Published online before print February 16, 2004, doi: 10.1161/01.HYP.0000118921.66329.da
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(Hypertension. 2004;43:780.)
© 2004 American Heart Association, Inc.
Scientific Contributions |
Evidence for a Gene on Chromosome 13 Influencing Postural Systolic Blood Pressure Change and Body Mass Index
From the Department of Epidemiology (K.E.N., K.M.R.), University of North Carolina, Chapel Hill; Washington University School of Medicine (I.B.B.), St. Louis, Mo; Division of Preventive Medicine (A.O.), Department of Medicine, University of Alabama at Birmingham; Cardiovascular Genetics Research Clinic (S.C.H.), Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City; Division of Epidemiology (M.B.M., J.S.P.), University of Minnesota, Minneapolis; and Department of Genetics (J.B., L.A.), Southwest Foundation for Biomedical Research, San Antonio, Tex.
Correspondence to Kari North, Department of Epidemiology, University of North Carolina Chapel Hill, Bank of America Center, 137 E. Franklin St., Suite 306, Chapel Hill, NC 27514. E-mail kari_north{at}unc.edu
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Abstract |
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Previous analysis in the Hypertension Genetic Epidemiology Network (HyperGEN) of the National Heart Lung and Blood Institute (NHLBI) Family Blood Pressure Program, a multicenter study of genetic and environmental factors related to hypertension, indicated regions of linkage for blood pressure traits together with several coincident regions for phenotypically correlated traits, including systolic blood pressure (SBP) response to a postural challenge and body mass index (BMI). Motivated by these findings and by our desire to better understand the physiology of these traits, we conducted bivariate linkage analysis of postural SBP change and BMI. Sibships in HyperGEN were recruited from 5 field centers in Massachusetts, North Carolina, Minnesota, Utah, and Alabama. All available affected siblings, their parents, and selected nonmedicated offspring were recruited. Among 1636 whites and 1747 blacks, we performed a maximum likelihood bivariate genome scan for quantitative trait loci influencing postural SBP change and BMI, similarly adjusted for race, study center, sex, age, and age-by-sex interactions. Genome scans were performed using SOLAR (version 2.0) and race-specific marker allele frequencies derived from founders. The maximum genome-wide logarithm of odds (LOD) score of 3.2 was detected on chromosome 13 at 24 cM. This marker (D13S493) lies within 20 cM of a marker previously linked to BMI in the Family Heart Study and is substantially higher than the univariate linkage for each trait (LOD scores for BMI and postural SBP change were 2.4 and 0.9, respectively). These findings suggest that a gene(s) on chromosome 13q jointly regulates the SBP response to postural change and BMI.
Key Words: genetics • hypotension • posture • body mass index • blood pressure
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Introduction |
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The response of systolic blood pressure (SBP) to a change in body position has been used in studies as a measure of cardiovascular reactivity. A strong decrease in blood pressure on standing (orthostatic hypotension) is associated with an increased risk of coronary heart disease (CHD),1 stroke,2 incident hypertension,3 and all-cause mortality.4,5
Epidemiological studies assessing the association between the SBP response to postural challenge and body mass index (BMI) have been inconsistent.1,4,6–10 Studies evaluating the association between BMI and orthostatic hypotension, although not consistent, have most often noted lower BMIs among those with a strong SBP decrease in response to a change in posture. The association between BMI and an increase in SBP in response to a change in posture has not been frequently reported. However, in a cohort of middle-aged participants, those with a strong SBP increase in response to a change in posture had higher BMIs.1,10
Studies have identified chromosomal regions harboring quantitative trait loci (QTL) for obesity11 and SBP response to postural challenge,12 with coincident linkage for obesity13 and SBP response to postural challenge at 18q21.12 Moreover, a polymorphism (C825T) in the gene encoding the G protein beta3 subunit (GNB3) has recently been associated with hypertension and obesity in several studies.14–16 Despite the evidence for a possible correlation between these phenotypes, there is still much to be learned about specific loci jointly influencing obesity and SBP response to postural challenge.
