(Circulation. 1995;92:3240-3248.)
© 1995 American Heart Association, Inc.
Articles |
Plasma HDL Cholesterol, Triglycerides, and Adiposity
A Quantitative Genetic Test of the Conjoint Trait Hypothesis in the San Antonio Family Heart Study
From the Department of Genetics, Southwest Foundation for Biomedical Research (M.C.M., J.B., A.G.C., J.L.B, J.W.M.) and Division of Epidemiology, University of Texas Health Science Center at San Antonio (M.P.S.).
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Abstract |
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 Top Abstract IntroductionMethods Results Discussion References |
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Background The conjoint trait hypothesis proposes that combined low HDL cholesterol (HDL-C) and high triglyceride (TG) levels represent a single, inherited phenotype that adiposity may influence in an unspecified manner. We conducted formal statistical genetic tests of the conjoint trait hypothesis and the relation of the conjoint trait to adiposity using data for 569 subjects in 25 pedigrees from the San Antonio Family Heart Study.
Methods and Results We conducted
multivariate genetic analyses to detect the
effects of genes and environmental factors on variation in plasma
concentrations of HDL-C and TG, fat mass (as percent body weight
[FM%], determined by bioelectric impedance), and body mass index
(BMI). We used maximum-likelihood methods to
simultaneously estimate the phenotypic means and SDs,
heritabilities (h2), effects of sex, age-by-sex,
eight dietary and medical covariates, and genetic and environmental
correlations. Likelihood ratio tests disclosed significant
heritabilities (P<.001) for all traits
(h2HDL-C=0.55,
h2TG=0.53,
h2FM%=0.37,
h2BMI=0.44) but significant genetic
correlations (P<.001), indicating pleiotropy, between two
trait pairs only: HDL-C and TG (
G=-0.52) and
fat mass and BMI (G=0.86). We obtained significant
environmental correlations between all trait pairs except HDL-C and BMI
(P>.05).
Conclusions Both shared genes (pleiotropy) and shared environmental factors contribute to the commonly observed inverse phenotypic association between plasma levels of HDL-C and TG. Rather than low HDL-C and high TG being a single, genetically transmissible entity, it is the inverse relation between these two phenotypes throughout their normal ranges of variation as well as at the extremes that is influenced by shared genes and shared environments. However, common environmental factors, not shared genes, account for reported associations of plasma HDL-C and TG levels with measures of adiposity.
Key Words: genetics • lipids • lipoproteins • obesity
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Introduction |
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TopAbstractIntroductionMethods Results Discussion References |
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Plasma concentrations of HDL-C and TG generally are reported to be inversely related.1 2 Negative correlations ranging from -0.21 to -0.65 have been reported by the Framingham study,3 the Honolulu Heart Study,4 the Lipid Research Clinics Prevalence Study,5 and the Modesto, Calif study.6 Because there is a substantial loss of cholesteryl ester from the HDL particle as it becomes enriched with TG, elevated plasma TG levels have been suggested to be a major independent cause of depressed HDL-C levels.7 8 Although the exact mechanisms responsible for the relation are yet to be elucidated, the positive association of depressed plasma HDL-C and increased plasma TG with CVD is well known. Indeed, results from several clinical trials repeatedly have provided logical support for interventions that are directed toward altering multiple plasma lipid and/or lipoprotein levels, including elevating HDL-C and lowering TG, to reduce the risk of CVD.9 10 11 12
Measures of fatness and obesity also are associated with increased risk of CVD as well as non–insulin-dependent diabetes mellitus.13 14 15 16 Both metabolic and endocrinologic mechanisms undoubtedly contribute to the associations of obesity with various disease states and with the combination of low HDL-C and high TG.17 18 19 20
The association of low HDL-C with high TG is observed in many heritable disorders of lipoprotein metabolism,21 eg, familial combined dyslipidemia,22 23 familial hypertriglyceridemia,22 24 familial hypoalphalipoproteinemia,25 and familial dyslipoproteinemic hypertension.26 Similarly, increased adiposity has been observed in association with some of these disorders as well as with mutations that influence lipoprotein risk factors for CVD.27 28 29
Recently, in two studies using Cincinnati Lipid Research Clinic Family
Study data, Sprecher et al30 31 advanced the
hypothesis
that low HDL-C and high TG occur conjointly and are transmitted across
generations as a "combined phenotype" or "conjoint
trait." They first arrived at this hypothesis after comparing the
prevalence of combined HDL-C and TG abnormalities in first-degree
relatives of probands with low HDL-C (
10th percentile) only, high TG
(
90th percentile) only, and those with the low HDL-C/high TG trait.
