© 1995 American Heart Association, Inc.
Articles |
A Major Locus Influencing Plasma High-Density Lipoprotein Cholesterol Levels in the San Antonio Family Heart Study
Segregation and Linkage Analyses
From the Department of Genetics, Southwest Foundation for Biomedical Research, and the Division of Epidemiology (M.P.S.), University of Texas Health Science Center at San Antonio, San Antonio, Tex.
Correspondence 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.
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Abstract |
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Abstract To detect and measure the effects of a single locus on quantitative variation in plasma concentrations of HDL cholesterol (HDL-C), we conducted statistical genetic analyses on data from 526 Mexican American individuals in 25 randomly ascertained pedigrees. By using maximum-likelihood complex segregation analysis, we found evidence for a major locus with a codominant mixture model that included the phenotypic means, standard deviations, relative frequency of a low HDL-C allele, and heritability for plasma HDL-C levels, plus the effects of sex (genotype specific), age-by-sex, age2-by-sex, plasma concentrations of apolipoprotein (apo)AI and triglycerides (genotype specific), exogenous sex hormone use, and menopausal status under an unrestricted general model. Inclusion of the four covariates (in addition to the sex and age-by-sex effects) accounted for nearly 79% of the variance in total plasma HDL-C levels. Of the remaining 21% of the variance, the detected major locus accounted for approximately 55% in men and 21% in women; the total genetic contributions to the variance by genes were approximately 82% in men and 69% in women. Linkage analyses with penetrance parameter estimates from the segregation analysis excluded tight linkage between the detected major locus and markers for the following candidate loci: the apoAI/apoCIII genomic region (P<.05), apoB (P<.01), hepatic lipase (P<.001), lipoprotein lipase (P<.001), and the LDL receptor (P<.001). While not excluding the apoE locus (LOD=-0.348, P<.21), the analysis provided no support for tight linkage between it and the detected major locus.
Key Words: HDL cholesterol • inheritance • linkage • candidate genes • genetic epidemiology
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Introduction |
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Extensive epidemiological evidence supports associations between variation in the concentrations of the major plasma cholesterol carriers and variation in risk for CHD in humans. Plasma HDL-C exhibits a well-documented inverse relationship with CHD risk.1 2 3 4 5 6 7 Because of its apparent "protective" or anti-CHD properties at higher plasma concentrations, many research groups have directed their efforts toward elucidation of the genetic and environmental determinants of variation in plasma levels of HDL-C.
The fact that genes influence quantitative variation in plasma HDL-C levels is well established. The heritability (or the proportion of the phenotypic variance caused by the additive effects of genes) in plasma HDL-C concentration has been estimated in numerous twin and family studies to range between 0.16 and 0.79.8 9 10 11 12 13 14 15 16 17 18 19 20 21 Attempts to discern the number and actions of loci responsible for these significant genetic effects on the variance in HDL-C levels have yielded varied and apparently inconsistent results.
A number of studies have sought evidence for major locus effects on plasma levels of HDL-C, but a consensus has been difficult to reach because of differing sample sizes and compositions, ascertainment schemes, analytical approaches, and scales of trait variables analyzed (ie, discrete versus continuous).22 23 For example, complex segregation analyses of four data sets ascertained through probands with CHD or other diseases detected no HDL-C gene.12 24 25 26 However, analyses of data from the randomly ascertained participants in the Lipid Research Clinic (LRC) Family Study,27 members of 3074 randomly ascertained nuclear families from Israel,28 a large collection of Utah pedigrees with mixed ascertainment schemes,29 and a large, single pedigree ascertained with excess CHD30 each detected major gene effects. While four analyses of hypoalphalipoproteinemia in two groups of families suggested that a major gene might be responsible for low HDL-C levels,31 32 33 34 similar studies in other families did not.35 36 37 38 More recently, a family study of cardiac catheterization patients39 found no evidence for a single locus with a major effect on HDL-C levels.
At the eighth Genetic Analysis Workshop (GAW8), several research groups analyzed a lipoprotein data set that was partially ascertained on a proband with low HDL-C and partially randomly ascertained. In this data set, little or no evidence for a major locus effect on plasma HDL-C was found by investigators using various single and two locus models,40 41 while another group tentatively detected one only after adjusting the HDL-C values for TG levels.42 In general, evidence for single loci with major effects on quantitative variation in plasma HDL-C concentrations has been neither greater nor more consistent in samples that were enriched for CHD or for extreme phenotypes than in randomly ascertained families. This suggests that differences in ascertainment schemes alone cannot account for the diversity of conclusions concerning the mode of inheritance for plasma HDL-C levels.
