Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Circ Cardiovasc Genet. Author manuscript; available in PMC 2012 June 1.
Published in final edited form as:
PMCID: PMC3116070

High Resolution Identity by Descent Mapping Uncovers the Genetic Basis for Blood Pressure Differences Between SHR Lines



The recent development of a large panel of genome-wide single nucleotide polymorphisms (SNPs) provides the opportunity to examine genetic relationships between distinct SHR lines that share hypertension, but differ in their susceptibility to hypertensive end-organ disease.

Methods and Results

We compared genotypes at nearly 10,000 SNPs obtained for the hypertension end-organ injury-susceptible SHR-A3 (SHRSP, SHR-stroke prone) line and the injury-resistant SHR-B2 line. This revealed that that the two lines were genetically identical by descent (IBD) across 86.6% of the genome. Areas of the genome that were not IBD were distributed across 19 of the 20 autosomes and the X chromosome. A block structure of non-IBD comprising a total of 121 haplotype blocks was formed by clustering of SNPs inherited from different ancestors. To test the null hypothesis that distinct SHR lines share a common set of hypertension susceptibility alleles we compared blood pressure in adult SHR animals from both lines and their F1 and F2 progeny using telemetry. In 16–18wk old animals fed a normal diet, systolic blood pressure (SBP, mm Hg) in SHR-A3 was 205.7 ± 3.86 (mean ± SEM, n = 26), while in similar SHR-B2 animals SBP was 186.7 ± 2.53 (n = 20). In F1 and F2 animals, SBP was 188.2 ± 4.23, (n = 19) and 185.6 ± 1.1 (n = 211) respectively (p<10−6, ANOVA). In order to identify non-IBD haplotype blocks contributing to blood pressure differences between these SHR lines we developed a high throughput SNP genotyping system to genotype SNPs marking non-IBD blocks. We mapped a single non-IBD block on chromosome 17 extending over less than 10Mb at which SHR-A3 alleles significantly elevate blood pressure compared with SHR-B2.


Thus hypertension in SHR-A3 and -B2 appears to arise from an overlapping set of susceptibility alleles, with SHR-A3 possessing an additional hypertension locus that contributes to further increase blood pressure.

Keywords: Hypertension, SNP, mapping, QTL, SHR, identity by descent


The SHR strain was produced during the 1960’s in Japan by inbreeding of Wistar rats with selection on the trait of elevated blood pressure 1, 2. All SHR animals are descended from two founder animals with no external additions to the gene pool. Brother-sister mating of SHR progenitors was continued for 8 generations to fix the trait of hypertension whereupon several parallel lines of SHR were generated, presumably to accommodate risk of reproductive depression as further inbreeding was performed. Three major SHR clades resulted: SHR-A, -B and -C. In addition, at about the F13 generation incompletely inbred animals that appear to have been contributed from progenitors of both the B and C clades were transferred to NIH where subsequent further inbreeding lead to the widely used SHR/N line. While all SHR lines have been reported to demonstrate robust hypertension and a similar ontogeny of blood pressure during development, it is unclear to what extent the genetic basis of hypertension overlaps across SHR lines. The objectives of our study were to compare the genome-wide extent and distribution of identity by descent (IBD) across SHR lines, to determine whether hypertension arises from within regions of IBD and to identify any genomic regions contributing to hypertension susceptibility that are not shared by both lines.

The issue of the genetic architecture of hypertension in SHR is important because the widely used SHR-A3 line differs from lines derived from SHR-B, SHR-C and the SHR/N line by being susceptible to hypertensive end-organ injury 2, 3. An important objective is to know whether SHR-A3 acquires end-organ injury susceptibility because of unique elements of the pathogenesis of hypertension in this line or whether injury susceptibility arises independently of blood pressure 3, 4. If SHR lines differ in the genetic basis of elevated blood pressure, a further opportunity is provided. This is the opportunity to map blood pressure loci across SHR lines. While the contrast in parental blood pressure phenotype would be less than in contrasts between SHR and normotensive strains 57 genetic background effects may be much smaller 8 and the shared IBD would reduce the total region of the genome in which differences in blood pressure could arise. The same approach has recently been used in closely related mouse lines to yield high resolution mapping of trait genes 9.



