Published online before print November 14, 2005, doi: 10.1161/01.HYP.0000191706.41980.29
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(Hypertension. 2005;46:1286.)
© 2005 American Heart Association, Inc.
Original Articles |
Genome-Wide Linkage Analysis for Loci Affecting Pulse Pressure
The Family Blood Pressure Program
From the Division of Epidemiology and Community Health (S.J.B., A.I.L., M.B.M., D.K.A.), University of Minnesota, Minneapolis; University of Michigan Hospitals (A.W.), Ann Arbor; Loyola University Medical Center (R.C.), Maywood, Ill; UAB Division of Preventive Medicine (A.O.), Birmingham, Ala; Cedars-Sinai Medical Center (Y.-D.I.C., M.P.), Los Angles, Calif; Division of Nephrology and Hypertension (S.T.T.), Mayo Clinic, Rochester, Minn; Institute of Molecular Medicine (M.F.), Houston, Tex; Division of Biostatistics (M.P.), Washington University School of Medicine, St. Louis, Mo; and Department of Epidemiology (D.K.A.), University of Alabama, Birmingham.
Correspondence to Donna K. Arnett, PhD, MSPH, Professor and Chair, Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Ryals Public Health Building 220E, 1530 Third Ave S, Birmingham, AL 35294-0022. E-mail Arnett{at}ms.soph.uab.edu
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Abstract |
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Pulse pressure, the difference between systolic and diastolic blood pressure, is an independent risk factor for cardiovascular disease. Increased pulse pressure reflects reduced compliance of arteries and is a marker of atherosclerosis. To locate genes that affect pulse pressure, a genome-wide linkage scan for quantitative trait loci influencing pulse pressure was performed using variance components methods as implemented in sequential oligogenic linkage analysis routines. The analysis sample included 10 798 participants in 3320 families who were recruited as part of the Family Blood Pressure Program and were phenotyped with an oscillometric blood pressure measurement device using a consistent protocol across centers. Pulse pressure was adjusted for the effects of sex, age, age2, age-by-sex interaction, age2-by-sex interaction, body mass index, and field center to remove sources of variation other than the genetic effects related to pulse pressure. Significant linkage was observed on chromosome 18 (logarithm of odds [LOD]=3.2) in a combined racial sample, chromosome 20 (LOD=4.4), and 17 (LOD=3.6) in Hispanics, chromosome 21 (LOD=4.3) in whites, chromosome 19 (LOD=3.1) in a combined sample of blacks and whites, and chromosome 7 (logarithm of odds [LOD]=3.1) in blacks from the GenNet Network. Our genome scan shows significant evidence for linkage for pulse pressure in multiple areas of the genome, supporting previous published linkage studies. The identification of these loci for pulse pressure and the apparent congruence with other blood pressure phenotypes provide increased support that these regions contain genes influencing blood pressure phenotypes.
Key Words: blood pressure • genetics • hypertension, genetics
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Introduction |
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Pulse pressure, the difference between systolic and diastolic blood pressure, is an independent risk factor for cardiovascular disease and in many studies shows a stronger association with cardiovascular disease than either systolic or diastolic blood pressure, although inconsistent results have been reported.1–4 Increased pulse pressure reflects reduced compliance of the arteries and is a marker of atherosclerosis.
Pulse pressure is influenced by cardiac and vascular factors. The cardiac ventricular ejection generates a primary pressure wave of which shape is influenced by the heart rate. The initial wave is propagated as the pulse wave velocity. The geometry of the vascular system contributes to pulse pressure such that any point of branching of the arterial tree generates a reflective wave that moves backward toward the ascending aorta. Among those with stiffer central arteries, such as older or hypertensive individuals, pulse wave velocity is increased significantly. In these cases, reflected waves will increase systolic blood pressure and reduce aortic pressure during diastole, leading to an increased pulse pressure.5
Pulse pressure is influenced by genetic factors.6–9 Snieder et al reported pulse pressure heritabilities of 0.54 for black and European American twins.10 Family studies using extended pedigrees report estimates of pulse pressure heritabilities of 0.21 and 0.25 in Mexican American and white families.6,7 The difference in the heritability estimates between these 2 kinds of studies is likely attributable to the considerable age dependency of pulse pressure. In twin studies, the impact of age is negligible because of the inherent age matching as well as matching for a host of other confounding variables; family studies rely on statistical adjustment to account for differences in age and other potential confounders. What remains clear is that pulse pressure has a genetic component, and therefore research to elucidate genes affecting pulse pressure is warranted.