It was observed that first-degree relatives of low HDL-C/high TG
probands were at increased risk of the combined phenotype
compared with relatives of probands from the other two classes. In the
subsequent study, Sprecher et al31 focused on a subsample
from the same clinical population, which they enriched for
hypertriglyceridemia by selecting probands
with TG levels above the 95th percentile. Results obtained from a
comparison of lipoprotein profiles for first-degree relatives of
probands who were high TG/normal HDL-C with those of probands who were
high TG/low HDL-C were interpreted to support the hypothesis that
inheritance of a conjoint trait, rather than elevation of TG alone, was
responsible for the well-known association. These studies also
identified increased body mass, as measured by the Quetelet index (ie,
QI=[weight/height2]x1000), as a factor that
". . .
may be partially responsible for familial CT [conjoint
trait]. . . ."30 While high TG/normal HDL-C and
high TG/low HDL-C probands exhibited elevated QIs and those with the
hypothesized conjoint trait were higher than those with high TG alone,
the QIs of the first-degree relatives of these proband classes were
not significantly different.
However, without a formal genetic analysis or test of the conjoint trait hypothesis, there is little basis for discrimination between the many possible causes for the conjoint, or more appropriately correlated, appearance of these traits in families. Many reasonable environmental and genetic scenarios can be invoked to account for familial aggregation of correlated low HDL-C, high TG, and obesity. As Sprecher et al31 recognize, the conclusion of a genetic basis for this correlation, even if warranted, still does not discriminate between the pleiotropic effects of additive polygenes and single loci or linkage. Given the range of reasonable functional scenarios for observed phenotypic associations between low HDL-C, high TG, and obesity, the shared effects of genes, or pleiotropy, is a much more parsimonious hypothesis for the conjoint inheritance of the two traits than is linkage. While the concept of pleiotropy has generally been applied to the multiple phenotypic effects of single loci, polygenic systems have also been shown to exhibit pleiotropic effects in quantitative characters.32 33
To discern whether quantitative variation in plasma levels of HDL-C and TG and measures of adiposity are inherited as a single conjoint trait, it is necessary to determine the proportion of the total phenotypic variance in HDL-C, TG, and measures of adiposity due to the additive effects of genes and the correlation between HDL-C, TG, and measures of adiposity due to shared genes and the effects of shared environment. We tested the conjoint trait hypothesis by conducting a multivariate statistical genetic analysis of quantitative variation in plasma HDL-C, plasma TG, and two measures reflective of obesity: BMI and fat mass.
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Methods |
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TopAbstractIntroductionMethods Results Discussion References |
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The data used in this study were obtained from a large sample of Mexican Americans, most of whom reside in San Antonio, Tex, who are participants in the San Antonio Family Heart Study, a broader project investigating the genetic determinants of atherosclerosis and its risk factors. The
1250
participants in the San Antonio Family Heart Study include 40 probands,
40- to 60-year-old men and women residing in a low-income
Mexican American barrio, and their first-, second-, and
third-degree relatives who were 16 years old. All probands were
randomly ascertained with respect to disease status; the only
eligibility criterion, apart from age, was that the proband had a
spouse who also was willing to participate and six living
first-degree relatives, excluding parents, who were 16 years old.
