© 2001 American Heart Association, Inc.
Atherosclerosis and Lipoproteins |
Genome-Wide Linkage Analysis Reveals Evidence of Multiple Regions That Influence Variation in Plasma Lipid and Apolipoprotein Levels Associated With Risk of Coronary Heart Disease
From the Department of Human Genetics (K.L.K., C.F.S.) and the Department of Epidemiology (S.L.R.K.), University of Michigan, Ann Arbor; the Department of Human Genetics (R.E.F.), University of Pittsburgh, Pittsburgh, Pa; the Division of Hypertension, Department of Medicine, Mayo Clinic (S.T.T.), Rochester, Minn; and the Human Genetics Center, University of Texas Health Science Center (E.B.), Houston.
Correspondence to Dr Charles F. Sing, University of Michigan, Department of Human Genetics, 5928 Buhl Building, 1241 E. Catherine St, Ann Arbor, MI 48109-0618. E-mail csing{at}umich.edu
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Abstract |
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Abstract—Results of genome-wide linkage analyses to identify chromosomal regions that influence interindividual variation in plasma lipid and apolipoprotein levels in the Rochester, Minn, population are reported. Analyses were conducted for total cholesterol (total-C), triglycerides (TGs), high density lipoprotein cholesterol (HDL-C), apolipoprotein A-I, apolipoprotein A-II, apolipoprotein B, apolipoprotein C-II, apolipoprotein C-III, apolipoprotein E, the total-C/HDL-C ratio, and the TG/HDL-C ratio. Genotypes were measured for 373 genome-wide marker loci on 1484 individuals distributed among 232 multigeneration pedigrees sampled without regard to health status. LOD scores and estimates of additive genetic variance associated with map locations were obtained by using the variance-component method of linkage analysis. No evidence of linkage with genes influencing variation in age served as a negative control. Plasma apolipoprotein E levels and the apolipoprotein E gene served as a positive control (LOD score 4.20). Evidence (LOD score >2.00) was provided that was suggestive of a gene or genes on chromosomes 4 and 5 influencing variation in the apolipoprotein A-II level, on chromosome 12 influencing variation in the apolipoprotein A-I level, and on chromosome 17 influencing variation of total-C/HDL-C. These analyses provide new information about genomic regions in humans that influence interindividual variation in plasma lipid and apolipoprotein levels and serve as a basis for further fine-mapping studies to identify new genes involved in lipid metabolism.
Key Words: genetic linkage • apolipoprotein E • apolipoprotein A-I • apolipoprotein A-II • total cholesterol • high density lipoproteins
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Introduction |
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One of the greatest challenges that the medical and public health communities face is to identify and characterize the genes that influence the risk of coronary artery disease (CAD). Only a small fraction of the genes that are expected to be involved in determining the risk of CAD are known. Measures of lipid metabolism are well-established intermediate traits that translate genetic variation into variation in the risk of CAD. Increased risk of developing CAD has been associated with elevated triglyceride (TG),1 2 total cholesterol (total-C),3 4 5 and LDL cholesterol6 7 levels and with decreased levels of HDL cholesterol (HDL-C).6 8 CAD has also been associated with low levels of plasma apoA-I and apoA-II and high levels of apoB and apoE.9 10 11 12 13 14 Only a fraction of the genetic variation in intermediate measures of lipid metabolism is explained by variation in known candidate genes. For example, the total genetic contribution to variation in total-C, HDL-C, and other lipid traits is estimated to be between 40% and 80%.15 16 However, single polymorphisms typically explain only 5% to 15% of that variation.16 17 18 19 20 The present study is part of an effort to find new genes that influence the genetic architecture of CAD by carrying out genetic studies of intermediate measures of lipid metabolism that translate genetic variation into variation in risk of CAD. The genetic architecture of a trait is defined by the number of genes, the number of alleles per gene and their relative frequencies, and the gene-gene and gene-environment interactions that combine to influence interindividual variation within a population.21 Genome-wide linkage scans have the potential for identifying regions of the genome not previously recognized as sources of interindividual variation in intermediate traits that influence variation in the risk of CAD.22 23 24 25 26
To identify regions of the genome predictive of interindividual variation in plasma lipid levels for the purpose of identifying new candidate genes, we have applied a variance component–based linkage method to phenotypic data in pedigrees ascertained without regard to health status. Evidence of the linkage of marker loci with a gene(s) influencing interindividual variation in plasma apoE level was observed in the region of chromosome 19 containing the apoE gene. LOD scores provided suggestive evidence of the linkage of marker loci with previously unknown genes on chromosomes 4 and 5 influencing apoA-II, on chromosome 12 influencing apoA-I, and on chromosome 17 influencing the total-C/HDL-C ratio.
