Atherosclerosis Susceptibility Loci Identified From a Strain Intercross of Apolipoprotein E–Deficient Mice via a High-Density Genome Scan
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.
Common diseases such as atherosclerosis are complex traits with influences from many genes and the environment. Although rare monogenic disorders, such as low-density 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 high-density 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 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 Kolmogorov-Smirnov 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 ≥1.6.
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 χ2 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.
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 μm2) and smallest lesion (4.2×103 μm2), 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 μm2) and smallest lesion (10.3×103 μm2). 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.
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 μm2, 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 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 (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.
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 μm2 (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) μm2, 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 μm2, respectively (Figure 3B).
A similar analysis was performed for the log lesion area in the male F2 mice according to their chromosome 17 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) μm2, respectively, whereas the DD males had a mean log lesion are of 4.76 (antilog &55.7×103) μm2. 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 μm2, 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).
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 high- and 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.
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 diet-induced 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 in which the risk for myocardial infarction was associated with genetic variation in the 5-lipoxygenase activating protein.16
This work was supported by SCCOR grant P50HL077107 from the National Heart Lung and Blood Institute of the National Institutes of Health.
- Received October 5, 2005.
- Accepted December 7, 2005.
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