In addition to data on various lipoproteins, their subfractions, and
enzymes and other proteins associated with them, extensive data
pertaining to body composition, diet, nutrition, medications, physical
activity, and socioeconomic status also have been collected. An
Institutional Review Board (University of Texas Health Science Center
at San Antonio) approved the data collection procedures, and all
subjects gave informed consent. For the present study, data were available for 569 individuals, 362 women and 207 men, whose ages ranged from 16 to 92 years (mean age, 39.4 years). Of these 569, 13 were unrelated to any other individual in the present sample, while the remaining individuals were distributed in 25 pedigrees. The sizes of these pedigrees ranged from 3 to 71 individuals, and all 25 pedigrees contained at least three generations of relatives. There are 117 sibships represented, ranging in size from 2 to 9, with a mean size of 3.02 and a modal size of 2 (n=56). This sample contains 3752 pairings of relatives for whom there are complete data on all variables. There are 850 first-degree relative pairs, including 464 sibling pairs, 261 mother-offspring pairs, and 125 father-offspring pairs; 1082 second-degree relative pairs, 1149 third-degree relative pairs, 570 fourth-degree relative pairs, and 92 fifth-degree relative pairs in the sample.
The study focused on four quantitative phenotypes: the two inversely related plasma phenotypes, HDL-C and TG, plus the two measures of relative weight and body composition, BMI and fat mass. Data on plasma concentrations of HDL-C (mmol/L) and TG (mmol/L) were obtained in accordance with standard clinical chemical methods34 from blood samples drawn in a clinical setting after a 12- to 14-hour fast. BMI was calculated as kg/m2 from weight (kg) and height (m) measurements, also obtained in the clinical setting by trained individuals following standard anthropometric protocols.35 BMI was included because of its common use in clinical and research settings as a measure of relative weight that is correlated with adiposity in adults. But, because the genetic and environmental correlations between these traits in this population were unknown before this study, we also included a more direct estimate of adiposity. FM% was estimated by means of bioelectrical impedance using a Valhalla 1990B body composition analyzer. The rationale, methodology, and utility of bioimpedance in body composition studies are well documented.36 37 38 39
In addition to age and sex, an extensive list of potential covariates of the four phenotypes was available for use in this study. These data were obtained from interview instruments used during the clinical visit that yielded the phenotype data. The instruments consisted of medical history interviews, which included personal and family history of diabetes, myocardial infarction, hypertension, stroke, vascular surgery, and current medications, and the Rose angina questionnaire40 ; and an environmental exposures interview, during which questionnaires on cigarette smoking, alcohol consumption, physical activity,41 and food frequency42 and dietary behavior43 were administered. In the analyses reported below, the additional covariates used included the ratio of dietary polyunsaturated fat to saturated fat and the percentage of dietary saturated fat (from the food frequency questionnaires) plus the following dichotomous (ie, 0,1) variables: postmenopausal status, exogenous sex hormones, diabetic status, diabetic medication use, cigarette smoking, and use of lipid-lowering medication.
Pedigree and phenotype data management and preparation were done with the computer package PEDSYS.44 Statistical genetic analyses used our modified version of the Pedigree Analysis Program, PAP, version 3.0,45 which uses maximum-likelihood methods to compute the likelihoods of genetic models on pedigrees.
According to classic quantitative genetic theory,46 the
total phenotypic variance in a trait,
2P, can be partitioned into
2G, the variance due to the effects
of genes, and 2E, the variance due to
environmental effects. These components are additive, such that
 | (1) |
Each of these components can be further partitioned.
Heritability, h2, the proportion of the total
phenotypic variance due to the additive effects of genes, is obtained
as 2G/2P.
On the basis of established quantitative genetic theory and method, it
is possible to extend univariate genetic analysis
to encompass the multivariate
state.32 46 47 48 Following
an approach presented in
Williams-Blangero and Blangero33 and Comuzzie et
al,49 we can model 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, the covariates and their regression coefficients, plus
the additive genetic values and random environmental deviations. From
such a model, we can obtain the phenotypic
variance-covariance matrix, from which we can estimate
the additive genetic and random environmental components, given the
relationships (kinship coefficients) obtained from the pedigree and
standard quantitative genetic theory. From the genetic and
environmental variance-covariance matrices, it is a
straightforward matter to estimate the additive genetic correlation,
G, and the environmental correlation,
E, between trait pairs.