One group has opined recently that inconsistencies in the literature indicate that "... plasma HDL-C levels do not segregate as a mendelian trait in most families..." and that currently available segregation analysis methods are not adequate for differentiating the major effects of genes at one or a few loci from the additive effects of genes at many loci.43 However, there exists another, more parsimonious explanation for such apparent inconsistencies. Plasma HDL-C concentration is a complex phenotype. Indeed, raw measurements of plasma HDL-C also are measurements of several of its components, including apoAI, for which major gene and residual additive genetic effects have been detected.44 Consequently, plasma HDL-C measures actually may represent more than one complex phenotype. This view is supported by the work of Atwood et al,45 which revealed a complicated likelihood surface with multiple local maxima for plasma HDL-C indicative of both genetic and environmental effects. We interpret such observations to suggest that the genetic determination of plasma HDL-C levels can be unraveled only by considering interactions with other phenotypes and biologically salient environmental factors. Currently available maximum-likelihood methods for segregation analysis are adequate to the task, but only if the genetic hypotheses (models) adequately specify such interactions.
Several groups have addressed the question of major locus effects on variation in plasma HDL-C or its components by means of association studies and preliminary linkage screening approaches that focus on candidate loci whose products are known or likely to be involved in the metabolism or transport of cholesterol. Allelic variation at marker loci for several such candidates has been reported to be associated with variation in plasma HDL-C levels. Two studies suggested that variation at the apoAIV locus exerts a modest influence on HDL-C levels.46 47 These results are perhaps consistent with those of Weinberg et al,48 who described an association between the apoAIV-2 allele and increased lecithin:cholesterol acyl transferase (LCAT) activation. Another study49 reported that a PvuII polymorphism just 5' of the apoAIV locus is associated with lower plasma HDL-C levels in Italian children. Recently, Cohen et al43 reported major contributions of allelic variation at the apoAI/CIII/AIV region and at the hepatic lipase locus (LIPC) to variation in plasma HDL-C concentrations in normolipidemic subjects, detected with the use of sibling-pair linkage screening methods. This result was reported to be consistent with lipoprotein profiles in familial hepatic lipase deficiency.50
We have conducted statistical genetic analyses of interindividual quantitative variation in plasma HDL-C concentrations, the general objectives of which were to detect and characterize the genetic determinants of quantitative variation in plasma levels of HDL-C throughout its entire range of variation in pedigrees established from randomly ascertained probands. Our more specific aims included (1) identifying biologically relevant covariates whose incorporation in segregation models would facilitate the unambiguous detection of a major gene effect and (2) using penetrance parameters obtained from the segregation analyses to search for unequivocal support for or exclusion of linkage between the detected major locus and selected candidate genes.
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Methods |
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Subjects
The data used in this study were obtained from a large sample of Mexican Americans, most of whom reside in San Antonio, Tex, and are participants in the San Antonio Family Heart Study, a broader project investigating the genetic determinants of atherosclerosis and its risk factors. The approximately 1250 participants in the San Antonio Family Heart Study include 40 probands who are 40- to 60-year-old men and women residing in a low-income Mexican American barrio, their spouses, and their first-, second-, and third-degree relatives who were 16 years of age or older. 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 or more years of age. In addition to data on plasma lipoproteins, their subfractions, lipids, 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 procedures, and all subjects gave informed consent.
For the present study, we identified a sample of 526 individuals, 313 women and 213 men whose ages ranged from 16 to 87 years (mean age, 39.5 years) for whom complete data were available on plasma HDL-C concentrations and 19 potential covariates (ie, phenotypes and environmental factors that we hypothesized might contribute significantly to the variance in plasma HDL-C levels and whose contributions to segregation models could be tested; see below). Of these 526 subjects, 13 were unrelated to any other individual in the present sample while the remaining 513 individuals were distributed in 25 pedigrees. The sizes of these pedigrees ranged from 3 to 70 individuals, and all 25 pedigrees contained at least three generations of relatives. There are 108 sibships represented, ranging in size from 2 to 9, with a mean size of 2.9 and a modal size of 2 (n=54). This sample contains 3110 pairings of relatives for whom there are complete data on all variables. There are 1242 first-degree relative pairs, including 390 sibling pairs, and 852 parent-offspring pairs; 1054 second-degree relative pairs, 623 third-degree relative pairs, 177 fourth-degree relative pairs, and 14 fifth-degree relative pairs in the sample.
Phenotypes and Covariates
Data on the principal phenotype, plasma HDL-C, and two
covariates, plasma TG and apoAI, were assayed from frozen plasma
aliquots obtained from blood samples drawn in a clinical setting after
a 12- to 14-hour fast. Cholesterol and TG concentrations
were assayed enzymatically with a Gilford SBA-300 clinical chemistry
analyzer with reagents supplied by Boehringer-Mannheim
Diagnostics and Stanbio. To measure HDL-C, apoB–containing
lipoproteins were precipitated from plasma by use of dextran
sulfate–Mg2+.51 The interassay
coefficients of variation for control products were 5.6% for HDL-C
and 3.2% for TG. ApoAI concentrations were measured in a commercial
laboratory (Medical Research Laboratories, Cincinnati, Ohio) by means
of nephelometry.52 53 The interassay coefficients of
variation for quality control samples of apoAI were 4.4%.