Previous analysis in the Hypertension Genetic Epidemiology Network (HyperGEN) of the National Heart Lung and Blood Institute (NHLBI) Family Blood Pressure Program, a multicenter study of genetic and environmental factors related to hypertension, indicated several regions of suggestive linkage for blood pressure traits17 together with several coincident regions for phenotypically correlated traits, including SBP response to postural challenge and BMI.12 However, these previous genome scans were conducted on a subset of the data presented here, considering only affected sibling pairs. Motivated by these preliminary findings and by our desire to more accurately model the underlying determinants of these traits and make it easier to detect, localize, and identify genes contributing to their variation and co-variation, we conducted bivariate linkage analysis of postural SBP change and BMI.
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Methods |
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Subjects
The Hypertension Genetic Epidemiology Network (HyperGEN18), is 1 of 4 networks in the National Heart Lung and Blood Institute (NHLBI) Family Blood Pressure Program.19 HyperGEN study methods have previously been reported.18 In brief, the original populations were defined and enumerated from the NHLBI Framingham Heart Study,20 the Minneapolis, Minn and Forsyth County, NC field centers of the Atherosclerosis Risk in Communities (ARIC) Study, the Utah Health Family Tree Study, and from various studies at the Birmingham field center.
The present report includes 1636 white and 1747 black hypertensive siblings and their relatives. Additionally, a random sample of age-matched persons from the same base populations was included to calculate population parameters. The sample of examined individuals included 4453 relative pairs: over 595 parent offspring pairs, 2017 sibling pairs, 850 avuncular pairs (aunt/uncle–niece/nephew), and 192 first-cousin pairs, recruited from the 5 clinical centers.
Phenotypes
Anthropometric measurements were obtained as reported previously.18 BMI was computed as weight (kg)/height (m)2. Blood pressure measurements were taken with a DINAMAP model 1846 SX/P automated oscillometric device using a standard protocol.21 Briefly, after a 5-minute rest period, a supine blood pressure reading was taken using the dominant arm and the participant was asked to stand. On standing, a blood pressure measurement was immediately taken. Two minutes later, a second standing measurement was taken. The SBP response to a postural change was calculated as the first standing measurement minus the supine measurement.
Venipuncture was performed on fasting participants following a previously described standard protocol.18 Use of antihypertensive medications was determined based on participants’ responses to questions about medications regularly used during the 4 weeks preceding the examination. Informed consent was obtained from all participants, and this project was approved by the Institutional Review Boards of all participating institutions.
Genotypes
Genomic DNA was isolated from whole blood using standard procedures. Genotyping was provided by the NHLBI Mammalian Genotyping Service (Marshfield, Wis). Details on gel preparation and polymerase chain reaction conditions are available from the Mammalian Genotyping Service web site (http://marshmed.org/genetics). The CHLC screening set 8 was used, which includes nearly 400 microsatellite markers equally spaced (
9-cM distance) throughout the genome. The average marker heterozygosity was 77.7%. Analyses and assignment of the marker alleles were performed using computerized algorithms. Relationship status among the purportedly full sibs was tested using ASPEX, a likelihood-based method22 and MERLIN23.
Analytical Methods
Variance Components Linkage Analysis
Multipoint linkage analysis as implemented in SOLAR, version 2.0,24 was performed to detect and localize QTL jointly influencing variation in BMI and postural SBP change. Details of this model have been described previously.24–27
The use of the variance component approach requires an estimate of the identity-by-descent (IBD) matrix. The small size of our families allowed us to compute exact conditional probabilities using the Lander-Green algorithm as implemented in MERLIN.23 Allele frequencies from founders were calculated separately in black and white groups. The IBD probabilities computed by MERLIN were then combined into a single set of multipoint IBD files in the SOLAR format using the program Mer2sol (http://taxa.epi.umn.edu/mer2sol/) developed by Michael Miller at the University of Minnesota.
