Download Barnard Jonathan D. Smith, Jeffrey M. Bhasin, Julie Baglione

Atherosclerosis Susceptibility Loci Identified From a Strain Intercross of Apolipoprotein E
−Deficient Mice via a High-Density Genome Scan
Jonathan D. Smith, Jeffrey M. Bhasin, Julie Baglione, Megan Settle, Yaomin Xu and John
Barnard
Arterioscler Thromb Vasc Biol. 2006;26:597-603; originally published online December 22,
2005;
doi: 10.1161/01.ATV.0000201044.33220.5c
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Atherosclerosis Susceptibility Loci Identified From a Strain
Intercross of Apolipoprotein E–Deficient Mice via a
High-Density Genome Scan
Jonathan D. Smith, Jeffrey M. Bhasin, Julie Baglione, Megan Settle, Yaomin Xu, John Barnard
Objective—Apolipoprotein (apo) E-deficient mice are hypercholesterolemic and develop atherosclerosis on low-fat chow
diets; however, the genetic background strain has a large effect on atherosclerosis susceptibility. This study aimed to
determine the genetic regions associated with strain effects on lesion area.
Methods and Results—We performed a strain intercross between atherosclerosis sensitive DBA/2 and atherosclerosis
resistant AKR apoE-deficient mice. Aortic root lesion area, total cholesterol, body weights, and complete blood counts
were ascertained for 114 male and 95 female F2 progeny. A high-density genome scan was performed using a mouse
single nucleotide polymorphism chip yielding 1967 informative polymorphic markers. Quantitative trait locus (QTL)
statistical analyses were performed. Novel loci associated with lesion or log lesion area were identified for the female
and male F2 cohorts. The atherosclerosis QTLs in female mice reside on chromosomes 15, 5, 3, and 13, and in male mice
on chromosomes 17, 18, and 2. QTL were also identified for body weight, total cholesterol, and blood count parameters.
Conclusions—Loci were identified for atherosclerosis susceptibility in a strain intercross study. The identity of the
responsible genes at these loci remains to be determined. (Arterioscler Thromb Vasc Biol. 2006;26:597-603.)
Key Words: atherosclerosis 䡲 mouse genetics 䡲 quantitative trait locus 䡲 QTL
C
ommon diseases such as atherosclerosis are complex
traits with influences from many genes and the environment. Although rare monogenic disorders, such as lowdensity lipoprotein receptor deficiency, lead to hypercholesterolemia and premature atherosclerosis, common genetic
variations that lead to atherosclerosis susceptibility are difficult to ascertain in human studies. Mouse models can be used
to identify atherosclerosis susceptibility genes by use of a
candidate gene approach, or via unbiased genomic methods.
This latter approach can identify new genes and pathways not
previously associated with atherosclerosis, which in turn can
be tested for human genetic variation and association with
cardiovascular disease. The unbiased genomic method uses
mice on different background strains to map the chromosomal
location of genes affecting a trait via the use of quantitative trait
locus (QTL) mapping. This method has been applied to study
diet-induced atherosclerosis in wild-type mice, as well as atherosclerosis in genetically engineered apolipoprotein (apo)
E-deficient and low-density lipoprotein (LDL) receptor-deficient
mice. These studies1,2 have identified atherosclerosis QTLs,
defined as the chromosomal locations of genes associated with
atherosclerosis severity. For several phenotypes, QTLs and their
causative genes identified in mice and rats have yielded power to
illuminate human disease pathways and human genetic variation
associated with disease.3– 6
We have used apoE-deficient mice as a model of hypercholesterolemia and atherosclerosis and an unbiased genomic
method to identify atherosclerosis susceptibility genes. We
have previously characterized aortic root lesion areas in
apoE-deficient mice on a total of 7 different inbred strains,
and of these the DBA/2 strain has the largest lesions and the
AKR strain was one of several strains with much smaller
lesions.7–9 For the current study, we bred an F2 cohort of 95
female and 114 male mice derived from an intercross between apoE-deficient AKR (atherosclerosis resistant) and
DBA/2 (atherosclerosis sensitive) parental stains. The mice
were maintained on a chow diet and aortic root lesion area,
plasma cholesterol, body weight, and complete blood counts
were measured at 16 weeks of age. We used a mouse
single-nucleotide polymorphism (SNP) chip to obtain a highdensity genome scan, which mapped the positions of QTLs
associated with atherosclerosis and other phenotypes.
Materials and Methods
Strain Intercross Study
ApoE-deficient mice10 on the C57BL/6 genetic background were
bred 10 generations onto the AKR/J and DBA/2J genetic backgrounds. ApoE-deficient F1 hybrids were bred using males and
females from both parental strains and were brother–sister-mated to
yield the F2 cohort. All mice were maintained on a chow diet until 16
Original received October 5, 2005; final version accepted December 7, 2005.
From the Departments of Cell Biology (J.D.S., J.M.B., J.B., M.S.), Cardiovascular Medicine (J.D.S.), and Quantitative Health Sciences (Y.X.,
J.Barnard), Cleveland Clinic Foundation, Cleveland Ohio; and the Department of Molecular Medicine (J.D.S.), Case School of Medicine, Cleveland Ohio.
