Genetic Analysis of Blood Pressure in 8 Mouse Intercross Populations
The genetic basis of hypertension is well established, yet very few genes that cause common forms of hypertension are known. Quantitative trait locus (QTL) analyses in rodent models can guide the search for human hypertension genes, but the excellent genetic resources for mice have been underused in this regard. To address this issue, we surveyed blood pressure variation in mice from 37 inbred strains and generated 2577 mice in 8 intercross populations to perform QTL analyses of blood pressure. We identified 14 blood pressure QTL in these populations, including ≥7 regions of the mouse genome not linked previously to blood pressure. Many QTL were detected in multiple crosses, either within our study or in studies published previously, which facilitates the use of bioinformatics methods to narrow the QTL and focus the search for candidate genes. The regions of the human genome that correspond to all but 1 of the 14 blood pressure QTL in mice are linked to blood pressure in humans, suggesting that these regions contain causal genes with a conserved role in blood pressure control. These results greatly expand our knowledge of the genomic regions underlying blood pressure regulation in mice and support future studies to identify the causal genes within these QTL intervals.
Blood pressure is a highly heritable phenotype affected by multiple genes and environmental factors. The genetic basis of hypertension has been investigated extensively in humans through genome-wide and candidate-gene association studies, as well as genome-wide linkage analyses. Despite the substantial effort made to identify genes underlying polygenic hypertension (see Cowley1 for review), few causal genes have been identified to date.
One alternative approach to studying the genetic basis of hypertension in humans is to identify genes affecting blood pressure in model organisms, mainly rodents, and then test those genes for a role in human blood pressure control. A common strategy for identifying genomic regions linked to a phenotype in rodent models is quantitative trait locus (QTL) analysis, and rodent blood pressure QTL often correspond with regions of the human genome containing genes affecting blood pressure.2,3⇓ This finding suggests that the same genes may be linked to blood pressure control in humans and rodents. In fact, parallel studies in humans and rats successfully identified the genes encoding adducin4 and 11β-hydroxylase5 as important in blood pressure control. Recently, Chang et al6 combined human linkage analysis with published mouse linkage and haplotype analysis7 to identify 9 candidate genes on human chromosome (Chr) 1q that they tested for association with blood pressure in humans; 2 of the 3 genes significantly associated with blood pressure are within the mouse haplotype region. These findings support the approach of using animal models to identify genes affecting blood pressure and then translating the findings to humans through association studies.
The resources available for genetic mapping in mice are exemplary, yet rats are the preferred rodent model for blood pressure QTL analysis. The rat genome database (www.rgd. mcw.edu) lists 292 blood pressure–related QTL in rats, but only 13 QTL linked to blood pressure in mice. Therefore, we performed QTL analyses of blood pressure in 8 mouse intercross populations to better understand the genetic regulation of blood pressure in mice and to facilitate comparative genomic mapping between mice and humans.
Breeding and Phenotyping Inbred Mice for the Strain Survey of Blood Pressure
Mice from 37 inbred strains were purchased from The Jackson Laboratory (Bar Harbor, ME), Clea Japan (Tokyo, Japan), or Charles River Japan (Yokohama, Japan) and bred at the Laboratory Animal Resource Center, University of Tsukuba. Tail-cuff systolic blood pressures (SBPs) were measured using a BP-98A blood pressure system (Softron; please see the online Data Supplement at http://hyper.ahajournals.org for details). All of the blood pressure measurements were taken from 10-week–old male mice in the morning, and the values from 100 successful readings (20 readings on each of 5 consecutive days) per mouse were used to calculate individual averages. Study protocols were approved by the university animal experimental committee of the University of Tsukuba.
Breeding and Phenotyping F2 Populations
Mice from 14 inbred strains were purchased from The Jackson Laboratory and bred at Novartis Pharmaceuticals Corp to generate 8 F2 populations for QTL analysis (summarized in Table 1). All of the F1 mice for each cross were generated in the same direction and intercrossed to produce the F2 progeny, meaning that maternal, imprinting, and mitochondrial effects were fixed within each F2 population. Tail-cuff blood pressure was measured in 8-week–old male F2 mice using a CODA-6 noninvasive blood pressure monitoring system (Kent Scientific). The accuracy of the CODA-6 system has been validated by comparison with simultaneous telemetry measurements,8 and we determined that a training week was not required for this system (Figure S1, available in the online Data Supplement). All of the measurements were taken in the afternoon, and values from ≤100 measurement cycles (20 per day for 5 days) were used to calculate average SBPs and SDs for each mouse. Any reading >2 SD from the mean for an individual mouse was discarded, and final averages and SDs were recalculated. Only mice having a final average SBP calculated from ≥40 cycles, of 100 cycles maximum, were used for the QTL analyses.