Multivariate Linkage Analysis
In the multivariate linkage model, the phenotype covariance is decomposed to include the genetic correlation between traits caused by additive genetic effects and the shared effects of the QTL, such that the covariation among family members for 2 traits is given by: 
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Where a and b can be trait 1 or 2 and 
q, g, e are the QTL specific genetic, additive genetic, and individual-specific environmental correlations between the two traits, respectively. 
q, g, e, are the additive genetic variance due to the major locus, the genetic variance due to residual additive genetic factors, and the variance due to individual-specific environmental effects, respectively. 
is a matrix whose elements (
qij) provide the probability that individuals i and j are IBD at a quantitative trait locus which is linked to a genetic marker locus, 
is the kinship matrix, and I is an identity matrix. The conversion of a 2-degrees of freedom (df) bivariate logarithm of odds (LOD) score to a 1-degree of freedom effective LOD score is based on the conversion of the 
2 with 
1 df, 
2 df, and point mass at zero. This is then converted to a 1 df 2 of equivalent probability value, which is divided by 2ln10 to get the 1-df effective LOD score (for more details, please see Amos et al28).
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Results |
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Application to HyperGEN Data
Only individuals with complete phenotypic data were included in these analyses. We excluded 10 individuals who were part of a monozygotic twin pair and 6 white individuals from Alabama, because the sample size of 6 was too small to consider as a separate race-center grouping. To reduce skewness in the phenotypic data, outliers (any observation more than 4 standard deviations (SD) from the mean and at least 1 SD from the nearest data point) were also excluded. Table 1 presents the baseline characteristics of all study participants. The average age was approximately 58 years in whites and 49 years in blacks (Table 1). Blacks had significantly higher BMIs and were more often female. The mean SBP response to a postural challenge was positive (2.5±13.3 mm Hg) in blacks and near zero in whites (-0.34±13.5 mm Hg). In hypertensive participants, the prevalence of severe hypertension was 53% in whites and 59% in blacks.
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Postural SBP change and BMI were similarly adjusted for race, study center, sex, age, and age-by-sex interaction. We evaluated other possible predictors of postural SBP response and BMI, including plasma total cholesterol, educational attainment, smoking status, alcohol consumption, supine blood pressure, serum glucose level, menopausal status, estrogen use, hypertension status, hypertensive medication usage (y/n), use of beta blockers (y/n), use of calcium channel blockers (y/n), and number of antihypertensive medications used. Further adjustments for these variables had little effect on the magnitude of the genetic signal (data not shown); therefore, only the minimally adjusted models were interpreted further. SBP response to postural challenge and BMI exhibited a residual heritability (after accounting for covariate effects) of 17% (h2=0.17 ± 0.05; P
0.01) and 55% (h2=0.55 ± 0.04; P0.0001), respectively. Heritability did not strongly vary by race (Table 2).
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The genetic and environmental correlations of SBP response to a postural challenge with BMI in the combined and race-specific samples are given in Table 3. In all 3 analyses, significant genetic correlations were identified.
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Table 4 presents the multipoint genome-wide bivariate-adjusted LOD scores (LOD scores converted from a mixed distribution to a 1-degree of freedom distribution) and their location from the variance component analyses for all peaks 
1.9 (suggestive evidence of linkage). We detected a maximum LOD score of 3.2 at D13S1493 at approximately 24 cM in the combined sample (significant evidence of linkage29) (Figure). The 1-LOD unit support interval spanned from 16 to 30 cM pter (from the terminus). This QTL on chromosome13q (13q11-13q12) was detected in black (LOD of 1.0 at 15 cM) and white participants (LOD of 2.7 at 25 cM). The 1-LOD unit support interval in race-specific analyses spanned from 16 to 30 cM pter in white participants and 1 to 110 cM pter in black participants. The second largest bivariate-adjusted LOD score was detected at D18S858 at approximately 80 cM (18q21.31) in the combined sample (Table 4). The 1-LOD unit support interval spanned from 68 to 88 cM pter.
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Discussion |
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A QTL jointly influencing the SBP response to a postural challenge and BMI was detected on chromosome13q (13q11-13q12) (maximum LOD score=3.2 at D13S1493 at approximately 24 cM). This linkage signal is substantially higher than the univariate linkage signal for each trait at this same chromosomal location (LOD scores for BMI and postural SBP change were 0.9 and 2.4, respectively, data not shown).
