Published online before print
June 12, 2003, 10.1101/gr.1185803
Genome Res. 13:1654-1664, 2003
©2003 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/03 $5.00
Letter
Using Advanced Intercross Lines for High-Resolution Mapping of HDL Cholesterol Quantitative Trait Loci
Xiaosong Wang1,
Isabelle Le Roy2,
Edwige Nicodeme3,
Renhua Li1,
Richard Wagner1,
Christina Petros1,
Gary A. Churchill1,
Stephen Harris4,
Ariel Darvasi1,5,
Jorge Kirilovsky3,
Pierre L. Roubertoux6 and
Beverly Paigen1,7
1 The Jackson Laboratory, Bar Harbor, Maine 04609, USA
2 Genétiqué, Neurogenétiqué, comportement, CNRS,
45071 Orléans Cedex 2, France
3 GlaxoSmithKline, Centre de Recherches, 91951 Les Ulis Cedex,
France
4 GlaxoSmithKline, Genetics and Discovery Alliances, Medicine Research
Centre, Stevenage SG1 2NY, UK
5 Life Sciences Institute, the Hebrew University of Jerusalem, Jerusalem
91904, Israel
6 Institut de Neurosciences Physiologiques et Cognitives, INPC.CNRS, 13402
Marseille Cedex 20, France
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ABSTRACT
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Mapping quantitative trait loci (QTLs)with high resolution facilitates
identification and positional cloning of the underlying genes. The novel
approach of advanced intercross lines (AILs) generates many more recombination
events and thus can potentially narrow QTLs significantly more than do
conventional backcrosses and F2 intercrosses. In this study, we
carried out QTL analyses in (C57BL/6J x NZB/BlNJ)x C57BL/6J
backcross progeny fed either chow or an atherogenic diet to detect QTLs that
regulate high-density lipoprotein cholesterol (HDL)concentrations, and in
(C57BL/6J x NZB/BlNJ)F11 AIL progeny to confirm and narrow
those QTLs. QTLs for HDL concentrations were found on chromosomes 1, 5, and
16. AIL not only narrowed the QTLs significantly more than did a conventional
backcross but also resolved a chromosome 5 QTL identified in the backcross
into two QTLs, the peaks of both being outside the backcross QTL region. We
tested 27 candidate genes and found significant mRNA expression differences
for 12 (Nr1i3, Apoa2, Sap, Tgfb2, Fgfbp1, Prom, Ppargc1, Tcf1, Ncor2,
Srb1, App, and Ifnar). Some of these underlay the same QTL,
indicating that expression differences are common and not sufficient to
identify QTL genes. All the major HDL QTLs in our study had homologous
counterparts in humans, implying that their underlying genes regulate HDL in
humans.
Many cardiovascular diseases result from cholesterol imbalances. In the
past several years, prospective, randomized, controlled clinical trials have
demonstrated that lowering low-density lipoprotein cholesterol (LDL) can
significantly reduce the incidence of cardiovascular diseases. Still, 60% to
70% of treated patients develop adverse cardiovascular events
(Shah et al. 2001 ).
Substantial evidence from major epidemiological studies indicates that the
level of plasma high-density lipoprotein cholesterol (HDL), especially at
average to slightly above average concentrations, is inversely related to
coronary artery disease incidence in both women and men. Furthermore, these
studies show that coronary artery disease risk associated with HDL is
independent of plasma LDL, other lipid parameters (triglycerides, total
cholesterol), and other non-lipid risk factors
(Franceschini 2001). Many
studies indicate that therapies that raise HDL may significantly reduce the
incidence of cardiovascular diseases (Boden
and Pearson 2000). Although its beneficial properties are not
clearly understood, HDL may protect against atherosclerosis in several ways,
including stimulating reverse cholesterol transport, inhibiting oxidation of
LDL, inhibiting inflammation by suppressing the formation of adhesion
molecules and macrophage chemotactic proteins, reducing lipoprotein retention,
and attenuating endothelial dysfunction
(Libby 2001;
Nofer et al. 2002). Thus, it
is very important to find the genes that regulate HDL levels: They may provide
new therapeutic targets for preventing and curing atherosclerosis.
Plasma HDL levels are controlled by both genes and environment, and up to
70% of the variation of HDL levels in humans is genetically determined
(Rader and Maugeais 2000).
Some of the responsible genes have been found by searching for polymorphisms
in genes that affect HDL. Examples are Abca1
(Bodzioch et al. 1999;
Rust et al. 1999) and
Lipc (Jansen et al.
1999). Quantitative trait loci (QTL) mapping has been used to
identify genomic regions that contain genes regulating HDL. In fact, at least
27 mouse QTLs and 22 human QTLs for HDL levels have been found
(Wang and Paigen 2002). Those
QTLs often have large confidence intervals (CIs) because they were detected
from limited recombination events. Until these QTLs are narrowed, identifying
their underlying genes by positional cloning will be challenging.
At least eight different experimental strategies or populations can be used
to finely resolve QTLs (Darvasi
1998; McPeek
2000). These are selective phenotyping, selective genotyping,
recombinant progeny testing, interval-specific congenic lines
(Darvasi 1998), advanced
intercross lines (AILs; Darvasi and Soller
1995), recombinant inbred segregation test
(Darvasi 1998), recombinant
inbred intercross test (D.W. Threadgill, pers. comm.), and genetically
heterogeneous stocks (Talbot et al.