Respectively, these two correlations are estimates of the effects of
shared genes (ie, pleiotropy) and shared environmental factors on the
phenotypic variance in a trait. Just as the genetic and environmental
components of the phenotypic variance and covariance
matrices for the pedigree are additive, so too are the components of
the phenotypic correlation matrix. Therefore, by use of the
maximum-likelihood estimates of additive genetic and environmental
correlations, an estimate of the total phenotypic correlation between
two traits, P, can then be obtained by means of
the following identity:
 | (2) |
Standard errors for the phenotypic correlations, obtained from
Equation 2
, were estimated by means of a first-order Taylor
series
approximation.
We conducted a quadrivariate quantitative genetic analysis of
HDL-C, TG, BMI, and FM% using the simultaneous
orthogonalization methods of Blangero and Konigsberg,50
implemented in our modified version of PAP version
3.0.45 This multivariate approach enabled
simultaneous maximum-likelihood estimation of the 80
parameters, including the phenotypic means (µ),
phenotypic standard deviations (), heritabilities (h2),
and the effects of sex, age-by-sex,
age2-by-sex, and the eight additional covariates
(listed above) for all four traits, as well as the genetic and
environmental correlations between them. Before analysis, HDL-C
and TG were loge (ln) transformed to remove
skewness in the data and mitigate the effects of scale on
maximum-likelihood estimation. No other prior adjustments to the
data were made.
While the quadrivariate model applied in this study is a polygenic one, it is possible that major genes could actually be involved in determining shared variation in the four phenotypes. The maximum-likelihood methods used in the study rely on the assumption of multivariate normality as a "working model" and are robust to deviations from multivariate normality in the underlying distribution. 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.51
The significance of each of 68 of the estimated parameters
(excluding µi and i) was assessed by
likelihood ratio tests, in which -2xln likelihood of a
restricted model, in which a parameter value is fixed at 0,
is compared with the same statistic for the more general quadrivariate
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 two models being compared.52 Pleiotropy is
indicated by additive genetic correlations that are found by likelihood
ratio tests to be significantly different from zero. Genetic
correlations were subjected to an additional likelihood ratio test,
comparing the unrestricted model in which the correlation is estimated
to restricted models, in which it is fixed at 1.0 or -1.0. This
has been reported as a test of the extent of pleiotropy, with
G significantly different from ||±1.0||
indicative of
"incomplete" pleiotropy.53
Matrices of the maximum-likelihood estimates of the phenotypic, additive genetic, and random environmental correlations were each subjected to principal-components analysis to provide a graphical means of more readily appreciating the structures (ie, patterns of intercorrelations among the four phenotypes) of the three matrices. This approach allows us to represent the original four variables in a reduced multivariate space while maximizing the variance explained in the original data.54 This is accomplished by extracting the eigenvalues (ie, the characteristic values or latent roots) of a covariance matrix, the elements of which have been standardized to have means of 1 and variances of 0 (in other words, a correlation matrix). The eigenvalues indicate the variance explained by each of the principal components, which are the independent (ie, uncorrelated, orthogonal) linear functions of the original variates with coefficients given by the eigenvectors, nonzero scaling vectors corresponding to each eigenvalue. In principal-components analysis, the first eigenvalue explains the largest proportion of the variance in the data; the second, the next largest proportion; and so on. If the first two principal components account for a substantial proportion of the variance among the four phenoypes in this statistical genetic study, we find that plotting them against one another facilitates examination of their interrelations in two-dimensional space.
