Published online before print November 14, 2005, doi: 10.1161/01.HYP.0000192024.72367.c3
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(Hypertension. 2005;46:1300.)
© 2005 American Heart Association, Inc.
Original Articles |
Epistasis, Not Numbers, Regulates Functions of Clustered Dahl Rat Quantitative Trait Loci Applicable to Human Hypertension
From the Research Centre-Centre Hospitalier de l’Université de Montréal, Hôtel Dieu, 3840 rue St. Urbain, Montréal, Québec, H2W 1T8, Canada.
Correspondence to Alan Y. Deng, Research Centre, Centre Hospitalier de l’Université de Montréal (CHUM), 7-132 Pavillon Jeanne Mance, 3840, rue St. Urbain, Montreal, Quebec, H2W 1T8, Canada. E-mail alan.deng{at}umontreal.ca
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Abstract |
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Quantitative trait loci (QTLs) for blood pressure (BP) were found on chromosome 10 of Dahl salt-sensitive rats and are potentially important to human essential hypertension. But their identities and how they influence BP together were not known. Presently, we first fine mapped existing QTLs, C10QTL1, C10QTL2, and C10QTL3, by constructing congenic strains. In the process, a new QTL, C10QTL4, was identified. Because the intervals harboring C10QTL1 and C10QTL4 contain a maximum of 16 and 10 possible genes, respectively, a limited number of specific gene targets has been identified to be QTLs residing in human homologous regions on chromosome 17. Moreover, because none of these candidates encodes a gene known to influence BP, the 2 QTLs will represent novel genes for BP regulations. Second, we used congenic strains with QTL combinations to analyze the interactions between the QTLs. Consequently, a double combination of C10QTL4 and C10QTL1 possessed the same BP as each of the 2 QTLs alone. BP of a triple combination of C10QTL4, C10QTL1, and C10QTL3 was not different from BP of the C10QTL4 and C10QTL1 double combination. These results demonstrate that C10QTL4, C10QTL1, and C10QTL3 are epistatic to one another in their BP effects. In contrast, when adding C10QTL2 into the triple formation of the 3 QTLs above to create a quadruple QTL combination, BP increased proportionately, indicating that C10QTL2 acts independently of C10QTL4, C10QTL1, and C10QTL3. The epistatic and additive interactions uncovered in the animal model will help elucidate similar interactions playing a role in human essential hypertension.
Key Words: gene–gene interaction • fine QTL mapping • comparative homology • congenic combinations
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Introduction |
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Unraveling the pathogenesis of essential hypertension depends on revelations of functional roles of quantitative trait loci (QTLs) for blood pressure (BP) and on a comprehension of their interactions. Animal models can be used for gene discovery, as well as for providing insights into the organizational hierarchy of QTLs1 because of the availability and ethical permissibility in experimental manipulations. The list of BP QTLs localized in a Dahl salt-sensitive (DSS) strain has been accumulating.1,2 Those on chromosome (Chr) 10 especially drew our attention,3 because its homologous segments on Chr 17 also harbor QTLs for certain human populations.4–6 Identifications of individual QTLs and an unveiling of their interactions from rat Chr 10 are important in understanding the pathogenesis of essential hypertension contributed by CHR 17.7
Our previous work showed the presence of 3 QTLs on Chr 10.3 Their combined effect, as demonstrated by the congenic strain S.LC10, seemed to account for 75% of the total BP difference between DSS and the normotensive Lewis (LEW).3 Although this initial observation pointed toward an additive relationship among these QTLs, this conclusion was indirect and inferential only. More stringent experimental evidence is required to elucidate their true relationships.
Although linkage studies can provide certain clues,8 an unambiguous demonstration of an epistatic or additive interaction between 2 QTLs can only come from the study of their combinations using congenic strains.9–12 Also, fine mapping needs to progress to the point that the possibility of possessing >1 QTL in each congenic interval has been minimized.
Current studies were designed to, first, fine map individual BP QTLs on Chr 10 and to reveal limited candidates for gene discovery of a QTL that probably predisposes certain humans to hypertension. Second, systematic QTL combinations by congenic strains are to be investigated to reveal their relationships.