Studies were performed on male rats of the injury-prone spontaneously hypertensive-A3 (SHR-A3, SHRSP/Bbb) line that have been maintained in our facility for 15 years. We also used male animals from the SHR-B2 line that were bred in our facility from stocks originating from colonies held at Kinki University School of Medicine, Japan. Animals were housed under controlled conditions in an AAALAC-approved animal facility and provided a standard rodent chow diet and drinking water ad libitum. No dietary sodium loading was employed. SHR-A3 (males) and SHR-B2 (females) parental lines were crossed to generate the F1 progeny. This progeny was further crossed to generate a freely segregating F2 progeny. Only male parental, F1 and F2 animals were used in the studies performed. All animal use was prospectively reviewed and approved by the University’s Animal Welfare Committee.

Blood pressure

Blood pressure was measured by radiotelemetry (Data Sciences, St. Paul, MN) in adult animals 16–18 wks of age in SHR-A3 and SHR-B2 as well as the F1 and F2 progeny of the intercross. Catheters were implanted into the abdominal aorta above the bifurcation and below the renal arteries. Animals were allowed to recover from implantation for at least 7 days before measurement began. Implants were calibrated under pressure (120, 160 and 200 mm Hg) at 37°C prior to implantation and again after removal and observed blood pressures were adjusted to compensate for calibration drift.

Parental line SNP analysis

We submitted genomic DNA to the STAR Consortium for inclusion in the rat SNP genotype discovery program performed by that consortium 10. This program also genotyped DNA from numerous closely related SHR lines.


SNP genotyping was performed in multiplex reactions using the Sequenom MassARRAY system 11. PCR product was amplified utilizing 0.10 µM each of forward and reverse primers selected for each SNP locus, 10.0 ng DNA, 3.5 mM MgCl2, 1.0 Unit of Qiagen HotStart DNA Taq Polymerase, 1.25X buffer and 0.5 mM of each dNTP in a 5.0 µL reaction volume. The thermal cycle protocol was: 94°C for 15 min; 94°C at 20 sec, 56°C for 30 sec, and 72°C for 1 min for 45 cycles; followed by 72°C for 3 mins. PCR amplification was followed by the addition of a 2.0 µL Shrimp Alkaline Phosphatase (SAP) enzyme reaction mix that dephosphorylates residual dNTP’s and degrades residual PCR primers. After the heat-labile SAP was inactivated, an extension reaction extended a genotyping oligo annealed to the PCR product across the SNP nucleotide. The extension reaction contains approximately 10.0 µM of the SNP specific extension primer which anneals upstream of the dimorphic SNP site, buffer, ddNTPs, and a proprietary thermostable DNA polymerase (Sequenom, Inc, San Diego, CA). The extension products were then desalted with SpectroCLEAN resin and ~20 nanoliters of the analyte was transferred onto a 384-well SpectroCHIP using the Samsung nanodispenser (Sequenom, Inc). Each site on the chip was preloaded with a formulated 3-Hydroxypicolinic acid (3-HPA) matrix that has been optimized for use with the MassARRAY system. Mass spectrometry (MS) analysis of the extension reactions was performed using a Bruker Compact MALDI-time of flight mass spectrometer. The mass spectra were collected and SpectroCALLER software (Sequenom, Inc.) was used to automatically assign the genotype calls. We genotyped DNA from parental lines and 211 F2 animals in which blood pressure was also measured. We designed PCR and oligo extension systems targeting a total of 234 SNPs on the 121 non-IBD blocks that were combined into 7 high level multiplex reactions. These genotyping reactions yielded high quality genotype data for 203 of the 234 SNPs.

Genetic mapping

Mapping of blood pressure was performed using R/qtl with Haley Knott regression 12, 13. Physical map positions of the SNP markers were adjusted to approximate cM positions by dividing the physical position (in Mbp) by 2 14. Genome-wide significance was determined in R/qtl by permutation (n=1000) of the phenotypes with respect to the genotypes 15.


