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. 2007 Sep 19;8(Suppl 1):S2. doi: 10.1186/1471-2350-8-S1-S2

Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study

Ramachandran S Vasan 1,2,✉, Martin G Larson 1,3, Jayashri Aragam 4, Thomas J Wang 5, Gary F Mitchell 6, Sekar Kathiresan 5,7, Christopher Newton-Cheh 5,7, Joseph A Vita 2, Michelle J Keyes 1,3, Christopher J O'Donnell 1,8, Daniel Levy 1,8, Emelia J Benjamin 1,2
PMCID: PMC1995617  PMID: 17903301

Abstract

Background

Echocardiographic left ventricular (LV) measurements, exercise responses to standardized treadmill test (ETT) and brachial artery (BA) vascular function are heritable traits that are associated with cardiovascular disease risk. We conducted a genome-wide association study (GWAS) in the community-based Framingham Heart Study.

Methods

We estimated multivariable-adjusted residuals for quantitative echocardiography, ETT and BA function traits. Echocardiography residuals were averaged across 4 examinations and included LV mass, diastolic and systolic dimensions, wall thickness, fractional shortening, left atrial and aortic root size. ETT measures (single exam) included systolic blood pressure and heart rate responses during exercise stage 2, and at 3 minutes post-exercise. BA measures (single exam) included vessel diameter, flow-mediated dilation (FMD), and baseline and hyperemic flow responses. Generalized estimating equations (GEE), family-based association tests (FBAT) and variance-components linkage were used to relate multivariable-adjusted trait residuals to 70,987 SNPs (Human 100K GeneChip, Affymetrix) restricted to autosomal SNPs with minor allele frequency ≥0.10, genotype call rate ≥0.80, and Hardy-Weinberg equilibrium p ≥ 0.001.

Results

We summarize results from 17 traits in up to 1238 related middle-aged to elderly men and women. Results of all association and linkage analyses are web-posted at http://ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. We confirmed modest-to-strong heritabilities (estimates 0.30–0.52) for several Echo, ETT and BA function traits. Overall, p < 10-5 in either GEE or FBAT models were observed for 21 SNPs (nine for echocardiography, eleven for ETT and one for BA function). The top SNPs associated were (GEE results): LV diastolic dimension, rs1379659 (SLIT2, p = 1.17*10-7); LV systolic dimension, rs10504543 (KCNB2, p = 5.18*10-6); LV mass, rs10498091 (p = 5.68*10-6); Left atrial size, rs1935881 (FAM5C, p = 6.56*10-6); exercise heart rate, rs6847149 (NOLA1, p = 2.74*10-6); exercise systolic blood pressure, rs2553268 (WRN, p = 6.3*10-6); BA baseline flow, rs3814219 (OBFC1, 9.48*10-7), and FMD, rs4148686 (CFTR, p = 1.13*10-5). Several SNPs are reasonable biological candidates, with some being related to multiple traits suggesting pleiotropy. The peak LOD score was for LV mass (4.38; chromosome 5); the 1.5 LOD support interval included NRG2.

Conclusion

In hypothesis-generating GWAS of echocardiography, ETT and BA vascular function in a moderate-sized community-based sample, we identified several SNPs that are candidates for replication attempts and we provide a web-based GWAS resource for the research community.

Background

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality in the United States [1]. It is increasingly recognized that CVD is a life-course disease, with overt events being antedated by subclinical cardiovascular target organ damage [2,3]. Current research indicates a fundamental role of left ventricular (LV) chamber size, wall thickness (LV remodeling) and mass (LVM) in the pathogenesis of high blood pressure [4,5], and clinical CVD [6,7], including stroke [8,9] and heart failure [10-12]. On a parallel note, exercise treadmill stress testing (ETT) is used routinely to evaluate patients with chest pain suggestive of ischemic etiology and for identifying individuals at intermediate pre-test probability of CVD who are more likely to develop clinical events [13]. Likewise, endothelial dysfunction, as assessed via brachial artery (BA) flow-mediated dilation (FMD), has emerged as a fundamental component of atherosclerosis and a precursor of overt CVD [14-16]. Thus, traits obtained via echocardiography (Echo), testing for BA endothelial function and ETT can serve as intermediate phenotypes in the pathway from standard risk factor to overt CVD. Such intermediate phenotypes have been studied extensively to characterize their clinical and genetic correlates, have been reported to be heritable traits [14,17-28], and have been linked to select genetic loci in several reports [29-31].

More recently, several investigators have proposed genome-wide association studies (GWAS) as a strategy to map causal genes with modest influences on traits associated with complex diseases such as CVD [32,33]. The availability of 100K genotype data on a subset of related Framingham Heart Study participants [34] provides a unique opportunity to conduct both genome-wide association and linkage analyses to explore the genetic underpinnings of LV remodeling, endothelial function and exercise performance in a community-based sample.

Methods

The design and selection criteria of the Original Framingham Study [35] and the Offspring Study [36] have been described elsewhere. As detailed in the Overview [37], 1345 participants (1087 Offspring and 258 Original Cohort) underwent genotyping using the Affymetrix GeneChip Human Mapping 100K single nucleotide polymorphism (SNP) set [34]. Participants were eligible for the present investigation if they had available genotypes and the echocardiographic, vascular and ETT traits of interest (as defined below). The Institutional Review Board at Boston University Medical Center approved the study and all participants gave written informed consent (including for genetic research).