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Methods |
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Sample and Laboratory Methods
As part of the Rochester Family Heart Study, multigeneration pedigrees were ascertained without regard for health through households with
2 children enrolled in the primary and
secondary schools of Rochester, Minn. Sampling details, the clinic
examination protocol, and baseline characteristics have been described
by Moll et al27 and Turner et
al.28 Marker genotype
data were obtained for 1484 individuals (779 females and 705 males) of
the 4486 individuals identified with 232 pedigrees (see
Table 1
). There was an average of 2.94 children per
pedigree. Individuals were genotyped for 373 highly
polymorphic microsatellite marker loci located on the 22 human
autosomes. The median distance between adjacent markers was 8.3 cM
(range 0 to 40.2 cM). Genotyping was performed by standard methods with
use of an Applied Biosystems/Perkin Elmer 377 automatic DNA sequencer.
The genotype data were analyzed for typings
inconsistent with the pedigree structure by use of the Lange
and Goradia29 algorithm as
implemented by Ped Check.30
Observed discrepancies were evaluated for typing errors in the
laboratory. Instances that could not be resolved were treated as
missing data.
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Plasma total-C and TG levels were measured by standard enzymatic methods.31 32 LDL particles were precipitated with polyethylene glycol 6000, and an aliquot of the supernatant was used for determination of HDL-C by an enzymatic method as described by Kaprio et al.33 Plasma apoA-I, apoA-II, apoC-II, apoC-III, and apoE were measured by radioimmunoassay.34 Plasma apoB was measured by ELISA.33 Complete lipid and apolipoprotein measurements were available for 778 females and 704 males.
Linkage Analysis
The lipid levels were adjusted for gender-specific
age, height, and weight to the third order, as well as body mass index
(BMI), before carrying out the linkage analysis. Marker
relative allele frequencies were estimated as the sample relative
allele frequencies. Genetic map location estimates were taken from
the maps prepared by the Center for Medical Genetics at the Marshfield
Medical Research
Foundation.35 Locations for
markers not contained in the Marshfield map were inferred from their
map locations in the Genetic Location
Database.36
Linkage analyses were conducted by the
variance-components method37
with use of the SOLAR computer program. In this method, a linear mixed
model was fit to the trait data so that the phenotypic variance about
the trait mean is partitioned into a monogenic component
(
2QTL)
representing the contribution of a quantitative trait locus
(QTL) linked to the marker locus, a residual familial component
(2R) attributable
to familial genetic and shared environmental effects, and a component
(2E) attributable
to environmental effects unique to the individual. With the assumption
of no recombination between trait and marker loci, the phenotypic
variance-covariance matrix (
) for individuals in a pedigree
may be written as
=
2QTL+II2R+I2E,
where is a matrix of the proportion of alleles shared identical
by descent (IBD) at a point in the genome estimated from the genotypic
data, II is a matrix of the expected proportion of alleles shared
IBD for pairs of relatives, and I is an identity matrix. Multipoint
estimates of IBD were obtained as a weighted average of the IBD at each
individual marker.38 The
estimates of IBD for individual markers were obtained by the computer
program SOLAR37 by using the
relationship information from the 4486 individuals making up the 232
pedigrees and the genotype data for the subset of 1484
individuals who were genotyped for the present study.