In addition to sex, age (in decimal years), and plasma apoAI and TG levels, the two remaining covariates used in the statistical genetic analyses were exogenous sex hormone use and menopausal status. Scored as dichotomous (ie, 0,1) variables, both were assessed in medical history interviews conducted during the clinic visit at which blood samples were drawn. These four covariates were selected by means of likelihood ratio tests (see below) from a subgroup of 19 (of the 53 potential) covariates that included fasting and 2-hour insulin levels; total daily caloric intake; percent dietary calories from proteins, carbohydrates, and alcohol; dietary cholesterol intake; dietary saturated fat intake; ratio of polyunsaturated to saturated fat in diet; body mass index; lipid-lowering medication use; diabetic status; diabetic medication use; smoking behavior; and physical activity (T-Mets). Of these 19, only plasma apoAI and TG, exogenous sex hormone use, and menopausal status were found to make a statistically significant (at P<.10) contribution to the maximum likelihoods of three nested models for quantitative variation in plasma HDL-C levels (see below). We used this less restrictive requirement (ie, P<.10 rather than P<.05) to increase the probability of including all important covariates in the eventual genetic model. Individual TG levels were transformed to their natural logarithms to reduce extreme positive skewness. No other transformations were applied to the data for HDL-C or the four selected covariates before statistical genetic analyses were performed.
Candidate Locus Genotypes
We selected candidate loci whose products are known to be
involved in the metabolism or transport of
cholesterol or those at which allelic variation has been
reported to be associated with variation in plasma HDL-C levels. They
include the loci for apoAI, apoAIV, apoCIII, apoB, and apoE;
lipoprotein lipase; hepatic lipase; and the LDL cholesterol
receptor. Genotypes were determined at 7 candidate loci. The
markers were typed by polymerase chain reaction (PCR) with the use of
lymphocyte DNA prepared from 20-mL blood samples as described
elsewhere.54 PCR amplification reactions used 0.1 to 0.5
µg of lymphocyte DNA, 1.0 pmol/µL of each primer, 0.025 unit/µL
Taq polymerase (Perkin-Elmer Cetus), and buffer and
nucleotide components described by the supplier of the
Taq polymerase. The sequences of amplification primers and
PCR reaction temperatures for each marker are available from the
references in Table 1
.55 56 57 58 59 60 61 To increase the
informativeness of our markers for the genomic region containing the
candidate loci for apoAI, apoAIV, and apoCIII, we combined individual
data for the two markers in this region into a three-allele
APOA1/APOC3 haplotype, which served as the sixth marker in
the linkage analyses.
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Statistical Genetics
Pedigree, phenotype, and genotype data
management and preparation were accomplished with use of the computer
package PEDSYS.62 Statistical genetic
analyses were conducted with the use of our modified version of
the package of pedigree analysis programs, PAP, v
3.0,63 which uses maximum-likelihood methods to
compute the likelihoods of alternate nongenetic and genetic models on
data in pedigrees.
Complex Segregation Analysis
The primary objective of the current study was to detect and
measure the contribution of a single locus (although not necessarily
the only locus) to quantitative phenotypic variation in plasma levels
of HDL-C with use of data from the 25 Mexican American pedigrees
described above. The statistical genetic approach used to do this was
complex segregation analysis.64 This approach
entails the statistical comparison of the likelihoods for alternate
models-a nested subset of more restricted nongenetic and genetic
models, each representing different transmission hypotheses
for plasma concentrations of HDL-C-with that of an unrestricted
general model. The general transmission model65 assumes a
mixture of three normal phenotypic distributions with a common standard
deviation and residual additive genetic contribution to the phenotypic
variance. The three phenotypic distributions are referred to as
ousiotypes66 and, in the case of a segregating major
locus, are interpreted to reflect unobservable genotypes.