1999; Mott et al.
2000). The advantages and disadvantages of each depend on the
mapping resolution desired; the size, effect (dominant or additive) and
expected number of QTLs; the difficulty of phenotyping the animal models; and
the available resources (time, animal space, money).
Regardless of the strategy used, narrowing a QTL interval in a mouse cross
depends on generating many recombinations and detecting them by typing the
cross progeny with closely spaced polymorphic markers. The large number of
recombinations required is usually not generated in F2 and
backcross populations, even large ones. On the other hand, repeated
intercrossing does generate them, often eliminates linkage disequilibrium, and
may resolve a QTL to one-fifth the size possible in backcross and
F2 populations (Darvasi and
Soller 1995). Such repeated intercrossing is the basis of AILs. To
produce such lines, mice of two inbred strains assumed homozygous for
alternative alleles at a series of QTLs are used to produce F2,
F3, F4, F5, through F10 or more
generations, by randomly intercrossing mice in each generation to produce the
next, avoiding sibling or cousin pairs. Thus, instead of producing a few
recombinations in either a single large F2 or backcross population,
the many recombinations required to finely resolve QTLs are accumulated in a
single relatively small population produced over the course of many
generations. Since AIL were first proposed in 1995
(Darvasi and Soller 1995),
their use has been reported only once, to narrow a QTL interval for
trypanosomiasis resistance (Iraqi et al.
2000).
C57BL/6J (B6) mice have relatively low plasma HDL levels and are
susceptible to atherosclerosis, whereas NZB/BlNJ (NZB) mice have relatively
high plasma HDL levels and are resistant to atherosclerosis. In this study, we
used a (B6 x NZB)F1 x B6 backcross to detect
significant QTLs that either individually or interactively regulate HDL
levels. We then used a (B6 x NZB)F11 AIL to finely resolve
those QTLs and statistically show that many QTLs exist on the same chromosome.
Each QTL was examined for candidate genes, which were tested for mRNA
expression differences between the two parental strains.
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RESULTS
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Plasma Cholesterol Concentrations and Distribution in the Parental
Strains, F1, N2, and F11 Progeny
Plasma HDL concentration was twice as high in chow-fed NZB mice than it was
in B6 mice (Table 1). Whereas 6
weeks of eating a high-fat diet raised the HDL concentration of the NZB mice
by 60%, it did not affect HDL concentrations in B6 mice. These results were
consistent with previous research showing that the high-fat diet can increase
HDL levels in female NZB mice (Pitman et al.
1998,
2002). The age of the mice had
nothing to do with the change of plasma HDL levels: 12-week-old B6 and NZB
mice fed chow had the same HDL levels as when they were 8 weeks old (data not
shown).
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Table 1. Plasma Lipid Concentrations in the Female Mice From Various B6 x
NZB Crosses and Their Parental Strains Before and After High-Fat Diet
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Non-HDL levels did not differ between NZB and B6 mice fed either diet. The
difference in total cholesterol levels between these strains, before and after
they were fed a high-fat diet, was due to the difference in HDL levels between
the two strains (Table 1).
Although the total and HDL cholesterol levels of F1 mice were
intermediate between those of the parental strains, they were closer to those
of the B6 parents, whether they ate either a chow or a high-fat diet
(Table 1). The distributions of
HDL levels among the F11 AIL progeny were unimodal, implying that
many genes affect the phenotypes (Fig.
1).
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Figure 1 Distribution of HDL concentrations and HDL inducibility in the 345 female
(B6 x NZB)F11 AIL progeny. HDL concentrations were determined
before (A) and after (B) the mice consumed the high-fat diet
for 6 weeks. (C) HDL inducibility was calculated as follows: ([HDL
concentrations of mice fed a high-fat diet - HDL concentrations of mice fed
chow]/HDL concentrations of mice fed chow) x 100%.
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Correlation of Plasma Cholesterol Levels and Atherosclerotic Lesions
in the Backcross
The atherosclerotic lesions of all 104 female backcross progeny were
measured after the progeny had consumed the high-fat diet for 15 weeks. The
total plasma cholesterol and the HDL of each progeny were determined three
times: immediately before and 4 and 15 weeks after the progeny began eating
the atherogenic diet. The result of a hierarchical clustering analysis is
shown in Figure 2. HDL levels
and atherosclerosis in mice fed a high-fat diet for 15 weeks were most closely
clustered, indicating they were very closely related. Preatherogenic diet
cholesterol levels formed the next most closely related cluster to
atherosclerosis (75% of total cholesterol in chow-fed mice is HDL; data not
shown). Thus, the three cholesterol parameters most closely correlated with
atherosclerosis were HDL levels induced by the 15-week atherogenic diet and
the basal cholesterol levels before the mice were fed atherogenic diet (total
cholesterol and HDL at the start of high-fat diet). In contrast, the four
parameters least correlated with atherosclerosis were non-HDL levels induced
by the high-fat diet (the bottom four parameters in
Fig. 2). Thus, atherogenesis
was more closely related to HDL levels than to non-HDL levels. However, we did
not find any significant QTL for atherosclerotic lesions in the backcross
progeny after they consumed high-fat diet for 15 weeks, because the majority
of the mice did not develop significant aortic atherosclerosis.