The expected close genetic relationship between the parental SHR lines requires a marker set of high resolution in order to localize regions of the genome that were inherited from different ancestors. We obtained SNP genotypes generated using Affymetrix genotyping array methodology from the STAR Consortium for ~10K SNPs for the parental SHR lines in our study (available at Table 1 indicates the main results of this genotyping effort. Overall accuracy of genotyping is reflected in a 0.27% heterozygosity rate. Since both lines are inbred over >100 generations, actual heterozygosity rate should be less than 0.0001%. The small deviation from this level may reflect either very limited genetic contamination, or perhaps more likely, genotyping errors. We examined the distribution of SNPs that are not IBD across the genome to determine whether non-IBD SNPs are randomly distributed through the genome or whether a haplotype block structure of clustered non-IBD SNPs can be identified. We first computed the physical distance at which there was a 5% chance of the random occurrence of two SNPs using a nominal physical size of the rat genome of 3Gb and this threshold was calculated at ~2Mb. To assign a block, we considered that a new haplotype block would be created every time a non-IBD SNP occurred more than 2Mb from the nearest non-IBD SNP. Applying this parameter across the genome we uncovered a block pattern of non-IBD demarcated by 1264 non-IBD SNPs by which the two lines were shown to differ. This clear clustering of SNPs within blocks reduces the complexity of the genome and allows the delineation of regions of the genome by which the two lines have arisen from distinct ancestors into a compact distribution comprised of 121 blocks that is illustrated in Figure 1. The overall 86.6% of the genome that is IBD is distributed across chromosomes that range from 69% IBD (chr 18) to 100% IBD (chr 20).

Figure 1
Using genotypes obtained from DNA provided to the STAR Consortium we constructed a 10K SNP map to identify genomic regions in which SHR-A3 and SHR-B2 were identical by descent (IBD, solid black line) or from which the inbred lines were descended from ...
Table 1
Genotype information for SNPs typed in parental lines (SHR-A3 and SHR-B2). Based on this dense marker panel, the two lines were found to be identical by descent at nearly 87% of the genome.

Systolic, mean and diastolic blood pressures from 16–18 wk old parental strains, the F1 progeny of crosses between SHR-A3 males and SHR-B2 females and the F2 progeny of an F1 A3 x B2 intercross are shown in Table 2. Distribution of systolic blood pressure across the lines and crosses is shown in Figure 2. If SHR-A3 and SHR-B2 raised under identical conditions acquire the shared trait of hypertension through alleles at which they are IBD, then it would be expected that there is no statistically significant difference in blood pressure between them. We tested this hypothesis by one-way analysis of variance to determine whether there were significant differences in blood pressure between individual groups. The ANOVA produced a highly significant p value (p<10−6) indicating that differences in blood pressure existed between the groups. Post hoc tests indicated that significant differences existed between SHR-A3 and SHR-B2, SHR-A3 and F1 and SHR-A3 and F2 animals.

Figure 2
Distribution of systolic blood pressure in SHR-A3, SHR-B2, F1A3B2 and F2A3B2 rats. ANOVA followed by post hoc testing indicated that no differences exist between systolic blood pressure comparing SHR-B2 with F1A3B2, SHR-B2 with F2A3B2 and F1A3B2 with ...
Table 2
Blood pressures (mm Hg) measured by radiotelemetry in SHR rat lines and crosses and analysis of variance, n = number of animals studied.

In order to localize genomic regions containing alleles that differentially influence blood pressure in SHR-A3 and SHR-B2 we performed an F2 intercross mapping study. We designed a genotyping approach to capture SNP markers distributed across the 121 non-IBD haplotype blocks. We used R/qtl to perform mapping of systolic, mean and diastolic blood pressure. Physical positions were obtained from the Dec 2004, Baylor 3.4/rn4 genome sequence assembly ( and were divided by 2 to provide estimated genetic positions of the markers and the genetic map was calculated with this information 14. Figure 3 provides the LOD plots for each blood pressure phenotype. A single significant blood pressure QTL was mapped in the genome. It was located on chromosome 17 where SHR-A3 alleles are associated with increased blood pressure (Tables 3 and and4).4). The LOD score for this QTL for systolic blood pressure was significantly greater than the genome-wide LOD score expected if no QTL was present (p<0.002). Mean BP was also significantly associated with sequence variation in this region.

Figure 3Figure 3
a.) Genome-wide LOD scores for systolic, mean and diastolic blood pressure measured by telemetry in 211 male F2 progeny of a cross between SHR-A3 and SHR-B2. b) Chromosome 17 interval mapping. The only regions at which SHR-A3 and SHR-B2 are not identical ...
Table 3
Mapping of blood pressure traits in the F2 progeny of an SHR-A3 x SHR-B2 intercross. A single significant blood pressure QTL was obtained.
Table 4
Effect on systolic blood pressure associated with genotyped marker alleles in the non-IBD haplotype block closest to the mapped chromosome 17 LOD peak. ANOVA followed by post hoc testing indicates that highly significant differences are associated with ...