Ousiotypes are the product of two discrete factors, A
and/or a. In ousiotype notation, upper case letters indicate
factors associated with lower levels of the trait and lower case
letters indicate higher levels. The expected relative frequencies of
the three possible ousiotypes-AA, Aa, and
aa-are assumed to conform to the classic Hardy-Weinberg
proportions such that, given a single parameter
pA=p, the ousiotype relative frequencies are predicted by
p2:2p(1-p):(1-p)2. Associated with each of
the three ousiotypes is an arbitrary probability of transmitting a
factor (A) from parent to offspring dependent on the
parental ousiotype (denoted 
AA,
Aa, and aa). The 18
parameters estimated in the simplest of the general models
for plasma levels of HDL-C in this study were the frequency of the
factor (or allele in a genetic model) producing lower plasma HDL-C
levels; the means of the three phenotypic distributions
(µAA, µAa, and µaa);
an additive genetic residual (h2), commonly referred to as
the heritability; a common phenotypic standard deviation (
), assumed
to be the same for each distribution; three transmission probabilities
(AA, Aa, and aa);
plus the effects of sex (ßsex), age-by-sex
(ßagef and ßagem),
age2-by-sex (ßage2< ARRANGE="STAGGER">f and
ßage2< ARRANGE="STAGGER">m), plasma levels of Ln TG (ßLnTG),
plasma levels of apoAI (ßapoAI), exogenous sex hormone use
(ßhormones), and menopausal status (ßmenopausal). Because our
modified version of PAP includes
genotype-specific regression penetrance
subroutines,67 68 this general model could readily be
expanded to simultaneously estimate the 18 additional
parameters to test hypotheses of
genotype-specific effects for all nine covariates. All
parameters were estimated by numerical maximization of the
likelihood of the data given the hypothesized transmission model.
We tested four classes of restricted models against the most general
model by using the unified approach of Lalouel et al.65
The simplest alternate model to be considered was the sporadic model,
which allows only random environmental effects and no genetic
transmission (h2=0). The second alternate model, the
polygenic model, assumes a single distribution to which the genetic
contribution is entirely due to the additive effects of genes. The
third class of alternate models, the mendelian models, assumes the
segregation of a major locus effect and incorporates transmission
probabilities fixed at classic mendelian expectations (ie,
AA=1, Aa=0.5, aa=0).
Additionally, mendelian mixture models allow for residual polygenic
background. The fourth model class tested, the environmental
transmission model, assumes random environmental effects for major
factors with the transmission probabilities constrained to equal
pA. Environmental mixture models permit the additional
estimation of residual polygenic inheritance.
Each restricted model was compared with the unrestricted general model
with use of likelihood ratio test statistics obtained as twice the
difference between the loge (Ln) likelihoods (
) of the
two models.69 These test statistics are approximately
asymptotically distributed as 
2 variates with
degrees of freedom equal to the difference in the number of
parameters in the two models. The best alternate model was
the one having the fewest estimated parameters while not
being significantly worse than the most general model.
A similar approach was taken to select the four covariates used in this
study from the 19 potential covariates. The likelihoods of three nested
models-sporadic, polygenic, and codominant mixture-which included a
potential covariate, were compared by likelihood ratio test with those
of the same three nested models in which the regression
parameter for that covariate was fixed at 0. The tested
covariate was retained for estimation in the subsequent segregation
analyses if the 2 associated with the
likelihood ratio test was significant at the 
=0.10 level.
Linkage Analysis
The second major objective of the current study was to determine
if there was sufficient evidence to support or exclude linkage between
a detected major locus affecting plasma HDL-C levels and 6 candidate
gene (one of which is a two-locus haplotype) locations. Subsequent
to detection of a major locus effect on plasma HDL-C levels, formal
parametric quantitative trait linkage analyses were
conducted also with the use of PAP v 3.063
with extensions.68 70 The use of formal LOD score linkage
analyses based on a well-fitting segregation model allows
powerful tests of both linkage and exclusion. Briefly, we used a
genetic model in which we fixed the penetrance parameters
at the values estimated in the prior complex segregation
analysis. This model was reparameterized (with
respect to the segregation model) to include the relative frequencies
of marker alleles at a candidate locus plus the probability of
recombination (
) and a measure of linkage disequilibrium
(D') between the marker and detected major locus. With
linkage disequilibrium assumed to be absent (D'=0), the
model was maximized under two conditions: complete linkage and no
linkage between the detected major locus and the marker (ie, =0
and =.50, respectively).
Linkage and exclusion of linkage were determined by classic LOD scores and likelihood ratio tests. The LOD (or log10 odds ratio) score was obtained as the difference between the log10 likelihood of the linkage models maximized at the two recombination probabilities. By convention, a LOD score of 3.0 or greater usually has been taken as strong evidence (1000:1 odds) for linkage and a LOD score of -2.0 or less has been considered strong evidence (100:1 odds) against linkage.71 However, these values are based on an inappropriate prior probability for the current study because our linkage tests involved candidate loci, with prior support for effects on lipoprotein metabolism and transport. Inheritance of plasma HDL-C levels is complex (ie, not strictly monogenic), and we used a genetic transmission model that was dictated by the data themselves. Therefore, the prior probability of detecting true linkage is increased and the likelihood of falsely excluding a true linkage is decreased.