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Methods |
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Animals
Protocols for handling, as well as maintaining, animals were approved by our institutional animal committee. The breeding procedure, markers used, and the screening protocol for generating congenic strains were essentially the same as reported previously.9,13,14 S.LC10, S.L10, S.L11, S.L1, and DSS are the same as produced previously,3 and S.L4 was the same as used before.15 LEW is a normotensive strain.13,14
Constructions of New Congenic Substrains
S.L10,3 S.L6,3 and S.L1115 were used to derive congenic substrains. The procedure was similar to that published previously.9,13 For the current work, 7 congenic substrains were produced (Figure 1) and are designated as: DSS.LEW-(D10Chm128-D10Chm182)/Lt (abbreviated as C2S.L15), DSS.LEW-(D10rat13-D10Rat11)/Lt (abbreviated as C2S.L16), DSS.LEW-(D10Chm224-D10Chm222)/Lt (abbreviated as C2S.L17), DSS.LEW-(D10Chm224-D10Chm6)/Lt (abbreviated as C2S.L20), DSS.LEW-(D10Mco30-D10Got92)/Lt (abbreviated as C2S.L22), DSS.LEW-(D10Rat204-D10Rat9)/Lt (abbreviated as C2S.L23), and DSS.LEW-(D10Chm128-D10Chm169)/Lt (abbreviated as C2S.L24).
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BP Measurements
BP studies on the congenic strains were essentially the same as reported previously.3,11,15–21 In brief, male rats were weaned at 21 days of age, maintained on a low-salt diet (0.2% NaCl, Harlan Teklad 7034), and then fed a high-salt diet (2% NaCl, Harlan Teklad 94217) starting from 35 days of age until the end of the experiment. Telemetry probes were implanted when rats were 56 days old (ie, after 3 weeks of the high-salt diet) with their body weights between 250 and 320 g.
BPs for all of the strains were measured at least at 2 different times to exclude seasonal, as well as environmental, influences. Thus, the BP data were pooled from separately reproducible measurements for each strain. In the presentation of telemetry data, averaged 24-hour readings are shown during the course of measurements. Because systolic and diastolic pressures were consistent with mean arterial pressures (MAP) of the strains, only MAP is presented.
Statistical Analysis
Repeated measures ANOVA followed by Dunnett (which permits a correction for multiple comparisons and sample sizes) was used to compare a parameter between 2 groups as presented previously.9,13,14 During the BP comparison, ANOVA was first used to analyze the data to see whether there was any difference among the groups. If the difference was significant, then the Dunnett test ensued to establish which group was different and how much more significant it was from the DSS strain. The 2x2 ANOVA determined a QTL-QTL interaction (or a lack of it) by evaluating whether the observed BP effect of a congenic strain combining separate QTLs was significantly different from a predicted sum of BP effects from each individual QTL.9
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Results |
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New Congenic Substrains Identified a Cluster of BP QTLs and Specific Candidates for 2 QTLs
Chromosome spans for new congenic strains are indicated in Figure 1. These strains were derived from an intensified effort to define a QTL interval to the number of genes amenable for positional cloning and to provide a limited number of gene candidates for human genetic investigations.
Our previous work showed that S.L10 contained a larger chromosome segment than S.L9 defining C10QTL1 and had a BP slightly lower than S.L9.3 This could be because of the presence of another QTL or simply because of the influence of the genetic background. To resolve this issue, C10S.L15 and C10S.L24 were derived (Figure 1). C10S.L17 and C10S.L20 were designed for fine mapping C10QTL1 (Figure 1). C10S.L16 and C10S.L23 were designed for fine mapping C10QTL2 and C10S.L22 for fine mapping C10QTL3 (Figure 1).
Figure 2 shows the actual tracings MAP of DSS and congenic strains by telemetry. For the simplicity of comparisons among the strains, averaged MAPs were shown at the bottom of Figure 1. Figure 3 shows how QTLs interact with one another.
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2 C10QTLs. The BP effect for the QTLs and their combinations (
Because BP of C10S.L15 was lower (P>0.001) and BP of C10S.L24 was not (P>0.30) lower than that of DSS (Figure 2), a new QTL, C10QTL4, definitely exists in the chromosome segment of 820 kb, which harbors 10 genes and undefined loci (Locs; Figure 4). The C10QTL1 interval as defined by C10S.L20, spans &1.17 Mb and harbors 16 genes and Locs (Figure 5). All of these genes and Locs except 1 have corresponding homologues in humans (Figure 4 and 5 
).
