BMC Medical Genetics

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Genome-wide association study of electrocardiographic and heart
rate variability traits: the Framingham Heart Study
Christopher Newton-Cheh*1,2,3,6, Chao-Yu Guo1,4,6, Thomas J Wang1,3,6,
Christopher J O'Donnell1,3,5,6, Daniel Levy1,5,6 and Martin G Larson1,4,6
Address: 1The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA, 2Program in Medical and Population
Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA, 3Cardiology Division,
Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA, 4Departments of Mathematics and Statistics, Boston University,
Boston, MA, USA, 5National Heart, Lung and Blood Institute, Bethesda, MD, USA and 6On behalf of the Framingham Heart Study 100K
Cardiovascular Disease Working Group
Email: Christopher Newton-Cheh* - cnewtoncheh@partners.org; Chao-Yu Guo - chaoyu@bu.edu; Thomas J Wang - tjwang@partners.org;
Christopher J O'Donnell - odonnellc@nhlbi.nih.gov; Daniel Levy - levyd@nih.gov; Martin G Larson - mlarson@bu.edu* Corresponding author

Published: 19 September 2007
<supplement>
S Vasan</editor><title><note>Research</note><p>The Framingham <url>http://www.biomedcentral.com/content/pdf/1471-2350-8-S1-info.pdf</url>
Heart Study 100,000 single nucleotide polymorphisms resource</p> </title> <editor>Emelia</supplement>
J Benjamin, Caroline S Fox, Daniel J Gottlieb, Cashell E Jaquish, Daniel Levy, James B Meigs, Christopher Newton-Cheh, Christopher J O'Donnell and Ramachandran

BMC Medical Genetics 2007, 8(Suppl 1):S7

doi:10.1186/1471-2350-8-S1-S7

This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S7© 2007 Newton-Cheh et al; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract
Background: Heritable electrocardiographic (ECG) and heart rate variability (HRV) measures, reflecting pacemaking,conduction, repolarization and autonomic function in the heart have been associated with risks for cardiac arrhythmias.
Whereas several rare monogenic conditions with extreme phenotypes have been noted, few common genetic factors
contributing to interindividual variability in ECG and HRV measures have been identified. We report the results of acommunity-based genomewide association study of six ECG and HRV intermediate traits.
Methods: Genotyping using Affymetrix 100K GeneChip was conducted on 1345 related Framingham Heart Study
Original and Offspring cohort participants. We analyzed 1175 Original and Offspring participants with ECG data (meanage 52 years, 52% women) and 548 Offspring participants with HRV data (mean age 48 years, 51% women), in relation
to 70,987 SNPs with minor allele frequency ≥ 0.10, call rate ≥ 80%, Hardy-Weinberg p-value ≥ 0.001. We usedgeneralized estimating equations to test association of SNP alleles with multivariable-adjusted residuals for QT, RR, andPR intervals, the ratio of low frequency to high frequency power (LF/HFP), total power (TP) and the standard deviationof normal RR intervals (SDNN).
Results: Associations at p < 10-3 were found for 117 (QT), 105 (RR), 111 (PR), 102 (LF/HF), 121 (TP), and 102 (SDNN)
SNPs. Several common variants in NOS1AP (4 SNPs with p-values < 10-3; lowest p-value, rs6683968, p = 1 × 10-4) wereassociated with adjusted QT residuals, consistent with our previously reported finding for NOS1AP in an unrelated sampleof FHS Offspring and other cohorts. All results are publicly available at NCBI's dbGaP at http://www.ncbi.nlm.nih.gov/
projects/gap/cgi-bin/study.cgi?id=phs000007.
Conclusion: In the community-based Framingham Heart Study none of the ECG and HRV results individually attained
genomewide significance. However, the presence of bona fide QT-associated SNPs among the top 117 results for QT
duration supports the importance of efforts to validate top results from the reported scans. Finding genetic variants
associated with ECG and HRV quantitative traits may identify novel genes and pathways implicated in arrhythmogenesis
and allow for improved recognition of individuals at high risk for arrhythmias in the general population.