Missing genotype values in the subset of 1484 were imputed by
using information from flanking markers before full multipoint
estimation.37
All LOD scores reported below are based on multipoint
analyses. The LOD scores were calculated as the difference
between the maximum of the log10 likelihood of
the full model, including estimates of
2QTL,
2R, and
2E, and the
maximum of the log10 likelihood of the reduced
model in which
2QTL was
constrained to equal 0. Twice the difference between the maximum of the
loge likelihood of these 2 models is a
likelihood ratio test statistic asymptotically distributed as a 1/2:1/2
mixture of a 
2 with a point mass at 0 and
1 df. In 3 cases, the linkage
analysis yielded evidence of 1 QTL influencing
interindividual variation in a trait (multipoint LOD >2.00). In these
cases, a second linkage analysis was performed with the
contribution of the first QTL to trait variation fixed in the full and
reduced models.39 All linkage
analyses were carried out in SOLAR with use of the
t distribution function, which
provides a better fit to data that do not meet assumptions of
normality. We have used a LOD score of 3.00 to indicate adequate
evidence of linkage, a LOD threshold of 2.00 as suggestive, and a LOD
score of 1.30 as tentative evidence of
linkage.40
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Results |
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The phenotypic characteristics of the 1482 individuals in 232 pedigrees used in these linkage analyses are summarized in Table 2
. The average age of males was 36.3 years (range 5.2
to 89.3 years) and of females was 39.0 years (range 5.4 to 90.3 years).
The average BMI was 23.4 kg/m2 (range 13.7
to 50.8 kg/m2) for females and 23.7
kg/m2 (range 13.8 to 43.3
kg/m2) for males. On average, 1263 (range
819 to 1392) individuals were genotyped for a marker locus. The
average individual was successfully genotyped for 320
markers.
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The multipoint linkage analysis results of the
genome scan are presented in
Table 3 as the highest LOD score observed on each
chromosome for each of 11 plasma lipid and apolipoprotein
traits. LOD peaks >2.00 are indicated in boldface type; LOD scores
>3.00 were observed for the apoE level on chromosome 19 in the
region containing the apoE
gene. In the analysis of age, we observed no LOD score >0.00
(Table 3).
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Figure 1 depicts the linkage results for 4 traits (apoA-I,
apoA-II, apoE, and total-C/HDL-C) for the chromosomes for which LOD
peaks >1.30 were observed. The highest LOD score observed was 4.20,
corresponding to a chromosomal region influencing the plasma apoE level
on 19q, near the marker D19S246. An interval 40 cM wide between markers
D19S245 and D19S254 had LOD scores >2.00. Tentative evidence for
linkage to a region influencing variation in apoE was also found on
chromosome 12p near the marker GATA91H06 (LOD score 1.50). To test
whether the multiple QTLs identified as influencing variation in apoE
levels formed a true multilocus system, we ran a multipoint linkage
scan of the genome by using full and reduced models, which both
included the contribution of the QTL on chromosome 19. A LOD score from
this analysis of 1.30 on chromosome 12 near the marker
GATA91H06 supports the initially observed evidence of linkage to a QTL
in this region that influences variation in apoE levels.
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Suggestive evidence of a QTL near the marker D4S2368 on chromosome 4q (LOD score 2.35) and a QTL on 5p (LOD score 2.13) near the marker D5S2500, which influence variation in apoA-II levels, was detected. Tentative evidence was observed for a third QTL influencing apoA-II variation on chromosome 18 near the marker D18S976 (LOD score 1.53). Linkage analysis in which the full and reduced models included the contribution of the chromosome 4 QTL resulted in a LOD score of 1.55 for a second QTL on chromosome 5 near D5S2500. Suggestive evidence was found of linkage near marker D12S2070 on chromosome 12 (LOD score 2.02) with a QTL influencing plasma apoA-I levels. Evidence suggestive of a QTL near marker D17S928 at the end of the long arm of chromosome 17 (LOD score 2.48) that influences plasma total-C/HDL-C variation was observed. Additional tentative evidence of a QTL influencing variation in total-C/HDL-C was found on chromosome 5q near the marker D5S408 (LOD score 1.57). Linkage analysis with the contribution of the chromosome 17 QTL included in the full and reduced models resulted in a LOD score of 1.26 for a second QTL at the chromosome 5 location.