Correspondence to Jonathan D. Smith, Department of Cell Biology, NC10, The Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195. E-mail
[email protected]
© 2006 American Heart Association, Inc.
Arterioscler Thromb Vasc Biol. is available at http://www.atvbaha.org
DOI: 10.1161/01.ATV.0000201044.33220.5c
597
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Arterioscler Thromb Vasc Biol.
March 2006
weeks of age, at which time they were weighed, anesthetized with
ketamine/xylazine (170 and 5 mg/kg, respectively), and bled via the
retroorbital plexus. Mice were perfused transcardially with saline,
and the hearts with the aortic root were removed for quantitative
assessment of aortic root lesion area, as described.11 Whole blood
was used for automated complete blood counts on an Advia 120
Hematology System, calibrated for mouse blood cells. The following
parameters were determined: white blood cell (WBC) number, red
blood cell (RBC) number, hematocrit, hemoglobin level, percent
neutrophils, percent lymphocytes, percent monocytes, and percent
eososinophils. Total plasma cholesterol was assayed using an enzymatic assay (Stanbio Laboratory, Boerne, Tex). Each phenotype was
assessed for normal Gaussion distribution by the KolmogorovSmirnov test and, if it passed, parametric statistics were used,
whereas if it failed, nonparametric statistics were used. Lesion areas
were not normally distributed and log10 lesion values were also
studied as a distinct phenotype. Correlations between phenotypes
were calculated by linear regression. Descriptive statistics were
performed using Prism 4.0 software (GraphPad, San Diego, Calif).
Genome Scan and QTL Analysis
DNA was prepared from frozen spleen of each mouse and used for
SNP genotyping on a 5K mouse SNP chip that was performed by
ParAllele Biosciences (South San Francisco, Calif). We also performed a polymerase chain reaction and gel-based assay for one
polymorphic marker on the Y chromosome (marker name zfy2,
accession ID MGI:8565 in Mouse Genome Information website,
http://www.informatics.jax.org/). Each SNP allele was verified using
DNA from 2 apoE-deficient AKR mice, 2 apoE-deficient DBA/2
mice, and 2 F1 mice bred from these parental strains. SNPs that did
not show the expected pattern in these control samples were not used
for the genome scan, yielding 1991 SNPs on the 19 autosomes and
the x chromosome. Genome scan data were obtained for 95 female
and 114 male F2 mice. On average, for each mouse, 98.1%⫾3.3%
(mean⫾SD) of the SNPs were assigned genotypes. Over the whole
F2 cohort, the percent of each parental allele for each SNP was
calculated, and 11 SNPs were removed because the ratio of allele of
the most prevalent parental strain over the least prevalent parental
strain was ⱖ1.6.
Phenotypic and genotypic data for each mouse were assembled
and analyzed using the r/qtl software package (version 0.99-24) run
in the R statistical package (version 2.1.0).12 For mapping purposes,
the chromosome number and megabase (Mb) position of each SNP
was ascertained from the NCBI mouse genome build 34. Information
from each SNP was retrieved from the dbSNP database (http://
www.ncbi.nlm.nih.gov/SNP/index.html), and the sequence surrounding the SNP was used in a BLAT search (http://genome.
ucsc.edu/cgi-bin/hgBlat?command⫽start) against the mouse genome
to verify its position. r/qtl was used to calculate the recombination
frequency, and any markers that were not placed appropriately were
evident by visual plotting of the recombination frequency for each
chromosome. We removed an additional 13 SNP markers for which
we could not assign a Mb position, or if the assignment appeared in
error by recombination frequency calculation, yielding a total of
1967 SNP markers. The EM algorithm was used for interval
mapping within the r/qtl software, which calculated LOD scores (log
of the odds ratio) for each phenotype across the mouse genome at
every SNP position and in 2-Mb intervals in regions where marker
SNPs were not present. Lesion size was analyzed before and after
log10 transformation, which normalizes the distribution and gives
equal weighting to fold-differences across the distribution. All other
phenotypes were analyzed without log transformation.
For phenotypes that were significantly different in males and
females, QTL analyses were performed in each sex separately and in
both sexes combined using sex as an interactive covariate; for these
gender combined analyses, the r/qtl software does not calculate an
adjusted phenotype value for each mouse, and therefore we could not
determine inheritance model or percent variation due to the QTL. For
phenotypes in which sex had no significant effect, QTL analyses
were performed in each sex separately and in both sexes combined
without further correction. The nominal probability values of the
LOD score peaks were calculated by converting the LOD score to a
␹2 statistic, as described by Lander and Kruglyak,13 using 1 degree of
freedom for nonadjusted analyses and 2 degrees of freedom for
analyses with sex as an interacting covariate. Genome-wide probability values for LOD score peaks were ascertained by permutation
analysis within r/qtl, using 10 000 permutations of each phenotype
assignment. We determined the LOD score for each analysis that met
the genome-wide probability value cutoffs of 0.01, 0.05, 0.10, 0.15,
0.20, and 0.25. Thus, we could assign each LOD score probability
value as less than one of these cutoffs. Percent of the phenotype
attributed to each locus was determined by linear correlation analysis
using both dominant and codominant (additive) models, and the
model that yielded the highest correlation coefficient was selected.
QTL symbol names have been approved by the Mouse Genomic
Nomenclature Committee.