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Background
Quantitative non-invasive measures of cardiac electricalactivity recorded in electrocardiographic (ECG) and heart
rate variability (HRV) studies are widely available in community-based samples and have been found to be predictive of cardiovascular events including sudden cardiac
death [1-7]. Aggregation of these measures within families
suggests a heritable component.
Prior studies, including our own, have reported the heritability of common indices of myocardial repolarizationand HRV measures. The heritability of electrocardiographic QT interval duration, a measure of myocardial
repolarization, has been reported to be approximately
35%, indicating that 35% of the variability in adjusted QTinterval duration is attributable to heritable factors [8-11].
Electrocardiographic RR interval, or its inverse heart rate,
has been observed to have a heritability ranging from 32–
40% in family studies [12,13] and 54–77% in twin studies[11,14,15]. Electrocardiographic PR interval has reported
heritability estimated at 34% [15]. We have previously
reported the heritability of HRV measures including theratio of low frequency to high frequency power, total
power and the standard deviation of normal RR intervals[16].
The heritability of ECG and HRV traits suggests there is asignificant genetic component to the determination ofthese measures of myocardial repolarization, sinus nodefunction, atrioventricular conduction, and autonomic
function. Rare variants in ion channel genes have been
implicated in rare Mendelian Long QT Syndromes. Historically, efforts to identify genetic determinants of common, complex traits have been focused on linkage orassociation of a small number of such biologic candidate
genes with limited success and conflicting results.
Genome-wide association studies (GWAS) offer theopportunity to test a large fraction of common genetic variation using high-throughput genotyping arrays. Suchstudies have recently identified genes previously unrecognized to contribute to disease, including complement factor H and age-related macular degeneration [17], INSIG2
and obesity [18], and the IL23R and inflammatory bowel
disease [19]. To date the most convincing association with
an ECG or HRV trait is that of a common variant in
NOS1AP with QT interval variation identified through
GWAS, reported by us in collaboration with others [20].
This study, using a fixed genotyping array to survey
100,000 variants, discovered a novel gene involved in
myocardial repolarization and demonstrated the power ofsuch unbiased methods to identify previously unrecognized genes or pathways involved in cardiovascular physiology. One such array, the Affymetrix 100K GeneChip
has been shown to capture about 30% of common genetic

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variants among the European ancestry HapMap CEU sample [21]. We therefore sought to relate 70,987 common
genetic variants genotyped on this array to ECG and HRV
phenotypes in participants in the Framingham Heart
Study as a first step toward identifying genetic variants
that influence important cardiovascular traits.

Methods
Study sample
The Framingham Heart Study is a cohort of predominantly European ancestry; the study sample is more fully
described in the Overview Methods section [22]. ECG
traits were measured between 1968 and 1975 using theentire sample of FHS Original and Offspring Cohort participants (examination cycles 11 and 1, respectively) withavailable measures, and free of prevalent coronary heartdisease, atrial fibrillation and anti-arrhythmic medicationuse (n = 7356). The heritability sample for ECG analyses
comprised 1951 individuals in 355 pedigrees. HRV traits
were measured between 1983 and 1987 using the entire
sample of FHS Original (examination cycle 18) and Offspring Cohort participants (examination cycle 3) withavailable measures, and free of prevalent myocardial infarction, congestive heart failure, atrial fibrillation, diabetes, and antihypertensive or cardioactive medication use(n = 1966). The heritability sample for HRV traits comprised 747 subjects in 307 pedigrees. The sample examined for GWAS included the subset of individuals on the
FHS Related plates (n = 1175 for ECG analyses, n = 548 for
HRV analyses). Each study participant provided written
informed consent for genetic analyses and the study wasapproved by the Boston University Medical Center Institutional Review Board.
Phenotype definition
Digital caliper measurements were made on scanned
paper ECGs recorded at 25 mm/sec, with good reproducibility as previously shown [9]. QT interval duration on
the ECG was taken from the onset of the QRS to the endof the T wave or the nadir between the T wave and U wave
if present, as previously described [9]. The QT phenotype
was defined as the averaged, standardized residuals from
sex-, lead-(II, V2, V5) and cohort-specific linear regression
on age and RR interval. RR interval duration was measured as the time in msec from one R wave to the next R
wave. The RR phenotype was defined as the averaged,
standardized residuals from sex-, lead-(II, V2, V5), andcohort-specific linear regression on age. PR interval duration was measured from the onset of the P wave to theonset of the QRS interval on lead II only. The PR phenotype was defined as the standardized residual from sexand cohort-specific linear regression on age and RR interval.