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Discussion |
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The purpose of the present study was to identify genomic regions containing genes influencing interindividual variation in plasma lipid and apolipoprotein levels in nonhispanic white families from Rochester, Minn, selected without regard to health status. The results of our linkage scan provide evidence of regions of the genome that may contain previously unknown genes that influence normal interindividual variation in apoA-I, apoA-II, apoE, and total-C/HDL-C. Positional candidate genes in regions identified by multipoint linkage analyses are summarized in Table 4
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The genome-wide linkage scan for loci influencing age serves
as a negative control for the variance-components linkage method in
this population.
Figure 2 contrasts the multipoint linkage results for age
with those for plasma apoE on chromosome 19. The fact that no LOD score
>0.00 was observed for age on any chromosome is in accord with the low
type I error rate (0.0025) estimated for this method in the GAW10
problem set of simulated nuclear
families.41
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The apoE gene region
on chromosome 19q serves as a positive control for the
variance-components linkage method. There is a large body of evidence
supporting the major contribution of the
apoE gene to variation in
plasma apoE level and to the risk of developing
CAD.11 14 42 43 44 45
The 3 major apoE isoforms (E2, E3, and E4) account for between 11% and
19% of the interindividual variation in levels of apoE in this and
other
populations.16 33 46
The highest LOD score observed in the present study (LOD score
4.20, near the marker D19S246) identifies a region of chromosome 19
that includes the apoE/C-I/C-II
gene cluster. The width of this peak, covering more than one third of
chromosome 19 (LOD score >2.00), may be due to the major effect of the
apoE gene on interindividual
variation in the apoE level. Alternately, the width of the region with
LOD scores suggestive of linkage may indicate overlapping peaks that
are due to genes other than
apoE within this region with
effects on lipid metabolism. To test whether variation in
the apoE gene was responsible
for the multipoint LOD peak observed on chromosome 19, we adjusted
plasma apoE levels for apoE
genotype means (after gender-specific adjustment for variation
in age, height, and weight to the third order and BMI). The resulting
peak multipoint LOD, 1.10 at 78 cM
(Figure 2
), is below our threshold for tentative evidence of
linkage but may still reflect variation in the
apoE gene not measured by the
2,
3, and
4
alleles47 (J.H.
Stengård, personal communication, 2000).
The ability to detect linkage of markers with the
apoE gene region, when the
plasma apoE level is considered, documents the utility of this method
to localize genes influencing interindividual variation in plasma lipid
traits of complex inheritance. Simulations by Blangero et
al39 indicate that a
variance-components linkage analysis of 1000 individuals in
randomly ascertained extended pedigrees has a power of 80% to detect a
QTL that accounts for as little as 20% of trait variation with a LOD
score of 3.0. However, the pedigrees used in the study of Blangero et
al were much larger than the pedigrees used in the present study,
and the power of the variance-components method to detect linkage
decreases with declining QTL-specific
heritability.48 Thus, the
many QTLs that are each expected to have considerably smaller effects
on genetic variance may not be detected by a linkage analysis
performed with this simple model on small randomly ascertained
pedigrees.