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Results |
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Univariate summary statistics for the quantitative variables for the sample of 526 individuals included in the statistical genetic analyses are presented in Table 2
, and the distribution of raw (ie, untransformed,
nonadjusted) plasma HDL-C values for this same sample is
presented in the Figure. We have reported
elsewhere72 that both female and male age-and
sex-specific mean plasma HDL-C levels from this distribution are
within a standard error of their respective HHANES73 age-
and sex-specific means, with 67% of the values (mean±SD) falling
between the HHANES 15th and 85th percentiles. Similar comparisons place
the age- and sex-specific means from the present study
intermediate to HDL-C values for United States blacks and whites,
obtained by NHANES II investigators.74
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Complex Segregation Analysis
By means of complex segregation analysis, we found clear
evidence of a major locus effect on quantitative variation in plasma
HDL-C concentrations in 526 Mexican Americans in the San Antonio Family
Heart Study. The results of complex segregation analysis are
summarized in Table 3. With the exception of the genetic
(mendelian) mixture model, the likelihoods of all restricted alternate
models for quantitative variation in plasma HDL-C levels were
significantly worse than that of the unrestricted general model. The
maximum-likelihood estimates of the transmission probabilities in
the general model (AA=1.0, Aa=0.56±0.08,
and aa=0) clearly correspond to those expected under
mendelian segregation (ie, 1.0, 0.5, and 0). The best fitting model
describes a major locus at which is segregating a common allele,
A, with a relative frequency,
pA, of 0.843. Given the assumption of
Hardy-Weinberg equilibrium and this estimated allele frequency, the
approximate genotypic proportions are
0.71(AA):0.26(Aa):0.03(aa). While a
codominant model of inheritance best fits the pedigree data, the
A allele does appear to exert some degree of dominance
over the a allele. The homozygote AA mean
plasma HDL-C level was estimated at just over a standard deviation
lower than that for the Aa heterozygotes and more than 6
standard deviations lower than the aa homozygotes'
estimated mean. The A allele is associated with low to
midrange values and the less frequent a allele is
associated with elevated plasma levels of HDL-C. The model also
estimated a significant residual additive genetic effect,
h2=0.600.
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The detection of a major locus effect on plasma HDL-C concentrations
required that the four covariates (plasma TG, plasma apoAI, exogenous
sex hormones, and menopausal status) be included in the models.
Likelihood ratio tests for each covariate indicated significant effects
for sex ([3]< ARRANGE="STAGGER">2 =28.970,
P=.000002), Ln plasma TG levels
([3]< ARRANGE="STAGGER">2 =234.69, P<.000001),
plasma apoAI levels ([1]< ARRANGE="STAGGER">2=634.90,
P<<.000001), exogenous hormone use
([1]< ARRANGE="STAGGER">2 =5.166, P=.023033), and
menopausal status ([1]< ARRANGE="STAGGER">2=3.785,
P=.0517) but not for the age-by-sex or
age2-by-sex terms (P>.400). Tests comparing
likelihoods of models in which the effect of each covariate
(i) was uniform across genotypes (ie,
ß(i)AA=ß(i)Aa=ß(i)aa)
with those of models in which the effects varied by genotype
(ie, ß(i)AA 
ß(i)Aa ß(i)aa)
revealed that both the effect of sex, ie, the estimated displacement of
female genotypic means from those of males, and the effect of Ln plasma
TG levels were genotype specific (sex:
[2]< ARRANGE="STAGGER">2=16.142, P=.000312; Ln TG:
[2]< ARRANGE="STAGGER">2=5.99, P=.050037). The
genotype-specific effect of sex is most pronounced in the
homozygote aa class, with the estimated mean plasma HDL-C
levels being nearly 0.465 mmol/L (18 mg/dL) lower than that of the men.
The genotype-specific negative effect of plasma TG on
plasma HDL-C concentrations is also greatest in the aa
homozygotes.
The ratio of the variance of a polygenic model in which apoAI, TG,
exogenous sex hormones, and menopausal status were included
(2=28.5285) to that of the same model without
these four covariates (2=135.6483) was 0.2103,
indicating that the four covariates accounted for approximately 79% of
the variance in the original plasma HDL-C levels. The magnitude of the
effect exerted by plasma apoAI concentration was by far the greatest of
the covariates included in the model. Similar variance ratio
comparisons indicated that this covariate alone accounts for
approximately 75% of the variance in plasma HDL-C levels. Taken
together, the four covariates leave approximately 21% of the variance
in raw plasma HDL-C levels to be explained by the major gene model
presented above.