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The BP of C10S.L22 was not lower than that of DSS (P>0.41; Figure 2). The C10QTL3 interval spans &9.17 Mb and harbors &122 genes and Locs. This size could not be additionally reduced, because after having screened >250 meioses, only 1 crossover was obtained in the region containing C10QTL3, which gave rise to C10S.L22 (Figure 1). Thus, this experimental condition limited an additional narrowing of the C10QTL3 interval. Two substrains, C10S.L16 and C10S.L23, showed BPs lower than that of DSS (P<0.014; Figure 2). The C10QTL2 interval can be defined in &4.6 Mb and contains nearly 64 genes and Locs (Figure 1). During the current work, we screened nearly 120 microsatellite markers in the region harboring C10QTL2, and they were not polymorphic between DSS and Lewis. Consequently, the C10QTL2 interval could not be additionally narrowed.
BP of Clustered QTLs Is Controlled by Epistatic Interactions
As 4 QTLs were revealed and each had a BP effect between 27 and 43 mm Hg (Figure 1), it became intriguing as to how these QTLs would function together in controlling BP. One way to analyze their interactions was to make a "double" QTL combination between every 2 of the 4 QTLs. This approach, nevertheless, was limited by one’s ability to obtain crossovers between closely linked markers, particularly between those separating C10QTL4 from C10QTL1 and/or C10QTL3. For example, to create a combination of C10QTL4 and C10QTL3, one would need to have a double crossover in the same rat, that is, one between D10Chm147 and D10Chm212 and another between D10Chm216 and D10Rat27 (Figure 1). Such a probability is beyond the practical capability in most rat breeding facilities.
However, we did have available congenic strains that contain double, triple, and quadruple combinations of the QTLs in question. We reasoned that a systematic investigation of these combinations might provide insights into the relationship among the 4 QTLs. With this goal in mind, we restudied BP effects of a double QTL combination (S.L10) between C10QTL4 and C10QTL1, a triple QTL combination (S.L4) of C10QTL4, C10QTL1, and C10QTL3, and a quadruple QTL combination (S.LC10) of C10QTL4, C10QTL1, C10QTL3, and C10QTL2 (Figure 1). Their BP effects were given at the bottom of Figure 1.
Figure 3 summarizes the interactions or a lack of them among the 4 C10QTLs as analyzed by a 2x2 factorial ANOVA,9 assuming that 1 QTL gene was involved in each QTL interval. BPs for the group of strains in Figure 3a were measured simultaneously; the same applied to those for Figure 3b and c. There is clearly an epistatic interaction between C10QTL4 and C10QTL1 (P<0.002; Figure 3a). Adding C10QTL3 into C10QTL4 and C10QTL1 double combination to form the triple combination did not increase BP (Figure 3b), indicating that C10QTL3 interacts epistatically (P<0.009), as well with both C10QTL4 and C10QTL1. The BP of the triple QTL combination was not different from that of C10QTL4 or C10QTL1 alone (data not shown). Therefore, a numerical accumulation of C10QTL4, C10QTL1, and C10QTL3 did not increase the BP effect beyond that of the single QTL of the 3 QTLs, as if being confined by a "ceiling" effect. In contrast, when C10QTL2 was added into the combination of the other 3 QTLs (Figure 3c), BP increased proportionately, indicating that there is no epistatic interaction (P<0.939) between C10QTL2 and the other 3 QTLs.
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Discussion |
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A major finding from the current work is that several BP QTLs are found within a segment of Dahl rat Chr 10, which is homologous to a section of human CHR 17. The intervals for 2 of these QTLs, C10QTL4 and C10QTL1, harbor only 10 and 16 genes and undefined Locs, respectively. This limited number of candidates for each of C10QTL4 and C10QTL1 will, no doubt, facilitate the mutation screening for gene discovery of homologous QTLs in humans. Because none of these genes and Locs is known to influence BP, the human homologous genes for C10QTL4 and C10QTL1 will likely provide novel therapeutic targets and diagnostic tools for human hypertension. The second major finding from the current work is that functions of 3 BP QTLs clustered are controlled by epistasis, a feature revealed only by experimental combinations of congenic strains. Their numerical aggregation has no influence on BP. This QTL-QTL interaction will help elucidate the relationships among homologous human QTLs in population studies and perspective pharmacogenomic designs.