The peak chromosome 17 LOD score for systolic blood pressure corresponds with a haplotype block that extends from SNPs located at 10.4Mb to 19.6Mb. The nearest region of non-identity between SHR-A3 and -B2 on this chromosome is at 81Mb. Thus the block at 10.4–19.6Mb is highly resolved in an extensive region of sequence identity between SHR-A3 and -B2 lines. We compared systolic blood pressure effects in F2 animals associated with inheritance of SHR-A3 or -B2 alleles at the two SNP markers typed in the block spanning 10.4Mb to 19.6MB (Table 5). The results indicated that A3 alleles are associated with increased blood pressure at this locus and that the magnitude of the effect (~14mmHg) contributes substantially to the overall difference in blood pressure observed across these parental lines.

Table 5
Differential gene expression of genes in the chromosome 17 locus determined by Affymetrix RG-U34A arrays, n= 12 SHR-A3, 15 SHR-B2, age 4–18wks). Expression values for SHR-A3 and SHR-B2 are chip normalized fluorescence intensity signals.

To determine whether the blood pressure locus we have identified is exclusive to the SHR-A3 x SHR-B2 contrast we have made, we have compared SNP genotypes in the SHR lines for which high-density SNP genotype information is available from the SNP-STAR consortium ( All injury-prone SHR-A3 lines (5 geographically distinct lines) share the same genotypes in the chromosome 17 block we have identified. In contrast, all injury-resistant SHR lines (9 distinct lines) are genetically dimorphic with respect to SHR-A3 in this region. Furthermore, the ~62Mb of IBD lying between the proximal non-IBD block we have associated with blood pressure and the two small distal non-IBD blocks on chromosome 17 (Figure 1) is also IBD in all 14 SHR lines and is created by 484 contiguous SNPs in which not a single SNP in any SHR line is dimorphic. Knowledge of the identity of the chromosome 17 blood pressure locus will allow it to be evaluated for a contribution to end-organ injury susceptibility in SHR-A3 and thereby clarify whether end-organ injury-susceptibility arises from blood pressure differences between SHR-A3 and injury-resistant SHR lines or from alleles that are not related to blood pressure and that create injury susceptibility by non-pressor mechanisms.

The chromosome 17 block of non-IBD that we have mapped contains 78 RefSeq genes (see supplemental data). To add further to our identification of candidate genes in this chromosome 17 locus we have examined our previously reported kidney gene expression data using Affymetrix RG-U34A arrays from SHR-A3 and SHR-B2 rats 16. This array provides gene expression data for 37 genes in this block. Difference in gene expression was observed for several genes in this region (Table 5). Among renal tubular sodium transport proteins, differential expression for SLC34A1, the sodium-dependent, phosphate co-transporter (Npt2), was robustly detected via several independent probe-sets reporting expression of this gene. Resequencing of this gene in SHR-A3 and SHR-B2 revealed several sequence variants in the proximal promoter that were associated with changes in predicted transcription factor binding sites. These variants provide a potential mechanism for the differential Slc34A1 gene expression we observed. A single SNP was observed in the coding sequence of Slc34A1 that produced a non-synonymous change in the COOH-terminal region of the transporter. Other genes in this block are also candidate genes for the observed blood pressure trait. The gene encoding the dopamine 1A receptor has been implicated in blood pressure regulation via control of renal ion transport function 17, 18. We also examined this gene for sequence variation across our lines but found no sequence differences across the lines. We have also examined the sequence of ATP/GTP binding protein 1 and have observed the presence of a insertion/deletion polymorphism affecting 3 bases that deletes a single amino acid in SHR-B2, while SHR-A3 retains the same sequence as the Brown-Norway rat genome sequence. Further studies will be required to investigate the functional significance of the altered gene expression and sequence variation we have observed.


Using a recently developed high density rat SNP panel, we have been able to uncover the extent and distribution of IBD across SHR-A3 and SHR-B2 lines. The high level of IBD observed means that mapping trait loci by crossing these lines involves mapping across a much-reduced genome. Rather than mapping across the whole genome (2.8Gb), we are effectively only mapping across the 13.4% (0.37Gb) of the genome that is not identical by descent. Mapping resolution is increased further because the space in which alleles can segregate is organized into discrete blocks that can be both isolated and small. Thus, the approach to mapping employed here is a reductionist one. Blood pressure loci common to both SHR lines are removed from mapping in our cross because they are shared due to shared ancestry. A single quantitative trait locus (QTL) is identified in the cross we have constructed and is localized to a narrow, well isolated haplotype block of non-IBD on chromosome 17 extending across ~10Mb.