Measurement of phenotypes

Echocardiography

All attendees underwent routine transthoracic two-dimensionally-guided M-mode echocardiography at the second (1979–1982), fourth (1987–1990), fifth (1991–1995) and sixth (1996–1998) Offspring cohort examinations. Echocardiographic equipment for image acquisition varied across these examinations: at examination cycle 2, a Hoffrel 201 ultrasound receiver (and Aerotech transducer) was used; at examinations 4 and 5, a Hewlett Packard (model 77020AC) ultrasound machine was used; at examination 6 images were acquired using a Sonos 1000 Hewlett-Packard machine. At all four examinations, however, measurements of LV internal dimension in diastole (LVDD) and systole (LVDS), the thicknesses of the posterior wall (PW) and interventricular septum (IVS), and the diameters of the aortic root (all measured at end-diastole) and left atrium (LA) at end-systole were obtained by using a 'leading edge' technique [38], averaging measurements in 3 cardiac cycles according to the American Society of Echocardiography guidelines. We calculated LV wall thickness (LVWT) as the sum of PW and IVS measurements. LV mass was calculated by using the formula 0.8[1.04(LVDD+IVS+PW)3 - (LVDD)3] + 0.6 [39]. The reproducibility of Echo measurements was systematically assessed at the sixth examination [40].

ETT measures

At the second Offspring examination (1978–1981), all attendees underwent submaximal exercise test according to the standard Bruce protocol for up to five incremental 3-minute stages. The test was terminated (without a cool down period) when participants reached their target heart rate (85% age-predicted peak heart rate). Blood pressure measurements and electrocardiograms were recorded during exercise at the midpoint of each 3-minute exercise stage, and for each minute for up to 4 minutes into the recovery period.

BA endothelial function

As described previously [14], BA flow-mediated dilation (FMD; percent change in diameter from baseline; i.e. 100 * [hyperemic diameter at 1 minute - baseline diameter]/baseline diameter) and mean hyperemic flow velocity (cm/sec) were determined during the seventh clinical examination cycle (1998–2001). A Toshiba SSH-140A ultrasound system with a 7.5 MHz linear array transducer and commercially available software (Brachial Analyzer version 3.2.3, Medical Imaging Applications) were used. Investigators, blinded to participant clinical and genetic data, determined brachial artery diameter at baseline and 1 minute after reactive hyperemia induced by 5-minute forearm cuff occlusion. The coefficient of variation for baseline and hyperemic diameters were 0.5% and 0.7%, respectively.

Doppler flow was assessed at baseline and during reactive hyperemia using a 3.75 MHz carrier frequency and with correction for the insonation angle [41]. Mean baseline and hyperemic flow velocities were analyzed from digitized audio data using semiautomated signal averaging (Cardiovascular Engineering, Waltham, MA). Baseline and deflation flow measurements were reproducible on repeated analysis of 30 subjects with correlations of >0.98.

Genotyping methods

The Overview [37] details the genotyping performed with the Affymetrix 100K SNP GeneChip http://gmed.bu.edu/about/genotyping.html[34] and with the Marshfield STR marker set at the Mammalian Genotyping Service http://research.marshfieldclinic.org/genetics.

Statistical methods

We generated normalized sex-specific residuals adjusting for the following covariates: for the echocardiographic phenotypes, age, sex, height, weight, smoking, systolic and diastolic blood pressure, hypertension treatment; for ETT measures, age, sex, body mass index, baseline heart rate, diabetes, smoking, ratio of total to high-density lipoprotein cholesterol, and treatment for hypertension (additional adjustments for select variables is detailed in Table 1); for BA function, a set of 15 covariates previously reported [14] to be associated with endothelial function in our sample (see Table 1). Covariates were from the same exam as the phenotype measures. Next, we used residuals for the phenotypes of interest to test for potential association with 100K SNPs using additive family-based association tests (FBAT) and linear regression models with general estimating equations (GEE; additive genetic models) to account for correlation among related individuals from nuclear families, as detailed in the Overview [37]. We chose 70,987 SNPS for association analysis that met the following criteria: autosomal SNPs with genotypic call rate ≥80%, minor allele frequency ≥10%, Hardy-Weinberg equilibrium test p ≥0.001, and ≥10 informative families for FBAT. The choice of an 80% genotyping call rate threshold may appear unusually liberal. We chose this threshold to be more inclusive in terms of associations reported. Also, the algorithm for the genotype calls was the Dynamic Modeling algorithm, which is less precise than other algorithms that have been introduced more recently. The association analyses were complemented by linkage analyses that used variance components methods and a subset of 100K markers and Marshfield STRs; the selection of markers and methods for calculating identity-by-descent are also described in the Overview [37].

Table 1.

Echocardiographic, exercise testing and brachial artery function traits analyzed in participants with 100K genotype data

Trait Number of Traits* Offspring Exam cycles Adjustment† Heritability
A. Echocardiographic Traits Averaged Across 4 Examinations
LV mass (LVM) 10 2,4,5,6 - age- and sex-
- multivariable-**		 0.36
LV diastolic dimension (LVDD) 10 2,4,5,6 0.38
LV systolic dimension (LVDS) 10 2,4,5,6 0.30
LV wall thickness (LVWT) 10 2,4,5,6 0.41
LV fractional shortening (LVFS) 10 2,4,5,6 0.20
Left atrial diameter (LAD) 10 2,4,5,6 0.25
Aortic root diameter (AOR) 10 2,4,5,6 0.52
B. Exercise Treadmill Test (ETT) Traits
Stage 2 Exercise systolic blood pressure (SBP) 2 2 - age- and sex-
- multivariable-**		 0.28
Stage 2 Exercise diastolic blood pressure (DBP) 2 2 0.22
Stage 2 Exercise heart rate 2 2 0.25
Post-exercise 3 minute recovery SBP 2 2 0.20
Post-exercise 3 minute recovery DBP 2 2 0.16
Post-exercise 3 minute recovery heart rate 2 2 0.40
C. Brachial Artery (BA) Endothelial Function Traits
Baseline BA diameter 2 7 - age- and sex-
- multivariable-**		 0.25
Baseline BA flow velocity 2 7 0.32
BA flow-mediated dilation (FMD) percent 2 7 0.19
BA hyperemic flow velocity 2 7 0.06
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BA = brachial artery. LV = left ventricular. SBP = systolic blood pressure. DBP = diastolic blood pressure.