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Figure 2 Hierachical clustering analysis for atherosclerosis and plasma lipid levels
in the 104 (B6 x NZB)F1 x B6 backcross progeny. Total and HDL
cholesterol were measured in mice fed chow (0 w), mice fed a high-fat diet for
4 weeks (4 w), and mice fed a high-fat diet for 15 weeks (15 w). Non-HDL was
calculated by subtracting HDL from total cholesterol. Atherosclerotic lesions
were determined by averaging the lesion sizes of five aortic root cross
sections from each mouse. The length along the X-axis represents the
distance between two clusters, expressed in semipartial R2
distance. The more similar the traits are, the closer they will appear in the
cluster tree.
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QTL Analysis of Genetic Factors Affecting Plasma HDL
Concentrations
Backcross
Genome scans in the backcross progeny fed chow and in those fed a high-fat
diet revealed QTLs for HDL levels on both Chrs 1 and 5
(Fig. 3A,B). The positions, 95%
CIs, LOD scores, and the nearest markers for these QTLs are shown in
Table 2. The Chr 1 QTL
explained more of the HDL variation than did the Chr 5 QTL (22.3% compared
with 9% for chow-fed mice, and 23.3% compared with 19.2% for fat-fed mice;
Table 2). Homozygous B6
genotypes at Chrs 1 and 5 QTLs were associated with lower HDL concentrations,
and heterozygous genotypes were associated with higher HDL levels
(Fig. 4A).
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Figure 3 Genome scans for HDL concentrations in (B6 x NZB)F1
x B6 backcross progeny fed chow (A) and high-fat diet for 4
weeks (B), and HDL concentrations in the (B6 x
NZB)F11 AIL progeny fed chow (C) and a high-fat diet for 6
weeks (D). The Y-axis indicates LOD ratio scores, and the
X-axis indicates chromosome positions. Horizontal lines indicate
suggestive (P < 0.10) and significant (P < 0.05)
thresholds, calculated by permutation tests.
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Figure 4 Allele effect and interaction plots for the QTLs found in the (B6 x
NZB)F1 x B6 backcross and in the (B6 x
NZB)F11 AIL. (A) Genotypes at marker alleles nearest each
QTL identified in the backcross and AIL-affected HDL concentrations in
chow-fed and high-fat diet–fed progeny (4 weeks for backcross progeny
and 6 weeks for AIL progeny). (B) Genotypes at marker alleles closest
to the Chr 5 and 16 QTLs identified in the backcross and AIL interacted to
affect HDL concentrations of chow-fed backcross AIL progeny. All HDL
concentrations are plotted as mean ± SEM (mg/dL).
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A pair-wise genome scan for HDL in chow-fed backcross mice revealed that a
Chr 5 QTL near marker D5Mit161 (70 cM) interacted with a Chr 16 QTL
near marker D16Mit70 (57 cM). When the D16Mit70 genotype was
B/B, HDL levels were higher with a B/N than with a B/B genotype at
D5Mit161; when the genotype at D16Mit70 was B/N, HDL levels
were the same for either a B/B or B/N genotype at D5Mit161
(Fig. 4B).
Advanced Intercross Lines
Because significant QTLs for HDL levels were found on Chrs 1 and 5 and an
interacting locus on Chr 16 in the backcross, we genotyped the AIL progeny
with 23 Chr 1 markers, 30 Chr 5 markers, and eight Chr 16 markers. The HDL
QTLs subsequently found are shown in Figure
3, C and D, and their positions, 95% CIs, LOD scores, and nearest
markers are shown in Table 2. The QTLs were named Hdlq (HDL QTLs) followed by a number. The Chr 1
QTLs for HDL levels in AIL progeny fed chow and in those fed a high-fat diet
confirmed and significantly narrowed Hdlq5 found in the backcross
progeny (both in those fed chow and in those fed a high-fat diet). In addition
to Hdlq5, a second QTL for HDL levels was found on Chr 1 and was
given a locus name Hdlq6.
Whereas analysis of AIL chow-fed mice resolved the backcross Chr 5 HDL QTL
into two separate ones, it resolved that in AIL mice fed the high-fat diet
into three distinct ones. The first, at 29 cM in both chow- and fat-fed mice,
was given a locus name Hdlq7; the second, at 60 cM in fat-fed mice,
was given a locus name Hdlq8; and the third, at 69 cM in both chow-
and fat-fed mice, had already been named Hdlq1
(Pitman et al. 2002).
The Chr 1 QTLs explained more of the variation than did any of the Chr 5
QTLs (39.0% compared with 14.8% and 9.0% for chow-fed mice, and 30.7% [16.6% +
14.1%] compared with 9.9%, 4.7%, and 7.4% for mice fed the high-fat diet;
Table 2). Homozygous B6
genotypes at all these QTLs were associated with the lowest HDL
concentrations, homozygous NZB genotypes were associated with the highest HDL
concentrations, and heterozygosity at these loci was associated with
intermediate HDL levels (Fig.
4A).