Fine Congenic Mapping Identified Limited Number of Gene Candidates for Each of 2 BP QTLs on Dahl Chr 10 Potentially Relevant to Human Hypertension
Building congenic substrains for fine mapping BP QTLs has yielded 2 intervals harboring only 10 and 16 genes and Locs for each of C10QTL4 and C10QTL1 (Figures 1, 4, and 5
). These candidates can serve as molecular targets for positional cloning of C10QTL4 and C10QTL1. Because they have homologous human counterparts, this limited number of candidates provides gene targets for immediate mutational screening in human populations for association studies,7 although the molecular identities of the 2 QTLs have not been identified.
It is worth noting that, because the obvious genes known to affect BP, such as Ace, Nos2, and Wnk4, have been excluded11,21 as candidate genes for a QTL (Figure 1), each of C10QTL4 and C10QTL1 will be a brand new gene for regulating BP homeostasis. It remains to be determined whether there could be more QTLs present in the broad intervals harboring C10QTL2 and C10QTL3 (Figure 1).
Strategy for QTL Discovery
Regarding the molecular identification of a QTL once it has been restricted to an interval harboring a limited number of genes, a brief discussion on the strategy of QTL discovery is in order. An extensive and detailed discourse has been presented recently by Deng.1
Much like the intervals harboring C10QTL1 and C10QTL4 (Figures 4 and 5 ), most of the genes residing in them, known or undefined, are not reputed to influence BP. Consequently, the identification of the QTL in question impinges on the strategy of positional cloning, that is, discovering the identity of the QTL based purely on its location on the chromosome without any other physiological, biochemical, or signaling clues. To accomplish positional cloning of a QTL, several approaches are available.
Fine Congenic Mapping
Ideally, if one can create a congenic strain that changes BP, and the QTL interval contains only one gene that possesses alleles contrasting between 2 comparing strains, it unambiguously proves that this gene is the QTL in question. Limitations in this line of work lie in the ability of obtaining crossovers flanking solely 1 gene. However, it is practically achievable to restrict a QTL interval to 100 to 200 kb,22–24 and such an interval can contain 1 to 2 genes.24
Gene Profiling Followed by Functional Testing
One way to quickly and directly find a candidate gene for a QTL can be by gene profiling based on a genome-wide microarray.25 However, this approach depends on narrowly defined functional assumptions for the selection of a targeted tissue, time course, and age. Also, this approach assumes that the QTL in question is differentially expressed between 2 contrasting strains. As a consequence, it is prone to produce false positives.26 Therefore, stringent functional verifications have to follow after a candidate gene has been found by gene profiling. These functional authentications include, but are not limited to, a transgenic rescue of an appropriate phenotype27 and/or fine interval mapping by congenic strains.21,23
More relevant to the intervals harboring C10QTL1 and C10QTL4, if any gene profiling is to be conducted, a region-specific microarray rather than a genome-wide microarray is appropriate. In this case, expressions of all of the genes in the intervals containing C10QTL1 and C10QTL4 will need to be examined to identify the pertinent tissues and all of the tissues in which a gene is expressed. Subsequently, the region-specific microarray for C10QTL1 and C10QTL4, respectively, will be hybridized with RNAs extracted from all of the relevant tissues. A candidate gene(s) thus found has to reside inside the C10QTL1 or C10QTL4 interval to be a valid candidate.
Region-Saturated Gene Sequencings
Allelic differences for either C10QTL1 or C10QTL4 can be present in coding rather than in the regulatory regions. In this case, mutation detections in functional domains of a gene are essential. In order to find a candidate gene with significant nucleotide differences defining differing alleles for either C10QTL1 or C10QTL2, exons and exon-intron boundaries for all of the known and undefined Locs in the interval (Figures 4 and 5) need to be sequenced and compared. If there is a significant mutation(s) found in only 1 gene in either the C10QTL1 or C10QTL4 interval, this gene will become a candidate. Functional testing on this gene to be the QTL in question will be conducted. If there are significant mutations found in multiple genes in either the C10QTL1 or C10QTL4 interval, fine definition congenic mapping will need to be accomplished to narrow the gene candidate, or/and functional testings on each of these genes need to be performed.
Functional Testing on an Individual Gene as a BP QTL
There are several techniques that allow the functional testing of a specific gene. They include fine congenic mapping presented above, transgenic rescues,28 gene targeting by homologous recombination,29 and interference RNA.30 In the rat, except for gene targeting, all of the other approaches can be accomplished.