This remaining (residual) phenotypic variance in plasma concentrations
of HDL-C was decomposed into proportions attributable to the effects of
the major locus, the residual additive effects of genes other than the
major locus, and the random environment for both women and men. The
consequences of genotype-specific sex effects on this
decomposition are evident in Table 4. While the
proportion of the residual phenotypic variance due to the effects of
genes is less in women than in men, the major locus accounts for more
than twice the total variance in plasma HDL-C levels in men than in
women. The effects of residual additive genes other than the major
locus are greater in women, as is the proportion of the variance due to
random environmental factors.
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Linkage Analyses
Results of the tests for linkage between the detected major
locus influencing plasma HDL-C levels and the candidate loci are
summarized in Table 5. None of the results supported
tight linkage of any of the candidate loci with the detected major
locus. Linkage was excluded at the P<.001 level for three
of the loci, LDLR (LOD=-35.461), LPL3
(LOD=-3.231), and LIPC (LOD=-3.151); at the
P<.01 level for APOB (LOD=-2.429); and at the
P<.05 level for the APOC3/APOA1 haplotype
(LOD=-0.898).
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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Our general objective was to detect and characterize unique genetic determinants of quantitative variation in plasma levels of HDL-C throughout its entire range of variation. To accomplish this, we used maximum-likelihood methods to conduct statistical genetic analyses in which we fit alternate models for variation in this trait to data from 526 Mexican American subjects in 25 randomly ascertained pedigrees. Furthermore, we incorporated in our analyses the effects of several covariates that contributed significantly to the variance of plasma HDL-C levels in the pedigreed data. This approach was successful in defining a 21% portion of the variance in the total HDL-C phenotype in which the mode of inheritance could be inferred definitively. We determined that much of this 21%, nearly 70% in women and more than 80% in men, is attributable to the effects of genes. We also determined that a single locus is responsible for up to two thirds of the genetic contribution to this variance and that the most common allele at this locus is associated with borderline to low plasma concentrations of HDL-C in 97% of the subjects, while the less common allele is associated in 3% of the subjects with desirably elevated plasma HDL-C levels.75 We further determined that this major locus is not linked tightly to any of the candidate loci that we tested.
Plasma HDL-C level is a complex trait because patterns of quantitative variation are influenced by more than one locus as well as by endogenous and exogenous environmental factors. To detect and characterize the genetic determinants of any portion of this complex phenotype, it is essential to acknowledge and account for this complexity in the analyses by fitting models that not only allow for mixtures of major and minor locus effects but also incorporate covariates that reflect some portion of the underlying physiological complexity. This point is well illustrated by the results of the current study. With the use of a genetic model that incorporated such effects, maximal likelihood segregation methods were sufficiently powerful to detect single locus effects contributing approximately 11% and 4% of the phenotypic variance in the original plasma HDL-C measures in men and women, respectively. In the pedigree sample studied, analyses (results not shown) that did not include apoAI, TG, hormone use, and menopausal status failed to resolve the effect of a major locus. Given that these covariates together account for nearly 79% of the variance in total plasma HDL-C concentrations, it is easy to understand how their combined influences would obscure the signal of a major locus influencing a portion the remaining 21% if they were not included in the analysis.
These observations are consistent with the results of a segregation analysis of data from the Donner Laboratory Family Study reported by Cupples and Myers,42 who, while not able to discriminate between a dominant and an additive model for plasma HDL-C levels, found that regression adjustment of the data for TG improved the likelihoods of their genetic models significantly. Researchers who described a codominant mode of inheritance for plasma HDL-C in a single pedigree from the Bogalusa Heart Study with excess CHD76 also reported that "knowledge of apolipoprotein levels can be used to define more clearly major gene effects on lipoprotein levels."30 It is clear that the inclusion of plasma concentrations of apoAI and TG as covariates in our segregation analysis for plasma HDL-C redefines the phenotype under study. Given the probable structures of the circulating HDL-C particles,77 it is likely that the genetic model that best fits the data in the segregation analysis is a model for the inheritance of the amount of cholesterol per HDL particle. If so, the locus characterized in this study may be the same one that was detected in the Bogalusa pedigree by Amos et al.30
The influences of the included covariates on plasma levels of HDL-C are both genetic and environmental in nature. The inclusion of plasma ApoAI in the statistical genetic analysis of plasma HDL-C accounts for the effects of a major locus for variation in plasma apoAI levels also detected in this population.44 Adding plasma TG to the model accounts for the additive effects of shared genes and shared random environments that have been demonstrated previously72 to be largely responsible for the well-documented inverse relation between plasma levels of HDL-C and TG. Shared genes and environments recently shown to influence both sex steroids and lipoproteins in this population78 may explain the value of adding sex, exogenous hormones, and menopausal status to the genetic model for plasma HDL-C levels.