The LOD score plot patterns in Figure 5,
A and B, were similar. Thus, although Hdlq6 was
statistically significant only in high fat-fed mice, we still think it exists
in the chow-fed mice. Similarly, although Hdlq8 was found only in
high fat-fed mice in AIL and appeared as a peak proximal to Hdlq1 in
the LOD score plot (Fig. 5D),
it may also exist in chow-fed mice, as indicated by a shoulder proximal to
Hdlq1 (Fig. 5C).
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Figure 5 Comparison of QTLs detected in the (B6 x NZB)F1 x B6
backcross and (B6 x NZB)F11 AIL. The fine scans compare the
Chr 1 HDL QTLs (A, in chow-fed progeny; B in high fat-fed
progeny) and Chr 5 HDL QTLs (C in chow-fed progeny; D in
high fat-fed progeny) identified in the backcross (dashed line) and AIL (solid
line) progeny. The Y-axis indicates LOD ratio scores, and the
X-axis indicates chromosome positions. Solid bars along the
X-axis represent 95% confidence intervals (CIs) for the AIL QTLs, and
the open bars represent the 95% CI for the backcross QTLs, calculated
according to the posterior probability densities of the QTL locations. Some
candidate genes are shown along the X-axis.
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Because the single marker genome scan showed multiple QTLs on Chr 1 and Chr
5, we carried out a statistical analysis to determine if they were separate
QTLs. Simulation tests declaring either one versus two or two versus three
QTLs on the same chromosome were carried out 1000 times by using MAKEF2
function in Pseudomarker (release version 0.9;
http://aretha.jax.org/jax-cgi/churchill/index.cgi).
Maximum LOD scores were calculated based on 1-QTL, 2-QTL, and 3-QTL models.
The LOD score difference between 2-QTL and 1-QTL models
( LOD2-1) was used to judge whether
there were two separate QTLs on the same chromosome; the LOD score difference
between 3-QTL and 2-QTL models
(LOD3-2) was used to judge whether
there were three separate QTLs on the same chromosome. Threshold LOD
differences (LOD) were computed by permutation tests, and they were
LOD of 1.58 (P = 0.20), LOD of 1.83 (P =
0.10), and LOD of 1.99 (P = 0.05). This means that the LOD
score for the 2-QTL model must be at least 1.99 higher than the LOD for the
1-QTL model to accept that a chromosome contains two separate QTLs, and the
LOD score for the 3-QTL model must be at least 1.99 higher than the LOD for
the 2-QTL model to accept that a chromosome contains three separate QTLs. As
shown in Table 3, the LOD score
difference for one versus two QTL models
(LOD2-1) for the Chr 1 QTLs obtained
by analyzing the high-fat diet–fed AIL progeny was 1.5 (not
significant); for the Chr 5 QTLs obtained by analyzing the chow-fed progeny,
the LOD score difference for one versus two QTL models
(LOD2-1) was 2.2 (P <
0.05); for the Chr 5 QTLs obtained by analyzing the high fat-fed progeny, the
LOD score difference for one versus two QTL models
(LOD2-1) was 2.4 (P <
0.05), and for two versus three QTL models
(LOD3-2) was 1.9 (0.10 > P
> 0.05; Table 3). Therefore,
there were at least two Chr 5 QTLs for HDL levels in both chow and high
fat-fed progeny, and possibly three in high fat-fed progeny. Likewise, there
is an 80% chance (P 0.2) that there were two distal Chr 1 HDL QTLs in
high fat-fed progeny.
A pair-wise scan for Chrs 1, 5, and 16 in the AIL confirmed the interaction
between a Chr 16 and a Chr 5 QTL revealed in the backcross: Loci on Chrs 16
(near marker D16Mit227, 58.8 cM) and 5 (Hdlq7, near marker
D5Mit55, 28 cM) were found to interactively affect HDL levels
(Fig. 4B). However, although
the AIL analysis revealed that the position of the Chr 5 interacting locus was
 30 cM, the backcross analysis revealed it to be 70 cM, perhaps
because QTLs are more finely resolved and accurately mapped in AIL than in
backcrosses. The B allele was associated with lower HDL levels than was the N
allele, and the lowest HDL level was found when both the Chr 5 and Chr 16 loci
were homozygous for B alleles (Fig.
4B).
Finally, all main effect QTLs detected by genome scans were entered into a
multiple regression model to assess their combined effect on HDL levels. To
assess those effects, we used F statistics based on adjusted (type
III) sum of squares (Table 4).
In chow-fed progeny, Hdlq5, Hdlq7, Hdlq1, and Hdlq9
significantly (P < 0.05) regulated HDL concentrations, with
Hdlq5 explaining more variance (28.7%) than Hdlq7 and
Hdlq1 combined (6.8%). In high fat-fed progeny, Hdlq5, Hdlq6,
Hdlq7, Hdlq8, and Hdlq1 each significantly (P < 0.05)
affected HDL levels. The multiple regression model indicated that the Chr 1
QTLs affected HDL levels less in high fat-fed progeny than in chow-fed
progeny.