The mean SBP response to postural challenge was positive (2.5±13.3 mm Hg) in blacks and close to zero in whites (-0.34±13.5 mm Hg). In race-specific analyses, we detected a signal in both black (LOD of 1.0 at 15 cM) and white (LOD of 2.7 at 25 cM) participants. Thus, this QTL may be important in both subpopulations.
The marker that demonstrated the strongest evidence for linkage coincides with a signal reported by Rice et al,30 which is a peak linkage signal of 1.5 (P=0.0040) on 13q11 for blood pressure response to training. Additionally, our signal is within 20 cM of a linkage (at 13q14) to BMI reported by Feitosa et al31 in the Family Heart Study (LOD=3.2, P=0.00006) and Knoblauch et al32 in a sample of dizygotic twin pairs (P=0.0001).
One plausible candidate gene in the 14-cM (1 LOD unit) support interval is Insulin Promoter Factor 1 (IPF1), a key regulator of islet peptide hormone expression that may be involved in several disorders, most notably, diabetes mellitus (13q12.1).33 Altered IPF1 gene expression may contribute to the beta-cell dysfunction that characterizes type 2 diabetes and may also be related to hypertension and obesity, 2 traits often displayed in diabetic subjects, for which common genes have been hypothesized. Indeed, murine models of insulin resistance have shown the importance of impaired insulin action in pancreatic beta cells for normal glucose sensing and the prevention of obesity.34 Moreover, insulin directly affects vascular endothelium or smooth muscle by causing changes in skeletal muscle blood flow.35
Three other bivariate-adjusted LOD scores 1.9 (suggestive evidence for linkage) were detected at 12q, 18q, and 21p. The bivariate signal on 18q, although lower than the univariate LOD at the same location (univariate LOD=3.6, data not shown), overlays an interesting candidate gene for BMI and SBP response to a postural challenge, melanocortin 4 receptor (MC4R). MC4R deficiency is the commonest monogenic form of obesity36 and may play a role in obesity-related hypertension through the action of leptin,37,38 because the renal sympathetic effects of leptin may depend on the interactions with other neurochemical pathways in the hypothalamus, including MC4R.39,40
Although participants in our study were not selected on the basis of orthostatic hypotension status, the pattern of blood pressure responses to a postural challenge may be atypical, because the study sample is largely hypertensive. Use of some antihypertensive and other medications is often cited as a potential cause of orthostatic hypotension in the clinical literature. However, findings from population studies have been inconsistent.1,7,41 Our ability to assess whether use of prescription antihypertensive medications may have obscured normal physiological responses to the postural stressor is limited in our study because 75% of participants in our study were current users of antihypertensive medications. Nonetheless, when correcting for antihypertensive medication use, hypertension status, and class of hypertensive medication, the magnitude of the peaks reported decreased only slightly. For example, LOD scores with and without a correction for antihypertensive medication use, hypertension status, and class of hypertensive medication were reduced by 0.2, 0.1, and 0.5 LOD units, respectively. However, correcting for class of hypertensive medication reduced the sample by >700 participants, therefore substantially reducing the power of the analysis.
In population-based studies, the blood pressure response to a change in posture is reported to be normally distributed and wide, with a mean close to zero and with a substantial portion of individuals having either strong increases or decreases.4,10 Yet very few studies examine this response as a quantitative trait. Indeed, much of the literature to date focuses on the health-related correlates of orthostatic hypotension. Elevated blood pressure has been consistently associated with orthostatic hypotension.1,6,9 Studies have reported a lack of association of BMI with orthostatic hypotension1,7 but predominantly lower BMIs among those with orthostatic hypotension.4–6
In contrast, relatively little is known about health-related correlates of strong postural blood pressure increases. One study of normotensive individuals reported a positive association of BMI with strong postural blood pressure increases.3,10 Similarly, strong increases in blood pressure in response to a change in posture have also been associated with higher BMI.10 We explored the correlation between BMI and SBP response to a change in posture in hypertensive and random participants and found small but significant correlations (r=0.17, P<0.0001 and r=0.15, P<0,0001, respectively) when adjusted for age sex, age-by-sex interaction, race, center, menopausal, hormone use, alcohol consumption, smoking status, and supine blood pressure. Unfortunately, in examining the 10 families with the highest positive LOD scores, we could not detect a pattern of segregation of low BMI and a strong negative SBP response to a change in posture (data not shown). Clearly, more research is needed to better understand the interrelationship between the full range of BMI and the SBP response to a change in posture. Indeed, the role of genes in the joint regulation of these 2 traits may help us to better understand their interrelationship.