* For Echo traits, the phenotypes listed include those based on averaged values across 4 examinations. Overall, the number of Echo phenotypes includes individual traits at each exam (× 2 for two levels of adjustment in models) plus the averaged traits across 4 exams (× 2 for two levels of adjustment in models) listed above. For ETT and BA traits, the number of individual traits includes traits at single exams (× 2 for two levels of adjustment in models).

**covariates in multivariable models include:

For Echo phenotypes: age, sex, height, weight, smoking, systolic blood pressure, diastolic blood pressure, hypertension treatment.

For ETT phenotypes: age, sex, BMI, diabetes, current smoking, baseline heart rate, hypertension treatment, total/HDL cholesterol. Additional adjustments were ETT phenotype-specific: Exercise SBP was also adjusted for systolic BP at rest; exercise DBP for diastolic BP at rest; exercise heart rate for heart rate at rest; Recovery SBP for systolic BP at rest, systolic BP during second stage of exercise, and peak systolic BP during exercise; Recovery DBP for diastolic BP at rest, diastolic BP during second stage of exercise, and peak diastolic BP during exercise; and recovery heart rate for heart rate at rest, during second stage of exercise, and peak heart rate during exercise.

For BA phenotypes: age, sex, mean arterial pressure, pulse pressure, heart rate, diabetes, body mass index, fasting blood glucose, prevalent cardiovascular disease, hormone replacement therapy use, walk test before and after BA test, Total/HDL cholesterol, smoking within 6 hrs of BA test, hypertension, lipid-lowering treatment use.

We used an unfiltered approach and report the top 25 SNPs associated with echocardiographic traits, ETT measures and BA phenotypes (15, 5 and 5 SNPs, respectively; relative proportions chosen empirically because of the larger number of echocardiographic traits analyzed) according to their degree of statistical significance (lowest p values) in GEE and FBAT models separately. For echocardiographic phenotypes, we analyzed the mean of values for traits averaged across the 4 examinations, as well as traits at individual examinations separately. In order to evaluate potential pleiotropic effects, we examined SNP associations across related sets of traits and listed the top 25 SNPs with the lowest geometric mean of p values for all echocardiographic traits (averaged across the four examinations), for all ETT measures and for all BA traits (15, 5 and 5 SNPs, respectively, for the 3 groups). Because we analyzed individual echocardiographic traits at each of the four examinations, we also listed the top SNP associations based on the geometric mean of p values for these individual echocardiographic traits across the four examinations. Thus, we use the term 'pleiotropic effects' to refer to whether there were SNPs that were associated with multiple traits within the 3 subgroups. Additionally, we examined associations of SNPs in or within 200 Kb of the start or terminus of six selected genes (ACE, AGT, AGTR1, ADRB1, VEGF, NOS3) that have been previously reported to be associated with Echo, ETT and BA function phenotypes [30,42-51]. We view these analyses as exploratory because the coverage of the Affymetrix 100K GeneChip for these genes was quite limited.

Results

Table 1 lists the phenotypes analyzed from the three groups (Echo, ETT and BA endothelial function), the number of traits evaluated within each group, and the covariates included in regression models to create residuals. We observed moderate to high heritability of most of the traits evaluated (Table 1; estimates are multivariable-adjusted, for covariates noted above under methods, and listed in table footnote). Heritability estimates were 52% for aortic root dimension, 36–40% for LV mass, internal dimensions and LVWT, and 25% for LA size. Estimates for ETT measures varied from 41% for post-exercise recovery heart rate, 28% for exercise systolic blood pressure, and 16–25% for other phenotypes. For BA function, baseline flow velocity and vessel diameter were most heritable (32 and 25%, respectively) and hyperemic flow the least (6%), with intermediate values for FMD (19%).

Results of all association analyses and detailed linkage results are web-posted at http://ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007, the database of genotype and phenotype public repository (dbGaP) at the National Center for Biotechnology Information. Overall, nine SNPs yielded a p value <10-5 in either GEE or FBAT models for Echo traits. Eleven SNPs were associated with ETT traits with a p value <10-5, and one SNP yielded a p value below this threshold for BA function traits. A conservative Bonferroni correction for the number of statistical tests (0.05/1,000,000) yields an approximate threshold of genome-wide significance of 5*10-8.

Table 2A displays the 25 most significantly associated SNPs in GEE analyses (15 for echo phenotypes; 5 each for ETT and BA phenotypes; additive genetic models) sorted by p values along with the corresponding p value using FBAT. The top SNPs associated with averaged Echo traits included: rs1379659 and rs666088 (both in SLIT2) with LV diastolic dimension, rs1935881 (FAM5C) and rs10493389 (PDE4B) with LA diameter, and rs10504543 (KCNB2) with LV systolic dimension. The top SNPs associated with ETT traits included: rs6847149 (NOLA1) and rs2056387 (RYR2) with stage 2 exercise heart rate, and rs2553268 (WRN) with stage 2 exercise systolic blood pressure. The top 5 SNPs associated with BA traits included rs3814219 (OBFC1) and rs10515508 (NRG2) with BA baseline flow, and rs4148686 (CFTR) with FMD. Only one of these SNPs (SLIT2) had a p value < 10-3 in FBAT.

Table 2.