The present studies indicate that adult SHR-A3 consuming a normal rodent chow diet and in the absence of sodium loading have higher blood pressures than SHR-B2. Since these animals are raised under identical environmental conditions, it is expected that trait differences are likely to be largely genetic. However, the variance of blood pressure in the F1 and F2 progeny provide another indicator of heritability and are very similar (Table 2), suggesting limited genetic influence on the blood pressure trait in this cross. Indeed, the heritability of blood pressure in crosses between SHR-A3 and SHR-B2 is expected to be limited because IBD is very high and the majority of the blood pressure trait can be expected to arise from genetic variation that is common to the two lines. Mapping suggests that the difference in blood pressure between the parental lines arises from just one of the 121 genomic haplotype blocks from which SHR-A3 and SHR-B2 arose from distinct ancestors.

Resolution of the genetic basis of hypertension in the SHR/N line has advanced to the identification of one gene, cd36, that contains variation that contributes to hypertension in this line 19, 20. This identification was facilitated by the existence of gene expression effects associated with variation in cd36. Our own studies have identified one gene, Slc34A1, which has a robust expression phenotype across our lines. Resequencing reveals that the promoter sequence differs across our lines. We have examined these differences to determine whether they affect any predicted transcription factor binding sites and find that several sites are altered. These variations may underlie the differential gene expression, however further work will be required to determine whether functional differences in the encoded sodium-dependent, phosphate co-transporter result and whether trait differences can be ascribed to these functional differences. A mouse knock-out for Slc34a1 has been reported 21, 22. As expected, the major phenotype observed relates to altered urinary phosphate excretion (this being the major proximal transporter of phosphate) and consequent effects on serum phosphate. There do not appear to be any reports assessing blood pressure in these animals. Nonetheless, Slc34A1 plays a clear role in renal adaptations to increased blood pressure, thereby allowing the possibility that altered function of the protein may blunt adaptive responses to elevated blood pressure 23. In humans Slc34a1 plays a similar renal role 24. Genome-wide association studies have implicated intronic sequence variation in this gene in susceptibility to renal injury, though this does not appear to be related to a primary effect of hypertension to induce this injury 25. One other gene potentially involved in blood pressure regulation was identified in the chromosome 17 mapped interval as the dopamine 1a receptor gene (Drd1a) 26. This gene is involved in regulation of proximal tubule sodium reabsorption. Our gene expression data did not uncover differential expression across the strains and we did not detect any sequence variation across the coding sequence of this intron-less gene.

It is possible that trait differences in blood pressure between SHR-A3 and SHR-B2 could be secondary to alleles that do not directly affect blood pressure, but rather contribute to renal injury and thereby produce a secondary effect on blood pressure. However, we have previously reported on the time-course of histologically assessed hypertensive renal injury and contrasted its development in SHR-A3 with SHR-B2 27. We observed that histological renal injury does not emerge until 24 weeks of age in SHR-A3. Thus it is reasonable to infer that the blood pressure differences measured here in animals 16–18 weeks of age are not secondary to renal injury.

The difference in systolic blood pressure between parental lines is 19 mm Hg (Table 2). The maximum difference attributed to alleles at the chromosome 17 locus in the F2 progeny is 14 mmHg (Figure 4). The chromosome 17 locus appears to account for a substantial part of the difference in blood pressure across the parental strains. It may be that there are other loci capable of influencing blood pressure in the SHR-A3 x SHR-B2 cross. However, even with the use of telemetry measurements and a highly accurate genotyping system, any effects arising in such loci are likely too small to be detected by mapping. Variation in the region that we have identified on chromosome 17 has been previously associated with blood pressure in rats. Mapping an inter-cross of Brown-Norway and Dahl salt-sensitive rats led to the identification of a blood pressure (BP response to acetylcholine after l-NAME) locus on chromosome 17 with a peak LOD score in the vicinity of the block we have identified 28. A salt-sensitive BP locus on chromosome 17 was identified in female, but not male Sabra rats and its peak was localized more distally on the chromosome 17 than the locus we have identified 29. Chromosome 17 also appears to harbor a blood pressure locus in the Lyon hypertensive rat, though again the peak LOD score arises from a more distal region of the chromosome 30. Two papers report mapping of blood pressure alleles to the syntenous region of rat chromosome 17 in mouse 31, 32.