Results
Phenotypic Analysis
Aortic root lesion areas were compared in 16-week-old,
chow-diet–fed, female and male apoE-deficient mice on the
DBA/2 and AKR genetic background, as well as in the F1 and
F2 mice derived from intercrossing these strains (Figure 1A
and 1B). As previously observed in apoE-deficient mice,9
female mice on the DBA/2 and AKR backgrounds have
larger lesions in the aortic root than male mice (P⬍0.01 by
Mann Whitney test for DBA/2 and AKR parental strains).
Female DBA/2 mice had a median lesion area ⬎11-fold
larger than lesions in the female AKR mice (P⬍0.001). The
F1 and F2 female mice had intermediate median lesion areas
that were ⬇2-fold higher than the levels in the AKR mice,
and much closer to AKR strain in size than to the DBA/2
strain. Male DBA/2 mice had a median lesion area ⬎14-fold
larger than lesions in the male AKR mice (P⬍0.001). Again,
the F1 and F2 mice had intermediate median lesion areas that
were much closer to AKR strain in size than to the DBA/2
strain. This suggests that the AKR alleles of some of the
major lesion susceptibility genes may be dominant over the
DBA/2 alleles for both sexes. Lesion values of the F2 mice
were log10 transformed and frequency distributions were
plotted for both sexes (Figure 1C and 1D). The F2 females had
a very broad lesion distribution with a 66-fold range between
the mouse with the largest lesion (266⫻103 ␮m2) and smallest
lesion (4.2⫻103 ␮m2), and the distribution was skewed with
a major and minor modes evident. The F2 males had a less
broad lesion distribution with a 16-fold range between the
mouse with the largest lesion (166⫻103 ␮m2) and smallest
lesion (10.3⫻103 ␮m2). The lesion distribution in the male F2
mice was markedly bimodal, with ⬇55% of the mice in the
low lesion peak, and ⬇45% of the mice in the high lesion
peak. This bimodal distribution is consistent with a major
gene effect on a sex chromosome, although other models
could also explain such a distribution.
We then performed correlation analysis of the log lesion
area for each F2 mouse with its corresponding body weight,
total cholesterol level, and each of the blood count parameters. There were no significant correlations between log
lesion and any of these parameters for the female F2 cohort.
For the male F2 cohort, total plasma cholesterol accounted for
only a small proportion in the variation in log lesion area
(7.8%, P⫽0.003). None of the other parameters were significantly correlated with log lesion area at the P⬍0.05 level.
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Smith et al
Atherosclerosis QTLs From ApoE-Deficient Mice
599
Figure 1. Aortic lesions in the parental
strains and the strain intercross cohorts.
Lesion areas in individual female (A) and
male (B) mice by strain, lines and values
represent medians, and probability values are derived from Dunn’s posttest of
a nonparametric ANOVA analysis. Log
lesion frequency histogram for female (C)
and male (D) F2 cohorts.
QTL Analysis
the 12 phenotypes: log lesion area, lesion area, body weight,
total cholesterol, WBC count, and percent eosinophils (Table
1). We then performed QTL mapping for these 12 phenotypes. Significance of each LOD peak was determined by 2
methods: (1) using the nominal probability values, as previously described;13 and (2) using the genome wide probability
values determined by permutation analysis. All of the LOD
peaks shown in Table 2 and Table I (available online at
http://atvb.ahajournals.org), at a minimum, reach the suggestive threshold level as suggested by Lander and Kruglyak.13
For the genome-wide probability values, we arbitrarily assigned the following descriptors: P⬍0.05 as significant;
P⬍0.25 as likely, and P⬎0.25 as suggestive. For the 95
female F2 mice, we observed a peak LOD score for log lesion
area on chromosome 15 (LOD⫽3.29, likely), named Ath22
(Figure 2A and Table 2). The other LOD peaks on chromosomes 3 (LOD⫽2.73, Ath23), 5 (LOD⫽2.59, Ath24), and 13
A high-density genome scan was performed using 1967 SNP
markers covering the 19 autosomes and the X chromosome;
in addition, we confirmed the direction of strain intercross for
the male mice by genotyping one polymorphic marker on the
Y chromosome. We first examined the effect of the Y
chromosome in the 114 male F2 mice to see whether it could
explain the bimodal distribution observed. The median lesion
areas of males with the AKR and DBA/2Y chromosomes
were 34.0⫻103 and 30.6⫻103 ␮m2, respectively, which were
not significantly different (Mann-Whitney P⫽0.87). Thus,
the Y chromosome could not account for the observed
distribution in the male F2 mice.
Before QTL analysis, we determined the effect of sex on
log10 transformed lesion areas, lesion areas, body weight,
total plasma cholesterol, and 8 parameters derived from the
complete blood counts. Sex had a significant effect on 6 of
TABLE 1.