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Table 4. Multiple Regression Model for Loci Affecting Plasma HDL Concentrations
in Female Mice From C57BL/6JxNZB AIL
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Comparison of the QTLs for HDL Levels in Backcross and AIL
The 95% CIs computed for the AIL QTLs were considerably narrower than those
computed for the QTLs identified in the backcross
(Table 2;
Fig. 5). Whereas the backcross
CI for Hdlq5 in chow-fed mice was 16 cM, it was 6 cM in AIL; whereas
the backcross CI for the same QTL in high fat-fed mice was 23 cM, it was 6 cM
in AIL. Likewise, the Chr 5 QTLs identified in the AIL were considerably
narrower than the Chr 5 QTL identified in the backcross. Furthermore, the AIL
analysis resolved the broad Chr 5 QTL identified in the backcross into
separate loci (Fig. 5C,D; Table 2). The backcross Chr 5
QTL (whether found in the mice fed chow or the high-fat diet) was between the
first and second main effect QTLs found in comparable AIL mice
(Fig. 5C,D).
Real-Time PCR Quantification of mRNA Expression of Candidate Genes
for Plasma HDL Concentrations in B6 and NZB Mice
Candidate genes for the HDL QTLs detected in the AIL were scrutinized in
the Ensembl Mouse Genome Server
(http://www.ensembl.org/Mus_musculus/).
Real-time PCR with mRNA extracted from B6 and NZB mouse liver tissue was used
to quantify expression levels of 27 candidate genes (a list of the candidate
genes and the primer sequences for these genes are listed in Supplemental
Table 1 available at
www.genome.org).
We normalized these expression levels to those of glyceraldehyde-3-phosphate
dehydrogenase (Gapd), the expression of which does not differ between
chow- and high fat-fed mice of the same strain (data not shown). Among the six
Hdlq5 candidate genes tested, mRNA expression levels of
Nr1i3 (in chow-fed mice only), Apoa2, and Sap were
significantly (P < 0.05) different between B6 and NZB mice.
Transforming growth factor ( 2 Tgfb2), a candidate
gene for Hdlq6, was expressed significantly more (P <
0.05) in B6 than in NZB mice. Among the 13 Chr 5 candidate genes tested, mRNA
expression levels of Fgfbp1, Prom, Ppargc1, Tcf1, Ncor2, and
Srb1 were significantly (P < 0.05) different between B6
and NZB mice. Among the seven Chr 16 candidates tested, mRNA expression levels
of App and Ifnar were significantly (P < 0.05)
different between B6 and NZB mice (Fig.
6). In summary, NZB mice expressed more mRNA copies of Nr1i3,
Sap, Fgfbp1, Ppargc1, Tcf1, Ncor2, App, and Ifnar than did B6
mice, and B6 expressed more mRNA copies of Apoa2, Tgfb2, Prom, and
Srb1 than did NZB mice.
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Figure 6 Real-time PCR quantification of mRNA expression levels of candidate genes
for plasma HDL concentrations in B6 and NZB mice. Total RNA was extracted from
the livers of female mice-fed chow and from mice fed a high-fat diet for 4
weeks. It was then transcribed into cDNA. mRNA expression levels for each
candidate gene was quantified with real time PCR by using fluorescent SYBR.
Results were normalized to Gapd and expressed as mRNA copies of
candidates per 1000 copies of Gapd. (*P <
0.05, and **P < 0.01, compared with B6 mice on the same
diet).
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DISCUSSION
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Atherosclerosis is a progressive process contributing to the pathogenesis
of coronary artery and cerebral vascular diseases. Its very complex etiology
involves many genetic and environmental risk factors. A great deal of evidence
indicates that it is attenuated by relatively high HDL concentrations
(Gordon and Rifkind 1989). To
find the genes that control the HDL concentrations, many QTL studies in mice
and humans have been carried out. In all, these studies have identified 27
mouse and 22 human HDL-regulating QTLs
(Wang and Paigen 2002).
In this study, we used a backcross to detect HDL QTLs and AIL to confirm
and finely resolve them. AIL proved to be a very powerful tool for narrowing
QTL: The 16 cM and 23 cM CI for Hdlq5 identified in the chow- and
fat-fed backcross progeny, respectively, were each narrowed to 6 cM by using
AIL. Not only did AIL significantly narrow Hdlq5, it also detected a
second HDL QTL (Hdlq6) on Chr 1 in high fat-fed mice.
Most interestingly, the AIL approach resolved the backcross Chr 5 QTL into
three separate ones, each with a higher LOD score than that of the backcross.
However, none of the three localized to the backcross QTL position. Thus,
which are the real QTLs? Haley and Knott
(1992) analyzed a simulated
regression in which a presumed single QTL may localize in the middle of what
are actually two linked QTLs. This appears to be the situation in our study:
The Chr 5 QTL (54 cM) identified in the chow-fed backcross progeny was between
the first (Hdlq7, 29 cM) and the second (Hdlq1, 69 cM) QTL
identified in the AIL; the Chr 5 QTL (38 cM) identified in the high fat-fed
backcross progeny was between the first (Hdlq7, 29 cM) and the second
(Hdlq8, 60 cM) QTLs identified in the AIL. Perhaps the backcross Chr
5 QTL is a "ghost" QTL, and the flanking AIL Chr 5 QTLs are the
real ones. A ghost QTL is the result of model mis-specification
(Martinez and Curnow 1992). In
a genome scan, we assume that there is only one QTL. When there are in fact
two (or more) QTLs and they are moderately tightly linked, the LOD curve can
present a peak that does not correspond to either of the two QTLs. Such a
ghost peak is most likely to occur in the middle, and as we have demonstrated,
the CI based on the ghost peak may exclude both of the real QTLs. It is
interesting that QTLs for HDL levels with similar positions to our backcross
Chr 5 QTL have been found before: once in a backcross
(Pitman et al. 2002) and three
times in three different intercrosses
(Machleder et al. 1997;
Mehrabian et al. 2000;
Pitman et al. 2002). Thus,
AIL, with many more recombination events, and thus more resolving power than
conventional backcrosses and intercrosses, revealed the true and much narrower
QTLs. Without a refined mapping strategy such as AIL, pursuing genes
underlying ghost QTLs would be fruitless.