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Heart rate variability measures were extracted from twohour ambulatory ECG recordings as previously described[16,23]. We excluded recordings with nonsinus rhythm,>10% premature beats, <1 hour recording time, or processed time <50% recording time. Fast Fourier transform
analysis was performed on 100-second blocks of RR interval data from 32 Hz recordings (Cardiodata Corp) sampled at 180 samples/second (Mortara Instrument Co).
Power density spectra were averaged across all 100-second
blocks. HRV phenotypes include two frequency domain
measures, ratio of low frequency to high frequency power(LF/HF) and total power (TP) and one time domain measure: standard deviation of normal RR intervals (SDNN).
The phenotype studied was the standardized, log-transformed residuals from sex and cohort-specific linear
regression of HRV trait on age, heart rate, systolic anddiastolic blood pressures, coffee intake and alcohol intake[23].
Genotyping and annotation
Affymetrix 100K SNP genotyping is described in the Overview Methods section [22]. Genotype annotation using
dbSNP and UCSC Genome Browser [24,25] are describedin the Overview Methods section [22].
Statistical analysis
The general statistical methods for linkage and GWA analyses are described in the Overview Methods section [22].
Heritability was estimated using the variance components
methods implemented in SOLAR [26]. The primary analysis of SNP associations with the 6 ECG and HRV phenotypes involved use of linear regression of minor allele
copy number (additive genetic model) on phenotype
using generalized estimating equations (GEE) to accountfor relatedness among individuals as described in the
Overview [22]. Secondary analyses involved family-based
association testing using FBAT [27] and linkage using
SOLAR [25] after exact identity-by-descent estimation
using Merlin [28] on a subset of 11,200 SNPs and STRs.
Affymetrix 100K GeneChip SNPs tested for association
with phenotypes included the 70,987 autosomal SNPs
with minor allele frequency ≥ 10%, call rate ≥ 80% andHardy-Weinberg equilibrium p-value ≥ 0.001. For all
results, nominal p-values are shown, unadjusted for multiple tests. For positive control analyses of rs10494366, weaccessed HapMap CEU genotypes at the NOS1AP locus
December 11, 2005 (http://www.hapmap.org). Correlation (r2) among HapMap CEU SNPs was determinedusing HaploView 4.0 beta 11 (http://www.broad.mit.edu/
mpg/haploview).

Results
Clinical characteristics of the FHS sample of 1345 subjects
are presented in the Overview [22]. Table 1 displays thevariables that were studied in our analyses of ECG andhttp://www.biomedcentral.com/1471-2350/8/S1/S7

HRV traits. Further information on these traits can befound at http://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?id=phs000007.
ECG and HRV measures are heritable traits
We have previously shown in the Framingham Heart
Study that electrocardiographic QT interval duration,
adjusted for RR interval, age, and sex has substantial heritability. In the current study, including a subset from theoriginal report, heritability was estimated at 0.39 (Table
1) [9]. We have also previously shown that HRV phenotypes show familial aggregation [16]; in the current report,
after adjustment for covariates, heritability estimates were0.36 (LF/HF), 0.41 (TP), and 0.32 (SDNN) (Table 1). For
the electrocardiographic RR interval (inverse heart rate),adjusted for age and sex, in the related sample of FHS original and offspring participants, we observed a heritabilityof 0.29. For the electrocardiographic PR interval, adjusted
for age, sex and RR interval, we observed a heritability of
0.34.
Association tests approximate the null distribution
After filtering all autosomal SNPs on call rate ≥ 80%,
minor allele frequency ≥ 0.10 and Hardy-Weinberg pvalue ≥ 0.001, we observed a distribution of the 70,987 pvalues that approximated a null distribution. The proportions of p < 0.0001 or p < 0.001 averaged across all six
ECG and HRV phenotypes were 0.00018 and 0.0016,
respectively (Table 2). The proportion of tests with ap ≥ 0.10 was 0.89. The p-value distributions were stable
across increasingly stringent call rate thresholds andshowed only a minor trend toward fewer excess p-values
with increasing minor allele frequency (data not shown).
Genome-wide association results
Results can be found at http://www.ncbi.nlm.nih.gov/
projects/gap/cgi-bin/study.cgi?id=phs000007. From theprimary GEE analyses, the strongest associations for the
QT, RR and PR phenotypes were for SNPs rs10507380 (p= 8.4 × 10-6), rs2179896 (p = 1.7 × 10-5), and rs882300 (p= 3.2 × 10-7), respectively (Table 3). From the primary
GEE analyses, the strongest associations for LF/HF, TP,
SDNN were for SNPs rs1395479 (p = 6.9 × 10-6),
rs9315385 (p = 7.7 × 10-6), and rs2966762 (p = 2.0 ×
10-5), respectively (Table 3). SNP associations by GEE
analysis with nominal p < 10-3 were found for 117 (QT),
105 (RR), 111 (PR), 102 (LFHFP), 121 (TP), and 102
SNPs (SDNN).
Positive controls in NOS1AP support the promise of
GWAS in this sample
We have previously reported the association of a common
variant rs10494366 (MAF 38%) in the NOS1AP gene withadjusted QT interval variation in 3 independent cohorts,
including an unrelated set of Framingham Heart Study

