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. Author manuscript; available in PMC: 2010 Jan 28.
Published in final edited form as: Am J Hypertens. 2008 Jan 10;21(2):194–199. doi: 10.1038/ajh.2007.34

GENOME-WIDE LINKAGE ANALYSIS OF PULSE PRESSURE IN AMERICAN INDIANS: THE STRONG HEART STUDY

Nora Franceschini 1, Jean W MacCluer 2, Kathreen M Rose 1, Sue Rutherford 2, Shelley A Cole 2, Sandy Laston 2, Harald HH Göring 2, Vincent P Diego 2, Mary J Roman 3, Elisa T Lee 4, Lyle G Best 5, Barbara V Howard 6, Richard R Fabsitz 7, Kari E North 1
PMCID: PMC2812893  NIHMSID: NIHMS91953  PMID: 18188160

Abstract

Background

Pulse pressure, a measure of central arterial stiffness and a predictor of cardiovascular mortality, has known genetic components.

Methods

To localize the genetic effects of pulse pressure, we conducted a genome-wide linkage analysis of 1,892 American Indian participants of the Strong Heart Family Study. Blood pressure was measured three times and the average of the last two measures was used for analyses. Pulse pressure, the difference between systolic and diastolic blood pressures, was log-transformed and adjusted for the effects of age and sex within each study center. Variance component linkage analyses were performed using marker allele frequencies derived from all individuals and multipoint identity-by-descent matrices calculated in Loki.

Results

We identified a quantitative trait locus influencing pulse pressure on chromosome 7 at 37 cM (marker D7S493, LOD=3.3) and suggestive evidence of linkage on chromosome 19 at 92 cM (marker D19S888, LOD=1.8).

Conclusions

The signal on 7p15.3 overlaps positive findings for pulse pressure among Utah population samples, suggesting that this region may harbor gene variants for blood pressure related traits.

Keywords: Genetics, pulse pressure, American Indian

INTRODUCTION

Pulse pressure, the difference between systolic (SBP) and diastolic (DBP) blood pressures, is an independent predictor of cardiovascular (CVD) morbidity and mortality in the general population [1, 2] and among American Indian individuals [3]. Elevated pulse pressure is associated with central artery stiffness and carotid intima-media hypertrophy, a measure of subclinical atherosclerosis [4]. Hemodynamic changes due to increased central artery stiffening occurs with aging, and in association with hypertension, diabetes [5], smoking, and obesity [6]. In addition, arterial stiffness is present in individuals with microalbuminuria [7, 8].

The localization of genetic effects that influence pulse pressure has been studied in several populations but not among American Indian individuals [9–12]. Genome-wide linkage for pulse pressure was identified on chromosomes 7q, 17q, 18q, 19p, 20q and 21q in the Family Blood Pressure Program (FBPP) [10] and additional loci were identified on chromosomes 6p, 10q and 22q in a recent genome-wide scan meta-analysis of Caucasians and Mexican American individuals [13]. In this study, we sought to identify quantitative trait loci (QTLs) for pulse pressure among American Indians, a population enriched for multiple risk factors for atherosclerosis.

METHODS

THE STRONG HEART FAMILY STUDY

The Strong Heart Family Study (SHFS), funded by the National Heart, Lung, and Blood Institute (NHLBI), was initiated in 1998 to study the genetics of CVD among American Indian populations [14]. The SHFS recruited family members of the original cohort of participants of the Strong Heart Study and it has examined over 3,600 American Indians aged 14 to 93 years from 13 tribes. Recruitment centers were located in Arizona, North and South Dakota, and Oklahoma. All participants provided informed consent. The SHFS protocols were approved by the Indian Health Service Institutional Review Board, by the Institutional Review Boards of the participating Institutions, and by the Indian tribes participating in these studies [14].

Participants were interviewed during a clinical visit. Tobacco exposure and alcohol intake were quantified using standardized questionnaires. Blood pressure was measured using a standard protocol across the three recruiting centers [14]. Forearm seated blood pressures were measured three times by a trained technician using a mercury column sphygmomanometer (WA Baum Co) and size-adjusted cuffs. The first and fifth Korotkoff sounds were recorded. The average of the last two of the three measures was used in the analyses. Pulse pressure was calculated using the difference between the SBP and DBP. We also estimated the mean arterial pressure using the equation DBP+ (pulse pressure/3). Anthropometric measures of height and weight were used to calculate body mass index (BMI).