Regardless of the ascertainment scheme used, most of the previous studies reporting a major gene effect on plasma HDL-C levels estimated a low relative frequency for an allele responsible for low levels of plasma HDL-C.28 29 30 31 This directly contrasts with our observations on these 25 Mexican American pedigrees in which a majority of the 71% of the pedigreed individuals estimated to possess the two lower value genotypes in this study can be characterized as having borderline to low HDL-C levels. Indeed, 7% of the sample have plasma HDL-C levels below the 0.905 mmol/L (35 mg/dL) threshold for high CHD risk and only 17.3% of the subjects, including approximately 75 individuals from the tail of the heterozygote distribution and the 16 aa homozygotes, have plasma HDL-C levels above the 1.552 mmol/L (60 mg/dL) threshold which would place them in the desirable low CHD risk category.75
Given our random ascertainment scheme and the number of separate pedigrees from which data were obtained, it is unlikely that the very common allelic effect on low plasma levels of HDL-C inferred in this analysis (after accounting for the genetic and environmental effects of apoAI and TG) represents a private polymorphism for hypoalphalipoproteinemia. Indeed, in those studies of families ascertained in whole or in part on hypoalphalipoproteinemic probands,29 31 the mean HDL-C levels for the low genotype individuals are approximately 0.517 mmol/L (20 mg/dL) lower than those observed in the present analysis. Rather, we suggest that we have detected a locus influencing the plasma concentration of HDL-C in both normal and pathophysiological ranges of the distribution of HDL-C values. If this locus is the major genetic determinant of plasma levels of HDL-C, after taking into account the genes influencing apoAI levels, then genetic variation at this locus could explain a substantial portion of CHD in the population studied. The fact that none of the previous studies reporting the detection of a major locus effect on quantitative variation in plasma HDL-C levels were conducted in Mexican American families should not call into question the general applicability of these results. The high frequency of this low HDL-C allele within these pedigrees probably reflects its frequency among the founders of the Mexican American population. Intergroup similarities in plasma HDL-C concentration provide further insight regarding the generalizability of the results of this statistical genetic analysis from one population to another. Given that the majority of the plasma HDL-C measurements in the present study fall within normative standards for United States populations of both similar and dissimilar ethnic composition and given the random ascertainment of the probands from which the San Antonio Family Heart Study pedigrees were reconstructed, we believe that these results–obtained from the statistical genetic analysis of data from 526 Mexican Americans predominantly residing in San Antonio, Tex–are generalizable to the United States Mexican American population and beyond to other populations and ethnic groups.
Full characterization of the detected major locus requires its identification and localization. To this end, we have excluded linkage between the detected major locus and the selected candidate loci in the genomic regions encoding apoAI, apoCIII, apoAIV, apoB, apoE, LDL cholesterol receptor, lipoprotein lipase, and hepatic lipase. For the detected major locus in this population, these results are unequivocal. However, these results should not be construed to imply that the candidate loci we tested have no influence over variation in total plasma HDL-C levels. The detected major locus is not the single major locus that determines plasma HDL-C levels. Rather, it is a single locus that exerts a detectable effect on a proportion of the variation in plasma levels of HDL-C; an as yet anonymous locus that our linkage analyses demonstrate is not one of the candidate loci tested. As we have pointed out above, total plasma HDL-C, the unrefined trait analyzed in most previous studies, may represent more than the one refined phenotype for which our analysis has detected a major locus effect. Variation in the several components of the HDL particle undoubtedly is influenced by multiple loci, some with major effects and some with minor additive effects. Again, we believe that the detected major gene, residual polygenic, and pleiotropic effects of other loci on plasma levels of apoAI44 79 must be considered potential confounders in analyses of plasma HDL-C levels that have not taken into account the effects of variation in apoAI levels.
The multiple major locus, or oligogenic determination of variation in total plasma HDL-C levels, is one possible explanation for the different results reported by others investigating candidate locus effects on that trait. Different groups may have detected the effects of the several different major loci, each exerting substantial influence on plasma HDL-C concentrations. However, the known tendency for false-positive detection of associations between candidate loci and quantitative traits for methods used in other analyses also must be considered. For example, many studies, including the recent report of an association between variation at the hepatic lipase locus and plasma HDL-C levels,43 have used so-called "model-free" linkage analysis methods, the most prominent of which is the sibling-pair method.80 The originator of the sibling-pair linkage method has shown that failure to allow explicitly for statistical nonindependence among siblings can contribute to underestimation of the probabilities for linkage test statistics,81 which, in turn, can contribute to false-positive linkage determinations. While inaccurately considered "model free" by many (in opposition to the supposedly "model-bound" approach of complex segregation analysis), these methods clearly assume a simple model of inheritance, ie, additive effects at a single locus. Such an assumption limits the utility of these methods to that of preliminary screening tools for traits about which little of the genetics is known or for traits without substantial dominance components, genotype-environment interaction, pleiotropy, or epistasis. In such situations, these tests can prove useful for reducing a large number of candidate loci to a more manageable subsample for further, more in-depth analyses. In fact, the results of published sibling-pair and other association analyses were used as preliminary screens, directing our selection of candidate loci for formal, parametric, model-based linkage analyses in the present study.
