This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Atwood, L. D. Articles by MacCluer, J. W. Search for Related Content PubMed PubMed Citation Articles by Atwood, L. D. Articles by MacCluer, J. W. Related Collections Other hypertension Genomics Hypertension - basic studies Genetics of cardiovascular disease

(Hypertension. 2001;37:425.)
© 2001 American Heart Association, Inc.

Scientific Contributions

Genome-Wide Linkage Analysis of Pulse Pressure in Mexican Americans

Larry D. Atwood; Paul B. Samollow; James E. Hixson; Michael P. Stern; Jean W. MacCluer

From the Division of Epidemiology and Institute of Human Genetics (L.D.A.), University of Minnesota, Minneapolis; the Department of Genetics (P.B.S., J.W.M.), Southwest Foundation for Biomedical Research, San Antonio, Tex; the Human Genetics Center (J.E.H.), University of Texas Health Science Center, Houston; and the Department of Medicine (M.P.S.), University of Texas Health Science Center, San Antonio.

Correspondence to Dr Larry Atwood, Division of Epidemiology, University of Minnesota, 1300 South Second St, Suite 300, Minneapolis, MN 55454-1015. E-mail atwood{at}epi.umn.edu

	Abstract

Top Abstract Introduction Methods Results Discussion References

 
Pulse pressure, a measure of aortic stiffness, is a strong predictor of cardiovascular mortality. To locate genes that affect pulse pressure, we performed genetic analysis on randomly ascertained families in the San Antonio Family Heart Study. Pulse pressure was defined as the difference between systolic and diastolic blood pressures. Likelihood methods were used to construct a model that had both single-locus and polygenic components for 46 families (1308 individuals). The single-locus component included sex-specific and genotype-specific effects of both age and body mass index. Using this model, we then performed 2-point linkage analysis in 10 families (440 individuals) that were among the largest of the 46 families and that had been genotyped for 399 polymorphic markers. The model that contained only the polygenic component and simple effects of the covariates showed pulse pressure heritability of 0.21. When the single-locus component was added, the sex-specific and genotype-specific effects of age and body mass index were highly significant (P<0.002). The full model accounted for 73% of the total variation of pulse pressure. Linkage analysis using this model with each marker revealed 4 markers with lod scores >1.9, which is the Lander-Kruglyak suggestive linkage standard. D21S1440 had a lod score of 2.78 with a recombination fraction () of 0.02. D7S1799 had a lod score of 2.04 (=0.01), D8S1100 had a lod score of 1.98 (=0.08), and D18S844 had a lod score of 1.95 (=0.11). These results are highly correlated with results involving systolic blood pressure, indicating that pulse pressure may not be genetically distinct from systolic blood pressure.

Key Words: population • genetics • blood pressure • hypertension, genetic • cardiovascular diseases

	Introduction

Top Abstract Introduction Methods Results Discussion References

 
Pulse pressure (PP), a measure of aortic stiffness, is a strong predictor of cardiovascular morbidity and mortality.¹ An association between PP and cardiovascular events has been shown in both normotensive and hypertensive subjects.² Among individuals aged >65 years, PP is the best measure of blood pressure that predicts mortality.³ Unfortunately, little is known about the genetics of PP.

The strong effects of sex, age, and body mass index (BMI) on blood pressure in the general population are well known.⁴ The high correlation between systolic blood pressure (SBP) and PP suggests that these effects exist for PP as well. In most genetic analyses, a simple correction that is held constant across genotypes is used to account for these effects. A possible interaction between genes and PP has not been reported in any study. Indeed, only a few studies have looked for interactions between common covariates and SBP or diastolic blood pressure (DBP). A segregation analysis⁵ of a large number of randomly ascertained nuclear families showed that there were significant genotype-specific effects of both sex and age on SBP. Recently, Turner et al⁶ have shown that the ACE insertion/deletion polymorphism has genotype-specific effects on blood pressure. Furthermore, Kardia⁷ has called for a more explicit consideration of gene-gene and gene-environment interactions in the study of blood pressure traits. In the present study, we modeled PP with sex-specific and genotype-specific effects of age, age², and BMI. Then, using the resulting model, we performed genome-wide 2-point lod score (z) analysis on PP. We used conservative criteria⁸ for claiming significant (z>3.3) and suggestive (z>1.9) linkage. We also report results that are weakly suggestive (z>0.59, which corresponds to a value of P<0.05). Given the known high correlation between PP and SBP, we also compare the genome scans for the 2 measures.