Hypertension was defined using the 7th Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) as blood pressure levels of 140/90 mm Hg or higher, or use of antihypertensive drugs. The American Diabetes Association (ADA) criteria of fasting plasma glucose levels ≥ 126 mg/dl, treatment with oral agents or insulin were used to define diabetes mellitus. Categories of obesity were defined using the National Institutes of Health (NIH) guidelines as normal (BMI < 25 kg/m2), overweight (BMI of 25 to < 30 kg/m2) and obese (BMI ≥ 30 kg/m2) (references available online supplemental material). Albumin and creatinine were measured in a random urine sample using nephelometric immunochemistry and alkaline picrate methods, respectively [15]. Urine albumin to creatinine ratio (ACR, mg/g) was used in analyses. Serum creatinine was measured by the picric acid method [15].

The aortic root diameter was measured at the sinuses of Valsalva with the parasternal long-axis view using phased-array echocardiographs with M-mode, 2D, pulsed, continuous, and color-flow Doppler capabilities as described before [16].

Genotyping

Genotype data were generated using the ABI PRISM Linkage Map Set-MD10 which includes ~ 400 markers spaced at approximately 10 cM intervals (references available online). Pedigree relationships have been verified using the PREST (pedigree relationship statistical tests) package, which employs likelihood-based inference statistics for genome-wide identity-by-descent (IBD) allele sharing. Mendelian inconsistencies and spurious double recombinants were detected using the SimWalk2. Marker allele frequencies were derived using maximum likelihood methods estimated from all individuals. The overall blanking rate for both types of errors was less than 1% of the total number of genotypes for Arizona, the Dakotas and Oklahoma. Multipoint identity-by-descent matrices (mIBD) were computed using LOKI 2.4. Our chromosomal maps are based on the deCODE map. We used the University of California Santa Cruz (UCSC) website (http://genome/ucsc.edu) and Online Mendelian Inheritance in Man (OMIM) (http://www3.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM) databases to determine the cytogenic location of markers and to search for candidate genes.

STATISTICAL ANALYSES

We used generalized linear models to estimate the mean age-adjusted blood pressure in categories of potential confounders (Table 1). Quantitative traits with skewed distributions were log-transformed (pulse pressure, BMI, ACR, and SBP). We obtained residuals from linear regression predictive models of the log of pulse pressure using a backwards elimination strategy (alpha = 0.10) in SAS version 9.1 (Cary, NC). A minimally adjusted model accounted for the effects of age, age2, sex and age-by-sex interaction within center (Model 1). Additional models adjusting for smoking (ever vs. never), alcohol intake (current vs. not), continuous log-transformed BMI, continuous log-transformed ACR, type 2 diabetes and hypertension treatment (yes vs. no) were also performed (Model 2). In addition, we explored models adjusting for aortic root diameter. The residuals’ kurtoses from minimal and maximal models were 0.42 and 0.75, respectively.

TABLE 1.

Characteristics of the American Indian participants: The Strong Heart Family Study (n=1,892)

Mean pulse pressure mm Hg (95% CI) Mean systolic/diastolic blood pressures, mm Hg
Age < 50 years (n=1340) 42 (41, 43) 119/77
Age 50 or older (n=552) 56 (55, 57) 131/75
Female (n=1169) 46 (45, 46) 120/75
Male (n=723) 47 (46, 48) 126/80
Normotensive subjects (n=1250) 43 (43, 44) 117/74
Hypertensive participants not on treatment (n=229) 52 (50, 54) 142/90
Hypertensive participants on treatment (n=413) 51 (50, 53) 129/78
Non-diabetic participants (n=1443) 46 (45, 46) 122/77
Diabetic participants (n=449) 48 (46, 49) 124/76
Never smokers (n=789) 46 (45, 47) 122/76
Ever smokers (n=1103) 46 (45, 47) 123/77
No alcohol intake (n=791) 47 (46, 48) 121/74
Current alcohol intake (n=1101) 46 (45, 46) 124/78
BMI lower than 25, kg/m2 (n=286) 46 (45, 48) 117/71
BMI 25 to less than 30, kg/m2 (n=481) 45 (44, 47) 122/77
BMI 30 or more, kg/m2 (n=1125) 46 (46, 47) 124/78
Urine albumin <30 mg/g creatinine (n=1530) 45 (45, 46) 122/76
Urine albumin 30 to 300 mg/g creatinine (n=283)* 48 (47, 50) 125/77
Urine albumin >300 mg/g creatinine (n=79)** 57 (54, 60) 136/79
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CI, confidence interval; BMI, body mass index

Values are age-adjusted estimates of mean pulse pressure, and mean systolic and diastolic blood pressures, except for categories of age.