Top associations for Echo, ETT and BA function traits based on lowest p value for GEE test (2a), FBAT (2b), and Linkage (2c)*

2A. Top Associations based on lowest GEE p values
Trait SNP Chromosome Physical position GEE p-value FBAT p-value In/near Gene (within 60 kb)
I. Top 15 SNPs associated with Echocardiographic Traits (Averaged across exams)
LV diastolic dimension rs1379659 4 20296952 1.17*10-7 0.001 SLIT2
LV fractional shortening rs366676 6 88774485 2.44*10-6 0.059 SPACA1
LV diastolic dimension rs666088 4 20171404 5.10*10-6 0.008 SLIT2
LV systolic dimension rs10504543 8 73941196 5.18*10-6 0.247 KCNB2
Left atrial diameter rs1935881 1 186798043 5.56*10-6 0.003 FAM5C
LV mass rs10498091 2 221724949 5.68*10-6 0.162
Left atrial diameter rs10493389 1 66022886 6.56*10-6 0.119 PDE4B
LV diastolic dimension rs4920799 5 84642284 6.84*10-6 0.041
LV wall thickness rs10519181 15 76076030 1.04*10-5 0.027 TBC1D2B
LV diastolic dimension rs6104740 20 11284809 1.15*10-5 0.021
LV diastolic dimension rs2900208 12 11769731 1.20*10-5 0.021 ETV6
LV systolic dimension rs3766377 1 157613632 1.25*10-5 0.032 CD244
LV diastolic dimension rs10511762 9 25621130 1.29*10-5 0.002 TUSC1
LV mass rs4936770 11 122434085 1.37*10-5 0.019 HSPA8
LV diastolic dimension rs4485619 20 11251676 1.57*10-5 0.043
II. Top 5 SNPs associated with ETT Traits
Stage 2 Exercise heart rate rs6847149 4 111157701 2.74*10-6 0.014 NOLA1
Stage 2 Exercise heart rate rs2819770 1 234237045 3.53*10-6 0.010 RYR2
Post-exercise 3 minute recovery SBP rs746463 11 109501154 4.88*10-6 0.564
Stage 2 Exercise heart rate rs2056387 1 234250153 5.17*10-6 0.002 RYR2
Stage 2 Exercise SBP rs2553268 8 31055898 6.32*10-6 0.001 WRN
III. Top 5 SNPs associated with BA endothelial function Traits
Baseline BA flow velocity rs3814219 10 105637085 9.48*10-7 0.325 OBFC1
BA FMD percent rs4148686 7 116728468 1.13*10-5 0.025 CFTR
Baseline BA flow velocity rs1471639 14 33552277 1.26*10-5 0.001
Baseline BA diameter rs1045182 6 116705168 1.44*10-5 0.058 TSPYL1
Baseline BA flow velocity rs10515508 5 139355254 1.71*10-5 0.016 NRG2
2B. Top associations based on lowest FBAT p value
Trait SNP Chromosome Physical position GEE p-value FBAT p-value In/near Gene (within 60 kb)
I. Top 15 SNPs associated with Echocardiographic Traits (Averaged across exams)
LV systolic dimension rs1392284 3 114584100 0.139 6.39*10-6 WDR52
LV fractional shortening rs10515040 17 48859319 0.211 1.29*10-5
LV fractional shortening rs9312006 3 8234491 0.002 1.64*10-5
LV systolic dimension rs10504591 8 76199715 0.046 1.95*10-5
LV fractional shortening rs448458 15 60591780 0.206 2.26*10-5
LV diastolic dimension rs580859 13 68030441 0.046 2.68*10-5
LV systolic dimension rs1959290 14 86208482 2.07*10-5 6.42*10-5
Aortic root diameter rs2468680 8 140590402 0.690 6.12*10-5
LV systolic dimension rs448458 15 60591780 0.019 5.60*10-5
LV systolic dimension rs2918268 5 148586949 0.574 4.82*10-5 ABLIM3
LV mass rs965036 6 20099022 0.033 3.69*10-5
Left atrial diameter rs1701821 7 112544622 0.137 3.66*10-5
Aortic root diameter rs7544568 1 38308301 0.194 3.21*10-5
LV fractional shortening rs10504591 8 76199715 0.112 2.92*10-5
LV diastolic dimension rs1488745 3 1976802 0.141 2.72*10-5
II. Top 5 SNPs associated with ETT Traits
Post-exercise 3 minute recovery SBP rs2016718 8 96813381 0.006 2.20*10-7
Post-exercise 3 minute recovery heart rate rs1029947 7 150713400 0.013 9.20*10-7 PRKAG2
Post-exercise 3 minute recovery heart rate rs1029946 7 150713454 0.022 3.89*10-6 PRKAG2
Stage 2 Exercise heart rate rs1958055 14 33254537 0.040 8.55*10-6 NPAS3
Post-exercise 3 minute recovery SBP rs7828552 8 71862761 0.057 9.34*10-6 XRG9
III. Top 5 SNPs associated with BA endothelial function Traits
BA hyperemic flow velocity rs1859634 7 100758808 0.030 1.21*10-5 AK124120
BA FMD percent rs1106494 14 62201734 0.003 1.61*10-5 KCNH5
BA hyperemic flow velocity rs2389866 4 120872866 0.423 2.12*10-5 PDE5A
Baseline BA diameter rs774227 9 91273496 0.002 2.34*10-5 NFIL3
Baseline BA diameter rs10502887 18 43433881 0.002 2.79*10-5
2C. Magnitude and location of peak LOD scores ≥2.0 for Echo, ETT and BA function traits
Trait SNP or STR Chromosome Physical position Maximum LOD score LOD-1.5 interval LOD+1.5 interval
I. Echocardiographic Traits (Averaged across examinations)
LV mass rs10515509 5 139270238 4.37 133816612 150634674
Aortic root diameter rs10513442 3 154474088 4.22 150552924 161813142
LV wall thickness rs3813713 10 51241137 3.16 32818116 58684005
LV wall thickness rs10511550 9 10638555 3.11 10220368 15384347
LV fractional shortening AFM254ve1 3 198506417 2.78 195278503 199138789
LV wall thickness rs2438085 2 105339005 2.59 103478258 106924695
Left atrial diameter rs7327514 13 78334100 2.55 68655834 90511230
LV wall thickness rs1719 15 83149176 2.45 64238853 86738287
LV mass rs10489725 1 181172198 2.41 176249023 201595809
LV mass rs1989051 8 129363300 2.38 127811630 133926399
LV wall thickness GATA164B08 3 8560016 2.25 2183832 20875136
II. ETT Traits
Stage 2 Exercise heart rate rs190982 5 88259176 2.93 71236666 96112374
Stage 2 Exercise heart rate GATA165C03 1 60383884 2.46 43070922 67006164
Stage 2 Exercise heart rate rs7286558 22 20504737 2.43 15786453 25685518
Stage 2 Exercise heart rate rs10483844 14 71902088 2.39 53240301 74937294
III. BA Endothelial Function Traits
Baseline BA flow velocity rs1425727 8 25642697 2.14 19459783 32261074
BA FMD percent D21S11 21 19476134 2.13 10000969 27037800
Baseline BA flow velocity rs3007456 9 42925816 2.12 36878975 76736108
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*SNPs are ordered by GEE p values. dbSNP positions are from NCBI Build 35 (hg17).