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Results |
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TopAbstractIntroductionMethods Results Discussion References |
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The univariate descriptive statistics for the raw measures for HDL-C, TG, BMI, and FM% are presented by sex in Table 1
. Both female and male age- and
sex-specific mean HDL-C and TG levels in the present study are
within 1 SEM of their respective HHANES55 age- and
sex-specific means, with 67% of the values (mean±SD) falling
between the HHANES 15th and 85th percentiles for HDL-C and between the
15th and 90th percentiles for TG. Similar comparisons place the age-
and sex-specific means from the present study intermediate to
to HDL-C and TG values for US blacks and whites obtained by NHANES II
investigators.56 Age- and sex-specific mean BMI values
in the present study sample were at the 75th and 85th HHANES
percentiles and at the 85th and 90th NHANES II percentiles for women
and men, respectively.57 58
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Fig 1, in which the data for both sexes are combined,
presents the univariate relative frequency
distributions for the four phenotypes and the gaussian
bivariate sample ellipses encompassing 86% of each pairwise
distribution. The major axes of the ellipses are centered on the sample
means, their lengths are determined by the unbiased SDs of the two
phenotypes, and their orientations are functions of the sample
covariances between them. Together, the
univariate presentations in Table 1 and Fig 1
display evidence of the scale differences between
HDL-C and TG and skewness of TG. Further, the gaussian bivariate sample
ellipses in Fig 1 provide initial graphic evidence of
the phenotypic associations between the four traits. The shapes of the
ellipses, reflecting the relative lengths of their major axes, and
their orientations, reflecting the slopes of these axes, reveal an
often observed negative association between HDL-C and TG and an
expected positive association between BMI and FM%.
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The maximum-likelihood estimates of the mean effects and variance
components and their SEEs from the quadrivariate quantitative genetic
analysis of ln HDL-C, ln TG, BMI, and FM% are
presented in Table 2. Likelihood ratio tests
disclosed significant heritabilities for all four phenotypes,
with point estimates of h2 for the plasma measures being
about 25% higher than those of the two indicators of adiposity. Sex
exerted significant effects on all phenotypes except BMI.
Similarly, only in the case of ln HDL-C were the age terms
nonsignificant contributors to the likelihood of the model. While the
effect of the dichotomous postmenopausal status variable was not
significant for any of the phenotypes, exogenous sex hormones
did contribute to ln HDL-C and ln TG. Diabetic status but not the
taking of diabetic medications had significant effects on ln TG, BMI,
and FM%. The ratio of dietary polyunsaturated fat to saturated fat
contributed significantly to the likelihood of the model but only in
the case of ln TG. The effects of the remaining covariates (ie,
smoking, percent of diet as saturated fat, lipid-lowering
medication use) on the four phenotypes were not significant in
this quantitative genetic model.
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Table 3 provides the maximum-likelihood estimates
and SEEs for the remaining parameters of the quadrivariate
quantitative genetic model: the additive genetic and environmental
correlations between the four phenotypes, plus the phenotypic
correlations, as estimated by Equation 2. Indications of
pleiotropy,
ie, significant additive genetic correlations, were observed only
between ln HDL-C and ln TG, G=-0.523, and between
BMI and FM%, G=0.864. No other genetic correlation
contributed significantly to the likelihood of the model. Likelihood
ratio tests also rejected the hypothesis of complete pleiotropy (ie,
G=±1.00) for all genetic correlations (including
that
for BMI and FM%, where 21=77.146,
P<.000001). Nearly all the environmental correlations
contributed significantly to the likelihood of the model. Only the
environmental correlation between ln HDL-C and BMI,
E=-0.187, was not significant
(21=2.52, P=.1124). All
environmental correlations between ln HDL-C and the other variables
were negative, while all those between TG and the adiposity measures
were positive.
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G) and
Environmental
Correlations (
When squared, the significant additive genetic correlations estimate
the proportion of the shared genetic variance attributable to the
additive effects of genes. The squared additive genetic correlation
between ln HDL-C and ln TG was
G2=0.274±0.123 and between BMI and
FM% was
G2=0.747±0.085. The estimate of the
proportion of the phenotypic variance accounted for by the phenotypic
correlation between ln HDL-C and ln TG was
P2=0.191±0.034 and between BMI and
FM% was
P2=0.576±0.030.