Perspectives
Our findings suggest that one or more genes on chromosome 13q jointly regulates the SBP response to postural change and BMI. Because the HyperGEN study population was not selected based on the SBP response to a change in posture or BMI, this region on 13q may have broader significance for blood pressure regulation and, by virtue of cross-study replication, likely harbors genes jointly influencing BMI and blood pressure regulation. Therefore, future research should type the biological candidate gene identified here, IPF1, to determine whether polymorphisms in this gene are the source of this and other related linkage signals. Identification of the exact functional mutations may suggest novel mechanisms in the development and regulation of hypertension and body mass.
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Acknowledgments |
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We acknowledge the participants of the HyperGEN study. We also acknowledge the HyperGEN Participating Institutions and Principal Staff. In addition, we acknowledge Brigitt Heier and Marilyn Knowles for assistance in the preparation of this manuscript. We also thank the University of Minnesota Supercomputing Institute for Digital Simulation and Advanced Computation for use of the IBM SP supercomputer. This hypertension network is funded by cooperative agreements (U10) with NHLBI (HL54471, HL54472, HL54473, HL54495, HL54496, HL54497, HL54509, and HL54515).
Received May 7, 2003; first decision June 13, 2003; accepted January 14, 2004.
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References |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
1. Rose KM, Tyroler HA, Nardo CJ, Arnett DK, Light KC, Rosamond W, Sharrett AR, Szklo M. Orthostatic hypotension and the incidence of coronary heart disease: the Atherosclerosis Risk in Communities study. Am J Hypertens. 2000; 13: 571–578.[CrossRef][Medline] [Order article via Infotrieve]
2. Eigenbrodt ML, Rose KM, Couper DJ, Arnett DK, Smith R, Jones D. Orthostatic hypotension as a risk factor for stroke: the atherosclerosis risk in communities (ARIC) study, 1987–1996. Stroke. 2000; 31: 2307–2313.
3. Rose KM, Holme I, Light KC, Sharrett AR, Tyroler HA, Heiss G. Association between the blood pressure response to a change in posture and the 6-year incidence of hypertension: prospective findings from the ARIC study. J Hum Hypertens. 2002; 16: 771–777.[CrossRef][Medline] [Order article via Infotrieve]
4. Davis BR, Langford HG, Blaufox MD, Curb JD, Polk BF, Shulman NB. The association of postural changes in systolic blood pressure and mortality in persons with hypertension: the Hypertension Detection and Follow-up Program experience. Circulation. 1987; 75: 340–346.
5. Masaki KH, Schatz IJ, Burchfiel CM, Sharp DS, Chiu D, Foley D, Curb JD. Orthostatic hypotension predicts mortality in elderly men: the Honolulu Heart Program. Circulation. 1998; 98: 2290–2295.
6. Applegate WB, Davis BR, Black HR, Smith WM, Miller ST, Burlando AJ. Prevalence of postural hypotension at baseline in the Systolic Hypertension in the Elderly Program (SHEP) cohort. J Am Geriatr Soc. 1991; 39: 1057–1064.[Medline] [Order article via Infotrieve]
7. Strogatz DS, Keenan NL, Barnett EM, Wagner EH. Correlates of postural hypotension in a community sample of elderly blacks and whites. J Am Geriatr Soc. 1991; 39: 562–566.[Medline] [Order article via Infotrieve]
8. Wu JS, Wu NH, Lu FH, Chang CJ. Factors associated with orthostatic hypotension in the Chinese population in Taiwan. Am J Hypertens. 1996; 9: 999–1005.[CrossRef][Medline] [Order article via Infotrieve]
9. Luukinen H, Koski K, Laippala P, Kivela SL. Prognosis of diastolic and systolic orthostatic hypotension in older persons. Arch Intern Med. 1999; 159: 273–280.