In an attempt to locate genes that affect pulse pressure, we conducted a genome-wide linkage scan for quantitative trait loci (QTL) influencing pulse pressure in the Family Blood Pressure Program (FBPP), which includes black, Asian, Hispanic, and white families. Because pulse pressure and systolic blood pressure are highly correlated, we also compare our linkage findings for pulse pressure with those for systolic blood pressure. The identification of genes contributing to variation in pulse pressure would allow for a better understanding of the unique pulse pressure QTLs and may point to etiologic pathways contributing to its variation.
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Methods |
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Subjects
This study includes participants from the FBPP. The FBPP was established in 1995 to investigate the genetics of hypertension in black, Hispanic (Mexican American), Asian, and non-Hispanic white populations. There are 4 separate networks in FBPP that all ascertained families having individuals with elevated blood pressure or a genetic predisposition to hypertension: GenNet, Genetic Epidemiology Network of Arteriopathy (GENOA), Hypertension Genetics Epidemiology Network (HyperGEN), and Stanford Asian Pacific Program in Hypertension and Insulin Resistance (SAPPHIRe). These networks have been described previously.11
Phenotyping
Eligibility criteria for this analysis required that participants were in a family of 
2 and had data for blood pressure, age, and body mass index (BMI). Systolic and diastolic blood pressures were measured using an automated oscillometric blood pressure measurement device with a consistent protocol across networks. Blood pressure was measured 3x on each participant by trained and certified technicians and then averaged for use in this analysis.11 Pulse pressure was calculated by taking the difference between the average systolic and diastolic blood pressure. Height was measured while participants were standing without shoes, with heels together, against a vertically mounted ruler, and weight was measured on a balance. BMI was calculated as weight (kg)/height (m2). All individuals who participated in FBPP gave informed consent; the institutional review board at each clinic site approved all protocols, and a certificate of confidentiality was obtained from the federal government for this study.
Genotyping
Genomic DNA was isolated from whole blood using standard procedures. Genotypes for 391 markers throughout the genome were typed by the Mammalian Genotyping Service (MGS) in Marshfield, Wis. Details about genotyping procedures can be found on the MGS website. Affected sibpair exclusion mapping (ASPEX) and graphical representation of relationships were used to verify family relationships, and MapMaker/SIBS and PedCheck were used to remove Mendelian errors within families.
Statistical Analysis
Heritability and covariate contributions to the trait variance were estimated, and a genome scan was performed using the variance components approach as implemented in sequential oligogenic linkage analysis routines (SOLAR).12 Variance component methods are predicated on the expected genetic covariances between relatives as a function of the identity-by-descent (IBD) relationships at a given locus.13 The variance component model tests the null hypothesis that there is no linkage (ie, the additive genetic variance attributable to the QTL is 0) by comparing the likelihood of the restricted (null) model to a model in which the variance of the QTL is estimated.14
Using allele frequencies specific to the founders in each racial group, we computed multipoint IBD matrices using Merlin (small families) and Loki (large families).15,16 These IBD matrices were converted into SOLAR format using the program MER2SOL.17 Because we accounted for differences in founder allele frequencies between the racial groups, our SOLAR analyses are valid for any race group or for any combination of race groups.
Pulse pressure was logarithmically transformed to normalize the distribution. Pulse pressure was adjusted for the effects of sex, age, age2, age-by-sex and age2-by-sex interactions, BMI, and field center. Other predictors of pulse pressure were evaluated and found to have little if any contribution to the trait variation; these included heart rate, alcohol use, tobacco use, estrogen use, waist-to-hip ratio, and diabetes status. Antihypertensive medication was used by 45% of the total study population. Reports about the effect of antihypertensive medications on pulse pressure are inconsistent in magnitude and direction.18–21 Therefore, antihypertensive medication status was not considered in this analysis.