We conducted a principal-components analysis on the
phenotypic, additive genetic, and random environmental correlation
matrices to provide a graphical representation of the structure
of each matrix. The first component axis commonly separates
variables as a function of magnitude differences, while successive
axes typically contrast variables on some other basis. The first
and second principal component axes account for 98.1%, 99.5%,
and 97.3% of the variation in the phenotypic, additive genetic, and
random environmental correlation matrices, respectively. Graphical
representations of the patterns of intercorrelations among the
four phenotypes in each of the three matrices are
presented in Fig 2a through 2c.
In all three plots, ln HDL-C and ln TG were separated along both
component axes, reflecting the large contributions of shared additive
gene effects and shared random environments to the well-documented
inverse phenotypic correlation between these two measures. The expected
positive association between the two adiposity measures is evident in
all three plots as well. The apparent phenotypic identity of these two
traits (ie, the overlap in Fig 2a) results from the
combined effects of shared additive genes (Fig 2b) and
shared random environments (Fig 2c). However, the
slight separation between the adiposity measures observed in Fig
2b and 2c confirms the results of likelihood
ratio
tests that rejected complete pleiotropy between FM% and BMI and
suggests that a portion of the variation in each of these traits is
affected by other unshared genes and other unshared random
environmental effects. The two sets of significant pairwise
correlations (ie, between ln HDL-C and ln TG and between FM% and BMI)
are responsible for the similarities of pattern among the three plots.
The differences in position, with respect to the adiposity measures, of
ln HDL-C and ln TG in Fig 2b and 2c reflect sign
changes in their nonsignificant correlations, rather than important
differences between shared genetic and random environmental effects on
the relations of the plasma lipid and lipoprotein measures to
adiposity.
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Discussion |
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TopAbstractIntroductionMethods Results Discussion References |
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We used a quadrivariate quantitative genetic analysis to determine the contributions of genes, shared genes, and shared environments to normal human phenotypic variation in plasma HDL-C and TG, plus two measures of adiposity, BMI and FM%, in extended pedigrees constructed following the random ascertainment of probands. In the Mexican American population represented by this pedigreed sample from the San Antonio Family Heart Study, the commonly reported inverse phenotypic association between plasma concentrations of HDL-C and TG is due in large part both to the additive effects of shared genes and to shared environments. The same is true for the positive phenotypic association between BMI and FM%. In the case of HDL-C and TG, this observation of additive genetic pleiotropy is consistent with the observations by other researchers and clinicians of what has been referred to as a conjointly inherited trait. The significant genetic correlation between BMI and FM% confirms that, in this population, the majority of genes that influence phenotypic variation in BMI also influence phenotypic variation in fat mass. However, interpretation of the phenotypic associations between the two plasma phenotypes and measures of adiposity as evidence for the conjoint inheritance of low HDL-C, high TG, and obesity (whether measured as FM% or as wt/ht2) is not supported by the results of this study. Rather, those phenotypic associations are attributable to shared environmental effects and not to shared genes.
The present study is not the first to have implications for understanding the pleiotropic relations between lipid and/or lipoprotein measures or between this class of variables and indicators of adiposity. Research efforts by several groups have produced results indicative of pleiotropic interrelations among plasma-borne risk factors for cardiovascular disease. Path analytical methods, applied to assess the sources of variation and covariation in HDL, LDL, and VLDL in 160 nuclear families from the Cincinnati Lipid Research Clinic Family Study, rejected the hypothesis that common environmental effects alone could explain the phenotypic correlation between HDL and VLDL,59 suggested low to moderate genetic correlations between HDL and LDL and between LDL and VLDL, and estimated a strong environmental correlation between HDL and VLDL.60 With an analytical approach more similar to that of the present study, Blangero et al61 used multivariate maximum-likelihood segregation analysis to detect major locus pleiotropy and significant residual additive genetic and environmental correlations between a major locus for apolipoprotein A-I and five HDL-C subfractions in data from the Donner Laboratory Family Study. A quantitative genetic analysis of body mass, fat pattern, and serum lipid measures in 665 whites from 135 kindreds by Towne et al,53 also using maximum-likelihood methods, detected no significant additive genetic correlation between LDL-C and either BMI or waist-to-hip ratio. Towne et al53 did observe negative additive genetic correlations between HDL-C and BMI, which bordered on significance, and an additive genetic correlation between HDL-C and waist-to-hip ratio, which was significant. In a statistical genetic study of data from 2184 households conducted in Gubbio, Italy,62 variance decomposition analyses revealed that the phenotypic correlation between HDL-C concentration and BMI was attributable to shared random environmental and household components rather than to shared genetic effects.
