© 1996 American Heart Association, Inc.
Articles |
Triiodothyronine Exerts a Major Pleiotropic Effect on Reverse Cholesterol Transport Phenotypes
From the Department of Genetics, Southwest Foundation for Biomedical Research (A.G.C., J.B., M.C.M., R.M.S., J.L.V., J.W.M.), San Antonio, Tex, and the Division of Clinical Epidemiology, Department of Medicine, University of Texas Health Science Center (M.P.S.), San Antonio, Tex.
Correspondence to Dr Anthony Comuzzie, Department of Genetics, Southwest Foundation for Biomedical Research, PO Box 28147, San Antonio, TX 78228-0147. E-mail agcom@darwin.sfbr.org.
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Abstract |
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Abstract The thyroid hormone triiodothyronine (T3) is known to be a potent mediator of APOA1 gene expression. With the use of multivariate quantitative genetic analysis, we have assessed the magnitude of shared effects of T3 on plasma concentrations of apolipoprotein AI (apo AI) and three related phenotypes: HDL-C, apo AII, and LpAI (which is a concentration of apo AI that contains HDL particles). Maximum likelihood techniques were used to simultaneously estimate mean effects and variance components in large, extended Mexican American families living in San Antonio, Tex. We found that T3 accounted for 16%, 23%, 21%, and 37% of the additive genetic variance in HDL-C, apo AI, apo AII, and LpAI, respectively, while explaining virtually none of the random environmental variance in these phenotypes. T3 also has a pronounced effect on the pairwise genetic correlations among the four phenotypes: After the pleiotropic effects of T3 concentrations are controlled for, the genetic correlations are reduced by 6% in the case of HDL-C and apo AI and 97% for apo AII and LpAI. Thus, genes that influence T3 have a significant effect on HDL-C, apo AI, apo AII, and LpAI and also on the correlations among these phenotypes.
Key Words: thyroid hormones • HDL-C • apo AI • apo AII • LpAI
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Introduction |
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TopAbstractIntroductionMethods Results Discussion References |
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High density lipoprotein cholesterol (HDL-C), apolipoproteins AI (apo AI) and AII (apo AII), and LpAI are all involved in reverse cholesterol transport, and their plasma concentrations are considered to be a strong indicator of risk for the development of atherosclerosis. Given their combined role in reverse cholesterol transport (ie, the removal of exchangeable cholesterol from extrahepatic tissues to the liver for excretion), it is not surprising that these HDL measures should exhibit considerable intercorrelation phenotypically. Furthermore, it can be hypothesized that much of the phenotypic correlation among these variables is in fact due to an underlying structure of genetic correlations, indicative of significant pleiotropy (ie, shared genes).
Because of their role in the regulation of metabolic levels and gene expression (via stimulation of messenger RNA production1 ), thyroid hormones are involved to some extent with practically all aspects of normal physiological activities. Specifically with regard to the apolipoproteins involved in reverse cholesterol transport, effects of thyroid hormones on apo AI levels have been noted in numerous studies. In women, plasma apo AI levels are decreased with hypothyroidism and increased with hyperthyroidism.2 3 4 Perhaps most important, triiodothyronine (T3) has recently been found to be a potent mediator of APOA1 gene expression.5 It has been shown that the regulation of APOA1 gene expression by T3 is the result of a thyroid hormone response element located at the 5' end of APOA1.5
Because of the functional relationship that has been reported among this set of traits, we have formally tested hypotheses concerning the extent to which the expected phenotypic correlations among these traits can be attributed either to shared genetic effects (ie, pleiotropy) or to shared random environmental effects. Here we have decomposed the phenotypic correlations among these reverse cholesterol transport phenotypes and T3 into their constituent additive genetic and random environmental correlations by use of variance decomposition techniques. From these analyses it was also possible to examine the degree to which the intercorrelations among HDL-C, apo AI, apo AII, and LpAI are accounted for in terms of each of their common additive genetic and random environmental correlations with T3.
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Methods |
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TopAbstractIntroductionMethods Results Discussion References |
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Population
In this study data were obtained from a large sample of Mexican American families living in San Antonio, Tex, who are participants in the San Antonio Family Heart Study (SAFHS), a broader project examining the genetics of atherosclerosis and its risk factors. Ascertainment of families for the SAFHS was made without regard to preexisting medical conditions. For this project, data were available for 445 individuals (182 men; 273 women). These individuals ranged in age from 16 to 87 years with an average age of 39.7 years. Of the 445 individuals, 10 are unrelated to any other individuals in the sample, whereas the remaining individuals are distributed among 25 pedigrees. The majority of these pedigrees contain at least three generations of relatives. They range in size from 3 to 71 individuals with an approximate mean size of 12 to 15 individuals. An Institutional Review Board (University of Texas Health Science Center at San Antonio) approved the procedures, and all subjects gave informed consent.