Polymorphism in the
apoE gene has been identified
as a predictor of variation in intermediate risk factors for CAD, apart
from apoE level, including total-C, TGs, apoB, and
HDL-C.33 45 49
For example, the traditional
apoE genotypes explain
3% and 23% of variation in TGs in men and women, respectively, in
Rochester, Minn.33 Peak
multipoint LOD scores on chromosome 19 for TGs did not exceed 2.00, nor
did the peak correspond to the position of the
apoE gene
(Table 3). However, the effect of
apoE on TG level varies in
contexts such as age and measures of body
size.50 51 52 53
The context-dependent effects of
apoE genotype on other
measures of lipid metabolism, and on the correlations among
them, are also well
known.52 53 Much of
the complexity of the genetic architecture of apoE and other plasma
lipid and apolipoprotein traits is ignored by the simple additive
variance-components model of linkage used in the present study. We
chose to initially examine the first-order effects to maximize the
power to detect a QTL by using the full sample in this first genome
scan. We expect that QTLs with large marginal effects will be detected
by this model but that QTLs whose effects are confined to particular
strata of the population may be associated with LOD scores below our
threshold for linkage.
Despite the limitations of the simple model used in the
present study, several regions suggestive of linkage with lipid and
apolipoprotein traits within which lie candidate genes for lipid
metabolism were identified
(Table 4). In addition to the evidence for linkage with a
QTL influencing variation in apoE level on chromosome 19, tentative
evidence for linkage was observed on chromosome 12. This region
contains the gene encoding the catalytic polypeptide of the apoB
mRNA–editing enzyme (APOBEC1),
which is involved in the metabolism of apoE. In LDL
receptor knockout mice, inactivation of
APOBEC1 resulted in a 60%
increase in plasma apoE.54
Thus, variation in the APOBEC1
gene may affect the risk of CAD by influencing variation in the apoE
level in particular contexts indexed by the level of LDL receptor
expression. Additionally, evidence of a locus on chromosome 12
influencing the risk of Alzheimer’s disease is stronger in
families without the apoE
4
allele,55 56 57
a risk factor for CAD and Alzheimer’s disease.
Regions on chromosomes 4 and 5 showed suggestive evidence of
the influence of QTL on apoA-II variation (LOD score >2.00)
(Figure 1). ApoA-II is the second most abundant
apolipoprotein in HDL (after apoA-I) and may affect the risk of CAD by
influencing the rate of cholesterol efflux from
peripheral
tissues.58 A gene on
chromosome 4 that codes for carboxypeptidase E is a positional
candidate gene for influencing apoA-II level. Carboxypeptidase E is an
enzyme responsible for prohormone sorting and processing, influencing
such signaling molecules as pro-opiomelanocortin and insulin, both of
which are involved in energy
homeostasis.59 60 61
Also near the position of the multipoint LOD peak for apoA-II on
chromosome 4 is the gene coding for the fibrinogen-ß polypeptide,
which has been associated with increased risk of peripheral
atherosclerosis.62
On chromosome 5, positional candidate genes include
cholesterologenesis enzymes
3-hydroxy-3-methylglutaryl-coenzyme A reductase
(HMGCR) and 3-hydroxy-3-methylglutaryl coenzyme A
synthase 1 (HMGCS1). Inhibition of
HMGCR in humans alters the plasma lipid profile,
including a significant reduction in the ratio of
cholesterol to
HDL-C.63 In rats,
administration of tetradecylthioacetic acid decreases apoA-II while
increasing HMGCS1 mRNA levels, evidence of a link
between HMGCS1 expression and the level of liver
apolipoproteins.64 Neither of
the 2 regions influencing variation in apoA-II that were identified in
this population included the structural gene for
apoA-II on chromosome 1q21-q23.
Variation in the apoA-II gene
can account for 10% to 11% of interindividual variation in the
apoA-II level.65 However, in
the sample considered here, variation in the
apoA-II gene does not
contribute to variation in apoA-II at a level detectable by linkage
analysis (LOD score <0.50,
Table 2).