	Methods

Top Abstract Introduction Methods Results Discussion References

 
Subjects
Probands in the San Antonio Family Heart Study (SAFHS) were 40- to 60-year-old men and women chosen at random from a low-income Mexican American barrio in San Antonio, Tex. Apart from age, there were 2 eligibility criteria: (1) the proband had a living spouse who was willing to participate in the study, and (2) the proband had at least 6 first-degree relatives, excluding parents, who were aged 16 years and living in the San Antonio area. Because these criteria show no obvious selection bias with respect to blood pressure, this data set can be considered a random sample from the San Antonio Mexican American population. The proband and all first-, second-, and third-degree relatives willing to participate were subjected to an extensive data-gathering protocol in which demographic information on morphometrics, cigarette smoking, alcohol consumption, dietary behavior, and physical activity were obtained. For all individuals, blood pressure was measured 3 times after a 5-minute seated rest period with a random zero sphygmomanometer. Blood pressure was calculated by dropping the first reading and averaging the latter 2 readings. PP was calculated as the difference between SBP and DBP. The model-building phase of the study included 41 extended families. The total number of individuals was 1777 (1308 with blood pressure data). Several families had marriages with no children with complete data. Removing these marriage links occasionally had the effect of turning 1 large family into multiple smaller families that are computationally more efficient. This increased the actual number of families analyzed to 46.

The SAFHS chose 10 families for initial genotyping, and the linkage reported in the present study is based on these 10 families. These 10 families (637 individuals, 495 with PP data) were chosen, on the basis of size, to maximize statistical power to detect linkage. Inasmuch as this selection shows no obvious bias with respect to PP, we regard the 10 families as a random sample with respect to PP. However, it must be recognized that this selection procedure may have introduced hidden bias with respect to PP.

All individuals who participated in SAFHS gave informed consent, and the Institutional Review Board of the University of Texas Health Science Center at San Antonio approved all protocols.

Genotyping
Genotyping of 399 polymorphic markers was accomplished by using standard methods described by Atwood et al.⁹ Polymorphic markers used were from the MapPairs 6 and 8 Linkage Screening Sets (Research Genetics, Inc). An AGT 3' dinucleotide polymorphism¹⁰ was typed as described by Atwood et al.¹¹ There were 399 polymorphic markers, both candidate loci and anonymous marker loci, that had heterozygosity of 0.60 and were included in the linkage analysis. The average number of genotyped individuals per marker used in the linkage analysis was 441 (SD 19) in 10 families. To save computation time in the linkage analysis, any marker that had >7 alleles was reduced to 7 alleles by using the DOWNCODE¹² program, which minimizes information loss. The average heterozygosity of all included downcoded markers was 0.75 (SD 0.06), ranging from 0.60 to 0.85. All genetic locations used in the present study are given in centimorgans from the p-terminus and are taken from the Genetic Location Database.¹³ The Genetic Location Database is the only database that contained all markers of interest and therefore provides consistent location estimates.

Statistical Analysis
Using version 4 of the Pedigree Analysis Package computer program,¹⁴ we defined a single-locus 2-allele mendelian model for PP. This model incorporated sex-specific genotypic means and sex-specific and genotype-specific effects of age, age², and BMI. Theoretical aspects of gene-covariate interactions were first developed for this type of model by Konigsberg et al.¹⁵ All 3 quantitative covariates were standardized to have a mean of 0 and an SD of 1. Genotype frequencies were assumed to be in Hardy-Weinberg equilibrium, and residual familial correlations were modeled with a heritability parameter. Residual variance was held equal across all genotypes. Under the assumption that these pedigrees constitute a population-based sample of the San Antonio Mexican American population, no ascertainment correction was necessary. Data for individuals with extreme values of PP (SD ±4) were removed from consideration. In a separate set of nuclear families, a Monte Carlo test showed no effect of medication on PP. This is consistent with the assumption that medication has the same effect on SBP and DBP. Thus, no correction for medication was performed in this analysis. Only 10% of the individuals in this data set were on high blood pressure medication.