10. Nardo CJ, Chambless LE, Light KC, Rosamond WD, Sharrett AR, Tell GS, Heiss G. Descriptive epidemiology of blood pressure response to change in body position. The ARIC study. Hypertension. 1999; 33: 1123–1129.
11. Comuzzie AG. The emerging pattern of the genetic contribution to human obesity. Best Pract Res Clin Endocrinol Metab. 2002; 16: 611–621.[CrossRef][Medline] [Order article via Infotrieve]
12. 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.
13. Ohman M, Oksanen L, Kainulainen K, Janne OA, Kaprio J, Koskenvuo M, Mustajoki P, Kontula K, Peltonen L. Testing of human homologues of murine obesity genes as candidate regions in Finnish obese sib pairs. Eur J Hum Genet. 1999; 7: 117–124.[CrossRef][Medline] [Order article via Infotrieve]
14. Hegele RA, Anderson C, Young TK, Connelly PW. G-protein beta3 subunit gene splice variant and body fat distribution in Nunavut Inuit. Genome Res. 1999; 9: 972–977.
15. Siffert W, Forster P, Jockel KH, Mvere DA, Brinkmann B, Naber C, Crookes R, Du PHA, Epplen JT, Fridey J, Freedman BI, Muller N, Stolke D, Sharma AM, Al Moutaery K, Grosse-Wilde H, Buerbaum B, Ehrlich T, Ahmad HR, Horsthemke B, Du Toit ED, Tiilikainen A, Ge J, Wang Y, Yang D, Husing J, Rosskopf D. Worldwide ethnic distribution of the G protein beta3 subunit 825T allele and its association with obesity in Caucasian, Chinese, and Black African individuals. J Am Soc Nephrol. 1999; 10: 1921–1930.
16. Siffert W, Rosskopf D, Erbel R, Ritz E. G protein beta3 subunit 825T allele and hypertension. Hypertension. 1999; 34: e8.[Medline] [Order article via Infotrieve]
17. Rao DC, Province MA, Leppert MF, Oberman A, Heiss G, Ellison RC, Arnett DK, Eckfeldt JH, Schwander K, Mockrin SC, Hunt SC. A genome-wide affected sibpair linkage analysis of hypertension: the HyperGEN network(1). Am J Hypertens. 2003; 16: 148–150.[CrossRef][Medline] [Order article via Infotrieve]
18. Williams RR, Rao DC, Ellison RC, Arnett DK, Heiss G, Oberman A, Eckfeldt JH, Leppert MF, Province MA, Mockrin SC, Hunt SC. NHLBI family blood pressure program: methodology and recruitment in the HyperGEN network. Hypertension genetic epidemiology network. Ann Epidemiol. 2000; 10: 389–400.[CrossRef][Medline] [Order article via Infotrieve]
19. Multi-center genetic study of hypertension: The Family Blood Pressure Program (FBPP). Hypertension. 2002; 39: 3–9.
20. Higgins M, Province M, Heiss G, Eckfeldt J, Ellison RC, Folsom AR, Rao DC, Sprafka JM, Williams R. NHLBI Family Heart Study: objectives and design. Am J Epidemiol. 1996; 143: 1219–1228.
21. DINAMAP Adult/Pediatric, and Neonatal Vital Signs Monitor: Models 1846 SX and 1846 SX/P Operations Manual. Tampa: Critikon Corp; 1988.
