Reciprocal rat chromosome 2 congenic strains reveal contrasting blood pressure and heart rate QTL

Alemayehu, Adamu; Breen, Laura; Křenová, D; Printz, Morton P.

doi:10.1152/physiolgenomics.00065.2002

Cited by 35 publications

(30 citation statements)

References 21 publications

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“…Our results are consistent with those of other investigators, 3,7,8 who showed the existence of several BP QTLs in similar regions. Specifically, in comparing our current results with those of Garrett and Rapp, 7 several features are evident.…”

Section: Comprehensive Congenic Coverage Divulging Multiple Bp Qtls Isupporting

confidence: 94%

“…ver since the revelation of a quantitative trait locus (QTL) for blood pressure (BP) on chromosome 2 (Chr 2) of Dahl salt-sensitive (DSS) rats, 1,2 Chr 2 seems to play a role in the development of hypertension in several of the hypertensive strains. [3][4][5][6][7][8][9][10][11][12][13] In our initial work, 2 BP QTLs designated C2QTL1 and C2QTL2 were localized to regions on Chr 2 of the DSS rat. 5,6 C2QTL1 was found between the markers D2Rat303 and D2Rat166, 6 and C2QTL2 was found between the markers D2Rat166 and D2Rat131.…”

mentioning

confidence: 98%

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Multiple Quantitative Trait Loci for Blood Pressure Interacting Epistatically and Additively on Dahl Rat Chromosome 2

Dutil¹,

Eliopoulos²,

Tremblay³

et al. 2005

Hypertension

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Abstract-Our previous work demonstrated 2 quantitative trait loci (QTLs), C2QTL1 and C2QTL2, for blood pressure (BP) located on chromosome (Chr) 2 of Dahl salt-sensitive (DSS) rats. However, for a lack of markers, the 2 congenic strains delineating C2QTL1 and C2QTL2 could not be separated. The position of the C2QTL1 was only inferred by comparing 2 congenic strains, one having and another lacking a BP effect. Furthermore, it was not known how adjacent QTLs would interact with one another on Chr 2. In the current investigation, first, a critical chromosome marker was developed to separate 2 C2QTLs. Second, a congenic substrain was created to cover a chromosome fragment thought to harbor C2QTL1. Finally, a series of congenic strains was produced to systematically and comprehensively cover the entire Chr 2 segment containing C2QTL2 and other regions previously untested. Consequently, a total of 3 QTLs were discovered, with C2QTL3 located between C2QTL1 and C2QTL2. C2QTL1, C2QTL2, and C2QTL3 reside in chromosome segments of 5.7 centiMorgan (cM), 3.5 cM, and 1.5 cM, respectively. C2QTL1 interacted epistatically with either C2QTL2 or C2QTL3, whereas C2QTL2 and C2QTL3 showed additive effects to each other. ver since the revelation of a quantitative trait locus (QTL) for blood pressure (BP) on chromosome 2 (Chr 2) of Dahl salt-sensitive (DSS) rats, 1,2 Chr 2 seems to play a role in the development of hypertension in several of the hypertensive strains. [3][4][5][6][7][8][9][10][11][12][13] In our initial work, 2 BP QTLs designated C2QTL1 and C2QTL2 were localized to regions on Chr 2 of the DSS rat. 5,6 C2QTL1 was found between the markers D2Rat303 and D2Rat166, 6 and C2QTL2 was found between the markers D2Rat166 and D2Rat131. 5 However, at the time, the position of C2QTL1 was solely inferred from comparing 2 overlapping congenic strains, one having and the other lacking a BP effect. 6 It was uncertain whether this deduction was valid in localizing a QTL for a polygenic trait. Another issue was that C2QTL1 defined by S.M1 6 and C2QTL2 defined by S.M5 and S.M6 5 shared a chromosome region of ambiguity. As a result, it could not be ruled out that there might be just 1 QTL instead of 2 in the Chr 2 region in question.Subsequent to our original work on Chr 2 QTL localizations, 1,2,5,6,14 another group found several QTLs situated adjacent to one another in 1 Chr 2 segment of DSS rats. 7 However, it was not clear how these QTLs could act with reference to one another.Based on these observations, 2 questions were addressed: are there truly multiple BP QTLs in a Chr 2 segment of the DSS rat? If there are, how do they interact with reference to one another in determining BP? Materials and Methods AnimalsCongenic strains, S.M, S.M1, S.M2, S.M5, and S.M6 and DSS strains are the same as used previously 5,6 (Figure 1). Constructions of Congenic SubstrainsS.M, S.M1, S.M5, and S.M6 5,6 were used to derive congenic substrains. The basic design was to systematically and as completely as possible cover the entire Chr 2 region of interest....