*

microalbuminuria;

**

macroalbuminuria

Heritability was estimated using maximum likelihood variance decomposition methods [17]. Multipoint variance component genome-wide linkage analyses were then performed using SOLAR version 2.1.4.[17]. The variance component approach tests for linkage between marker loci and the trait by partitioning the phenotypic variance into its additive genetic and environmental variance components and has been described in detail elsewhere [17]. We determined the 1-LOD unit drop support interval for all suggestive linkage results with a LOD score greater than or equal to 1.8 [18]. Family-specific LOD scores were computed using –pedlod in SOLAR. Linkage heterogeneity (joint test of linkage and heterogeneity) was estimated using –hlod in SOLAR and an alpha = 0.0001. We performed power calculations for a quantitative trait assuming that the trait was influenced by a single bi-allelic QTL and a residual additive genetic effect due to polygenes, using –power in SOLAR (see supplement Table 1). In addition, we performed analysis of SBP, DBP and mean arterial pressures to determine if identified QTLs for pulse pressure were also influencing other blood pressure phenotypes.

Genotype-by-environment interaction analyses

To test for additive genotype-by-environment interaction, the univariate variance component model was extended to include the genetic covariance between the two environment relative pairs [19]. The likelihood of a model including genotype-by- environment interaction is compared to the likelihood of restricted models in which such interactions are excluded. We tested a model in which the genetic correlation (rhoG) between the two groups is constrained to 1.0; and a model in which the genetic variance (σg) among groups is constrained to be equal. In addition, we tested a model in which the environment (residual) variances (σe) among the two groups were constrained to be equal. For genotype-by-sex interaction models, log-transformed pulse pressure trait was adjusted for age, age2 and recruiting centers. For genotype-by-age interaction, we used residuals of a linear model adjusting for sex and recruiting center, and a 50 year age cutpoint categorical variable. We also performed linkage analyses including the QTL-specific interaction term when significant (described in the online supplemental material).

RESULTS

Analyses were performed using an available subset of 1,892 of the total 3,600 individuals that have been genotyped in 14 large pedigrees. Among 18,349 relative-pairs, 1,530 were parent-offspring pairs, 1,381 were sibling pairs and 2,703 were avuncular pairs. Sixty-two percent of subjects were women, and the mean age for all participants was 42 years (standard deviation, SD, 16). Subjects were often smokers (58%), and overweight or obese (mean BMI of 32 kg/m2) (Table 1). Hypertension (34%) and diabetes (23%) were highly prevalent. Mean aortic root diameter was 3.3 cm (SD, 0.4). Mean serum creatinine was 0.8 (SD, 0.4) mg/dl. Microalbuminuria or macroalbuminuria were present in approximately 19% of individuals (Table 1).

Overall, the unadjusted mean pulse pressure was 46 mm Hg (SD 14). Pulse pressure increased after age 50, and the age-adjusted mean pulse pressure was similar among men and women (Table 1). Mean pulse pressure was also increased in hypertensive and diabetic subjects but not among smokers, those with current alcohol intake or obese participants. In addition, pulse pressure was elevated in increasing levels of urine albumin excretion (Table 1). For comparison, the mean age-adjusted SBP and DBP are also displayed in Table 1. In linear regression models, age, sex, smoking exposure, hypertension treatment, continuous log BMI and log ACR (p<0.01 for all), but not alcohol intake (p=0.10), type 2 diabetes (p=0.22) or aortic root diameter (p=0.31), were significant predictors of pulse pressure variation.

The estimated residual heritability of log of pulse pressure adjusted for age, age2, sex, age-by-sex interaction and center was 0.25 (standard error =0.04, p=1.3 × 10−12). We identified a QTL influencing pulse pressure on chromosome 7 at 37 cM (marker D7S493, LOD score 3.3, Figure 1) and suggestive evidence of linkage on chromosome 19 at 92 cM (marker D19S888, LOD=1.8, Figure 2) in models adjusted for age, sex and center. The 1-LOD support interval of the signal on chromosome 7 spanned from 32 cM (near marker D7S507) to 44 cM (marker D7S516) (Figure 1). Adjusting for continuous log BMI, alcohol exposure, diabetes and hypertension treatment did not substantially change the magnitude of the signal on chromosome 7 or 19 but additional adjustments for smoking exposure or log ACR attenuated the signal in chromosome 7 (LOD= 2.4 and 2.6, respectively) (Figure 1) but not the signal on chromosome 19 (Figure 2). Minimally and maximally adjusted models for all chromosomes are shown in supplemental Figure 1. Finally, models adjusting for aortic root diameter did not change our peak linkage signals on both chromosomes 7 and 19 (data not shown).

Figure 1.