Maximum likelihood estimates for this model were computed with the use of NPSOL, one of the maximization options available in Pedigree Analysis Package. To ensure that the global maximum was found, the model was maximized 500 times with different random initial estimates.

Is the maximum likelihood model valid for linkage analysis? Blood pressure is a complex trait, and the single locus model is certainly incorrect. However, Williamson and Amos¹⁶ have shown that linkage analysis is valid when the genetic trait model is incorrect but the marker model is correct. Because this sample shows no selection bias with respect to blood pressure, allele frequencies based on the data are a random sample of the population; ie, the marker model is correct. Therefore, the linkage analysis is valid. Would a major gene from a prior segregation analysis improve power? An analysis of Genetic Analysis Workshop 9 data¹⁷ showed that requiring a major gene from a segregation analysis before linkage analysis can actually reduce overall power to detect linkage for a complex trait. Thus, a segregation analysis is not necessary to achieve a valid error rate and may reduce power; therefore, we did not perform one on this data set.

To determine whether the sex-specific and genotype-specific parameters were significant, several subset models were analyzed to test these parameters with respect to age, age², and BMI. In these subsets, the sex-specific or genotype-specific parameters were removed, and the maximum likelihood of the resulting subset model was computed. Examination of all possible subsets is computationally impractical, so we tested subsets grouped by age (this group included both age and age²) and BMI, in that order. All subsets were compared with the full 27-parameter model by a ² test. If a sex-specific or genotype-specific component was found to be nonsignificant, it was removed from further consideration.

Using version 4 of the Pedigree Analysis Package,¹⁴ we then performed 2-point linkage analysis on the 10 SAFHS families that had been genotyped. In the linkage analysis, one locus that was constructed on all 46 families was described by the mendelian model, and the other locus was 1 of the 399 polymorphic markers. Marker allele frequencies were estimated from the data. Parameter estimates for both loci were fixed; thus, the only free parameter in the linkage model was the recombination fraction (). The lod score (z) at the maximum likelihood estimate of was computed as logL(_max)-logL(=0.5). For any significant linkage, we also tested for sex-specific recombination and heterogeneity between families.

	Results

Top Abstract Introduction Methods Results Discussion References

 
Tests of model parameters for PP showed that age and age² were significant as both sex-specific and genotype-specific effects (P=1.65x10^-15 and P=1.79x10^-35, respectively). BMI also had significant sex-specific and genotype-specific effects (P=1.96x10^-3 and P=1.94x10^-3, respectively). The full model was therefore the most parsimonious model and was used in the linkage analysis.

The Table shows the maximum lod score and recombination fraction for all markers with maximum lod scores >0.59 (equivalent to P=0.05 for a 1-tailed test) for PP. There were no markers with a lod score >3.3, which is the criterion for significant linkage in families proposed by Lander and Kruglyak.⁸ However, there were 4 markers with maximum lod scores >1.9, which is their criterion for suggestive linkage in families. D21S1440 had a maximum lod score of 2.78 at a value of 0.02. D7S1799 had a maximum lod score of 2.04 (=0.01), D8S1100 had a maximum lod score of 1.98 (=0.08), and D18S844 had a maximum lod score of 1.95 (=0.11). The lod scores for flanking markers were generally low, with the highest coming on the p-terminus side of D8S1100 at marker D8S373, which had a maximum lod score of 0.59.

View this table: [in this window] [in a new window]   Table 1. Markers with Lod Scores 0.59 (P=0.05) for PP

The raw variance of PP was 234.9. The polygenic model, ie, the model with no single locus component, yielded a heritability of 0.21 for PP and a residual variance of 128.4 after accounting for simple linear effects of sex, age, and BMI. Thus, the polygenic model accounted for 45% of the total variation in PP. The full model, ie, the polygenic model plus the single locus components, gave a residual variance of 62.4. Thus, the full model accounted for 73% of the total variation in PP. The correlation between PP and SBP was 0.84, and the correlation between PP and DBP was 0.