Our exclusion of linkage with these candidate loci also probably speaks as much to our limited understanding of the biology of plasma HDL-C as it does to the properties of sampling strategies and analytical approaches of various research efforts addressing it. While there is no doubt that the products of the structural loci tested are important participants in lipoprotein and cholesterol metabolism, these or other loci to which they are tightly linked do not directly influence the regulation of that portion of the quantitative variation in plasma HDL-C circumscribed by our genetic model. It is likely that other, still anonymous region(s) of the genome contribute to the regulation of gene expression at these structural loci and, by extension, to variation in the concentration of plasma HDL-C and other components of lipoprotein metabolism. In all likelihood, only by expanding our search beyond the candidates that represent our inadequate understanding of complex processes will we be able to identify and localize the locus detected in the present study.
Plasma HDL-C is recognized as one of the more predictive CHD risk factors. Clearly, the genetic and environmental determinants of interindividual quantitative variation in the plasma HDL-C levels are complex, perhaps even more so than is generally appreciated. There is no reason to believe that the relationship between plasma HDL-C and CHD susceptibility is any less complex. By accounting for this complexity in all our analyses, our understanding of these relationships may begin to better approximate biological reality.
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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. For technical contributions to this study, the authors wish to acknowledge M. Britten, E. Casanova, T. Dyer, G.R. Gilligan, P. Powers, and J.E. VandeBerg. The authors also express their appreciation to the anonymous reviewers for thorough reviews and thoughtful comments on an earlier draft of the manuscript.
Received June 8, 1995; accepted August 2, 1995.
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H. Coon, M. F. Leppert, J. H. Eckfeldt, A. Oberman, R. H. Myers, J. M. Peacock, M. A. Province, P. N. Hopkins, and G. Heiss Genome-Wide Linkage Analysis of Lipids in the Hypertension Genetic Epidemiology Network (HyperGEN) Blood Pressure Study Arterioscler. Thromb. Vasc. Biol., December 1, 2001; 21(12): 1969 - 1976. [Abstract] [Full Text] [PDF]
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J. M. Peacock, D. K. Arnett, L. D. Atwood, R. H. Myers, H. Coon, S. S. Rich, M. A. Province, and G. Heiss Genome Scan for Quantitative Trait Loci Linked to High-Density Lipoprotein Cholesterol: The NHLBI Family Heart Study Arterioscler. Thromb. Vasc. Biol., November 1, 2001; 21(11): 1823 - 1828. [Abstract] [Full Text] [PDF]
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G. Imperatore, W. C. Knowler, D. J. Pettitt, S. Kobes, J. H. Fuller, P. H. Bennett, and R. L. Hanson A Locus Influencing Total Serum Cholesterol on Chromosome 19p : Results From an Autosomal Genomic Scan of Serum Lipid Concentrations in Pima Indians Arterioscler. Thromb. Vasc. Biol., December 1, 2000; 20(12): 2651 - 2656. [Abstract] [Full Text] [PDF]
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K. L. Edwards, M. C. Mahaney, A. G. Motulsky, and M. A. Austin Pleiotropic Genetic Effects on LDL Size, Plasma Triglyceride, and HDL Cholesterol in Families Arterioscler. Thromb. Vasc. Biol., October 1, 1999; 19(10): 2456 - 2464. [Abstract] [Full Text] [PDF]
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S. M. Haffner, R. D'Agostino Jr, D. Goff, B. Howard, A. Festa, M. F. Saad, and L. Mykkanen LDL Size in African Americans, Hispanics, and Non-Hispanic Whites : The Insulin Resistance Atherosclerosis Study Arterioscler. Thromb. Vasc. Biol., September 1, 1999; 19(9): 2234 - 2240. [Abstract] [Full Text] [PDF]
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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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A. Zambon, S. S. Deeb, J. E. Hokanson, B. G. Brown, and J. D. Brunzell Common Variants in the Promoter of the Hepatic Lipase Gene Are Associated With Lower Levels of Hepatic Lipase Activity, Buoyant LDL, and Higher HDL2 Cholesterol Arterioscler. Thromb. Vasc. Biol., November 1, 1998; 18(11): 1723 - 1729. [Abstract] [Full Text] [PDF]
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D. L. Rainwater, B. D. Mitchell, M. C. Mahaney, and S. M. Haffner Genetic Relationship Between Measures of HDL Phenotypes and Insulin Concentrations Arterioscler. Thromb. Vasc. Biol., December 1, 1997; 17(12): 3414 - 3419. [Abstract] [Full Text]
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