Effect of Sex on Phenotype Values in the F2 Cohort
Normal
Distribution
Phenotype
Female
Mean
SD
Male
Mean
SD
t Test
Log lesion, ␮m
No
4.82
0.39
4.52
0.30
⬍0.0001
Lesion, ␮m2
No
66381
58284
32696
33893
⬍0.0001
Body weight, g
Yes
27.95
3.15
32.24
3.87
⬍0.0001
Total cholesterol, mg/dL
No
626
146
692
136
⬍0.01
2
WBC, 10 /␮L
Yes
7.1
2.2
8.4
3.0
⫽0.01
RBC, 106/␮L
No
11.5
2.1
11.4
1.8
NS
Hematocirt, %
No
55.0
10.3
54.8
9.0
NS
Hemoglobin, g/dL
No
16.5
3.7
16.3
2.7
NS
Neutrophils, %
No
21.9
0.9
21.9
1.0
NS
Lymphocytes, %
No
70.7
9.4
71.5
11.1
NS
Monocytes, %
No
2.4
1.5
2.5
1.4
NS
Eosinophils, %
No
3.4
1.8
2.7
1.3
⫽0.01
3
Nonparametric t tests were used for the phenotype values that were not normally distributed.
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600
TABLE 2.
Arterioscler Thromb Vasc Biol.
March 2006
Atherosclerosis QTLs
Sex
SNP*
Chr
Mb
(1 LOD)†
cM‡
LOD
Score§
Nominal
P Value¶
Linkage储
Genome P
Value**
Model††
%
Variance
Symbol
Log lesion
F
13482467
15
20 (10–29)
14
3.29
1.00E-4
Significant
⬍0.20
D
17.9
Ath22
Log lesion
F
13477166
3
68 (54–90)
33
2.73
3.95E-4
Suggestive
⬎0.25
A
12.7
Ath23
Log lesion
F
13478585
5
146 (130–149)
84
2.59
5.57E-4
Suggestive
⬎0.25
Co
15.1
Ath24
Log lesion
F
13481782
13
44 (39–66)
26
2.50
6.96E-4
Suggestive
⬎0.25
Het
ND
Ath25
Log lesion
M
13482966
17
36 (33–53)
20
4.25
9.80E-6
Significant
<0.05
A
14.5
Ath26
Log lesion
M
13483316
18
40 (38–56)
22
3.58
4.95E-5
Significant
⬍0.10
Het
ND
Ath27
Log lesion
M
13476938
2
179 (174–179)
107
3.28
1.03E-4
Significant
⬍0.20
Co
11.0
Ath28
Log lesion
F⫹M
8238029
17
34 (25–53)
19
5.49
3.28E-6
Significant
⬍0.10
ND
ND
Ath26
Log lesion
F⫹M
13482488
15
27 (10–29)
14
4.99
1.04E-5
Significant
⬍0.20
ND
ND
Ath22
Log lesion
F⫹M
13481782
13
44 (40–52)
26
4.27
5.43E-5
Suggestive
⬎0.25
ND
ND
Ath25
Log lesion
F⫹M
13477166
3
68 (60–88)
33
3.76
1.75E-4
Suggestive
⬎0.25
ND
ND
Ath23
Log lesion
F⫹M
13476938
2
179 (174–179)
107
3.57
2.72E-4
Suggestive
⬎0.25
ND
ND
Ath28
Log lesion
Trait
F⫹M
13478585
5
146 (131–149)
84
3.44
3.66E-4
Suggestive
⬎0.25
ND
ND
Ath24
Lesion
F
13478585
5
146 (137–149)
84
3.35
8.65E-5
Significant
⬍0.20
Co
15.0
Ath24
Lesion
F
13481782
13
43 (34–66)
26
2.76
3.66E-4
Suggestive
⬎0.25
Het
ND
Ath25
Lesion
F
13477166
3
68 (54–90)
33
2.39
9.14E-4
Suggestive
⬎0.25
A
12
Ath23
Lesion
M
13482966
17
47 (33–53)
20
4.27
9.34E-6
Significant
<0.05
A
13.9
Ath26
Lesion
M
13483340
18
46 (38–56)
24
3.24
1.13E-4
Suggestive
⬍0.20
Het
ND
Ath27
Lesion
F⫹M
13478585
5
145 (140–149)
84
5.49
3.28E-6
Significant
⬍0.20
ND
ND
Ath24
Lesion
F⫹M
13481782
13
44 (40–52)
26
4.99
1.04E-5
Significant
⬎0.25
ND
ND
Ath25
Lesion
F⫹M
8238029
17
34 (25–35)
14
4.53
2.99E-5
Significant
⬎0.25
ND
ND
Ath26
Lesion
F⫹M
13477166
3
68 (60–87)
33
4.01
9.87E-5
Suggestive
⬎0.25
ND
ND
Ath23
*LOD peak SNP marker, each SNP name is preceded by the characters 关rs兴.
†Mb position of LOD peak with 1 LOD drop-off confidence interval.
‡Approximate cM position of marker determined from nearby mapped marker using the Mouse Genomic Information database.
§For both sexes combined, the LOD score analysis used sex as an interactive covariate.
¶Nominal P values calculated as described by Lander and Kruglyak.13
储Level of linkage significance as defined by Lander and Kruglyak,13 suggestive and significant nominal P values for 1 degree of freedom (single sex analyses) are
⬍3.4⫻10⫺3, and ⬍1.0⫻10⫺4, respectively, and for 2 degrees of freedom (combined sex analyses) suggestive and significant nominal P values are ⬍1.6⫻10⫺3
and ⬍5.2⫻10⫺5, respectively.