	Discussion

Top Abstract Introduction Methods Results Discussion References

 
This is, to our knowledge, the first report of a genome scan for PP in a population-based sample. Given the strong correlation between PP and SBP, it is natural to ask whether genome scans of both traits found evidence for linkage in the same regions of the genome. For this data set, we have reported genome scans of SBP and DBP elsewhere.⁹ In the SBP genome scan, the 4 markers with the highest lod scores were the same as for PP reported in the present study. The lod scores for those 4 markers were 2.82 (D21S1440), 2.09 (D18S844), 1.64 (D8S1100), and 1.60 (D7S1799). Furthermore, 25 of the 31 markers that had lod scores >0.59 for PP also had lod scores >0.59 for SBP. The correlation between PP and SBP lod scores for those 25 markers was 0.92. This almost total overlap between PP and SBP is strong evidence that in this Mexican American population, PP and SBP have the same genetic etiology. This is surprising inasmuch as aortic stiffness is distinct from blood pressure and recent findings indicate that PP is a unique predictor of cardiovascular events. This may indicate that the uniqueness of PP is nongenetic. It should also be noted that PP might be too crude a measure of aortic stiffness to capture what is genetically unique about aortic stiffness.

In the model used for PP in the present study, we found significant sex-specific and genotype-specific effects of age, age², and BMI on PP. Given the strong correlation between SBP and PP, these results can be regarded as confirming the result of Perusse et al,⁵ who found sex-specific and genotype-specific effects of age on SBP.

Support for linkage of quantitative trait loci (QTL) affecting PP to the 4 regions located by the present study can be found in at least 1 independent study for each region. The suggestive linkage of a QTL affecting PP to chromosome 21 (D21S1440, z=2.78 and =0.02) is supported by evidence from 2 genome scans. Krushkal et al,¹⁸ in a study of SBP, found a maximum lod score of 0.86 with a locus 3 cM away from D21S1440. Further support comes from a genome scan of DBP performed by Xu et al,¹⁹ who found a maximum lod score of 2.20 with a locus 11 cM away from D21S1440.

The suggestive linkage of a QTL affecting PP to chromosome 7 (D7S1799, z=2.04 and =0.01) is supported by a genome scan of SBP reported by Rice et al.²⁰ They found a maximum lod score of 2.26 with a locus 20 cM from D7S1799.

The suggestive linkage of a QTL affecting PP to chromosome 8 (D8S1100, z=1.98 and =0.08) is supported by several studies. D8S1100 is 1.3 cM away from the aldosterone synthase gene (CYP11B2). A chimeric fusion of the aldosterone synthase gene and the adjacent 11 ß-hydroxylase gene (CYP11B1) is known to cause glucocorticoid-remediable aldosteronism, a rare monogenic form of hypertension.²¹ Further evidence that variation at this candidate gene influences blood pressure variation comes from Davies et al,²² who performed a case-control study of individuals from Glasgow, Scotland, in which polymorphisms in CYP11B2 were associated with hypertension and aldosterone excretion.

The suggestive linkage of a QTL affecting PP to chromosome 18 (D18S844, z=1.95 and =0.11) is supported by 2 studies. Pankow et al,²³ in a genome scan of the response of SBP to postural change, found a maximum lod score of 2.6 with a locus 27 cM away from D18S844. DeStefano et al,²⁴ in a study of orthostatic hypotension, found a significant maximum lod score of 3.21 with a locus 8.0 cM away from D18S844.

One of the 399 markers in the genome scan was a highly polymorphic marker at the angiotensinogen (AGT) locus. AGT is the most widely studied candidate gene for primary hypertension and has been linked to hypertension in this data set using hypertensive sibpairs.¹¹ AGT is therefore a natural candidate for linkage to PP; however, in this analysis, AGT was excluded from linkage to PP (z=-9.94 at =0.00). Thus, although AGT may have some effect in hypertensive subjects, it may not have a significant effect on the population.