By analyzing our AIL with a recently developed statistical approach to
detect gene interactions (Sen and
Churchill 2001), we detected a gene interaction between
Hdlq7 on Chr 5 and Hdlq9 on Chr 16. Finding gene
interactions has some practical implications. First, they can help determine
which genetic background is best for producing overlapping congenic lines.
Such lines, which are used to finely resolve QTL and confirm their association
with a phenotype, are constructed by introgressing a locus into a nonnative
background. Interactions between this locus and loci in the new background may
dramatically alter and lead to a misinterpretation of its effects. Second,
genes can interact in many ways: They may either co-activate or co-repress the
same target gene(s) in the same pathway, they may work antagonistically, and
their ligands and receptors may physically interact. Understanding such
dynamics might help an investigator choose the right candidates for testing.
Third, identifying interactions between mouse QTL genes may help identify
interacting genes involved in human disease
(Glorioso et al. 2001;
Herrera et al. 2001;
Korstanje and Paigen
2002).
Once the QTLs identified in the backcross were more finely resolved with
AIL, we searched for candidate genes in the Mouse Genome Server
(http://www.ensembl.org/Mus_musculus/)
and tested several of them. We were surprised to find that as many as 12 out
of the 27 candidates, some for the same QTLs, had twofold or more differences
in expression levels between B6 and NZB mice. This implies that interstrain
gene expression differences detected by the sensitive real-time PCR method may
be common and insufficient evidence that a gene underlies a QTL. It is
noteworthy that the mRNA expression level of a gene is only a crude predictor
of its functions. Proving that a gene underlies a QTL requires additional
evidence, such as either testing its expression in the F2 progeny
or showing that a polymorphism in its regulatory region affects transcription.
Of course, a mutation in a gene underlying a QTL may not result in an
expression difference between two strains; it may only affect the function of
a protein.
Many QTLs that regulate HDL concentrations in humans have been reported
(for review, see Wang and Paigen
2002), and some are homologous to the QTLs we found in mice: Two
QTLs on human Chr 1 (Coon et al.
2001; Elbein and Hasstedt
2002) are homologous to Hdlq5, a human QTL on
4p16–15.3 (Coon et al.
2001) is homologous to Hdlq7, a human QTL on 1p22
(Peacock et al. 2001) is
homologous to Hdlq8, and two human QTLs on chromosomes 12q24.2,
(Almasy et al. 1999) and
13q12–13,(Peacock et al.
2001; Elbein and Hasstedt
2002) are homologous to Hdlq1
(Table 2). This implies that
the human and mouse homologous HDL QTLs may have the same candidate genes.
In summary, we used a (B6 x NZB)F1 x B6 backcross to
detect significant QTLs that either individually or interactively regulate HDL
levels, and we used (B6 x NZB)F11 AIL to confirm and resolve
those QTLs finely enough for us to test their candidate genes. We have thus
shown that the AIL approach has the potential to finely resolve large QTLs
(some perhaps being ghost QTLs) and eliminate false ones. Future studies could
compare the sequences of the promoter and coding regions of promising
candidate genes between NZB and B6 mice, compare the protein levels of these
candidate genes between the two strains, and explore the functions of the
candidate genes in gene-targeted and transgenic mice. Once the genes
underlying these HDL QTL are found in mice, they may be directly tested in
humans.
|
METHODS
|
|---|
Mice and Diets
Backcross Progeny
We obtained NZB/BlNJ (NZB) and C57BL/6J (B6) mice from The Jackson
Laboratory, Bar Harbor, Maine, and mated them to produce 104 (B6 x
NZB)F1 x B6 backcross females. Mice were caged with pine
shavings at The Jackson Laboratory in a climate-controlled room (22°C to
23°C) with a 14-h/10-h light/dark cycle (lights on at 6:00 a.m.), and they
had free access to acidified water and a chow diet (Old Guilford 234A, CT)
until they were 8 weeks old. At that time, some mice were fed an atherogenic
diet containing 15% dairy fat, 50% sucrose, 20% casein, 0.5% cholic acid, 1.0%
cholesterol, cellulose, vitamins, and minerals
(Nishina et al. 1990) for 15
weeks. All experiments were approved by the Animal Care and Use Committee at
The Jackson Laboratory.
AIL Progeny
The AIL progeny were produced by Pierre L. Roubertoux at the University
Paris-René Descartes, and then maintained at the Centre National De La
Recherche Scientifique (CNRS) in Orléans, France. B6 and NZB mice were
chosen to construct the AIL because they met two critical criteria for
successful AIL analysis: simple sequence length polymorphisms (SSLP) are
common between the two strains, and the lipid levels in NZB mice are
considerably higher than those of B6 mice (see Mouse Phenome Database in MGI).