Epistatic and Additive QTL Interactions among C10QTLs
It is evident that the 3 QTLs, C10QTL4, C10QTL1, and C10QTL3, do not exert an incremental BP effect when acting in concert with one another. Thus, the absolute quantity of QTLs does not control the BP of an animal. Mechanistically, it is probable that C10QTL4 belongs to the same pathway/cascade as either C10QTL1 or C10QTL3.1,31 Because most of the genes residing in the intervals harboring C10QTL1 and C10QTL4 do not have clear functions, implications in shared cellular and/or biochemical pathways between the 2 QTLs still await their molecular identities to be defined. In contrast, it is likely that C10QTL2 belongs to different pathways/cascades from C10QTL4, C10QTL1, and C10QTL3.1,31
The revelation of epistatic interactions or a lack of it among BP QTLs has clear implications in the studies of human essential hypertension. First, considering the genetic bases of hypertension in a heterogeneous human population, 1 hypertensive individual can, understandably, have a defect in a QTL different from another hypertensive individual possessing a defect in another QTL on CHR 17. It is not surprising to observe that a genetic marker was found to be associated with hypertension in certain populations4–6,32,33 while not in others.34,35 Environmental influences aside, it is most likely that the varying results from analyzing various populations were attributable to the inherent genetic property of that population versus another, which is not necessarily an "inconsistency" of the genetic research. This type of genetic "population specificity" seems more of a true representation of hypertension caused by heterogeneous flaws than having a "master" gene responsible for all populations. Following a logical reasoning, in designing an antihypertensive drug and in applying clinical treatment, it seems to be more effective and efficient to administer a drug specifically targeting an appropriately designated population than aiming at most or all populations.
Second, a defect in 1 QTL will likely have a similar effect in changing BP as defects in >1 QTL sharing the same epistatic relationship. Moreover, a defect in the gene higher on the epistatic hierarchy can hide gene effects lower on the same epistatic grouping or pathway/cascade.1,11 Consequently, when analyzing the function of a gene in association with BP in human studies, a lack of an effect could also be attributable to the effect of another gene downstream in the epistatic hierarchy. In this case, separating as well as combining individual QTL effects are necessary to elucidate the relationships between the 2 QTLs.
Substitution Mapping Separating Closely Linked QTLs and Revealing Complex QTL-QTL Interactions
It is worth noting that the use of congenic strains constitutes a physical mapping of QTLs that cannot only define where exactly a QTL resides within a clearly designated interval, but also it can be combined to analyze QTL-QTL interactions. In comparison, linkage analyses are limited to indicating only an approximate location of a QTL on Chr 10.36,37 It cannot and does not define the number of QTLs present and their exact physical boundaries and unveil whether or not the QTLs act independently or dependently in relation to each other. Furthermore, linkage analysis cannot distinguish closely linked QTLs that have opposing BP effects.11,16 Therefore, substitution mapping is essential in achieving QTL identifications, as well as revealing QTL-QTL interactions.
Perspectives
The current work additionally refined intervals harboring 2 QTLs on Chr 10, C10QTL1 and C10QTL4, to regions of 16 and 10 genes, respectively. Because homologous sections on human CHR 17 are known to contain QTLs also, the limited number of candidates corresponding to C10QTL1 and C10QTL4 regions will facilitate gene discovery in human essential hypertension. The epistatic and additive QTL-QTL interactions divulged from our analyses will provide insights into how similar QTLs will interact in humans.
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Acknowledgments |
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This work was supported by a grant from the Canadian Institutes for Health Research (CIHR) to A.Y.D.
Received August 15, 2005; first decision September 1, 2005; accepted October 6, 2005.
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References |
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1. Deng AY. Functional genomics of blood pressure determination: dissecting and assembling a polygenic strait by experimental genetics. Curr Hypertens Rev. 2005; 1: 35–50.[CrossRef]
2. Rapp JP. Genetic analysis of inherited hypertension in the rat. Physiol Rev. 2000; 80: 135–172.
3. Palijan A, lambert R, Sivo Z, Dutil J, Deng AY. Comprehensive congenic coverage revealing multiple BP QTLs on Dahl rat Chromosome 10. Hypertension. 2003; 42: 515–522.
4. Baima J, Nicolaou M, Schwartz F, DeStefano AL, Manolis A, Gavras I, Laffer C, Elijovich F, Farrer L, Baldwin CT, Gavras H. Evidence for linkage between essential hypertension and a putative locus on human chromosome 17. Hypertension. 1999; 34: 4–7.