In summary, we have found suggestive evidence for QTL that affects PP in 4 distinct chromosomal regions in a randomly ascertained Mexican American population. A comparison of genome scans indicates that PP and SBP are likely to have the same genetic etiology.

	Acknowledgments

 
This research was funded by grants HL-54707 and HL-45522 from the National Heart, Lung, and Blood Institute. We also thank the University of Minnesota Supercomputing Institute for use of the 396-processor IBM SP supercomputer.

Received October 25, 2000; first decision December 11, 2000; accepted December 19, 2000.

	References

Top Abstract Introduction Methods Results Discussion References

 
1. Franklin SS, Khan SA, Wong ND, Larson MG, Levy D. Is pulse pressure useful in predicting risk for coronary heart disease?: the Framingham heart study Circulation. 1999;100:354–360.[Abstract/Free Full Text]

2. Benetos A, Rudnichi A, Safar M, Guize L. Pulse pressure and cardiovascular mortality in normotensive and hypertensive subjects. Hypertension. 1998;32:560–564.[Abstract/Free Full Text]

3. Glynn RJ, Chae CU, Guralnik JM, Taylor JO, Hennekens CH. Pulse pressure and mortality in older people. Arch Intern Med. 2000;160:2765–2772.[Abstract/Free Full Text]

4. Izzo J, Joseph L, Black HR, eds. Hypertension Primer. 2nd ed. Dallas, Tex: Council on High Blood Pressure, American Heart Association; 1999.

5. Perusse L, Moll PP, Sing CF. Evidence that a single gene with gender- and age-dependent effects influences systolic blood pressure determination in a population-based sample. Am J Hum Genet. 1991;49:94–105.[Medline] [Order article via Infotrieve]

6. Turner ST, Boerwinkle E, Sing CF. Context-dependent associations of the ACE I/D polymorphism with blood pressure. Hypertension. 1999;34(pt 2):773–778.

7. Kardia SLR. Context-dependent genetic effects in hypertension. Curr Hypertens Rep. 2000;2:32–38.[Medline] [Order article via Infotrieve]

8. Lander E, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995;11:241–247.[Medline] [Order article via Infotrieve]

9. Atwood L, Samollow P, Hixson J, Stern M, MacCluer J. Genome-wide linkage analysis of blood pressure in Mexican Americans. Genet Epidemiol. In press.

10. Kotelevtsev YV, Clauser E, Corvol P, Soubrier F. Dinucleotide repeat polymorphism in the human angiotensinogen gene. Nucleic Acids Res. 1991;19:6978.[Free Full Text]

11. Atwood LD, Kammerer CM, Samollow PB, Hixson JE, Shade RE, MacCluer JW. Linkage of essential hypertension to the angiotensinogen locus in Mexican Americans. Hypertension. 1997;30:326–330.[Abstract/Free Full Text]

12. Dyke B. PEDSYS: A Pedigree Data Management System. 2nd ed. San Antonio, Tex: Southwest Foundation for Biomedical Research; 1996.

13. Collins A, Frezal J, Teague J, Morton N. A metric map of humans: 23,500 loci in 850 bands. Proc Natl Acad Sci U S A. 1996;93:14771–14775.[Abstract/Free Full Text]

14. Hasstedt SJ. Pedigree Analysis Package. 4th ed. Salt Lake City, Utah: Department of Human Genetics, University of Utah; 1994.