Phenotypes
The phenotypes used in this analysis were
measured in serum samples collected after a 12-hour fast and include
total T3, HDL-C, apo AI, apo AII, and LpAI. Serum
concentration of total T3 was measured using a commercial
radioimmunoassay kit (Diagnostic Products Co). The
radioimmunoassay for total T3 had an intra-assay
coefficient of variation (CV) of 5.9% and an interassay CV of 8.7%.
Mass measurements of apo AI, apo AII, and LpAI were made at Medical
Research Laboratories, Highland Heights, Ky (Dr Evan Stein, Director).
Plasma concentrations of apo AI were measured by
nephelometry,6 apo AII by a competitive
immunoassay,7 and LpAI by differential electroimmunoassay
with a commercial kit (Sebia, 23 rue Maximilien-Robespierre). HDL-C was
measured according to the protocol of Warnick et al.8
Analytical Methods
Variance Decomposition
With
the use of established quantitative genetic theory,
it is possible to extend a univariate genetic
analysis to encompass the multivariate
state.9 10 11 12 Variance
decomposition
techniques,10 11 12 which use maximum
likelihood methods and
are implemented in a modified version of the computer program PAP
(Pedigree Analysis Package, University of Utah),13
were used to simultaneously estimate both the genetic and
environmental correlations among all pairs of traits as well as
individual trait means, phenotypic standard deviations, heritabilities,
sex and age effects, and selected environmental covariates for total
T3 and four reverse cholesterol transport
phenotypes (HDL-C, apo AI, apo AII, and LpAI). The phenotypic
correlation (
P) between a pair of traits can be
expressed in terms of the underlying genetic (G) and
environmental (E) correlations, correcting for the use
of related individuals, by the
equation
 | (1) |
where
h21 is the heritability of trait 1
and h22 is the heritability of trait 2. The
heritability (h2) represents the proportion of the
phenotypic variance accounted for by the total additive genetic
variance
(h2=
2G/2P)
or, in other words, the portion of the similarity in a given trait
between related individuals that is due solely to genetic factors. In
other words, the phenotypic correlation between a pair of traits can be
expressed as a function of the shared genetic and environmental effects
(expressed as the genetic and environmental correlations) between a
pair of traits conditional on the portion of their respective
phenotypic variances accounted for by the effect of genes (ie, their
heritabilities). It is possible to model the
multivariate phenotype of an individual as a
linear function of the measurements on the basis of the individual's
traits. From such a model we can obtain the phenotypic
variance-covariance matrix from which the additive
genetic and random environmental components are estimated, given the
relationships (kinship coefficients) obtained from the pedigree and
with the use of standard quantitative genetic theory. From the genetic
and environmental variance-covariance matrices, it is
then possible to directly estimate the additive genetic correlation
(G) (ie, the quantitative expression of pleiotropy) and
the environmental correlation (E) between pairs of
traits. Simply stated, a significant nonzero genetic correlation is a
direct measure of the extent of shared genes between a pair of traits
(ie, pleiotropy). A more detailed explanation of the extension of this
methodology to the multivariate state can be found in
previously published work.9 14 15
To examine the genetic effects of T3 on the reverse cholesterol transport phenotypes, we calculated the conditional phenotypic, genetic, and environmental covariance matrices with standard matrix formulas that use the observed maximum likelihood parameter estimates of the full model in which T3 was included as a separate phenotype, with these conditional estimates themselves being maximum likelihood estimates. We then obtained estimates of residual heritabilities (ie, heritabilities of reverse cholesterol transport phenotypes after removing the separate genetic and environmental effects of T3) and residual genetic correlations (ie, genetic correlations between reverse cholesterol transport phenotypes after correcting for the genetic effects of T3).16 In other words, the heritabilities as well as the genetic and environmental correlations among the four reverse cholesterol transport phenotypes were reestimated while accounting for their common interactions with T3.