Figure 4
Effect on systolic blood pressure in F2 animals of an SHR-A3 x SHR-B2 intercross of the inheritance of SHR-A3 (A) or SHR-B2 (B) alleles at the chromosome 17 BP locus (N = 211, p<0.002). The marker used for this analysis was rs63927241.

Our study has several limitations that should be considered. First, although a 10K SNP density provides a more highly detailed view of sequence variation in the rat genome than has been previously available, there may be blocks of non-IBD that are undiscovered at this resolution that might contribute to trait differences across our lines. Mitigating this limitation is the fact that such blocks are likely to be small and there is a positive relationship between the size of a block and its likelihood of containing relevant functional variation. Several small genomic blocks marked by only a single SNP could not be targeted by our multiplex genotyping approach. Here again, markers in nearby adjacent blocks mitigated this limitation for all but two blocks (chr 8 and chr X, indicated with asterisks in Figure 1).

This work is the first to report mapping of blood pressure in the rat using a newly developed high-density panel of single nucleotide polymorphisms. It is also the first report of blood pressure differences across SHR lines using telemetry measurements. These measurements reveal that differences in blood pressure exist between the SHR-A3 and the SHR-B2 lines and arise from a single non-IBD locus on chromosome 17 that extends over approximately 10Mb. SHR-A3 alleles at this locus increase blood pressure and act recessively. The approach used in these experiments opens two important new advances. First, highly resolved mapping can be achieved by mapping closely related SHR lines in which only a small fraction of the genome differs across lines. By combining this mapping with our renal gene expression data from these lines we can refine the list of candidate genes in this region and identify renally-expressed genes that differ in expression. This has lead to the identification of the proximal tubular sodium, phosphate co-transporter encoded by Slc34A1 as an important candidate gene in the locus. Second, other SHR blood pressure loci may be identified by mapping crosses using different combinations of SHR lines. Thus identification of SHR blood pressure genes may be achievable through a reductionist approach that increases the power and resolution of genetic mapping compared to crosses in less closely related strains.

Supplementary Material


Funding Sources: This work was supported by grants from NIH (R01-DK069632 and R01-DK081866) and from the American Heart Association (09GRNT2240045) to PAD


Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflict of Interest Disclosures: None