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Section: Comprehensive Congenic Coverage Divulging Multiple Bp Qtls Isupporting

confidence: 94%

mentioning

confidence: 98%

Multiple Quantitative Trait Loci for Blood Pressure Interacting Epistatically and Additively on Dahl Rat Chromosome 2

Dutil¹,

Eliopoulos²,

Tremblay³

et al. 2005

Hypertension

View full text Add to dashboard Cite

show abstract

“…Additionally, our signal localizes proximal to a homologous region identified in the rat, which contains a putative heart rate gene (Figure 3). 16 Using Ensembl homology maps, the flanking markers from our chromosome 4 QTL overlap with the flanking markers for the rat chromosome 2 QTL. 29 Because heart rate is correlated with measures of adiposity, insulin sensitivity, and blood pressure, we also were interested to determine whether our signal localized to regions with QTL for these phenotypes.…”

Section: Discussionmentioning

confidence: 99%

“…This signal is in the same region as a QTL for long QT syndrome 4 35 and a QTL for heart rate in rats. 16 Within the 1 LOD unit support interval, there are 2 strong candidates: ANKB and MYOZ2. Given the biological support for our 2 positional candidate genes and the strength of our LOD score (LODϭ3.9), our goal is to further explore this linkage signal by typing single nucleotide polymorphisms in both candidate genes.…”

Section: Perspectivesmentioning

confidence: 99%

Major Quantitative Trait Locus for Resting Heart Rate Maps to a Region on Chromosome 4

et al. 2004

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Abstract-Multiple studies have identified resting heart rate as a risk factor for cardiovascular disease independent of other cardiovascular disease risk factors (such as dyslipidemia and hypertension). Previous studies have examined heart rate in hypertensive individuals, but little is known about the genetic determination of resting heart rate in a normal population. Therefore, our objective was to perform a genome screen on a population containing normotensive and hypertensive individuals. We performed variance decomposition linkage analysis using maximum likelihood methods at Ϸ10 cM intervals in 2209 individuals of predominantly North European ancestry. We estimated the heritability of resting heart rate to be 26% and obtained significant evidence of linkage (logarithm of the odds [LOD]ϭ3.9) for resting heart rate on chromosome 4q. This signal is in the same region as a quantitative trait locus (QTL) for long QT syndrome 4 and a QTL for heart rate in rats. However, resting heart rate is often overlooked as a risk factor, even though multiple studies have identified resting heart rate as a CVD risk factor. [3][4][5][6][7][8][9][10][11] Moreover, although associated with other CVD risk factors, resting heart rate is an independent predictor of CVD. 3,6,12 Given the potential clinical significance of resting heart rate, mechanisms of heart rate control are of great biomedical interest. In brief, a heartbeat begins with a small group of specialized muscle cells in the sinoatrial node. These cells generate electrical signals that spread throughout the heart and cause it to contract with a regular, steady beat. Although the sinoatrial node generates the heartbeat, heart rate is under tight neurohumoral control.Interestingly, genetic factors also influence heart rate. Heart rate has been demonstrated to have a significant genetic component of variation. 13 Additionally, quantitative trait locus or loci (QTL) for heart rate have been identified in drosophila, 14 mice, 15 rats, 16 -18 and humans. 19 However, most genetic studies have used hypertensive subjects. Given that heart rate is associated with blood pressure, 4,8,10 it is unclear whether the QTL are applicable to the normal variation in heart rate or are specific to elevated blood pressure. Therefore, our objective was to perform a genome screen on a population not ascertained on hypertensive status. Furthermore, because heart rate has been associated with CVD risk factors, we will examine the relationship between heart rate and CVD risk factors in a subset of unrelated individuals. Methods SubjectsSubjects were participants of the Metabolic Risk Complications of Obesity Genes project, which recruited participants and their families from Take Off Pounds Sensibly, Inc (TOPS) membership in the midwestern United States. 20 Research protocols were approved by the Institutional Review Board of the Medical College of Wisconsin.In this study, 2209 individuals distributed across 411 white families of predominantly northern European ancestry participated. Exclusion c...