The mice were caged with dust-free sawdust in a climate-controlled room (23.5
± 0.5°C) with a 12-h/12-h light/dark cycle (lights on at 7:00
a.m.), and they had free access to water and chow (Old Guilford 234A, CT). B6
x NZB AIL (F2 through F11) were constructed by
randomly intercrossing mice (avoiding sibling and cousin pairs) in each
generation to produce the next (Darvasi and
Soller 1995). The first F11 litters of the
F10 females were discarded. When the F10 females were
visibly close to parturition for their second litters, they were isolated and,
1 d after they gave birth, their litters were culled to five to seven
pups to reduce postnatal effects of large litters. Litters smaller than five
were discarded. A total of 345 F11 females were produced. When they
were 28 ± 2 d old, they were weaned and fed the same atherogenic diet
as the backcross progeny for 6 weeks.
Lipid Measurements
Plasma lipid levels were measured three times in the same backcross
progeny: (1) when the progeny were 8 weeks old, immediately before they were
fed the atherogenic diet, (2) when they had been fed the atherogenic diet for
4 weeks, and (3) when they had been fed the atherogenic diet for 15 weeks.
Plasma lipid levels were measured twice in the same AIL progeny: (1) when the
progeny were 4 weeks old, immediately before they were fed the atherogenic
diet; and (2) when they had been fed the atherogenic diet for 6 weeks. Mice
were fasted for 4 h, and blood from their periorbital sinuses was collected in
EDTA-containing tubes. The plasma was separated by centrifuging the samples
for 5 min at 1500 rpm at 4°C. Plasma total cholesterol concentrations of
backcross progeny were measured with a commercial colorimetric enzymatic assay
(Pitman et al. 1998). HDL
concentrations were measured after apolipoprotein B-containing lipoproteins
were selectively precipitated with polyethylene glycol
(Pitman et al. 1998). We
measured very low density lipoprotein (VLDL), LDL, and HDL from the AIL
progeny by gel filtration by using a Superose 6 column. Briefly, plasma was
diluted 1/5 in 10 mM Tris (pH 7.4) containing 1 mM EDTA and 150 mM NaCl.
Twenty microliters of the sample were injected into the Superose 6 column
(preequilibrated in the same buffer), and lipoproteins were separated during a
56-min run. After the cholesterol detection enzymatic reagent
(Biomerieux-France) was added, the OD was measured at 500 nm after a 10-min
reaction at 37°C. Cholesterol concentration was then calculated using a
standard serum from Biomerieux, with the same protocol. Results are expressed
as mean ± standard error (SE) in milligrams per decaliter.
Evaluation of Aortic Atherosclerotic Lesions
After the 104 female progeny from the backcross had been on the atherogenic
diet for 15 weeks, they were killed by cervical dislocation. Their hearts and
ascending aortas were removed and fixed in 4% formaldehyde. Atherosclerotic
lesions on the aortic root of each mouse were measured as described previously
(Paigen et al. 1987). Lesions
were not measured in AIL progeny because 6 weeks of exposure to the
atherogenic diet is not sufficient to produce lesions.
DNA Isolation and Genotyping
DNA was isolated from either tail tips or spleens. Approximately either 1
cm of tail tip or one third of a spleen from each mouse was digested overnight
in 500 µL of 1x digestion buffer (50 mM Tris-Cl, 100 mM EDTA, 100 mM
NaCl, and 1% SDS at pH 8.0) containing 1mg/mL proteinase K in a 55°C water
bath. The digested products were mixed with one volume of 25:24:1
phenol:chloroform:isoamyl alcohol and centrifuged for 5 min at 1350 rpm at
room temperature. The aqueous phase was isolated, and DNA was precipitated
from it with two volumes of 100% ethanol. Strands of DNA were wound around a
glass capillary pipette and air-dried. The dried DNA pellets were resuspended
in 1 ml TE (10 mM Tris-HCl and 1 mM EDTA at pH 7.5–8.0).
We genotyped each backcross mouse with 97 SSLP markers (MIT MapPairs
primers; Research Genetics) spaced 15 to 20 cM apart throughout the mouse
genome (a density of three to seven markers per chromosome). Because most of
the genetic information for a quantitative trait can be determined from the
population members exhibiting the phenotypic extremes of the trait, we first
genotyped only the backcross progeny with HDL concentrations that were in the
upper and lower 30% of the HDL concentrations of the entire population, a
total of 61 of the 104 progeny. After the initial genome-wide scan identified
QTLs for HDL levels, we genotyped the remaining 43 mice for those chromosome
markers and genotyped all the mice for 11 more markers within the QTL regions
identified.
Because AIL are best suited for confirming and mapping QTLs initially
detected in either F2 or backcross progeny
(Darvasi and Soller 1995),
after the backcross genome scan detected main effect QTLs for HDL levels on
Chrs 1 and 5, and an interaction between QTLs on Chrs 16 and 5, we genotyped
the 345 AIL progeny with 23 polymorphic markers on Chr 1, 29 on Chr 5, and
eight on Chr 16.