Figure 1

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Chromosome 7 multipoint LOD scores for log-transformed pulse pressure. Model 1: log pulse pressure adjusted for age, age2, sex, age-by-sex interaction, center; Model 2: log pulse pressure adjusted for age, age2, sex, age-by-sex interaction, center, diabetes, smoking, log urine albumin excretion, log body mass index, hypertension treatment; Model 3: Model 2 without adjusting for log urine albumin excretion (smoking effect); Model 4: Model 2 without adjusting for smoking (urine albumin excretion effect).

Figure 2.

Figure 2

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Chromosome 19 multipoint LOD scores for log-transformed pulse pressure. Model 1: log pulse pressure adjusted for age, age2, sex, age-by-sex interaction, center; Model 2: log pulse pressure adjusted for age, age2, sex, age-by-sex interaction, center, type 2 diabetes, smoking, log urine albumin excretion, log body mass index, hypertension treatment; Model 3: Model 2 without adjusting for log urine albumin excretion (smoking effect); Model 4: Model 2 without adjusting for smoking (urine albumin excretion effect).

Although multiple families contributed to the described LOD score on chromosome 7, the highest family-specific LOD scores were found in two large pedigrees in Dakota and Oklahoma (LOD scores of 1.4 and 2.1, respectively)(supplemental Figure 2). The joint test of heterogeneity and linkage was not significant (p=0.12) and posterior probabilities for linkage were above zero for all pedigrees (supplemental Figure 2), indicating that the linkage to pulse pressure on chromosome 7 region was present in all families.

To determine if the QTLs on chromosomes 7 and 19 were influencing other measures of blood pressure, we also performed linkage analysis of DBP, SBP and mean blood pressures. The LOD scores for SBP were 0.03 and 0.97 on chromosomes 7 at 37 cM and 19 at 92 cM, respectively (previously reported in [20]). No genetic signals were detected for DBP or mean arterial pressure on chromosomes 7 and 19 (supplemental Figures 3 and 4).

In separate analysis using sex-specific residuals, we found no sex-specific additive genetic effects for pulse pressure (rhoG=0.50, p=0.09 for interaction by sex). However, we found a significant genotype-by-age interation for pulse pressure in models adjusting for sex and center, and using the age category cutpoint of 50 years (rhoG=−0.27 ± 0.12, p=1.41 × 10−9). The signal on chromosome 7 was attenuated in linkage analysis including the age interaction term (LOD score=1.8) and the chromosome 19 QTL was not detected.

DISCUSSION

We identified two QTLs influencing pulse pressure in American Indians: one of the QTLs shows significant evidence of linkage on chromosome 7 and the other shows suggestive evidence for linkage on chromosome 19. Our findings were consistent across different models of covariates adjustment, including adjustments for the effects of age, sex, BMI, diabetes, alcohol intake, aortic root diameter and hypertension treatment. However, the magnitude of the linkage peak for pulse pressure on chromosome 7 was slightly attenuated when adjusting for smoking status or albuminuria, both of which themselves are genetically determined.

Studies in US Caucasian and African American individuals identified QTLs for pulse pressure and other blood pressure related traits on chromosomes 7 and 19 but our study is the first to detect genome-wide significant linkage to this region [10, 11]. The pulse pressure QTL we identified at 7p15.3 overlaps positive findings for pulse pressure among Caucasian Utah population participants selected for early coronary artery disease (CAD), stroke or early onset hypertension (LOD=1.8, marker D7S1808), at approximately 6 Mb away for our linkage peak [11]. This QTL is also within 40 Mb of a QTL for pulse pressure identified among 779 FBPP GenNet African American participants (LOD= 3.1, marker D7S3046) [10] and among 1584 participants of the Framingham Heart Study (LOD= 2.4, located at 71 cM of chromosome 7) [12]. Also of interest, a QTL for carotid artery intimal medial thickness (IMT), a measure of subclinical atherosclerosis associated with increased pulse pressure, was recently identified in a nearby region on chromosome 7 (at 7p14.3) among the Offspring cohort of the Framingham Heart Study (LOD=1.6) [21]. In addition, a QTL for carotid-femoral pulse wave velocity, a more direct measure of aortic stiffness, was identified on chromosome 7 at 29 cM in the Framingham Heart Offspring Study[22]. The QTL on chromosome 19q has also been previously described by Camp et al for pulse pressure (marker D19S245, LOD=2.0) and more recently by the NHLBI FBPP in African American subjects (LOD=2.3) [10, 11]. Although a recent meta-analysis of pulse pressure linkage studies did not identify chromosomes 7 or 19 as regions of interest for pulse pressure, the study reported evidence for substantial genetic heterogeneity for this trait [13].