ApoA-I is the most abundant apolipoprotein in HDL-C and is a better predictor of CAD risk than is HDL-C alone.10 13 Studies of the genetic determination of apoA-I have found evidence of 1 major locus,66 2 major loci,67 or polygenes alone68 69 influencing the variation in different populations. The LOD score of 2.02 at 128 cM in the present study suggests that variation in plasma apoA-I may be influenced by a gene on chromosome 12q. A positional candidate gene, scavenger receptor class B type 1 (SRB1), is located near the position of this LOD peak. SRB1, with a binding affinity for multiple apolipoproteins including apoA-I, apoA-II, and apoC-III, acts as a receptor for the selective uptake of HDL cholesteryl ester in the liver and steroidogenic tissues and for the efflux of cholesterol from peripheral cells.70 The overexpression of SRB1 in mice is accompanied by a substantial, but transitory, decrease in HDL-C and apoA-I levels.71 It is possible that changes in SRB1 expression may also influence apoA-I levels in humans.
Evidence suggestive of the influence of a QTL on variation in total-C/HDL-C was observed from the analyses of chromosomes 5 and 17. Regulation of microsomal transfer protein (MTP) expression and activity may influence plasma cholesterol level. There are no obvious candidate genes near this location of the LOD peak on chromosome 5. A positional candidate gene located in the region of the peak LOD score on chromosome 17 is the procollagen proline 2-oxoglutarate 4-dioxygenase cellular thyroid hormone binding protein, a multifunctional protein that is the small subunit of the MTP heterodimer. MTP catalyzes the transport of TGs, cholesteryl esters, and phospholipids between phospholipid surfaces. Defects in the larger subunit of MTP have been previously associated with abetalipoproteinemia.72
Three regions identified in our genome-wide linkage scan, although associated with LOD scores <2.00, are worthy of further mention as supporting evidence for previously published reports of linkage. Aouizerat et al23 found suggestive evidence of linkage on chromosome 11p with a gene contributing to familial combined hyperlipidemia in Dutch pedigrees. The region, near the marker D11S1324 (35 cM), was associated with a combined lipid phenotype that included elevated total-C (>90th percentile). Tentative evidence for linkage (LOD score 1.84 at 34 cM) with a gene(s) on chromosome 11p (34 cM) influencing plasma total-C level was observed in the present study. Knoblauch et al25 observed strong evidence of linkage with a region of chromosome 13q near the markers D13S1241 and D13S786 (76 to 77 cM) influencing HDL-C, LDL, total-C, and BMI in an Arab family from Israel ascertained for familial hypercholesterolemia and in an independent sample of healthy white monozygotic and dizygotic twins from Germany. This region of 13q is very near the QLTs identified in the present study, which may influence TG (LOD score 1.64 at 86 cM) and TG/HDL-C (LOD score 1.37 at 64 cM) levels.
The results of our genome-wide linkage analyses of 11 lipid and apolipoprotein traits provide a focus for future genetic studies. This scan has identified chromosomal regions hypothesized to contain new genes influencing interindividual variation in apoA-I, apoA-II, and total-C/HDL-C and has provided supporting evidence of regions that may contain genes influencing variation in apoE, total-C, TGs, and TG/HDL-C. These results suggest a list of positional candidate genes for detailed DNA sequence analysis aimed at identifying functional mutations that affect variation in the plasma lipid profile. Identification of genes that influence lipid metabolism will lead to a better understanding of the etiology of CAD and provide suggestions for the development of new therapies for the treatment and prevention of disease. Finally, a comparison of our results with the findings of other studies suggests to us the importance of gene-environment interactions and the need for context-dependent linkage analyses to identify regions of the genome that contain genes that influence interindividual variation in plasma lipid and apolipoprotein levels only in particular subdivisions of the population at large.
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Acknowledgments |
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Funding for this research was provided by National Institutes of Health grants HL-39107, HL-51021, and HD-32197. We would like to thank Paul Kopec, Ken Weiss, and Sara Hamon at the University of Michigan and Terry Bertin and Kim Lawson at the University of Texas for their technical assistance and critical input.
Received February 9, 2001; accepted February 26, 2001.
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