Despite the fact that the multivariate model used in this study allows only for additive genetic effects, it is possible that major genes could actually be involved in determining the common variation shared among these five phenotypes. The maximum likelihood methods used here, however, are robust to deviations from multivariate normality in the underlying distribution. Therefore, valid maximum likelihood estimates for the parameters of the genetic model can be obtained.17 Also, as part of a general screening process we have not detected significant household effects for any of the traits presented here.
ln Likelihood Ratio Test
The
significance of both the genetic and environmental
correlations between any pair of traits and the significance of
heritability and covariate effects were tested by comparing the
likelihoods from restricted models, in which each of these
parameters was in turn constrained to equal 0.0, to the
likelihood for the general model in which all parameters
were estimated. The ln likelihood values of the general and the
restricted models were compared by use of a likelihood ratio test. This
test yields a statistic distributed asymptotically as a
2 with degrees of freedom equal to the difference
in the number of parameters estimated in the two models
being compared and is calculated as
 | (2) |
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In
this case, the comparisons of the restricted models to the
general model have one degree of freedom. An ln likelihood score for
any of the restricted models that was significantly worse than that of
the general model (P
.05) was considered evidence of a
significant, non-zero, correlation, heritability, or covariate
effect.
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Results |
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Table 1
provides the male and female means, and the
phenotypic standard deviations, along with their associated standard
errors for total T3, HDL-C, apo AI, apo AII, and
LpAI as estimated in the general model (ie, that is the model in which
all parameters were simultaneously estimated).
Maximum likelihood estimates were also obtained for age and sex effects
as well as for a variety of other covariates (eg, smoking, diabetic
status, use of diabetic medications, dietary cholesterol).
The selection of covariates considered was based on the results of
previous analyses by our group (unpublished data). None of the
covariates considered were found to have a significant effect on all of
the traits, and several were found to have no significant effect on any
of the traits. Maximum likelihood estimates of the significant
covariate effects, along with their associated standard errors and
probability values, are given in Table 2. It must be
remembered that no model is a true reflection of the natural world and
undoubtedly there exist as yet unidentified covariates that could have
a significant effect. However, the inclusion of such covariates would
serve only to strengthen the results (ie, increase the relative genetic
contribution).
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Maximum likelihood estimates and standard errors obtained from the
general model for the heritabilities (h2) of each trait, as
well as the genetic (G) and environmental
(E) correlations among them, are provided in Table
3. All five traits show moderate to pronounced levels of
heritability (0.33 to 0.52); and on the basis of likelihood ratio
tests, all are significantly different from zero at P.001
(Table 3). Significant (.001P.05) to highly
significant (P<.001) genetic correlations
(G=0.48 to 0.88) were detected among all pairs of
traits
except for T3:HDL-C and apo AII:LpAI (Table 3). With
respect to the environmental correlations only four pairwise
comparisons were found to be significant (ie, P.05); these
were HDL-C:apo AI, HDL-C:LpAI, apo AI:apo AII, and apo AI:LpAI (Table
3).
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When the estimates of heritabilities and genetic and environmental
correlations provided in Table 3
are inserted into Equation
1, it is
possible to calculate the phenotypic correlations among these traits
corrected for the use of related individuals. The pairwise phenotypic
correlations between T3 and HDL-C, apo AI, apo AII, and
LpAI are .096, .199, .257, and .098, respectively. With respect to the
phenotypic correlations among the reverse cholesterol
transport phenotypes, the values range from .230 for apo
AII:LpAI to .776 for HDL-C:apo AI.
Partitioning of the variance for each of the reverse
cholesterol transport phenotypes so as to account for
the portion attributable to the effect of T3 revealed a
reduction of 16%, 23%, 21%, and 37% of the additive genetic
variance in HDL-C, apo AI, apo AII, and LpAI, respectively (Table
4). With respect to the effects of T3 on the
random environmental variances of these four reverse
cholesterol transport phenotypes, the impact was
small to nonexistent, accounting for 0.6%, 0.0%, 1.4%, and 4.4%,
respectively, in HDL-C, apo AI, apo AII, and LpAI (Table 4).