**Genome-wide P values were calculated by permutation analysis, with genome-wide P values⬍0.05 in bold.
††Inheritance model determined by best linear regression coefficient, except for heterozygous model, determined by ANOVA.
A indicates AKR dominant; Chr, chromosome; cM, centimorgan position along the mouse chromosome; Co, codominant; D, DBA/2 dominant; F, female; Het,
heterozygous genotype different from the 2 parental genotypes; M, male; Mb, megabase position along the mouse chromosome; ND, not determined.
(LOD⫽2.50, Ath25) are suggestive. For the 114 male F2
mice, we observed peak LOD scores for log lesion area on
chromosomes 17 (LOD⫽4.25, significant, Ath26), 18
(LOD⫽3.58, likely, Ath27), and 2 (LOD⫽3.28, likely,
Ath28) (Figure 2B). Pooling both sexes and using sex as an
interactive covariate confirmed 2 of these loci, on chromosomes 17 (LOD⫽5.49) and 15 (LOD⫽4.99), both likely
(Figure 2C). The other LOD peaks on chromosomes 13
(LOD⫽4.27), 3 (LOD⫽3.76), 2 (LOD⫽3.57), and 5
(LOD⫽3.44) were suggestive.
We performed a similar QTL analysis using the non log
transformed lesion areas (Table 2), which gives more weighting to the F2 mice with larger lesion values. For the female F2
mice, this analysis identified the Ath24 locus chromosome 5
(LOD⫽3.35, likely), as well as the Ath25 and Ath23 loci on
chromosomes 13 (peak LOD⫽2.76) and 3 (peak
LOD⫽2.39), which were suggestive. The male analysis for
lesion area confirmed the Ath26 and Ath27 loci on chromosomes 17 (peak LOD⫽4.27, significant) and 18 (peak
LOD⫽3.24, likely). Pooling both sexes and using sex as an
interactive covariate confirmed the Ath24 loci on chromosome 5 (peak LOD⫽5.49, likely) and the Ath25, Ath26, and
Ath23 loci on chromosomes 13 (peak LOD⫽4.99), 17 (peak
LOD⫽4.53), and 3 (peak LOD⫽4.01), respectively, which
were all suggestive. Looking at both the log lesion and lesion
QTL analyses, we are most confident of 5 loci, Ath22 and
Ath24 on chromosomes 15 and 5, which derive their strength
from female F2 cohort, and Ath26, Ath27, and Ath28 on
chromosomes 17, 18, and 2, which derive their strength from
the male F2 cohort. Of these QTLs, the Ath26 locus on
chromosome 17 was the only one meeting the genome wide
criteria for significant in various analyses. The loci on
chromosomes 3 and 13 were associated with log lesion or
lesion area, and although they appeared as peaks in several
analyses, we are less confident of these loci as they did not
reach our genome-wide statistical threshold for likely, although they do meet the nominal probability value threshold
for suggestive.
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Smith et al
Figure 2. QTL maps for atherosclerosis. Whole genome LOD
score plots for log lesion area in the female F2 cohort (A), the
male F2 cohort (B), and both sexes combined using sex as an
interactive covariate (C).
The effect of these major loci on atherosclerosis was
evident when we analyzed lesion areas in female or male
mice divided into groups based on their genotype at a single
marker closest to the peak LOD position. The mean log lesion
areas⫾SD of the female F2 mice according to their chromosome 15 rs13482467 genotype (Ath 22) are shown in Figure
3A. The log lesion values were normally distributed and
regression analysis revealed that the DBA/2 allele was
dominant, with mean log lesion areas for the AA genotype
females of 4.44 ␮m2 (antilog ⬇27 400), whereas the DA and
DD genotype females had mean log lesion area of 4.85
(antilog ⬇71 000) and 4.79 (antilog ⬇61 100) ␮m2, respectively. Linear regression of the log lesion area using the
DBA/2 dominant model yielded an r2 value of 0.179, meaning
that 17.9% of the log lesion variation in the female F2 cohort
was associated with the parental inheritance of this single
marker (Table 2). The other significant atherosclerosis QTL
in females, ath24, was stronger using non-log-adjusted lesion
values. Linear regression analysis showed that the codominant model was strongest with 15% of lesion variation
associated with inheritance of the chromosome 5 rs13478585
marker. The mean lesion areas in females with rs13478585
AA, AD, and DD genotypes were 47.1, 82.1, and
110.8⫻103 ␮m2, respectively (Figure 3B).
A similar analysis was performed for the log lesion area in
the male F2 mice according to their chromosome 17
Atherosclerosis QTLs From ApoE-Deficient Mice
601
rs13482966 genotype (Ath26, Figure 3C). The AKR allele
was dominant, with mean log lesion areas for the AA and AD
males of 4.52 (antilog ⬇35.6⫻103) and 4.47 (antilog
⬇29.4⫻103) ␮m2, respectively, whereas the DD males had a
mean log lesion are of 4.76 (antilog ⬇55.7⫻103) ␮m2. Linear
regression of log lesion area using the AKR dominant model
revealed that 14.5% of the log lesion variation in the male F2
cohort was associated with the parental inheritance of this
marker. Although the chromosome 18 marker rs13483316
(Ath27) was significantly associated with log lesion area in
male mice, it was the AD heterozygous genotype that had
significantly smaller log lesion areas than either of the
parental genotypes (P⬍0.01), whereas the parental genotypes
had similar log lesion areas (Figure 3D). The other significant
QTL for log lesion area in males, ath28, fit the codominant
model, with mean log lesion areas in the AA, AD, and DD
genotypes of 4.45, 4.54, and 4.74 ␮m2, respectively (Figure
3E). We examined the remaining single sex atherosclerosis
QTLs in this fashion and report the best fit inheritance model
based on linear regression, and, for markers with dominant
and codominant inheritance patterns, we report the percent
variance in the trait associated with each marker (Table 2).