Approximately 86 genes underlie the 1-LOD support interval of the 7p15.3 QTL, of which two positional candidate genes have been associated with hypertension and/or atherosclerosis: interleukin-6 (IL-6 at 7p21) and neuropeptide Y (NPY at 7p15.1). IL-6 is an acute inflammatory cytokine and an inducer of C-reactive protein. High levels of IL-6 were seen in hypertension and increased pulse pressure [23], and were also associated with the development and the progression of atherosclerosis and CVD mortality [24]. A polymorphism in the promoter of the IL-6 gene (-174G/C) was associated with increased carotid artery IMT [25] and coronary artery disease [26] but has not been studied for association with pulse pressure. Neuropeptide Y is a vasoactive neuropeptide that regulates appetite and vascular reactivity. Polymorphisms of NPY were associated with hypertension, increased carotid artery IMT [27], myocardial infarct and stroke [28]. In addition, NPY variants were associated with insulin resistance [29] which is highly prevalent in American Indians in the Strong Heart Study.

Located in the chromosome region 19q13.3-13.41 is a cluster of 15 genes of the kallikrein family. Kallikreins are tissue-specific enzymes that generate kinins, which affect blood pressure and renal function. Variants of the kallikrein 1 (KLK1) gene are associated with hypertension in Han Chinese populations [30].

The identified QTL on chromosome 7 was specific to the pulse pressure phenotype but the QTL on chromosome 19 was also localized using the SBP trait. The FBPP has also showed some overlap between SBP and pulse pressure QTLs [10]. Interestingly, we did not see an overlap in QTLs for pulse pressure and mean arterial pressure, the later measure being the steady-flow component of the blood pressure curve. Minimal overlap in linkage peaks to blood pressure components have also been previously reported [12, 31].

We identified an interesting genotype-by-age interaction for pulse pressure, supporting a genetic basis for the physiologic differences observed in pulse pressure in young compared to older individuals. In linkage analysis accounting for age interaction, the QTL on chromosome 7 was attenuated and the evidence for linkage on chromosome 19 disappeared. These findings may be due to differences in the age effects within categories of risk factors for atherosclerosis, for example, the age effects may vary with gender or hypertensive status. In addition, reduced power to detect the effects may occur when using age categories. Lastly, the possibility that the chromosome 19 QTL is a false positive must also be considered. Further studies are needed to clarify the relationship between genotype, age, and pulse pressure.

Our study used averages of two blood pressure measures for estimation of pulse pressure which decrease the variability of the blood pressure trait. Misclassification of hypertension status could have occurred for individuals classified based on the single day average measure of blood pressure (36% of hypertensive individuals were classified using only blood pressure measures). Although two large families accounted for most of the linkage signal on chromosome 7, the joint test of heterogeneity and linkage was not significant, indicating that all families contributed to the linkage signal.

In summary, we found genome-wide evidence for linkage to pulse pressure on chromosome 7p and suggestive evidence for linkage on 19q among American Indian participants of the Strong Heart Family Study. Our study provides corroboration of genomic regions that possibly influence interindividual variation in pulse pressure on chromosomes 7p and 19q. In addition, the 7p region encompasses candidate genes known to affect blood pressure and hypertension, as well as markers of subclinical atherosclerosis. The identification and confirmation of QTLs for pulse pressure will bring us closer to the identification of the functional genes that influence pulse pressure and aid in disentangling the complex etiology of cardiovascular disease.

Supplementary Material

suppl-data
suppl-figs

Acknowledgments

FUNDING

This research was funded by a cooperative agreement that includes grants U01 HL65520, U01 HL41642, U01 HL41652, U01 HL41654, and U01 HL65521 from the National Heart, Lung, and Blood Institute. Development of SOLAR and the methods implemented in it are supported by US Public Health Service grant MH059490 from the National Institutes of Health. Dr Franceschini is supported by the American Heart Association award.

We thank the Strong Heart Family Study participants. In addition, the cooperation of the Indian Health Service hospitals and clinics, and directors of the SHS clinics, and the many collaborators and staff of the Strong Heart Study have made this project possible. The opinions expressed in this paper are those of the author(s) and do not necessarily reflect the views of the Indian Health Service.

Footnotes

This study has been presented in part at the 2006 International Genetic Epidemiology Meeting, November 16-17, 2006, St Pete’s, FL, USA.

No conflict of interest

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Associated Data

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Supplementary Materials

suppl-data
NIHMS91953-supplement-suppl-data.pdf (135.1KB, pdf)
suppl-figs
NIHMS91953-supplement-suppl-figs.pdf (853.4KB, pdf)
NIHMS91953-supplement-supplement_1.pdf (124KB, pdf)

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