Likewise,
the portion of the overall phenotypic variance in these four
phenotypes accounted for by T3 was negligible, with
reductions of 0.9%, 4.0%, 6.6%, and 1.0% for HDL-C, apo AI, apo
AII, and LpAI, respectively. Also, as can be seen from Table 5,
the removal of the common pleiotropic effect of
T3 on the pairwise correlations among the remaining four
phenotypes produced reductions in 2G
(ie, the portion of the covariance between a pair of traits
accounted for by the genetic correlation) ranging from 6% to 97%. The
first column of Table 5 shows the uncorrected genetic
correlation
(G) between the four reverse cholesterol
transport phenotypes (which are the same values as
presented in Table 3). Column 2 of Table 5
provides the
residual genetic correlation (G) between the four
reverse cholesterol transport phenotypes after
correcting for the common correlation with T3. Column 3
presents the percent reduction in the genetic variance explained
(which is the genetic analogue of the more familiar
r2) and is obtained by dividing the
squared value of column 2 (which is the genetic correlation between the
traits corrected for their common correlation with T3) by
the squared value of column 1 (which is the genetic correlation between
the traits uncorrected for their common correlation with
T3) and subtracting this number from 1 and then multiplying
by 100. The largest impact of the removal of the common effect of
T3 was seen on the genetic correlation between apo AII and
LpAI (a reduction of 97.5%), whereas the smallest was observed between
HDL-C and apo AI (a reduction of 5.6%).
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Discussion |
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TopAbstractIntroductionMethods Results Discussion References |
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The results of this analysis indicate that a substantial degree of pleiotropy exists among serum levels of T3, apo AI, apo AII, LpAI, and HDL-C, indicating that they all have some genetic determinants in common, while at the same time exhibiting little evidence of an extensive pattern of shared random environmental effects. Specifically, it appears that the correlation between T3 and these reverse cholesterol transport phenotypes is entirely due to common genetic effects, and in fact the corresponding small environmental correlations combined with the heritabilities of these traits (using Equation 1
) yield phenotypic correlations that
would not
be considered significantly different from zero in a statistical
analysis in which these genetic effects were ignored. The
finding that all the phenotypes examined had significant
heritabilities along with positive genetic correlations among this set
of phenotypes implies that the expression of these levels is to
some degree under genetic control and that those genes responsible for
elevated serum levels for one of these traits also produce elevated
levels in the others. That is, the phenotypic intercorrelation observed
among this group of variables is primarily driven by the influence
of a set of shared genes (ie, pleiotropy) as opposed to common
environmental factors. Although the existence of extensive pleiotropy
among the four reverse cholesterol transport
phenotypes is in itself not surprising, the rather
consistent and significant genetic correlation of
T3 with these variables is intriguing. This pattern of
genetic correlation between T3 and the reverse
cholesterol transport phenotypes becomes even more
interesting when the pairwise genetic correlations among the four
lipoprotein phenotypes are reconsidered after their correction
for the effects of their common correlation with T3.
Specifically, it was found that the removal of the common pleiotropic
effect of T3 resulted in reductions of 6% to 97% in the
portion of the genetic variance (2G)
explained by the pairwise genetic correlations among these four
phenotypes. In effect this demonstrates that a substantial
portion of the apparent pleiotropy among these reverse
cholesterol transport phenotypes actually comes
from genes that they all have in common, to varying degrees, with
T3. Common pleiotropy with T3 accounts for a
relatively small portion of the genetic correlation between HDL-C and
apo AI (
6%), thereby suggesting that most of the genes shared
between these two traits are unique to this pair of traits and not
shared with T3. At the other extreme, however, virtually
all of the pleiotropy between apo AII and LpAI (98%) is due to the
common pleiotropic effects of T3, indicating that
they have virtually no genes unique to themselves. The magnitude of the genetic impact of T3 on this set of reverse cholesterol transport phenotypes is also demonstrated when its contribution to the additive genetic variance of each of the remaining four phenotypes is considered. Partitioning the additive genetic variance of each phenotype into the portion accounted for by T3 resulted in the reduction of the overall additive genetic variance in these traits by 16% to 37%. Thus, it can be concluded that T3 contributes greatly to the additive genetic variance in each of these traits and as a result has a strong influence on their heritability. These findings lend further support to the conclusion that T3 exerts a significant pleiotropic effect on this group of lipoprotein phenotypes. It would appear, therefore, that serum T3 levels are an important factor to consider in the genetic analysis of any of these reverse cholesterol transport variables as well as perhaps other lipoprotein phenotypes. In fact, the recognition, and ultimate disentanglement, of such genetic interactions could open new avenues to understanding this group of lipoprotein variables and the normal metabolic as well as pathogenic processes in which they are involved.
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Acknowledgments |
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This study was supported by National Institutes of Health grants HL45522, GM15803, and DK44297.
Received June 7, 1995; accepted November 22, 1995.
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