We assessed linkage in race-specific data sets given the allele frequency differences in the 4 racial groups. Then we assessed linkage on a combined data set that included all racial groups to investigate genetic influences shared between the groups. Given consistent patterns of linkage observed in whites and blacks, these 2 racial groups were combined for linkage analysis. Furthermore, we assessed the impact of different ascertainment strategies used by each of the 4 FBPP networks by conducting race-network specific linkage scans for blacks and whites (Asians and Hispanics were only recruited in a single network).
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Results |
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Phenotype and genetic marker data were available on 10 798 subjects in 3320 families, with family sizes ranging from 2 to 23 (see supplemental Table I, available online at http:// hypertensionaha.org). The study population includes 37% black, 34% white, 15% Hispanic, and 14% Asian participants. Subjects ranged in age from 13 to 95 years, with a mean age of 52 years, and included &60% women. Baseline descriptive information for participants is summarized in Table 1.
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Pulse pressure heritability was estimated at 0.29 for the combined sample after accounting for covariate effects, and heritability did not vary considerably by race (Table 2). Pulse pressure was highly correlated with systolic blood pressure (r=0.85; P<0.001) and weakly correlated with diastolic blood pressure (r=0.23; P<0.001) for a sample that contained 1 randomly selected individual from each family. Multipoint logarithm of odds (LOD) scores for pulse pressure and systolic and diastolic blood pressure are plotted by map position for each chromosome in Figure 1 for all racial groups combined. We observed similar patterns of linkage signals for pulse pressure and systolic blood pressure throughout the genome in all data sets investigated (only combined data shown). Considering that antihypertension treatment lowers systolic blood pressure values considerably and treatment effects on pulse pressure are unpredictable in magnitude and direction, only pulse pressure linkage results are reported. Race-specific LOD scores for pulse pressure by chromosome are plotted by map position in Figure 2. Multipoint genome-wide adjusted LOD scores for pulse pressure are reported in Table 3 for all peaks 2.0.
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The most impressive results for pulse pressure were significant linkage on chromosome 18 at 71 cM (LOD=3.2) for all racial groups combined (Figure 3), chromosome 19 at 0 cM (LOD=3.1) for whites and blacks combined (supplemental Figure I), chromosome 17 at 89 cM (LOD=3.6) and chromosome 20 at 62 cM (LOD=4.4) for Hispanics, and finally, chromosome 21 at 18 cM (LOD=4.3) for whites. Also interesting was the significant linkage finding on chromosome 7 at 75 cM (LOD=3.1) for blacks in the GenNet Network and suggestive linkage on chromosome 1 at 212 (LOD=2.7) for blacks in the HyperGEN Network.
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Discussion |
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Our genome-wide scan conducted in a large sample of participants in the FBBP revealed similar patterns of linkage signals for pulse pressure and systolic blood pressure throughout the genome, indicating a common genetic etiology. The magnitudes of the LOD score peaks were consistently higher for pulse pressure than systolic blood pressure. This difference in magnitude was anticipated because of the confounding effects of antihypertensive treatment that has a large influence on systolic blood pressure but not pulse pressure.21 Antihypertensive medication use was pervasive in the study population, with 45% of the subjects on drug therapy, reducing the expected variance for systolic blood pressure and consequently attenuating the linkage signals.
