
described11 major gene (B locus) for
LDLCBase, however, explained variation in
LDLCBase only and in neither of the response
variables. Thus, we have evidence for 2 separate major loci that
regulate LDLC levels.
Nature of the Major Locus for Cholesterol Responsiveness
The magnitude of the effect of the R locus on
responsiveness is similar to that of the 7
-hydroxylase mutation in
the cholesterol-resistant
rabbit31 32 and the unidentified genetic variant
responsible for the JAX rabbit.33 The effect is
much greater than the differences among inbred and recombinant strains
of mice34 and among swine with apoB
variants.35 Physiological
mechanisms associated with dietary responsiveness have been studied
extensively in squirrel monkeys,36 African green
monkeys,37 rhesus
monkeys,38 and baboons,3
but no single gene affecting responsiveness to dietary
cholesterol has been identified in any nonhuman primate.
Given the close phylogenetic relatedness of baboon and human species
and their similarity in the physiological processes
of lipoprotein metabolism, this major locus for response to
dietary cholesterol may also exist in humans. Discovery of
such a gene in humans would be valuable in identifying diet-susceptible
people or, potentially, suggesting new strategies for developing drugs
targeted to control plasma LDLC.
We do not yet know the identity of the major locus that controls response to dietary cholesterol in baboons. An obvious candidate is the LDLR, which is regulated by intracellular cholesterol levels.39 In a previous study, we found a polymorphic site in intron 17 of baboon LDLR that was associated with serum LDLC and apoB levels on both the basal and high-cholesterol, high–saturated fat diets.6 We tested for linkage of this LDLR polymorphism with the R locus in these baboon families. However, we found no evidence of linkage (log of the odds score was -0.9; D.L.R. et al, unpublished observations, 1997) and conclude that the major gene for response to dietary cholesterol is not LDLR.
Other earlier studies that focused on high and low LDLC–responding baboons showed that high responders had increased cholesterol absorption, apoB production, and conversion of VLDL to LDL.40 41 High responders also had lower plasma levels of 27-hydroxycholesterol and lower hepatic levels of sterol 27-hydroxylase protein and activity.42 These latter observations suggest that a locus affecting bile acid metabolism may be a candidate for the LDLRC major gene.
Relationship of LDLC Response to Fat and to Cholesterol
The major locus affecting response to dietary
cholesterol also affects responsiveness to dietary
saturated fat under the conditions of this study and accounts for most
of the genetic correlation between the traits, as well as most of the
heritability for each of the 2 traits. Thus, the data suggest that the
product of the R locus, which affects both diet response
and baseline levels of LDLC, is central to LDL
metabolism.
A much greater interindividual variability has been observed in the response of baboons to dietary cholesterol compared with the response to saturated fat,12 16 43 and the same observation was made in this study. Dietary cholesterol has dominated most animal model studies of diet-induced hyperlipidemia, whereas its role in human hyperlipidemia is considered small relative to that of saturated fatty acids.44 A possible explanation for the differences in responsiveness to dietary cholesterol among animal species (including humans) is greater interspecies variation in the frequencies of alleles for genes affecting cholesterol response compared with those affecting fat response. That is, animals of all species show a consistent response to dietary fat, whereas the proportion of animals responding to dietary cholesterol varies considerably across species. This explanation suggests that, although lower in frequency, polymorphisms associated predominantly with responsiveness to dietary cholesterol may also exist in human populations.
Genetic Control of LDLCBase
The previously described11 major locus for
LDLCBase was confirmed in this study of a
different group of baboons. This B locus influences more
than a quarter of the variation in LDLCBase but
exerts no detectable effects on the response to dietary components. The
identity of this locus is also unknown, although our preliminary
results suggest that, like the R locus discussed above, this
locus is not linked to the candidate locus LDLR (log of the
odds score was -0.7; D.L.R. et al, unpublished observations, 1997).
There remains substantial genetic variation in LDLC (eg, 38% of
LDLCBase) that is not accounted for by either the
R or the B locus. Included in this set of genes
is the effect of variation at the LDLR locus, which explains
6% of total phenotypic variation in this
trait.6 Thus, in this study, we have detected the
actions of at least 2 different loci that exert substantial effects on
LDLC variation and that may also play important roles in LDLC variation
in humans.
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Selected Abbreviations and Acronyms |
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Acknowledgments |
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This work was supported in part by grant HL28972 from National Institutes of Health, Bethesda, Md. The authors are grateful to the veterinary staff who conducted the diet experiment and obtained the blood samples for these analyses. For technical assistance and data management, we thank Jim Bridges, Debbie Christian, and Mary L. Sparks.
Received September 24, 1997; accepted January 23, 1998.
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