15. Konigsberg LW, Blangero J, Kammerer CM, Mott GE. Mixed model segregation analysis of LDL-C concentration with genotype-covariate interaction. Genet Epidemiol. 1991;8:69–80.[Medline] [Order article via Infotrieve]

16. Williamson JA, Amos CI. Guess LOD approach: sufficient conditions for robustness. Genet Epidemiol. 1995;12:163–176.[Medline] [Order article via Infotrieve]

17. Atwood LD, Slifer SH. Prior segregation analysis and the power to detect linkage. Genet Epidemiol. 1997;14:755–760.[Medline] [Order article via Infotrieve]

18. Krushkal J, Ferrell R, Mockrin SC, Turner ST, Sing CF, Boerwinkle E. Genome-wide linkage analysis of systolic blood pressure using highly discordant siblings. Circulation. 1999;99:1407–1410.[Abstract/Free Full Text]

19. Xu X, Rogus JJ, Terwedow HA, Yang J, Wang Z, Chen C, Niu T, Wang B, Xu H, Weiss S, et al. An extreme-sib-pair genome scan for genes regulating blood pressure. Am J Hum Genet. 1999;64:1694–1701.[Medline] [Order article via Infotrieve]

20. 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.[Abstract/Free Full Text]

21. Luft FC. Molecular genetics of human hypertension. J Hypertens. 1998;16:1871–1878.[Medline] [Order article via Infotrieve]

22. Davies E, Holloway CD, Ingram MC, Inglis GC, Friel EC, Morrison C, Anderson NH, Fraser R, Connell JMC. Aldosterone excretion rate and blood pressure in essential hypertension are related to polymorphic differences in the aldosterone synthase gene CYP11B2. Hypertension. 1999;33:703–707.[Abstract/Free Full Text]

23. Pankow JS, Rose KM, Oberman A, Hunt SC, Atwood LD, Djousse L, Province MA, Rao DC. Possible locus on chromosome 18q influencing postural systolic blood pressure changes. Hypertension. 2000;36:471-476.[Abstract/Free Full Text]

24. DeStefano AL, Baldwin CT, Burzstyn M, Gavras I, Handy DE, Joost O, Martel T, Nicolaou M, Schwartz F, Streeten DH, et al. Autosomal dominant orthostatic hypotensive disorder maps to chromosome 18q. Am J Hum Genet. 1998;63:1425–1430.[Medline] [Order article via Infotrieve]

This article has been cited by other articles:

				P. Lacolley, P. Challande, M. Osborne-Pellegrin, and V. Regnault Genetics and pathophysiology of arterial stiffness Cardiovasc Res, March 1, 2009; 81(4): 637 - 648. [Abstract] [Full Text] [PDF]

				B. C. Rossier and L. Schild Epithelial Sodium Channel: Mendelian Versus Essential Hypertension Hypertension, October 1, 2008; 52(4): 595 - 600. [Full Text] [PDF]

				E. Zintzaras, G. Kitsios, D. Kent, N. J. Camp, L. Atwood, P. N. Hopkins, and S. C. Hunt Genome-Wide Scans Meta-Analysis for Pulse Pressure Hypertension, September 1, 2007; 50(3): 557 - 564. [Abstract] [Full Text] [PDF]

				P. Duggal, A. P. Klein, K. E. Lee, R. Klein, B. E. K. Klein, and J. E. Bailey-Wilson Identification of Novel Genetic Loci for Intraocular Pressure: A Genomewide Scan of the Beaver Dam Eye Study Arch Ophthalmol, January 1, 2007; 125(1): 74 - 79. [Abstract] [Full Text] [PDF]

				B. Guzman, B. Cormand, M. Ribases, D. Gonzalez-Nunez, A. Botey, and E. Poch Implication of Chromosome 18 in Hypertension by Sibling Pair and Association Analyses: Putative Involvement of the RKHD2 Gene Hypertension, November 1, 2006; 48(5): 883 - 891. [Abstract] [Full Text] [PDF]

				B. Llamas, C. Lau, W. A. Cupples, M.-L. Rainville, E. Souzeau, and C. F. Deschepper Genetic Determinants of Systolic and Pulse Pressure in an Intercross Between Normotensive Inbred Rats Hypertension, November 1, 2006; 48(5): 921 - 926. [Abstract] [Full Text] [PDF]

				S. T. Turner, S. L.R. Kardia, T. H. Mosley, A. D. Rule, E. Boerwinkle, and M. de Andrade Influence of Genomic Loci on Measures of Chronic Kidney Disease in Hypertensive Sibships J. Am. Soc. Nephrol., July 1, 2006; 17(7): 2048 - 2055. [Abstract] [Full Text] [PDF]