5. Julier C, Delepine M, Keavney B, Terwilliger J, Davis S, Weeks DE, Bui T, Jeunemaitre X, Velho G, Froguel P, Ratcliffe P, Corvol P, Soubrier F, Lathrop GM. Genetic susceptibility for human familial essential hypertension in a region of homology with blood pressure linkage on rat chromosome 10. Hum Mol Genet. 1997; 6: 2077–2085.
6. Levy D, DeStefano AL, Larson MG, O’Donnell CJ, Lifton RP, Gavras H, Cupples LA, Myers RH. Evidence for a gene influencing blood pressure on chromosome 17. Genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the framingham heart study. Hypertension. 2000; 36: 477–483.
7. Knight J, Munroe PB, Pembroke JC, Caulfield MJ. Human chromosome 17 in essential hypertension. Ann Hum Genet. 2003; 67: 193–206.[CrossRef][Medline] [Order article via Infotrieve]
8. Deng Y, Rapp JP. Cosegregation of blood pressure with angiotensin converting enzyme and atrial natriuretic peptide receptor genes using Dahl salt-sensitive rats. Nat Genet. 1992; 1: 267–272.[CrossRef][Medline] [Order article via Infotrieve]
9. Dutil J, Eliopoulos V, Tremblay J, Hamet P, Charron S, Deng AY. Multiple quantitative trait loci for blood pressure interacting epistatically and additively on dahl rat chromosome 2. Hypertension. 2005; 45: 557–564.
10. Monti J, Plehm R, Schulz H, Ganten D, Kreutz R, Hubner N. Interaction between blood pressure quantitative trait loci in rats in which trait variation at chromosome 1 is conditional upon a specific allele at chromosome 10. Hum Mol Genet. 2003; 15: 435–439.
11. Palijan A, Dutil J, Deng AY. Quantitative trait loci with opposing blood pressure effects demonstrating epistasis on Dahl rat chromosome 3. Physiol Genomics. 2003; 15: 1–8.
12. Rapp JP, Garret MR, Deng AY. Construction of a double congenic strain to prove an epistatic interaction on blood pressure between rat chromosomes 2 and 10. J Clin Invest. 1998; 101: 1591–1595.[Medline] [Order article via Infotrieve]
13. Eliopoulos V, Dutil J, Deng Y, Grondin M, Deng AY. Severe hypertension caused by alleles from normotensive Lewis for a quantitative trait locus on chromosome 2. Physiol Genomics. 2005; 22: 70–75.
14. Grondin M, Eliopoulos V, Lambert R, Deng Y, Ariyarajah A, Moujahidine M, Dutil J, Charron S, Deng AY. Complete and overlapping congenics proving the existence of a quantitative trait locus for blood pressure on Dahl rat chromosome 17. Physiol Genomics. 2005; 21: 112–116.
15. Sivo Z, Malo B, Dutil J, Deng AY. Accelerated congenics for mapping two blood pressure quantitative trait loci on Chromosome 10 of Dahl rats. J Hypertens. 2002; 20: 1–9.[CrossRef]
16. Ariyarajah A, Palijan A, Dutil J, Prithiviraj K, Deng Y, Deng AY. Dissecting quantitative trait loci into opposite blood pressure effects on Dahl rat Chromosome 8 by congenic strains. J Hypertens. 2004; 22: 1495–1502.[CrossRef][Medline] [Order article via Infotrieve]
17. Deng AY, Dutil J, Sivo Z. Utilization of marker-assisted congenics to map two blood pressure quantitative trait loci in Dahl rats. Mamm Genome. 2001; 12: 612–616.[CrossRef][Medline] [Order article via Infotrieve]
18. Dutil J, Deng AY. Further chromosomal mapping of a blood pressure QTL in Dahl rats on chromosome 2 using congenic strains. Physiol Genomics. 2001; 6: 3–9.