Abbreviations as in Table 1. The number of informative families for FBAT analyses ranged from a minimum of 80 to a maximum of 210.

Table 2B lists the top 25 SNPs associated with phenotypes in FBAT along with corresponding p values in GEE models. Only one of these SNPs (rs1959290 associated with LV systolic dimension) had a p value < 10-3 in GEE models. The top 5 SNPs associated with ETT traits included rs1029947 and rs1029946 (both in PRKAG2) with heart rate at 3 minutes of post-exercise recovery. The top 5 SNPs associated with BA traits included rs2389866 (PDE5A).

Table 2C lists the magnitude and the location of loci with LOD scores that exceeded 2.0. The peak LOD scores were: for Echo traits, 4.38 (chromosome 5) for LV mass and 4.23 (chromosome 3) for aortic root size; for ETT traits, 2.93 (chromosome 5), 2.46 (chromosome 1), and 2.43 (chromosome 22) for heart rate during stage 2 of exercise; for BA traits, 2.14 (chromosome 8) for baseline flow velocity.

Table 3 evaluates the potential pleiotropic effect of SNPs by evaluating the geometric mean of p values for associations across averaged echo, and single-exam ETT and BA traits, and by relating them to the individual Echo traits across 4 examinations. SNPs associated with 4 genes (SLIT2, WDR72, UBE2L3 and KCNB2) were related to individual echo traits across examinations, as well as associated with the averaged Echo traits. Likewise, RYR2 was associated with a low geometric p value when related to all ETT traits across the examination.

Table 3.

Top associations ordered by geometric mean of GEE p-values across traits (3 groups) and across examinations (echo traits)

Trait SNP Chromosome Physical position GEE p-value In/near Gene (within 60 kb)
IA. Top 15 SNPs associated with Echocardiographic Traits across individual exams
LV diastolic dimension rs4920799 5 84642284 0.001
LV diastolic dimension rs1379659 4 20296952 0.001 SLIT2
Aortic root diameter rs26438 5 165221499 0.001
LV mass rs473664 15 51598287 0.0016 WDR72
Aortic root diameter rs10488825 11 81688520 0.0017
LV mass rs10498091 2 221724949 0.0017
LV diastolic dimension rs10514431 16 76498035 0.0018 KIAA1576
LV diastolic dimension rs10505599 8 133812159 0.0019 FLJ33069
LV systolic dimension rs10504543 8 73941196 0.0019 KCNB2
LV diastolic dimension rs2900208 12 11769731 0.0019 ETV6
LV mass rs861857 22 20306894 0.0022 UBE2L3
LV systolic dimension rs10501940 11 99440078 0.0023 CNTN5
LV systolic dimension rs707025 2 155014031 0.0023 GALNT13
Aortic root diameter rs1395204 18 4477509 0.0025
LV diastolic dimension rs10513272 9 116143601 0.0026 PAPPA
IB. Top 15 SNPs associated with Averaged Echocardiographic Traits (across LV traits)
Across LV phenotypes rs1379659 4 20296952 0.0007 SLIT2
rs10498091 2 221724949 0.0009
rs861857 22 20306894 0.001 UBE2L3
rs473664 15 51598287 0.0012 WDR72
rs3766377 1 157613632 0.0016 CD244
rs10504543 8 73941196 0.0017 KCNB2
rs10518462 4 126562521 0.0018
rs1959289 14 86208319 0.0026
rs667269 3 175822307 0.0027
rs1959290 14 86208482 0.0027
rs1959291 14 86208525 0.0027
rs10485104 6 165966351 0.0030 PDE10A
rs10491574 9 116701992 0.0030 ASTN2
rs707025 2 155014031 0.0032 GALNT13
rs525960 1 149310939 0.0033 LCE5A
II. Top 5 SNPs associated with ETT Traits (across traits)
Across phenotypes rs1560916 17 28973892 0.0101 ACCN1
rs1432214 2 137712255 0.0106
rs10512056 9 76835140 0.0112 LOC442425
rs2056387 1 234250153 0.0115 RYR2
rs6560812 10 2363105 0.0132
III. Top 5 SNPs associated with BA endothelial Traits (across traits)
Across phenotypes rs2912991 7 52684247 0.0029
rs10510677 3 36168070 0.007
rs10493052 1 33614778 0.007 ZNF31
rs7155941 14 41681710 0.008
rs1954627 14 41689011 0.009
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dbSNP positions are from NCBI Build 35 (hg17).

Abbreviations as in Table 1.