For all atherosclerosis QTLs, except the 2 with the heterozygous effect, the DBA/2 allele was associated with larger
lesions than the AKR allele.
QTL analyses described were also performed for all of the
10 other phenotypes (Table I; supplementary data, please see
http://atvb.ahajournals.org). For body weight in the F2 females, we found a highly significant QTL locus on chromosome 12, named Bw20, and a likely QTL on chromosome
19 (Bw21). For male body weight, there was a likely QTL on
chromosome 2 (Bw22), which was also observed in both
sexes combined with sex as an interactive covariate. QTLs
were also found for total cholesterol, WBC count, RBC
count, hematocrit, hemoglobin level, percent monocytes,
percent eosinophils, and percent lymphocytes (Table I).
Discussion
We performed a strain intercross between apoE-deficient
mice on the AKR and DBA/2 genetic backgrounds to identify
loci associated with atherosclerosis. This study is the first that
we are aware of in which a mouse SNP chip was used to
obtain a high-density genome scan, increasing the marker
density ⬇10-fold over standard studies using microsatellite
markers. In comparing with a low-density genome scan using
109 microsatellite markers (data not shown), the high-density
genome scan confirmed all of the atherosclerosis QTLs and
identified 2 additional loci for which there were no nearby
markers in the low density genome scan. In addition, we
examined 12 QTLs which were identified in both the highand low-density genome scans, performed using Mb positions
of the markers rather than cM positions. In this analysis, the
size of the 1 LOD drop-off surrounding the peak LOD
position was 29.3 Mb and 19.7 Mb for the low- and
high-density genome scans, respectively (P⫽0.007 by paired
2-tailed t test). Thus, the high-density genome scan was better
than the low-density genome scan, both in terms identifying
more loci and in narrowing the interval in which the causative
gene is likely to reside.
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602
Arterioscler Thromb Vasc Biol.
March 2006
Figure 3. Effect of single SNP markers on atherosclerosis. A, The rs13482467 SNP on chromosome 15 is associated with log lesion
area in the female F2 mice, with the DBA allele dominant. B, The rs13478585 SNP on chromosome 5 is associated with lesion area in
the female F2 mice, with codominant inheritance. C, The rs13482966 SNP on chromosome 17 is associated with log lesion area in the
male F2 mice, with the AKR allele dominant. D, The rs13483316 SNP on chromosome 18 is associated with log lesion area in the male
F2 mice, with a heterozygous effect on the phenotype. E, The rs13476938 SNP on chromosome 2 is associated with log lesion area in
the male F2 mice, with codominant inheritance.
We identified the Ath26 QTL for atherosclerosis, which
met our genome-wide probability value criteria as significant
(Chr 17) and four QTLs which met our criteria as likely (Chr
2, 5, 15, and 18). In addition, 2 other QTLs (Chr 3 and 13)
gave substantial LOD peaks in ⬎1 analysis but failed to meet
our criteria as likely. The finding that the median lesion areas
in the F1 and F2 cohorts were closer to the median lesion area
of the AKR parental strain than of the DBA/2 strain suggested that some of the major loci might be dominant for the
AKR allele. We found that the AKR allele was dominant for
the Ath26 and Ath28 QTLs in male mice, as predicted.
However, Ath22, the major log lesion QTL in female mice
showed dominance for the DBA/2 allele, not in agreement
with our prediction.
The Ath27 QTL for males and the suggestive Ath25 QTL
for females altered atherosclerosis in the heterozygous genotype, but lesions were similar in both parental genotypes. This
phenomenon is similar to heterosis, or hybrid vigor, that is
often observed in F1 crosses. A protein that functions as a
homo dimer or oligomer provides one potential explanation
for this type of inheritance pattern. For example, the function
of the AKR or DBA/2 single isoform complex may be similar
to each other, whereas the mixed isoform complex could have
a loss or gain of function. For all of the remaining atherosclerosis QTLs with dominant/recessive or codominant inheritance patterns, the DBA/2 allele was always associated with
increased atherosclerosis, and thus these QTLs may explain
much of the variation observed in the parental strains. This
result differs from some other mouse atherosclerosis studies
in which strong QTLs were found in which lesion severity
tracked in the opposite direction as observed in the parental
strains.8,14
Mouse atherosclerosis susceptibility loci have been previously mapped using strain intercrosses or recombinant inbred
strains in apoE-deficient, LDL receptor-deficient, or dietinduced models of atherosclerosis. Twenty mouse atherosclerosis QTLs now appear on the Mouse Genome Informatics
(build 3.3) website (http://www.informatics.jax.org). However, none of the previously identified atherosclerosis QTLs
is coincident with the atherosclerosis QTLs on chromosomes
2, 3, 5, 13, 15, 17, and 18 that we have identified in the
current study. Recently, a mouse atherosclerosis QTL was
identified on chromosome 2 at 69 cM,15 but this is not
coincident with Ath28 on chromosome 2, which maps to the
distal end of the chromosome at ⬇107 cM. It appears that the
specific atherosclerosis loci identified in any one study may
primarily be a function of the parental strain pair used, and
none of the previous studies used the same strain pair as in the
current study.