19. Dutil J, Deng AY. Mapping a blood pressure quantitative trait locus to a 5.7-cM region in Dahl salt-sensitive rats. Mamm Genome. 2001; 12: 362–365.[CrossRef][Medline] [Order article via Infotrieve]
20. Moujahidine M, Dutil J, Hamet P, Deng AY. Congenic mapping of a blood pressure QTL on chromosome 16 of Dahl rats. Mamm Genome. 2002; 13: 153–156.[CrossRef][Medline] [Order article via Infotrieve]
21. Moujahidine M, lambert R, Dutil J, Palijan A, Sivo Z, Ariyarajah A, Deng AY. Combining congenic coverage with gene profiling in search of candidates for blood pressure QTLs in Dahl rats. Hypertens Res. 2004; 27: 203–212.[CrossRef][Medline] [Order article via Infotrieve]
22. Garrett MR, Rapp JP. Defining the blood pressure QTL on chromosome 7 in Dahl rats by a 177-kb congenic segment containing Cyp11b1. Mamm Genome. 2003; 14: 268–273.[CrossRef][Medline] [Order article via Infotrieve]
23. Garrett MR, Meng H, Rapp JP, Joe B. Locating a blood pressure quantitative trait locus within 117 kb on the rat genome: substitution mapping and renal expression analysis. Hypertension. 2005; 45: 451–459.
24. Olofsson P, Holmberg J, Tordsson J, Lu S, Akerstrom B, Holmdahl R. Positional identification of Ncf1 as a gene that regulates arthritis severity in rats. Nat Genet. 2003; 33: 25–32.[CrossRef][Medline] [Order article via Infotrieve]
25. Aitman TJ, Glazier AM, Wallace CA, Cooper LD, Norsworthy PJ, Wahid FN, Al Majali KM, Trembling PM, Mann CJ, Shoulders CC, Graf D, St Lezin E, Kurtz TW, Kren V, Pravenec M, Ibrahimi A, Abumrad NA, Stanton LW, Scott J. Identification of Cd36 (Fat) as an insulin-resistance gene causing defective fatty acid and glucose metabolism in hypertensive rats. Nat Genet. 1999; 21: 76–83.[CrossRef][Medline] [Order article via Infotrieve]
26. Pravenec M, Wallace C, Aitman TJ, Kurtz TW. Gene expression profiling in hypertension research: a critical perspective. Hypertension. 2003; 41: 3–8.
27. Pravenec M, Landa V, Zidek V, Musilova A, Kren V, Kazdova L, Aitman TJ, Glazier AM, Ibrahimi A, Abumrad NA, Qi N, Wang JM, St Lezin EM, Kurtz TW. Transgenic rescue of defective Cd36 ameliorates insulin resistance in spontaneously hypertensive rats. Nat Genet. 2001; 27: 156–158.[CrossRef][Medline] [Order article via Infotrieve]
28. Hanahan D. Transgenic mice as probes into complex systems. Science. 1989; 246: 1265–1275.
29. Smithies O, Maeda N. Gene targeting approaches to complex genetic diseases: atherosclerosis and essential hypertension. Proc Natl Acad Sci U S A. 1995; 92: 5266–5272.
30. Hasuwa H, Kaseda K, Einarsdottir T, Okabe M. Small interfering RNA and gene silencing in transgenic mice and rats. FEBS Lett. 2002; 532: 227–230.[CrossRef][Medline] [Order article via Infotrieve]
31. Deng AY. In search of hypertension genes in Dahl salt-sensitive rats. J Hypertens. 1998; 16: 1707–1717.[CrossRef][Medline] [Order article via Infotrieve]
32. Rutherford S, Johnson MP, Curtain RP, Griffiths LR. Chromosome 17 and the inducible nitric oxide synthase gene in human essential hypertension. Hum Genet. 2001; 109: 408–415.[CrossRef][Medline] [Order article via Infotrieve]
33. Rice T, Rankinen T, Province MA, Chagnon YC, Perusse L, Borecki IB, Bouchard C, Rao DC. Genome-wide linkage analysis of systolic and diastolic blood pressure: the Quebec Family Study. Circulation. 2000; 102: 1956–1963.
34. Knight J, Gardner GT, Clark AJ, Caulfield MJ. Investigation of chromosome 17q as a locus for human essential hypertension in African Caribbeans. J Hum Hypertens. 2000; 14: 385–387.[CrossRef][Medline] [Order article via Infotrieve]
35. Sharma P, Fatibene J, Ferraro F, Jia H, Monteith S, Brown C, Clayton D, O’Shaughnessy K, Brown MJ. A genome-wide search for susceptibility loci to human essential hypertension. Hypertension. 2000; 35: 1291–1296.