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“…Single QTLs identified in genome scans typically fractionated into multiple, closely linked QTLs, often with opposite effects. In mouse genetics this finding emerged from studies that chased single effects through the creation of congenics and includes QTLs influencing seizures (Legare et al 2000), obesity (Stylianou et al 2004), growth , blood pressure (Frantz et al 2001;Alemayehu et al 2002;Garrett and Rapp 2002a,b;Ariyarajah et al 2004), diabetes (Podolin et al 1998), antibody production (Puel et al 1998), and infection (Bihl et al 1999). In flies, highresolution mapping efforts using a higher density of recombination or performing quantitative complementation tests to deficiencies revealed similar complexity for QTLs affecting wing shape (Weber et al 1999(Weber et al , 2001Mezey et al 2005), longevity De Luca et al 2003;Wilson et al 2006), resistance to starvation stress (Harbison et al 2004), mating behavior (Moehring and Mackay 2004), olfactory behavior (Fanara et al 2002), locomotor reactivity (a startle response) , and numbers of sensory bristles (Dilda and Mackay 2002).…”

Section: Genetic Architecture: Many Loci Of Small Effectmentioning

confidence: 99%

Genetic architecture of quantitative traits in mice, flies, and humans

Flint¹,

Mackay²

2009

Genome Res.

404

364

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We compare and contrast the genetic architecture of quantitative phenotypes in two genetically well-characterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, with that found in our own species from recent successes in genome-wide association studies. We show that the current model of large numbers of loci, each of small effect, is true for all species examined, and that discrepancies can be largely explained by differences in the experimental designs used. We argue that the distribution of effect size of common variants is the same for all phenotypes regardless of species, and we discuss the importance of epistasis, pleiotropy, and gene by environment interactions. Despite substantial advances in mapping quantitative trait loci, the identification of the quantitative trait genes and ultimately the sequence variants has proved more difficult, so that our information on the molecular basis of quantitative variation remains limited. Nevertheless, available data indicate that many variants lie outside genes, presumably in regulatory regions of the genome, where they act by altering gene expression. As yet there are very few instances where homologous quantitative trait loci, or quantitative trait genes, have been identified in multiple species, but the availability of high-resolution mapping data will soon make it possible to test the degree of overlap between species.Recent successes in mapping quantitative trait loci (QTLs) that contribute to phenotypic variation in humans and model organisms make it possible to address important questions about the genetic architecture of quantitative phenotypes, such as the likely number of loci, their mode of genetic action, and interaction with each other and with the environment. An understanding of the genetic architecture of quantitative traits is not only important in its own right, it will also inform studies that seek to proceed to the identification of the quantitative trait genes (QTGs) and the quantitative trait nucleotide (QTN) variants, the functional changes in DNA sequence that contribute to trait variation.In this work we use examples from two genetically wellcharacterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, to discuss how an understanding of the genetic architecture of quantitative phenotypes in model organisms informs mapping experiments in human genetics. In our discussion of the latter, we draw upon the recent successful application of genome-wide association studies (GWAS) to both quantitative phenotypes (such as height and weight) and disease phenotypes (assuming that the genetic architecture of common disease is similar, if not identical, to that of quantitative phenotypes).Genetic architecture has been the subject of a number of recent reviews, most of which deal with information from a single model organism (Mackay 2004) or review a small number of phenotypes (Kendler and Greenspan 2006). Here, our purpose is different. We tackle three relate...

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Reciprocal rat chromosome 2 congenic strains reveal contrasting blood pressure and heart rate QTL

Cited by 35 publications

References 21 publications

Multiple Quantitative Trait Loci for Blood Pressure Interacting Epistatically and Additively on Dahl Rat Chromosome 2

Multiple Quantitative Trait Loci for Blood Pressure Interacting Epistatically and Additively on Dahl Rat Chromosome 2

Major Quantitative Trait Locus for Resting Heart Rate Maps to a Region on Chromosome 4

Genetic architecture of quantitative traits in mice, flies, and humans

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