DNA was isolated by phenol:chloroform extraction from the tail of each F2 mouse and genotyped by KBiosciences with ≈90 single-nucleotide polymorphism markers evenly spaced across the genome.9 This number of single-nucleotide polymorphism markers provides similar power to detect and resolve QTL as an infinite number of markers.10
To minimize the influence of extreme phenotype values on the QTL analyses, SBP values were converted to van der Waerden normal scores within each cross.11 A 3-step analysis12 was used to identify QTL linked to blood pressure. QTL mapping was performed in R/qtl.13 Because the single-nucleotide polymorphism markers used for genotyping are mapped to physical positions in the genome, centimorgan (cM) positions were approximated by dividing megabase positions by 2 for genetic mapping; we confirmed the validity of this approximation by comparison with the cM positions estimated from the genotype data from each cross. Main-effect QTLs were identified by calculating logarithm of the odds scores at 2-cM intervals across the genome and compared with genome-wide adjusted significance (P<0.05) thresholds calculated by permutation testing.12,14⇓ CIs were determined as 95% of the area under the posterior probability density curves. QTL from each cross were fit to a multiple regression model to assess their effects on blood pressure. Ultimately, all of the QTL were mapped to the physical mouse genome map.
The Tukey honestly significant differences test was used to test the significance of unplanned, pairwise comparisons among the 37 inbred strains. Values for groups sorted by genotype are presented as mean±SE and were compared by ANOVA followed by Bonferroni posttest using SigmaStat. P<0.05 was considered significant.
Survey of SBP in Inbred Mice
To evaluate SBP among inbred mice and identify strains useful for QTL analysis, we measured SBP in mice from 37 different inbred strains. The strain survey data, with individual values, is publicly available in the Mouse Phenome Database (http://www.jax.org/phenome). We found a wide variation in SBP between mice from different inbred strains, from C3H mice with SBP at ≈100 mm Hg to NZO mice with SBP >130 mm Hg (Figure 1; please see Table S1).
QTL Analyses of SBP in F2 Populations
Based on the strain survey data and genetic diversity between inbred mouse strains, we chose mice from 12 inbred strains to generate 8 F2 populations for QTL analyses. Although none of the strains are considered hypertensive (SBP >140 mm Hg), SBP was significantly different between each of the strain pairs used to generate the F2 populations (Table 1), and each F2 population displayed a wide blood pressure distribution (Figure S2). We performed QTL analysis on these 8 F2 populations and detected significant, main-effect QTL in all but 1 of the populations (Figure 2); the (PL×CBA)F2 population did not identify any QTL significantly linked to SBP. From the 7 analyses, 14 regions of the mouse genome were linked to SBP on 10 different chromosomes (peak locations, CIs, allele effects, logarithm of the odds scores, and modes of inheritance are summarized in Table 2).
We detected a significant QTL on Chr 1 affecting SBP in the (C3H×KK)F2 population (Figure 3A). Mice that inherited either 2 C3H or KK alleles at this locus had significantly higher blood pressure than heterozygous mice, indicating an overdominant pattern of inheritance (Figure 3B).
Chr 3 was significantly linked to SBP in 3 of the F2 populations tested, and the mapping plots suggest the presence of 2 blood pressure QTL on this chromosome (Figure 3C). Proximal Chr 3 was linked to SBP in the (129×D2)F2 and (BTBR×SWR)F2 populations. Although D2 mice contributed a recessive high blood pressure allele on Chr 3 (Figure 3D), SWR mice contributed an additive high blood pressure allele at this locus (Figure 3E). The CIs for these QTL were proximal to 45 cM, but Chr 3 distal to 45 cM was linked to SBP in the (SJL×RIII)F2 population (Figure 3C). Mice that inherited 2 RIII alleles on distal Chr 3 showed significantly higher blood pressure values than those that inherited 1 or 2 SJL alleles (Figure 3F). The mapping plot of Chr 3 for the (BTBR×SWR)F2 cross indicated that Chr 3 distal to 45 cM may also be linked to SBP in this population (Figure 3C).
The (C3H×KK)F2 population identified a significant QTL affecting SBP that spanned Chr 4 (Figure 4A). C3H mice contributed a recessive high blood pressure allele at this QTL (Figure 4B). Distal Chr 4 (≈45 to 60 cM) was also linked to SBP in the (FVB×RIII)F2 population, where a recessive high blood pressure allele was inherited from RIII mice (Figure 4C). The (C3H×KK)F2 mapping plot and distal Chr 4 QTL in the (FVB×RIII)F2 population suggest the presence of multiple blood pressure QTLs on Chr 4.
Chr 5 was significantly linked to SBP in both the (129×A)F2 and (SJL×RIII)F2 crosses (Figure 4D). SJL and 129 contributed recessive high blood pressure alleles in their respective populations (Figure 4E and 4F).
The Chr 11 mapping plot for the (AKR×NZW)F2 population shows a broad SBP QTL spanning from 6 to ≈52 cM (Figure 5E). At this locus, AKR mice contributed a recessive high blood pressure allele (Figure 5F).