Much further work is required to confirm these loci and
identify the causative genes. We hope that the identification
of mouse atherosclerosis susceptibility genes will illuminate
genes and pathways that play a role in human disease. These
so-called cross species QTLs have been identified for a
variety of complex traits, including atherosclerosis.5,6 For
example, the 5-lipoxygenase gene plays a role in atherosclerosis susceptibility in LDL receptor-deficient mice.3 The
elucidation of this pathway in mice helped in human studies
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Smith et al
in which the risk for myocardial infarction was associated
with genetic variation in the 5-lipoxygenase activating
protein.16
Atherosclerosis QTLs From ApoE-Deficient Mice
9.
Acknowledgments
This work was supported by SCCOR grant P50HL077107 from the
National Heart Lung and Blood Institute of the National Institutes
of Health.
References
1. Smith JD. Quantitative trait locus mapping for atherosclerosis susceptibility. Curr Opin Lipidol. 2003;14:499 –503.
2. Lusis AJ, Mar R, Pajukanta P. Genetics of atherosclerosis. Annu Rev
Genomics Hum Genet. 2004;5:189 –218.
3. Mehrabian M, Allayee H, Wong J, Shi W, Wang XP, Shaposhnik Z, Funk
CD, Lusis AJ, Shih W. Identification of 5-lipoxygenase as a major gene
contributing to atherosclerosis susceptibility in mice. Circ Res. 2002;91:
120 –126.
4. Wang X, Ria M, Kelmenson PM, Eriksson P, Higgins DC, Samnegard A,
Petros C, Rollins J, Bennet AM, Wiman B, de FU, Wennberg C, Olsson
PG, Ishii N, Sugamura K, Hamsten A, Forsman-Semb K, Lagercrantz J,
Paigen B. Positional identification of TNFSF4, encoding OX40 ligand, as
a gene that influences atherosclerosis susceptibility. Nat Genet. 2005;37:
365–372.
5. Wang X, Ishimori N, Korstanje R, Rollins J, Paigen B. Identifying novel
genes for atherosclerosis through mouse-human comparative genetics.
Am J Hum Genet. 2005;77:1–15.
6. Stoll M, Kwitek-Black AE, Cowley AW, Jr., Harris EL, Harrap SB,
Krieger JE, Printz MP, Provoost AP, Sassard J, Jacob HJ. New target
regions for human hypertension via comparative genomics. Genome Res.
2000;10:473– 482.
7. Dansky HM, Charlton SA, Sikes JL, Heath SC, Simantov R, Levin LF,
Shu P, Moore KJ, Breslow JL, Smith JD. Genetic background determines
the extent of atherosclerosis in ApoE-deficient mice. Arterioscler Thromb
Vasc Biol. 1999;19:1960 –1968.
8. Dansky HM, Shu P, Donavan M, Montagno J, Nagle DL, Smutko JS, Roy
N, Whiteing S, Barrios J, McBride TJ, Smith JD, Duyk G, Breslow JL,
10.
11.
12.
13.
14.
15.
16.
603
Moore KJ. A phenotype-sensitizing Apoe-deficient genetic background
reveals novel atherosclerosis predisposition loci in the mouse. Genetics.
2002;160:1599 –2608.
Smith JD, James D, Dansky HM, Wittkowski KM, Moore KJ, Breslow
JL. In silico quantitative trait locus map for atherosclerosis susceptibility
in apolipoprotein E-deficient mice. Arterioscler Thromb Vasc Biol. 2003;
23:117–122.
Plump AS, Smith JD, Hayek T, Aalto-Setala K, Walsh A, Verstuyft JG,
Rubin EM, Breslow JL. Severe hypercholesterolemia and atherosclerosis
in apolipoprotein E-deficient mice created by homologous recombination
in ES cells. Cell. 1992;71:343–353.
Plump AS, Scott CJ, Breslow JL. Human apolipoprotein A-I gene
expression increases high density lipoprotein and suppresses atherosclerosis in the apolipoprotein E-deficient mouse. Proc Natl Acad Sci U S A.
1994;91:9607–9611.
Broman KW, Wu H, Sen S, Churchill GA. R/qtl: QTL mapping in
experimental crosses. Bioinformatics. 2003;19:889 – 890.
Lander E, Kruglyak L. Genetic dissection of complex traits: guidelines
for interpreting and reporting linkage results. Nat Genet. 1995;11:
241–247.
Welch CL, Bretschger S, Latib N, Bezouevski M, Guo Y, Pleskac N,
Liang CP, Barlow C, Dansky H, Breslow JL, Tall AR. Localization of
atherosclerosis susceptibility loci to chromosomes 4 and 6 using the Ldlr
knockout mouse model. Proc Natl Acad Sci U S A. 2001;98:7946 –7951.
Seidelmann SB, De LC, Leibel RL, Breslow JL, Tall AR, Welch CL.