Table 4A–C displays results of association of Echo, ETT and BA function traits with SNPs in proximity to 6 genes (within 200 Kb of the start or terminus) chosen from the published literature; as noted earlier, the Affymetrix 100K GeneChip does not cover SNPs within these genes adequately. We observed weak associations of several SNPs in proximity to the genes of interest and Echo, ETT and BA function traits, none of which had a p value < 10-3). It is noteworthy, though, that several SNPs in proximity to ADRB1, AGT, and AGTR1 were associated weakly (p value of 0.05 to 10-3) with several Echo, ETT and BA function phenotypes.

Table 4.

Associations of traits with SNPs in or near (up to 200 kb away) 6 well-replicated genes in the published literature with a p-value < 0.05 in either FBAT or GEE.

4A. Associations of averaged echo traits
Candidate Gene FHS 100K SNP Physical Position Trait GEE p-value FBAT p-value
ACE no SNP was associated with a p value < 0.05 for any LV trait studies
ADRB1 rs10510001 115962372 LV diastolic dimension 0.004 0.0002
rs10510001 115962372 LV systolic dimension 0.001 0.001
rs10510000 115961985 LV diastolic dimension 0.052 0.001
rs10510000 115961985 LV systolic dimension 0.059 0.001
rs7902873 115884949 LV diastolic dimension 0.467 0.002
rs10509999 115917269 LV diastolic dimension 0.136 0.003
rs180940 115712401 LV wall thickness 0.003 0.072
rs180934 115720720 LV wall thickness 0.005 0.090
rs180935 115720249 LV wall thickness 0.005 0.160
rs998334 115957002 LV diastolic dimension 0.289 0.005
rs7902873 115884949 LV systolic dimension 0.249 0.006
rs10509999 115917269 LV systolic dimension 0.036 0.008
rs180935 115720249 LV mass 0.011 0.409
rs180934 115720720 LV mass 0.015 0.411
rs7902873 115884949 LV mass 0.646 0.016
rs998334 115957002 LV systolic dimension 0.110 0.018
rs10510001 115962372 LV fractional shortening 0.021 0.056
rs10510000 115961985 LV fractional shortening 0.382 0.027
rs180940 115712401 LV mass 0.029 0.303
rs10510001 115962372 LV mass 0.393 0.040
rs6585258 115739125 LV mass 0.047 0.481
rs10510000 115961985 LV mass 0.335 0.048
AGT rs10495300 227188497 Left atrial diameter 0.095 0.001
rs2478518 227174605 LV fractional shortening 0.028 0.238
rs1202585 227309949 LV wall thickness 0.030 0.028
rs2180478 227295655 LV diastolic dimension 0.800 0.030
rs758216 227005969 Aortic root diameter 0.031 0.341
rs1202524 227257907 LV diastolic dimension 0.323 0.037
rs2478518 227174605 LV systolic dimension 0.038 0.467
rs731824 227329806 Left atrial diameter 0.043 0.399
rs1202585 227309949 LV mass 0.044 0.117
AGTR1 rs1059502 150045008 Aortic root diameter 0.598 0.005
rs1357424 149801524 LV fractional shortening 0.075 0.006
rs1357424 149801524 LV systolic dimension 0.241 0.030
rs1059502 150045008 LV diastolic dimension 0.142 0.030
rs2331406 150048180 Aortic root diameter 0.589 0.032
rs10513333 149786557 Left atrial diameter 0.437 0.039
VEGF rs729761 43912549 LV fractional shortening 0.012 0.200
rs729761 43912549 Left atrial diameter 0.573 0.018
rs729761 43912549 LV systolic dimension 0.018 0.249
rs2396083 43912786 LV fractional shortening 0.026 0.250
rs2396083 43912786 Left atrial diameter 0.489 0.031
rs2396083 43912786 LV systolic dimension 0.040 0.224
NOS3 No SNP was associated with a p value < 0.05 for any LV trait studied
4B. Associations of ETT traits
Candidate Gene FHS 100K SNP Physical Position Trait GEE p-value FBAT p-value
ACE rs10491167 58780430 Stage 2 Exercise systolic blood pressure 0.046 0.137
rs10491168 58795195 Stage 2 Exercise systolic blood pressure 0.030 0.124
rs721575 59136652 Post-exercise 3 minute recovery heart rate 0.024 0.025
ADRB1 rs2419857 115607366 Stage 2 Exercise heart rate 0.852 0.030
rs4345919 115651155 Post-exercise 3 minute recovery heart rate 0.048 0.740
AGT rs1752189 227000046 Post-exercise 3 minute recovery SBP 0.145 0.021
rs758216 227005969 Post-exercise 3 minute recovery SBP 0.184 0.050
rs10495298 227120049 Stage 2 Exercise systolic blood pressure 0.288 0.050
rs2478516 227175387 Stage 2 Exercise systolic blood pressure 0.021 0.486
AGTR1 rs275678 149851039 Post-exercise 3 minute recovery SBP 0.216 0.049
rs427832 149949061 Post-exercise 3 minute recovery SBP 0.014 0.171
rs1949350 150089423 Stage 2 Exercise heart rate 0.010 0.335
VEGF rs729761 43912549 Stage 2 Exercise heart rate 0.041 0.035
NOS3 rs2303928 150176978 Stage 2 Exercise heart rate 0.012 0.056
4C. Associations of BA function traits
Candidate Gene FHS 100K SNP Physical Position Trait GEE p-value FBAT p-value
ACE no SNP was associated with a p value < 0.05
ADRB1 rs180940 115712401 BA hyperemic flow velocity 0.581 0.015
rs180935 115720249 BA hyperemic flow velocity 0.953 0.030
rs180934 115720720 BA hyperemic flow velocity 0.662 0.029
rs10509999 115917269 BA hyperemic flow velocity 0.001 0.137
rs10510000 115961985 BA hyperemic flow velocity 0.007 0.014
rs10510001 115962372 BA hyperemic flow velocity 0.017 0.239
AGT rs758216 227005969 Baseline BA flow velocity 0.049 0.488
rs1202585 227309949 Baseline BA diameter 0.002 0.080
rs1202585 227309949 BA hyperemic flow velocity 0.049 0.080
AGTR1 rs1492090 149884751 BA hyperemic flow velocity 0.026 0.293
rs427832 149949061 Baseline BA flow velocity 0.509 0.043
VEGF rs833048 43762514 BA hyperemic flow velocity 0.860 0.036
rs10498756 44046909 BA hyperemic flow velocity 0.312 0.008
NOS3 rs741067 149938103 Baseline BA diameter 0.043 0.024
rs1006581 149949571 Baseline BA diameter 0.009 0.097
rs2215564 150001612 BA hyperemic flow velocity 0.028 0.104
Open in a new tab

dbSNP positions are from NCBI Build 35 (hg17).