Distal Chr 13 contained a significant SBP QTL in the (129×A)F2 intercross (Figure 6C). F2 mice that inherited two 129 alleles at this locus displayed significantly higher SBP than heterozygotes or A homozygotes (Figure 6D), indicating a recessive 129 high blood pressure allele at this QTL.
Considering the limitations of available methods for blood pressure measurement in mice, we chose to use tail-cuff manometry because QTL analyses require high-throughput blood pressure phenotyping. We previously validated the accuracy of the CODA-6 tail-cuff system for measuring SBP by comparison with simultaneous radiotelemetry blood pressure measurements.8 Blood pressure can be greatly affected by environmental conditions and measurement technique; despite substantial environmental and methodologic differences, results from the strain survey performed at the University of Tsukuba enabled us to produce 7 effective mapping populations at Novartis. Within our QTL analyses, we carefully controlled time of measurement (afternoon only), ambient conditions (ie, temperature and noise), and operator handling to limit measurement variability. To further reduce variability, we conducted 5 daily measurement sessions of 20 measurements each and calculated the average blood pressure from ≤100 discrete measurements selected to exclude outliers >2 SD from the initial mean. We believe that this experimental strategy provides an accurate measurement of blood pressure in each mouse, and this approach has been used previously for QTL analyses of blood pressure in mice.2,7,15⇓⇓
In this study, we conducted QTL analyses of 8 intercrosses generated with mice from 14 inbred strains. Although none of the 14 inbred mouse strains used is considered hypertensive, each strain pair used to produce an F2 population had a significant difference in SBP (Table 1), and even strain pairs without significantly different SBPs can be used to identify significant QTL affecting blood pressure. For example, Sugiyama et al15 identified significant, main-effect blood pressure QTL in a (BALB×CBA)F2 intercross. BALB and CBA each contributed 1 high blood pressure allele at the main-effect loci,15 which could account for the significant blood pressure QTL in the F2 population despite no difference in blood pressure between these inbred mice. In our crosses, we detected significant QTL in F2 populations generated from inbred mice with SBP differences as low as 5.3 mm Hg. Overall, the number of QTL detected was not proportional to the phenotypic difference between the parental strains. Strain pairs with large SBP differences (BTBR versus SWR: 25.7 mm Hg; PL versus CBA: 13.4 mm Hg) produced F2 populations that identified only 1 significant QTL between them, whereas the (129×D2)F2 population identified 3 significant QTL despite the small SBP difference (5.9 mm Hg) between the parental strains.
Some of the QTL identified in our intercrosses replicate blood pressure QTL detected previously in other mouse crosses (Figure 7). For example, we identified a significant QTL on Chr 15 in the (129×D2)F2 intercross that overlaps a QTL found previously in crosses between CBA and BALB mice,15 A and B6 mice,2 and BPH and BPL mice.16 The concordant regions of the rat and human genomes have also been linked to blood pressure.17–19⇓⇓ Other previously reported blood pressure QTL on distal Chr 1,2,7,20⇓⇓ proximal Chr 4,2,20,21⇓⇓ and Chr 820 were replicated in our studies. In addition to these previously identified QTL, we also identified many novel blood pressure QTL in the 8 intercross populations (Figure 7). Proximal Chr 3 was linked to SBP in (129×D2)F2 and (BTBR×SWR)F2 populations, whereas distal Chr 3 was linked in the (SJL×RIIIS)F2 population. Distal Chrs 4 and 5 also contained novel QTL detected in multiple crosses, whereas Chrs 12 and 13 were each linked to SBP in a single intercross population.
Moreno et al22 found that kidney weight:body weight ratio can serve as an intermediate phenotype for blood pressure QTL on the basis of correlation between the phenotypes and QTL concordance. However, only 1 blood pressure QTL corresponded with a kidney weight QTL in the same cross (Table S2), suggesting that kidney weight is not an intermediate phenotype for blood pressure QTL in mice.
Blood pressure QTL identified in both mice and rats are often concordant with human blood pressure QTL.2,3⇓ The regions of the human genome corresponding to all but 1 of the QTL identified in the intercross populations that we analyzed contained blood pressure QTL (Table 2; see Cowley1 for review of human hypertension QTL). This high degree of concordance between mouse and human blood pressure QTL suggests evolutionary conservation of genes affecting blood pressure.
The 8 mouse populations examined in our study double the number of populations used for linkage analysis of blood pressure. The majority of QTL detected in our study have been replicated, either within these 8 intercross populations or in published studies; however, several QTL were detected in only 1 cross, suggesting that future crosses may detect additional new blood pressure QTL. New QTL crosses investigating the genetic basis of blood pressure may also replicate these QTL and provide more information for interval-specific haplotype analysis.23 Future studies to fine map these QTL and identify the causal genes could elucidate novel pathways affecting blood pressure in both mice and humans.
Sources of Funding
This work was supported by the Novartis Institutes for BioMedical Research.
- Received April 14, 2009.
- Revision received May 1, 2009.
- Accepted July 9, 2009.
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