Three linkage analyses for pulse pressure have been reported in the literature. Camp et al performed a genome wide analysis for pulse pressure on 26 Utah pedigrees and found suggestive linkage in 2 regions: chromosome 8 at 54 cM (LOD=2.89) and chromosome 12 at 109 cM (LOD=2.59).7 Our study was unable to replicate either of these findings. However, we found corroborating evidence for linkage signals reported by Atwood et al and DeStefano et al on chromosomes 21, 7, 5, and 18, although peak locations differed. Atwood et al performed a pulse pressure linkage analysis on 46 Mexican American families in the San Antonio Heart Study and found suggestive linkage on chromosomes 21 at 37 cM (LOD=2.78), 7 at 114 cM (LOD=2.04), 8 at 149 cM (LOD=1.98), and 18 at 116 cM (LOD=1.95).6 DeStefano et al performed a genome-wide analysis for pulse pressure on 2492 participants of the Framingham Heart Study and found several areas with suggestive evidence of linkage: chromosomes 15 at 122 cM (LOD=2.94), 7 at 122 cM (LOD=2.42), 5 at 122 cM (LOD=2.03), and 10 at 122 cM (LOD=1.83).22
Although there was modest overlap in linkage peaks with published genome scans for pulse pressure, we found corroboration of linkage peaks from studies using related blood pressure phenotypes such as systolic blood pressure, hypertension, early age–onset hypertension, systolic blood pressure longitudinal trends, and systolic blood pressure postural changes. This suggests either a common genetic substructure for blood pressure phenotypes or intrinsic correlation redundancy in the measurement across phenotypes (ie, various ways to measure the same underlying condition). Table 4
summarizes our findings (in bold) and linkage evidence reported previously in the same region. Of the 15 linkage peaks with a LOD 2 found in our study, 10 have peaks on or within 10 cM of previously reported loci for blood pressure phenotypes. Many of the previous linkage studies for hypertension-related phenotypes were conducted in 1 of the 4 FBPP networks and represent linkage for a subset of the population used in this study, although not all linkage peaks observed in this study replicated previous within-network results. Some of the discrepancies can likely be attributed to the differences in sample sizes between studies and the fact that previous scans may have used incomplete marker data.
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FBPP is a unique collaboration that provides a wealth of information on numerous pedigrees from various ethnic groups. The size and the diversity of the data set available for analysis are the biggest strengths of this study. Calculation of IBD probabilities is more accurate when using unique founder allele frequencies for each racial group, and large sample sizes within racial groups provide better estimates of these frequencies. Finally, standard protocols used in blood pressure measurement across the 4 networks assure comparable measurements for all FBPP subjects.
The major weakness of this study is that linkage analysis is an exploratory methodology that is sensitive to complexities in population structure, etiologic heterogeneity, and complex gene–gene and gene–environment interactions. Race-specific data sets were used to account for some of this heterogeneity, but residual heterogeneity is likely to persist. Each network used a different ascertainment scheme, and population differences within racial groups are likely. The FBPP cohort is culturally and geographically dispersed and gene–environment interactions may differ across racial groups and subgroups within each study population. The degree to which these factors impact the linkage results is unknown. Finally, the degree to which antihypertension medication use affected pulse pressure values and consequently linkage results is unknown.
Perspectives
Our genome scan shows significant evidence for linkage for pulse pressure in multiple areas of the genome, supporting previously published linkage studies. The identification of these loci for pulse pressure and the apparent congruence with other blood pressure phenotypes provide increased support that these regions contain genes influencing blood pressure phenotypes. Therefore, further characterization of these regions and identification of candidate genes is needed to elucidate the specific genetic components affecting pulse pressure. Given the limited resources, replication of linkage evidence in this large population provides valuable information for prioritizing genetic locations for further study. Ultimately, the goal is to use results from linkage studies to identify the functional mutations associated with blood pressure variation that may be clinically important in the diagnosis and treatment of hypertension.
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Acknowledgments |
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We are grateful for resources from the University of Minnesota Supercomputing Institute, the National Institutes of Health training grant in cardiovascular disease genetic epidemiology (No. 5 T32 HL007972), and the National Heart, Lung, and Blood Institute Family Blood Pressure Program, including networks GenNet (U01 HL54512, U01 HL54508, U01 HL54485, U01 HL54466, and U01 HL64777), GENOA (U01 HL54481, U01 HL54504, U01 HL54463, U01 HL54526, U01 HL54457, and U01 HL54464), HyperGEN (U01 HL54471, U01 HL54472, U01 HL54495, U01 HL54497, U01 HL54509, U01 HL54496, and U01 HL54473), and SAPPHIRe (U01 HL54527, U01 HL54498), http://www.biostat.wustl.edu/flopp/Acknowledgements.html.
Received June 1, 2005; first decision July 1, 2005; accepted October 5, 2005.
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