The present study provides the first direct quantification of the genetic and environmental correlations between the components of the hypothesized combined trait, HDL-C and TG, as well as between HDL-C, TG, and measures of adiposity in extended pedigrees. It is the first formal test of the hypothesis of the conjoint inheritance of—or, more aptly, pleiotropy between—HDL-C and TG. Because the present study includes data from the normal range of variation for plasma HDL-C and TG levels, the detection of additive genetic correlations between these traits extends beyond support for the hypothesis that low HDL-C/high TG is a distinct transmissible entity. Our results show that it is the relation between HDL-C and TG, throughout their normal ranges of variation as well as at the extremes, that is heritable. Genes that are involved in the elevation of TG levels also are involved in lowering HDL-C and, conversely, genes that contribute to the elevation of HDL-C also contribute to the lowering of TG.
These conclusions are consistent with the reported metabolic interactions between HDL-C and TG. Many mechanisms that are known to lower plasma HDL-C concentrations are involved in TG metabolism,63 64 65 66 67 while mechanisms known to produce major alterations in TG metabolism also alter the size and concentration of HDLs present in the plasma.64 68 69 Heritable phenotypes hypothesized to mediate some of these interactions have been described.70
The shared genetic effects detected in the present study probably do not account for the relations between HDL-C and TG reported in all dyslipidemias. While more than 25% of the genetic variance in HDL-C and TG is attributable to shared genes, a portion of the remaining variation in plasma HDL-C and TG levels undoubtedly can be influenced independently by other unshared genes and/or environmental factors. Such additional influences could account for observations of uncorrelated responses between HDL-C and TG under specific physiological conditions or in association with specific deficiencies. For example, Yamashita et al71 reported elevated TG but normal HDL-C in individuals with reduced cholesteryl ester transfer protein activity, and Eisenberg and coworkers68 reported a similar association with alterations in the ratio of lipoprotein lipase to hepatic lipase.
The lack of genetic correlation between the lipid and adiposity measures should not be interpreted to mean that adiposity is unrelated to genes that may influence lipoprotein metabolism. The results of the present study indicate that, when the normal range of variation for plasma HDL-C, TG, and adiposity (whether measured as BMI or FM%) is included in the analysis, the phenotypic correlation of adiposity with HDL-C and TG is due to the effects of shared environmental factors rather than to the additive effects of genes shared by all of these traits. It is possible that HDL-C, TG, and adiposity are all genetically correlated to other traits not included in this study. A multivariate maximum-likelihood–based pedigree analysis that detected pleiotropy between a new phenotype and HDL-C, TG, and adiposity would not alter the patterns of intercorrelations already observed.
The heritability estimates obtained for these four traits in this pedigree sample are each very similar to the average of the heritabilities for each trait reported in twin, family, and pedigree studies of non-Hispanic groups from North America and Europe.72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 Detection of identical or nearly identical heritabilities for a trait in different populations must reflect similar relations between the additive genetic and environmental components of the total phenotypic variance in those populations. However, interstudy comparisons of the narrow sense heritability, h2, which is the estimate of the proportion of the total phenotypic variance due to the additive effects of genes, are of dubious utility. Many factors, including different ascertainment schemes, study designs, methods of parameter estimation, and population-specific environmental contributions to the phenotypic variance can result in dissimilar heritability estimates, even when the genetic variance estimates in the different populations are essentially the same. Identical heritability estimates for a trait in different populations are not necessary or sufficient to demonstrate involvement of identical genes in the expression of a trait, and dissimilar heritability estimates in different populations are not necessary or sufficient to exclude involvement of the same genes in the expression of the trait.