Discussion

Principal findings

We report results of GWAS of Echo, ETT and BA function traits in a moderate-size community-based sample using several complementary analytical approaches. Our principal findings are five-fold. First, we observed modest to strong evidence of heritability for several Echo, ETT and BA function traits, underscoring the contribution of additive genetic effects to interindividual variation in these traits. Our heritability findings confirm prior reports for some of the traits [18,20,22,23,27,28,52], including from our group [14,24]. Second, notwithstanding the modest-to-high heritability, none of the SNP-trait associations we observed achieved genome-wide significance (conservative Bonferroni correction p of 5*10-8). Therefore, any associations presented should be viewed as hypothesis-generating, with need for replication in additional samples. Third, our investigation highlights some of the challenges inherent in the interpretation of GWAS results. We did not observe any overlap between the top SNPs noted in GEE-based versus FBAT-based analyses, in part due to the inherent differences in the two analytical methods (see Overview for details [37]). Fourth, notwithstanding the lack of genome-wide statistical significance, our data do suggest several interesting biological candidates among the SNPs most strongly associated with different traits in the various analytical approaches (see discussion below). Fifth, we were quite limited in our ability to replicate findings for genetic variants previously associated with the traits that we investigated because specific coverage of such genetic variation in these candidates was limited in the Affymetrix 100K GeneChip. Therefore, the lack of replication of SNPs in proximity to 6 genes previously reported to be associated with Echo, ETT and BA traits should be interpreted with great caution. It is interesting that several weak associations (p between 0.05 and 10-3) were observed between traits in the three groups and SNPs in proximity to selected candidate genes evaluated (ADRB1, AGT and AGTR1).

Potential biological candidates among observed associations

In our GWAS of Echo traits, a SNP in SLIT2 was associated with Echo LV diastolic dimension in several analyses. SLIT2 is an evolutionarily highly conserved gene that encodes a putative secreted protein, which contains conserved protein-protein interaction domains including leucine-rich repeats and epidermal growth factor-like motifs [53]. The gene has multiple effects but has been recently identified to have a novel role in vascular function by contributing to migratory mechanisms in vascular smooth muscle cells [54]. Likewise, the associations of LV mass with HSPA8, and of LA size with PDE4B are consistent with the key role of heat shock protein expression [55] and T-cell mediated immune responses [56], respectively, in myocardial hypertrophic responses to insults or hemodynamic overload.

Analyses of ETT traits provided some interesting results. The association of a SNP in RYR2 with exercise heart rate responses in multiple analyses is quite consistent with the fundamental role of the ryanodine receptor on the sarcoplasmic reticulum in calcium trafficking during cardiac muscle excitation-contraction coupling [57]. Furthermore, RYR2 has been implicated in exercise-induced polymorphic ventricular tachyarrhythmias [58]. Using FBAT, SNPs in PRKAG2 were associated with heart rate during the recovery period post-exercise. Mutations in PRKAG2, an enzyme that modulates glucose uptake and glycolysis [59], are associated with glycogen-filled vacuoles in cardiomyocytes. The phenotypic manifestations include cardiac hypertrophy, ventricular pre-excitation and conduction system disturbances, encompassed together in the Wolff-Parkinson-White syndrome [60].

Genetic linkage analyses of ETT traits identified peaks on chromosomes 5 and 22 for exercise heart rate. The 1.5 LOD support intervals for these peaks included MEF2C and MAPK1, respectively. MEF2C is a critical regulator of cardiac morphogenesis [61]. Additionally, overexpression of MEF2C in experimental studies is associated with disturbances in extracellular matrix remodeling, ion handling, and metabolism of cardiomyocytes [62]. The peak on MAPK1 is of interest because a recent investigations highlighted the role of MAPK signaling in mediating the responses of skeletal muscles to exercise training [63].

A SNP in NRG2 was associated with BA flow velocity at rest, and also was in proximity to the top LOD peak for LV mass, raising the possibility of pleiotropic effects of this gene on ventricular and vascular remodeling and function. NRG2, which encodes neuregulin-2, is a member of the epidermal growth factor (EGF) family and binds to ErbB receptors. ErbB signaling has been implicated in angiogenesis and endothelial cell proliferation [64]. Of interest, a SNP in CFTR was associated with FMD. It is noteworthy that CFTR is expressed in vascular smooth muscle cells and activation of CFTR chloride channels regulates contraction and relaxation of smooth muscle cells; disruption of the CFTR gene prevents cAMP-dependent vasorelaxation in experimental studies [65]. CFTR is also expressed in endothelial cells where it functions as a cyclic nucleotide-regulated chloride channel [66]. Of interest, a SNP in PDE5A was associated with BA hyperemic flow velocity in FBAT analyses. Phosphodiesterase 5 (PDE5) hydrolyzes cyclic guanosine monophosphate (cGMP) and cyclic adenosine monophosphate (cAMP), is widely expressed in the vasculature, and is best known as the target of sildenafil, a drug used to treat erectile dysfunction [67]. PDE5 degrades cGMP in smooth muscle cells so as to maintain the contracted state of blood vessels [67]. PDE5A may also play a critical role in the growth promoting effects of Angiotensin II on vascular smooth muscle cells [68].