1. Okamoto K, Yamori Y, Nosaka S, Ooshima A, Hazama F. Studies on hypertension in spontaneously hypertensive rats. Clin Sci Mol Med Suppl. 1973;(45 Suppl 1) 11s-14. [PubMed]
2. Okamoto K, Yamori Y, Nagaoka A. Establishment of the stroke-prone spontaneously hypertensive rat. Circ. Res. 1974;34/35:I-143–I-153.
3. Nagaoka A, Iwatsuka H, Suzuoki Z, Okamoto K. Genetic predisposition to stroke in spontaneously hypertensive rats. Am J Physiol. 1976;230:1354–1359. [PubMed]
4. Gigante B, Rubattu S, Stanzione R, Lombardi A, Baldi A, Baldi F, Volpe M. Contribution of genetic factors to renal lesions in the stroke-prone spontaneously hypertensive rat. Hypertension. 2003;42:702–706. [PubMed]
5. Hilbert P, Lindpaintner K, Beckmann JS, Serikawa T, Soubrier F, Dubay C, Cartwright P, De Gouyon B, Julier C, Takahasi S, Vincent M, Ganten D, Georges M, Lathrop GM. Chromosomal mapping of two genetic loci associated with blood-pressure regulation in hereditary hypertensive rats. Nature. 1991;353:521–529. [PubMed]
6. Jacob HJ, Lindpaintner K, Lincoln SE, Kusumi K, Bunker RK, Mao YP, Ganten D, Dzau VJ, Lander ES. Genetic mapping of a gene causing hypertension in the stroke-prone spontaneously hypertensive rat. Cell. 1991;67:213–224. [PubMed]
7. Petretto E, Mangion J, Dickens NJ, Cook SA, Kumaran MK, Lu H, Fischer J, Maatz H, Kren V, Pravenec M, Hubner N, Aitman TJ. Heritability and tissue specificity of expression quantitative trait loci. PLoS Genet. 2006;2 pe172. [PMC free article] [PubMed]
8. Gigante B, Rubattu S, Zee RY, Volpe M, Lindpaintner K. Non-random chromosomal distribution of sslps: Systematic assessment using a novel genetic linkage map between two closely related rat strains. Cytogenet Cell Genet. 2001;95:64–72. [PubMed]
9. Stylianou IM, Langley SR, Walsh K, Chen Y, Revenu C, Paigen B. Differences in dba/1j and dba/2j reveal lipid qtl genes. J Lipid Res. 2008;49:2402–2413. [PMC free article] [PubMed]
10. Saar K, Beck A, Bihoreau MT, Birney E, Brocklebank D, Chen Y, Cuppen E, Demonchy S, Dopazo J, Flicek P, Foglio M, Fujiyama A, Gut IG, Gauguier D, Guigo R, Guryev V, Heinig M, Hummel O, Jahn N, Klages S, Kren V, Kube M, Kuhl H, Kuramoto T, Kuroki Y, Lechner D, Lee YA, Lopez-Bigas N, Lathrop GM, Mashimo T, Medina I, Mott R, Patone G, Perrier-Cornet JA, Platzer M, Pravenec M, Reinhardt R, Sakaki Y, Schilhabel M, Schulz H, Serikawa T, Shikhagaie M, Tatsumoto S, Taudien S, Toyoda A, Voigt B, Zelenika D, Zimdahl H, Hubner N. Snp and haplotype mapping for genetic analysis in the rat. Nat Genet. 2008;40:560–566. [PubMed]
11. Gabriel S, Ziaugra L, Tabbaa D. Snp genotyping using the sequenom massarray iplex platform. Curr Protoc Hum Genet. 2009 Chapter 2:Unit 2 12. [PubMed]
12. Broman KW, Wu H, Sen S, Churchill GA. R/qtl: Qtl mapping in experimental crosses. Bioinformatics. 2003;19:889–890. [PubMed]
13. Haley CS, Knott SA. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity. 1992;69:315–324. [PubMed]
14. Jensen-Seaman MI, Furey TS, Payseur BA, Lu Y, Roskin KM, Chen CF, Thomas MA, Haussler D, Jacob HJ. Comparative recombination rates in the rat, mouse, and human genomes. Genome Res. 2004;14:528–538. [PubMed]
15. Broman KW, Sen S. A guide to qtl mapping with r/qtl. New York: Springer; 2009.
16. Dmitrieva RI, Hinojos CA, Grove ML, Bell RJ, Boerwinkle E, Fornage M, Doris PA. Genome-wide identification of allelic expression in hypertensive rats. Circ Cardiovasc Genet. 2009;2:106–115. [PMC free article] [PubMed]
17. Hussain T, Lokhandwala MF. Renal dopamine receptors and hypertension. Exp Biol Med (Maywood) 2003;228:134–142. [PubMed]
18. Jose PA, Eisner GM, Felder RA. Renal dopamine receptors in health and hypertension. Pharmacol Ther. 1998;80:149–182. [PubMed]
19. Pravenec M, Churchill PC, Churchill MC, Viklicky O, Kazdova L, Aitman TJ, Petretto E, Hubner N, Wallace CA, Zimdahl H, Zidek V, Landa V, Dunbar J, Bidani A, Griffin K, Qi N, Maxova M, Kren V, Mlejnek P, Wang J, Kurtz TW. Identification of renal cd36 as a determinant of blood pressure and risk for hypertension. Nat Genet. 2008;40:952–954. [PubMed]
20. Pravenec M, Kurtz TW. Recent advances in genetics of the spontaneously hypertensive rat. Curr Hypertens Rep. 2010;12:5–9. [PMC free article] [PubMed]