It is intergroup similarities in the phenotypes studied that provide useful insight regarding the generalizability of the results of any statistical genetic analysis from one population to another. Given that the majority of the measurements on the four phenotypes in the present study fall within normative standards for US populations of both similar and dissimilar ethnic composition and given the random ascertainment of the probands from which the San Antonio Familiy Heart Study pedigrees were reconstructed, we believe that these results, obtained from the statistical genetic analysis of data from 569 Mexican Americans predominantly residing in San Antonio, Tex, are generalizable to the US Mexican American population and beyond to other populations and ethnic groups.
In addition to providing insights into the patterns of genetic and environmental interactions among potentially related phenotypes, the multivariate approach used and the results obtained in this study have practical implications for other analyses. Large-magnitude genetic correlations obtained in multivariate quantitative genetic analyses can serve to delimit major locus hypotheses, including major locus pleiotropy and linkage, to be tested by multivariate segregation analysis and combined segregation and linkage analysis.93 Traits with high genetic or environmental correlations also can be used to great advantage as covariates in univariate segregation analysis. For example, on the basis of the results of the present study, we included TG as a covariate in a complex segregation analysis of plasma levels of HDL-C to account for shared additive genetic and random environmental contributions to the variance in HDL-C and TG levels. This helped to refine the HDL-C phenotype and increased the likelihood of the major gene model significantly.94
Plasma HDL-C and TG, the two components of the hypothesized conjoint trait, plus the measures of adiposity examined in the present study are all commonly accepted as complex phenotypes. To various extents, each of their observed patterns of quantitative variation is a function of interactions with genes and/or environmental factors that also influence quantitative variation in the other three phenotypes. Detecting pleiotropic and environmental interactions is an important first step in unraveling the determinants of phenotypic variation in cardiovascular risk factors such as HDL-C, TG, and adiposity. Statistical genetic models that in some way incorporate these interactions among phenotypes are more likely to approximate the biological reality of the phenotypes and consequently should be more likely to successfully detect, measure the effects of, localize, and identify genes contributing to commonly observed patterns of phenotypic variation and covariation.
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Selected Abbreviations and Acronyms |
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Acknowledgments |
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This study was supported by National Institutes of Health (NIH) grant HL-45522. The development of statistical genetic methods used in this study was supported by NIH grants HL-28972, HL-45522, GM-31575, and DK-44297. The critical comments and insights of Dr D.L. Rainwater and Dr B.D. Mitchell and the programming assistance of T. Dyer are gratefully acknowledged.
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Footnotes |
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Reprint requests to Michael C. Mahaney, PhD, Department of Genetics, Southwest Foundation for Biomedical Research, PO Box 28147, San Antonio, TX 78228-0147. E-mail mmahaney@darwin.sfbr.org.
Received April 24, 1995; revision received June 27, 1995; accepted July 24, 1995.
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References |
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M. C. Mahaney, J. Blangero, D. L. Rainwater, G. E. Mott, A. G. Comuzzie, J. W. MacCluer, and J. L. VandeBerg Pleiotropy and Genotype by Diet Interaction in a Baboon Model for Atherosclerosis : A Multivariate Quantitative Genetic Analysis of HDL Subfractions in Two Dietary Environments Arterioscler Thromb Vasc Biol, April 1, 1999; 19(4): 1134 - 1141. [Abstract] [Full Text] [PDF]
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L. Perusse, T. Rice, J. P. Despres, D. C. Rao, and C. Bouchard Cross-Trait Familial Resemblance for Body Fat and Blood Lipids: Familial Correlations in the Quebec Family Study Arterioscler Thromb Vasc Biol, November 1, 1997; 17(11): 3270 - 3277. [Abstract] [Full Text]
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