Quantitative trait locus mapping of genetic modifiers of metabolic
syndrome and atherosclerosis in low-density lipoprotein receptordeficient mice: identification of a locus for metabolic syndrome and
increased atherosclerosis on chromosome 4. Arterioscler Thromb Vasc
Biol. 2005;25:204 –210.
Helgadottir A, Manolescu A, Thorleifsson G, Gretarsdottir S, Jonsdottir
H, Thorsteinsdottir U, Samani NJ, Gudmundsson G, Grant SF, Thorgeirsson G, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE,
Johannsson H, Gudmundsdottir O, Gurney ME, Sainz J, Thorhallsdottir
M, Andresdottir M, Frigge ML, Topol EJ, Kong A, Gudnason V,
Hakonarson H, Gulcher JR, Stefansson K. The gene encoding 5-lipoxygenase activating protein confers risk of myocardial infarction and stroke.
Nat Genet. 2004;36:233–239.
Downloaded from http://atvb.ahajournals.org/ by guest on February 28, 2014
Supplemental Table 1. Non-atherosclerosis QTLs.
Trait
Body weight
Body weight
Body weight
Body weight
Cholesterol
Cholesterol
Cholesterol
WBC
WBC
RBC
Hematocrit
Hemoglobin
Hemoglobin
% Monocytes
% Eosinophils
% Eosinophils
% Eosinophils
% Lymphocytes
% Lymphocytes
% Lymphocytes
% Lymphocytes
% Lymphocytes
1
Sex
F
F
M
F+M
M
M
F+M
F
F+M
F+M
F+M
F+M
F+M
F+M
F
F+M
F+M
M
M
M
F+M
F+M
Adj
ic
ic
ic
ic
ic
1
SNP
2
3662628
3023497
13476656
13476754
6244558
13459120
6244558
13479943
13479941
3657938
3657938
3657938
13482409
6370004
4226520
6307428
4226520
13476557
13483042
8260738
13483042
13476534
Chr
12
19
2
2
13
10
13
8
8
4
4
4
14
5
7
13
7
2
17
4
17
2
Mb
75
56
98
125
48
37
48
100
99
124
124
124
115
98
23
17
23
68
55
140
55
62
cM
34
45
58
70
31
23
31
45
45
58
58
58
62
52
11
2
11
37
34
70
34
34
LOD
5.18
3.74
3.17
4.17
3.85
3.47
4.67
4.05
6.22
4.67
3.89
3.97
3.17
3.01
3.21
4.49
4.46
3.28
3.07
3.01
3.69
3.05
nominal
3
p value
genome
4
p value
1.05E-6
3.36E-5
1.34E-4
6.83E-5
2.57E-5
6.46E-5
2.16E-5
1.59E-5
6.12E-7
3.57E-6
2.34E-5
1.93E-5
1.34E-4
1.98E-4
1.22E-4
3.27E-5
3.51E-5
1.03E-4
1.71E-4
1.98E-4
3.79E-5
1.80E-4
<0.01
<0.10
<0.20
<0.25
<0.05
<0.10
<0.15
<0.05
<0.01
<0.05
<0.10
<0.10
<0.20
<0.25
<0.20
<0.25
<0.25
<0.15
<0.25
<0.25
<0.10
<0.25
Symbol
Bw20
Bw21
Bw22
Bw22
Chol15
Chol16
Chol15
Wbc1
Wbc1
Rbc1
Hct1
Hgb1
Hgb2
Mono1
Eosn1
Eosn2
Eosn1
Lymph1
Lymph2
Lymph3
Lymph2
Lymph1
Model
5
(% variance)
Het
A (15.5%)
D (11.5%)
ND
Co (10.9%)
D (8.7%)
ND
A (20.6%)
ND
A (10.2%)
A (8.8%)
A (9.0%)
D (7.5%)
D (12.6%)
Co (15.3%)
ND
ND
D (9.7%)
D (13.5%)
D (13.1%)
D (9.1%)
D (7.7%)
Genotype
w/ highest
6
phenotype
AA~DD
AA
AA
AA*
DD
AA
DD*
AA
AA*
DD
DD
DD
DD
AA
AA
AA*
AA*
AA
DD
DD
DD
AA
Adjustment for LOD score analysis: for traits with sex effects, sex was used as an interactive covariate
(ic), for traits without sex effects, no adjustments were applied.
2
LOD peak SNP marker, each SNP name is preceded by the charaters “rs”.
3
Nominal p values calculated as described by Lander and Kruglyak, Nat Genetic 1995;11,241-247.
4
Genome wide p-vaules were calculated by permutation analysis, with genome wide p values < 0.05 in
bold.
5
Inheritance model determined by best linear regression coefficient, except fro heterozygous model,
determined by ANOVA. The % variance attributed to each locus is the linear regression r2 value.
6
* denotes that the genotype with the highest phenotype was implied from single sex analysis.
Abbreviations: F, female; M, male; Adj, adjustment to QTL analysis; ic, sex as an interactive covariate;
Chr, chromosome; Mb, megabase position along the mouse chromosome; A, AKR dominant; D, DBA/2
dominant; Co, co-dominant; Het, heterozygous genotype different from the two parental genotypes; ND,
not determined.