21. Hoag HM, Martel J, Gauthier C, Tenenhouse HS. Effects of npt2 gene ablation and low-phosphate diet on renal na(+)/phosphate cotransport and cotransporter gene expression. J Clin Invest. 1999;104:679–686. [PMC free article] [PubMed]
22. Chau H, El-Maadawy S, McKee MD, Tenenhouse HS. Renal calcification in mice homozygous for the disrupted type iia na/pi cotransporter gene npt2. J Bone Miner Res. 2003;18:644–657. [PubMed]
23. McDonough AA. Mechanisms of proximal tubule sodium transport regulation that link extracellular fluid volume and blood pressure. Am J Physiol Regul Integr Comp Physiol. 2010;298:R851–R861. [PubMed]
24. Magen D, Berger L, Coady MJ, Ilivitzki A, Militianu D, Tieder M, Selig S, Lapointe JY, Zelikovic I, Skorecki K. A loss-of-function mutation in napi-iia and renal Fanconi's syndrome. N Engl J Med. 2010;362:1102–1109. [PubMed]
25. Kottgen A, Pattaro C, Boger CA, Fuchsberger C, Olden M, Glazer NL, Parsa A, Gao X, Yang Q, Smith AV, O'Connell JR, Li M, Schmidt H, Tanaka T, Isaacs A, Ketkar S, Hwang SJ, Johnson AD, Dehghan A, Teumer A, Pare G, Atkinson EJ, Zeller T, Lohman K, Cornelis MC, Probst-Hensch NM, Kronenberg F, Tonjes A, Hayward C, Aspelund T, Eiriksdottir G, Launer LJ, Harris TB, Rampersaud E, Mitchell BD, Arking DE, Boerwinkle E, Struchalin M, Cavalieri M, Singleton A, Giallauria F, Metter J, de Boer IH, Haritunians T, Lumley T, Siscovick D, Psaty BM, Zillikens MC, Oostra BA, Feitosa M, Province M, de Andrade M, Turner ST, Schillert A, Ziegler A, Wild PS, Schnabel RB, Wilde S, Munzel TF, Leak TS, Illig T, Klopp N, Meisinger C, Wichmann HE, Koenig W, Zgaga L, Zemunik T, Kolcic I, Minelli C, Hu FB, Johansson A, Igl W, Zaboli G, Wild SH, Wright AF, Campbell H, Ellinghaus D, Schreiber S, Aulchenko YS, Felix JF, Rivadeneira F, Uitterlinden AG, Hofman A, Imboden M, Nitsch D, Brandstatter A, Kollerits B, Kedenko L, Magi R, Stumvoll M, Kovacs P, Boban M, Campbell S, Endlich K, Volzke H, Kroemer HK, Nauck M, Volker U, Polasek O, Vitart V, Badola S, Parker AN, Ridker PM, Kardia SL, Blankenberg S, Liu Y, Curhan GC, Franke A, Rochat T, Paulweber B, Prokopenko I, Wang W, Gudnason V, Shuldiner AR, Coresh J, Schmidt R, Ferrucci L, Shlipak MG, van Duijn CM, Borecki I, Kramer BK, Rudan I, Gyllensten U, Wilson JF, Witteman JC, Pramstaller PP, Rettig R, Hastie N, Chasman DI, Kao WH, Heid IM, Fox CS. New loci associated with kidney function and chronic kidney disease. Nat Genet. 2010;42:376–384. [PMC free article] [PubMed]
26. Albrecht FE, Drago J, Felder RA, Printz MP, Eisner GM, Robillard JE, Sibley DR, Westphal HJ, Jose PA. Role of the d1a dopamine receptor in the pathogenesis of genetic hypertension. J Clin Invest. 1996;97:2283–2288. [PMC free article] [PubMed]
27. Dmitrieva RI, Hinojos CA, Boerwinkle E, Braun MC, Fornage M, Doris PA. Hepatocyte nuclear factor 1 and hypertensive nephropathy. Hypertension. 2008;51:1583–1589. [PMC free article] [PubMed]
28. Moreno C, Dumas P, Kaldunski ML, Tonellato PJ, Greene AS, Roman RJ, Cheng Q, Wang Z, Jacob HJ, Cowley AW., Jr Genomic map of cardiovascular phenotypes of hypertension in female dahl s rats. Physiol Genomics. 2003;15:243–257. [PubMed]
29. Yagil C, Sapojnikov M, Kreutz R, Katni G, Lindpaintner K, Ganten D, Yagil Y. Salt susceptibility maps to chromosomes 1 and 17 with sex specificity in the sabra rat model of hypertension. Hypertension. 1998;31:119–124. [PubMed]
30. Bilusic M, Bataillard A, Tschannen MR, Gao L, Barreto NE, Vincent M, Wang T, Jacob HJ, Sassard J, Kwitek AE. Mapping the genetic determinants of hypertension, metabolic diseases, and related phenotypes in the lyon hypertensive rat. Hypertension. 2004;44:695–701. [PubMed]
31. Feng M, Deerhake ME, Keating R, Thaisz J, Xu L, Tsaih SW, Smith R, Ishige T, Sugiyama F, Churchill GA, DiPetrillo K. Genetic analysis of blood pressure in 8 mouse intercross populations. Hypertension. 2009;54:802–809. [PMC free article] [PubMed]
32. Wright FA, O'Connor DT, Roberts E, Kutey G, Berry CC, Yoneda LU, Timberlake D, Schlager G. Genome scan for blood pressure loci in mice. Hypertension. 1999;34:625–630. [PubMed]