36. Deng AY, Rapp JP. Locus for the inducible, but not a constitutive, nitric oxide synthase cosegregates with blood pressure in the Dahl salt-sensitive rat. J Clin Invest. 1995; 95: 2170–2177.[Medline] [Order article via Infotrieve]
37. Garrett MR, Dene H, Walder R, Zhang Q, Cicila GT, Assadnia S, Deng AY, Rapp JP. Genomic scan and congenic strains for blood pressure quantitative trait loci using Dahl salt-sensitive rats. Genome Res. 1998; 8: 711–723.
38. Gibbs RA, Weinstock GM, Metzker ML, Muzny DM, Sodergren EJ, Scherer S, Scott G, Steffen D, Worley KC, Burch PE, Okwuonu G, Hines S, Lewis L, DeRamo C, Delgado O, Dugan-Rocha S, Miner G, Morgan M, Hawes A, Gill R, Celera, Holt RA, Adams MD, Amanatides PG, Baden-Tillson H, Barnstead M, Chin S, Evans CA, Ferriera S, Fosler C, Glodek A, Gu Z, Jennings D, Kraft CL, Nguyen T, Pfannkoch CM, Sitter C, Sutton GG, Venter JC, Woodage T, Smith D, Lee HM, Gustafson E, Cahill P, Kana A, Doucette-Stamm L, Weinstock K, Fechtel K, Weiss RB, Dunn DM, Green ED, Blakesley RW, Bouffard GG, De Jong PJ, Osoegawa K, Zhu B, Marra M, Schein J, Bosdet I, Fjell C, Jones S, Krzywinski M, Mathewson C, Siddiqui A, Wye N, McPherson J, Zhao S, Fraser CM, Shetty J, Shatsman S, Geer K, Chen Y, Abramzon S, Nierman WC, Havlak PH, Chen R, Durbin KJ, Egan A, Ren Y, Song XZ, Li B, Liu Y, Qin X, Cawley S, Worley KC, Cooney AJ, D’Souza LM, Martin K, Wu JQ, Gonzalez-Garay ML, Jackson AR, Kalafus KJ, McLeod MP, Milosavljevic A, Virk D, Volkov A, Wheeler DA, Zhang Z, Bailey JA, Eichler EE, Tuzun E, Birney E, Mongin E, Ureta-Vidal A, Woodwark C, Zdobnov E, Bork P, Suyama M, Torrents D, Alexandersson M, Trask BJ, Young JM, Huang H, Wang H, Xing H, Daniels S, Gietzen D, Schmidt J, Stevens K, Vitt U, Wingrove J, Camara F, Mar AM, Abril JF, Guigo R, Smit A, Dubchak I, Rubin EM, Couronne O, Poliakov A, Hubner N, Ganten D, Goesele C, Hummel O, Kreitler T, Lee YA, Monti J, Schulz H, Zimdahl H, Himmelbauer H, Lehrach H, Jacob HJ, Bromberg S, Gullings-Handley J, Jensen-Seaman MI, Kwitek AE, Lazar J, Pasko D, Tonellato PJ, Twigger S, Ponting CP, Duarte JM, Rice S, Goodstadt L, Beatson SA, Emes RD, Winter EE, Webber C, Brandt P, Nyakatura G, Adetobi M, Chiaromonte F, Elnitski L, Eswara P, Hardison RC, Hou M, Kolbe D, Makova K, Miller W, Nekrutenko A, Riemer C, Schwartz S, Taylor J, Yang S, Zhang Y, Lindpaintner K, Andrews TD, Caccamo M, Clamp M, Clarke L, Curwen V, Durbin R, Eyras E, Searle SM, Cooper GM, Batzoglou S, Brudno M, Sidow A, Stone EA, Venter JC, Payseur BA, Bourque G, Lopez-Otin C, Puente XS, Chakrabarti K, Chatterji S, Dewey C, Pachter L, Bray N, Yap VB, Caspi A, Tesler G, Pevzner PA, Haussler D, Roskin KM, Baertsch R, Clawson H, Furey TS, Hinrichs AS, Karolchik D, Kent WJ, Rosenbloom KR, Trumbower H, Weirauch M, Cooper DN, Stenson PD, Ma B, Brent M, Arumugam M, Shteynberg D, Copley RR, Taylor MS, Riethman H, Mudunuri U, Peterson J, Guyer M, Felsenfeld A, Old S, Mockrin S, Collins F. Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature. 2004; 428: 493–521.[CrossRef][Medline] [Order article via Infotrieve]
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