PCR genotyping for both the backcross and the AIL was carried out for 35
cycles under the following conditions: 30 sec at 94°C, 30 sec at 55°C,
and 1 min at 72°C. Polymorphisms were detected by electrophoresing the PCR
products on 4% Nusieve® 3:1 agarose gels in 1x Tris-borate-EDTA
running buffer for 2 h at 190 V. Gels were then stained with ethidium bromide
and photographed under UV light. All SSLP markers used in this study were
ordered according to their physical position retrieved from either Ensembl
Mouse Genome Server
(http://www.ensembl.org/Mus_musculus/)
or Celera Discovery System
(http://www.celera.com).
Real-Time PCR Analysis
Total RNA was extracted by using RNeasy Mini Kit (Qiagen), following the
manufacturer's instructions. For each RNA sample, spectrophotometric
absorption at 260 nm was measured, and RNA concentration was calculated as
A260 x 40 (µg/mL) x dilution factor. cDNA was
synthesized by reverse-transcribing 2 µg of total RNA with Omniscript RT
Kit (Qiagen), using oligo(dT)15 primer (Promega). Quantitative
real-time PCR was performed by using the ABI Prism 7700 sequence detection
system (PE Applied Biosystems). Primers were designed by using the primer
design software Primer Express 2.0 (PE Applied Biosystems). The forward and
reverse primers for each pair of primers are located on different exons. To be
sure the primers amplified a unique and desired cDNA segment, each potential
pair of primers was checked in the BLAST program in Ensembl Mouse Genome
Server
(http://www.ensembl.org/Mus_musculus/)
and Celera database
(http://www.celera.com).
The primers used are shown in Supplemental
Table 1.
cDNA samples were mixed with primers and SYBR Master Mix (PE Applied
Biosystems) in a total volume of 15 µL. PCR was conducted using the
following parameters: 2 min at 50°C, 10 min at 95°C, and 40 cycles of
15 s at 95°C and 1 min at 60°C. PCR reactions were performed in
96-well optical reaction plates (Applied Biosystems). Quantitative real-time
PCR was normalized to the copies of Gapd mRNA from the same sample.
Acquired data were analyzed by Sequence Detector software (PE Applied
Biosystems). All PCR assays were performed in triplicate.
Statistical Analysis
Single marker genome scans to detect main effect QTLs were performed by
using the method of Sen and Churchill
(2001). We performed marker
regression-based genome scans using Matlab Software (Mathworks, Inc.) and
computed the oneway ANOVA F statistic at each marker in the
genotyping array. LOD ratio scores were computed at 2-cM intervals for the
whole genome, and significance thresholds were assessed by permutation tests
(Churchill and Doerge 1994).
QTLs were deemed significant if they either met or exceeded the 95%
genome-wide threshold; they were deemed suggestive if they met the 90%
genome-wide threshold but were not significant. We used the more conservative
P = 0.1 instead of P = 0.37 suggested by Lander and Kruglyak
(1995) as a threshold
P value for suggestive QTLs so that fewer false positives would be
found. CIs of QTLs were calculated according to the posterior probability
density of the QTL locations, as described previously
(Sen and Churchill 2001).
Some loci may affect HDL levels through epistatic interactions, so we also
performed simultaneous pair-wise genome scans to search for pairs of
interacting loci (Sen and Churchill
2001). This method examines all pairs of marker loci for
association with HDL levels in a two-dimensional genome scan. The detailed
procedure has previously been described
(Sugiyama et al. 2001). We
chose a nominal level of 0.05 as a significance threshold for interacting
QTLs.
Finally, all main effect and interacting QTLs for HDL levels that had been
detected by genome scans (single marker and pair-wise) were entered into a
multiple regression model by using Minitab software, procedure GLM (Minitab
Inc.). We used F statistics based on adjusted (type III) sum of
squares to make final determinations on the contribution of a given QTL (main
and interacting effects) in combination with all other QTLs.
Hierarchical clustering analysis was performed by using Ward's
minimum-variance method in the procedure of Proc Cluster in SAS Program (SAS
Institute, Inc., version 8.1). In Ward's minimum-variance method, the distance
between two clusters is the ANOVA sum of squares between the two clusters
added up over all the variables. At each generation, the within-cluster sum of
squares is minimized over all partitions obtainable by merging two clusters
from the previous generation. The sums of squares are divided by the total sum
of squares to give proportions of variance (squared semipartial
correlations).
To compare the difference of the mRNA expression levels between B6 and NZB
strains, we used Student's t test. All values are expressed as mean
± SE.
|
Acknowledgements
|
|---|
This work was funded by GlaxoSmithKline, France, by the Centre National de
la Recherche Scientifique (CNRS) and Ministere de la Recherche et de la
Technology of France, and by the Program for Genomic Applications of the
Heart, Lung and Blood Institute, National Institutes of Health (HL66611). We
thank Coralie Outreville for technical assistance and Ray Lambert for his
assistance in preparing the manuscript.
The publication costs of this article were defrayed in part by payment of
page charges. This article must therefore be hereby marked
"advertisement" in accordance with 18 USC section 1734 solely to
indicate this fact.
|
Footnotes
|
|---|
Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.1185803.
7 Corresponding author.
E-MAIL
bjp{at}aretha.jax.org;
FAX (207) 288-6078. 
Article published online before print in June 2003.
[Supplemental material is available online at www.genome.org.]
|
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Received January 16, 2003;
accepted in revised format April 22, 2003.
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ABSTRACT
RESULTS