				S. J. Bielinski, A. I. Lynch, M. B. Miller, A. Weder, R. Cooper, A. Oberman, Y.-D. I. Chen, S. T. Turner, M. Fornage, M. Province, et al. Genome-Wide Linkage Analysis for Loci Affecting Pulse Pressure: The Family Blood Pressure Program Hypertension, December 1, 2005; 46(6): 1286 - 1293. [Abstract] [Full Text] [PDF]

				O. Staub and F. Verrey Impact of Nedd4 Proteins and Serum and Glucocorticoid-Induced Kinases on Epithelial Na+ Transport in the Distal Nephron J. Am. Soc. Nephrol., November 1, 2005; 16(11): 3167 - 3174. [Abstract] [Full Text] [PDF]

				C. J. Russo, E. Melista, J. Cui, A. L. DeStefano, G. L. Bakris, A. J. Manolis, H. Gavras, and C. T. Baldwin Association of NEDD4L Ubiquitin Ligase With Essential Hypertension Hypertension, September 1, 2005; 46(3): 488 - 491. [Abstract] [Full Text] [PDF]

				G. F. Mitchell, A. L. DeStefano, M. G. Larson, E. J. Benjamin, M.-H. Chen, R. S. Vasan, J. A. Vita, and D. Levy Heritability and a Genome-Wide Linkage Scan for Arterial Stiffness, Wave Reflection, and Mean Arterial Pressure: The Framingham Heart Study Circulation, July 12, 2005; 112(2): 194 - 199. [Abstract] [Full Text] [PDF]

				S. J. Zieman, V. Melenovsky, and D. A. Kass Mechanisms, Pathophysiology, and Therapy of Arterial Stiffness Arterioscler. Thromb. Vasc. Biol., May 1, 2005; 25(5): 932 - 943. [Abstract] [Full Text] [PDF]

				A. L. DeStefano, M. G. Larson, G. F. Mitchell, E. J. Benjamin, R. S. Vasan, J. Li, D. Corey, and D. Levy Genome-Wide Scan for Pulse Pressure in the National Heart, Lung and Blood Institute's Framingham Heart Study Hypertension, August 1, 2004; 44(2): 152 - 155. [Abstract] [Full Text] [PDF]

				C. A. Mein, M. J. Caulfield, R. J. Dobson, and P. B. Munroe Genetics of essential hypertension Hum. Mol. Genet., April 1, 2004; 13(90001): R169 - 175. [Abstract] [Full Text] [PDF]

				N. J. Camp, P. N. Hopkins, S. J. Hasstedt, H. Coon, A. Malhotra, R. M. Cawthon, and S. C. Hunt Genome-Wide Multipoint Parametric Linkage Analysis of Pulse Pressure in Large, Extended Utah Pedigrees Hypertension, September 1, 2003; 42(3): 322 - 328. [Abstract] [Full Text] [PDF]

				S. Francke, M. Manraj, C. Lacquemant, C. Lecoeur, F. Lepretre, P. Passa, A. Hebe, L. Corset, S. L. K. Yan, S. Lahmidi, et al. A genome-wide scan for coronary heart disease suggests in Indo-Mauritians a susceptibility locus on chromosome 16p13 and replicates linkage with the metabolic syndrome on 3q27 Hum. Mol. Genet., November 1, 2001; 10(24): 2751 - 2765. [Abstract] [Full Text] [PDF]

				L. S.-C. Cheng, R. C. Davis, L. J. Raffel, A. H. Xiang, N. Wang, M. Quinones, P.-Z. Wen, E. Toscano, J. Diaz, S. Pressman, et al. Coincident Linkage of Fasting Plasma Insulin and Blood Pressure to Chromosome 7q in Hypertensive Hispanic Families Circulation, September 11, 2001; 104(11): 1255 - 1260. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Atwood, L. D. Articles by MacCluer, J. W. Search for Related Content PubMed PubMed Citation Articles by Atwood, L. D. Articles by MacCluer, J. W. Related Collections Other hypertension Genomics Hypertension - basic studies Genetics of cardiovascular disease