Strengths and limitations

The moderate-sized community-based sample, availability of longitudinal Echo measurements, routine ascertainment of standardized and reproducibly-measured traits, and evaluation of multiple complementary analytical methods including assessment of potential pleiotropic genetic effects strengthen our investigation. By web-posting unfiltered aggregate data we provide a resource for the scientific community to conduct in silico replication. Nonetheless, several limitations must be emphasized. As noted previously, the lack of genome-wide significance for any association observed given the extent of multiple statistical testing does not exclude a potential role of genetic influences on the traits studied. We had limited statistical power to detect modest genetic effects, given the sample size and the extent of multiple testing. As detailed in the Overview paper [37], for a conservative alpha level such as 10-8, we have more than 90% power to detect an association with a SNP explaining 4% or more total phenotypic variation when 80% or more individuals are phenotyped. We also had limited ability to replicate previously reported findings, in view of the partial coverage of genetic variation in select candidates with the Affymetrix 100K gene chip. Additionally, genetic variants may influence phenotypes in a context-specific manner [69], being modulated by environmental influences. For instance, the associations of ACE and AGTR2 with LV mass were reported to vary according to dietary salt intake in one investigation [48]. We did not undertake an investigation of gene-environmental interactions in the present study. Likewise, some of the moderately strong associations may represent false-positive results, notwithstanding the evidence suggesting that some of the associated SNPs may be reasonable biological candidates. We averaged echocardiographic traits across multiple examinations, with a view to characterizing the phenotype better over a period of time using several observations. Such a strategy could limit regression dilution bias, if the examinations are repeated over a short period of time. In our study, however, these examinations spanned a time period of twenty years, and the examinations used different echocardiographic equipment that may introduce misclassification. Further, such averaging assumes that similar sets of genes and environmental factors influence traits over a wide age range. Such an assumption may not be true, i.e., age-dependent gene effects may be masked by averaging of observations across ages in participants. Lastly, our sample was white and of European descent. The generalizability of our findings to other ethnicities is unknown.

Conclusion

In hypothesis-generating GWAS of Echo, ETT response and BA vascular function in a moderate size community-based sample, we identified several SNPs that are potential candidates for replication. Overall, our investigation provides a scientific framework for analyzing and interpreting GWAS of phenotypes fundamental to our understanding of cardiac and vascular remodeling and hemodynamic responses to exercise testing. We expect the Framingham 100K SNP data to serve as a valuable scientific resource by virtue of the web-posting of unfiltered aggregate data

Abbreviations

BA = brachial artery. CVD = cardiovascular disease. ETT = exercise treadmill test. FBAT = family-based association test. FHS = Framingham Heart Study. FMD = flow-mediated dilation. GEE = generalized estimating equations. GWAS = genome-wide association. IVS = interventricular septum. LA = left atrium. LOD = logarithm of odds. LV = left ventricular. LVM = LV mass. LVWT = LV wall thickness. LVDD = LV diastolic diameter. LVSD = LV systolic diameter. PW = posterior wall. SNP= single nucleotide polymorphism.

Competing interests

None of the authors have a competing interest relevant to the subject of this manuscript. GFM is the owner of Cardiovascular Engineering, Inc, a company that designs and manufactures devices that measure vascular stiffness. The company uses these devices in clinical trials that evaluate the effects of diseases and interventions on vascular stiffness.

Authors' contributions

RSV conceived the study design, planned the analyses, drafted and critically revised the manuscript. MGL planned the study, assisted in securing funding for BA function, planned and conducted the analyses, and critically revised the manuscript. JA critically revised the manuscript. TJW contributed to the analysis and interpretation of data, revising of the manuscript for important intellectual content. GFM assisted in obtaining funding for BA function measures, contributed to data acquisition, and critically revised the manuscript. SK and CNC participated in the study design, interpretation of data, and reviewed the manuscript. JAV assisted in securing funding for BA function measures and revising the manuscript. MJK contributed to collecting the BA function data base and reviewing the manuscript. COD and DL participated in the study conception and design, interpretation of data and reviewed the manuscript. EJB provided critical input designing the study, securing funding for BA function and echo measures, acquiring measurements, planning the analyses and critically revising the manuscript.

Contributor Information

Ramachandran S Vasan, Email: vasan@bu.edu.

Martin G Larson, Email: mlarson@bu.edu.

Jayashri Aragam, Email: Jayashri.Aragam@va.gov.

Thomas J Wang, Email: tjwang@partners.org.

Gary F Mitchell, Email: GaryFMitchell@mindspring.com.

Sekar Kathiresan, Email: skathiresan1@partners.org.

Christopher Newton-Cheh, Email: cnewtoncheh@partners.org.

Joseph A Vita, Email: jvita@bu.edu.

Michelle J Keyes, Email: mjkeyes@bu.edu.

Christopher J O'Donnell, Email: codonnell@nih.gov.

Daniel Levy, Email: levyd@nih.gov.

Emelia J Benjamin, Email: emelia@bu.edu.

Acknowledgements

This work was supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (contract No. N01-HC-25195), the Boston University Linux Cluster for Genetic Analysis (LinGA) funded by the NIH NCRR (National Center for Research Resources) Shared Instrumentation grant 1S10RR163736-01A1, and NIH grants K23-HL-074077 (TJW), K23-HL080025 (Dr. Newton-Cheh), 6R01-NS 17950; 1R01 HL60040 (EJB); RO1 HL70100 (EJB), HL080124 (RSV) and K24-HL04334 (RSV).

This article has been published as part of BMC Medical Genetics Volume 8 Supplement 1, 2007: The Framingham Heart Study 100,000 single nucleotide polymorphisms resource. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2350/8?issue=S1.

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