22. Hinds DR, N. The ASPEX package: Affected sib-pair mapping. 1996.
23. Abecasis GR, Cherny SS, Cookson WO, Cardon LR. Merlin–rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet. 2002; 30: 97–101.[CrossRef][Medline] [Order article via Infotrieve]
24. Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998; 62: 1198–1211.[CrossRef][Medline] [Order article via Infotrieve]
25. Goldgar DE. Multipoint analysis of human quantitative genetic variation. Am J Hum Genet. 1990; 47: 957–967.[Medline] [Order article via Infotrieve]
26. Amos CI. Robust variance-components approach for assessing genetic linkage in pedigrees. Am J Hum Genet. 1994; 54: 535–543.[Medline] [Order article via Infotrieve]
27. Blangero J, Almasy L. Multipoint oligogenic linkage analysis of quantitative traits. Genet Epidemiol. 1997; 14: 959–964.[CrossRef][Medline] [Order article via Infotrieve]
28. Amos C, de Andrade M, Zhu D. Comparison of multivariate tests for genetic linkage. Hum Hered. 2001; 51: 133–144.[CrossRef][Medline] [Order article via Infotrieve]
29. Lander E, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995; 11: 241–247.[CrossRef][Medline] [Order article via Infotrieve]
30. Rice T, Rankinen T, Chagnon YC, Province MA, Perusse L, Leon AS, Skinner JS, Wilmore JH, Bouchard C, Rao DC. Genomewide linkage scan of resting blood pressure: HERITAGE Family Study. Health, Risk Factors, Exercise Training, and Genetics. Hypertension. 2002; 39: 1037–1043.
31. Feitosa MF, Borecki IB, Rich SS, Arnett DK, Sholinsky P, Myers RH, Leppert M, Province MA. Quantitative-trait loci influencing body-mass index reside on chromosomes 7 and 13: the National Heart, Lung, and Blood Institute Family Heart Study. Am J Hum Genet. 2002; 70: 72–82.[CrossRef][Medline] [Order article via Infotrieve]
32. Knoblauch H, Muller-Myhsok B, Busjahn A, Ben Avi L, Bahring S, Baron H, Heath SC, Uhlmann R, Faulhaber HD, Shpitzen S, Aydin A, Reshef A, Rosenthal M, Eliav O, Muhl A, Lowe A, Schurr D, Harats D, Jeschke E, Friedlander Y, Schuster H, Luft FC, Leitersdorf E. A cholesterol-lowering gene maps to chromosome 13q. Am J Hum Genet. 2000; 66: 157–166.[CrossRef][Medline] [Order article via Infotrieve]
33. Macfarlane WM, Frayling TM, Ellard S, Evans JC, Allen LI, Bulman MP, Ayres S, Shepherd M, Clark P, Millward A, Demaine A, Wilkin T, Docherty K, Hattersley AT. Missense mutations in the insulin promoter factor-1 gene predispose to type 2 diabetes. J Clin Invest. 1999; 104: R33–39.[Medline] [Order article via Infotrieve]
34. Mauvais-Jarvis F, Kulkarni RN, Kahn CR. Knockout models are useful tools to dissect the pathophysiology and genetics of insulin resistance. Clin Endocrinol (Oxf). 2002; 57: 1–9.[CrossRef][Medline] [Order article via Infotrieve]
35. Steinberg HO, Baron AD. Vascular function, insulin resistance and fatty acids. Diabetologia. 2002; 45: 623–634.[CrossRef][Medline] [Order article via Infotrieve]
36. Farooqi IS, Keogh JM, Yeo GS, Lank EJ, Cheetham T, O’Rahilly S. Clinical spectrum of obesity and mutations in the melanocortin 4 receptor gene. N Engl J Med. 2003; 348: 1085–1095.
37. Rahmouni K, W GH. Leptin and the central neural mechanisms of obesity hypertension. Drugs Today (Barc). 2002; 38: 807–817.[Medline] [Order article via Infotrieve]
38. Matsumura K, Tsuchihashi T, Fujii K, Iida M. Neural regulation of blood pressure by leptin and the related peptides. Regul Pept. 2003; 114: 79–86.[CrossRef][Medline] [Order article via Infotrieve]
39. Hall JE, Brands MW, Hildebrandt DA, Kuo J, Fitzgerald S. Role of sympathetic nervous system and neuropeptides in obesity hypertension. Braz J Med Biol Res. 2000; 33: 605–618.[Medline] [Order article via Infotrieve]
40. Hall JE, Hildebrandt DA, Kuo J. Obesity hypertension: role of leptin and sympathetic nervous system. Am J Hypertens. 2001; 14: 103S–115S.[CrossRef][Medline] [Order article via Infotrieve]
41. Rutan GH, Hermanson B, Bild DE, Kittner SJ, LaBaw F, Tell GS. Orthostatic hypotension in older adults. The Cardiovascular Health Study. CHS Collaborative Research Group. Hypertension. 1992; 19: 508–519.
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