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Table 1: Heritability of electrocardiographic and heart rate variability phenotypes examined in the Framingham 100K project
Phenotype

Adjustment*

Heritability† (SE)Acronym

Heritability sample

Original Exam

Offspring Exam

QTPOOLQTMEN**QTWOMEN**RRPOOLRRMEN**RRWOMEN**
PRAdjRRPOOL

1951
900
1051
1951
900
1051
1950

11
11
11
11
11
11
11

1
1
1
1
1
1
1

Age, RR, sex, cohort
Age, RR, cohort
Age, RR, cohort
Age, sex, cohort
Age, cohort
Age, cohort
Age, RR, sex, cohort

0.39 (0.04)†
0.43 (0.08)
0.46 (0.04)
0.29 (0.04)
0.28 (0.08)
0.31 (0.07)
0.34 (0.04)

Age, heart rate, SBP, DBP,
coffee & alcohol intake
Age, heart rate, SBP, DBP,
coffee & alcohol intake
Age, heart rate, SBP, DBP,
coffee & alcohol intake

0.32 (0.09)†

Electrocardiographic phenotypes
QT sex-pooled
QT men only
QT women only
RR sex-pooled
RR men only
RR women only
PR sex-pooled

Heart rate variability phenotypes
SDNN sex-pooled

SDNNHRV

747

18

3

Total power sex-pooled

TOTPWRHRV

747

18

3

Low frequency/high frequency
power sex-pooled

LFHFHRV

747

18

3

0.41 (0.10)†
0.36 (0.10)†

All phenotypes for ECG and HRV traits are shown (including pre-specified secondary traits not examined for this report), including covariates adjusted for in linear regression
models. Heritability estimates were generated in the superset of individuals with phenotype values in families (n ≤ 1951 for ECG traits, n ≤ 747 for HRV traits). For
electrocardiographic traits there were 1175 individuals with genotypes and for heart rate variability there were 548 individuals with genotypes in the 100K analyses. All results
are publicly available at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.
*Separate regression models were created by cohort and sex for ECG and HRV measures. †All heritability estimates <0.001.
**Prespecified secondary results in GWAS pipeline but not analyzed in this report (available at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007). QT
and HRV heritability estimates are consistent with previous reports in largely overlapping sample [9,16].

participants (on the Unrelated Plates), as part of a threestage genome-wide association study [20]. In the current
report, we have now validated the association of
rs10494366, genotyped on the Affymetrix 100K GeneChip array, with QT interval duration in the Related Plate
set examined (nominal 2-sided p = 0.0009, rank in 100K
analysis #102, call rate 91%). Moreover, among the 117
SNPs on the array associated with QT with p < 0.001 there
were three additional associated SNPs that were in or near
NOS1AP, all partially correlated with rs10494366:
rs6683968 (p = 0.0001, MAF 32%, 100K rank #10, r2 =0.05 to rs10494366 in HapMap CEU, call rate 93%),
rs945713 (p = 0.0002, r2 = 0.35, MAF 42%, rank #23, callrate 99%) and rs1932933 (p = 0.0004, r2 = 0.63, MAF
39%, rank #42, call rate 99%). For illustrative purposes,
we further considered a two-staged design in which one
genotyped all SNPs associated with the QT phenotype
with p < 0.001 (n = 117) found in the Related Plate set
100K analysis in an additional approximately 1500 independent Framingham offspring cohort participants on theunrelated plate set (the reverse of the order in which thisSNP was actually genotyped). In such a design the p-value
for SNP rs10494366 of 0.0009 would rise to a combined
p-value of 8.9 × 10-6 on joint analysis of the two samples.
Suggestive linkage results
We observed suggestive evidence of linkage to the following phenotypes with LOD scores exceeding 2.2: LOD 2.50
on chromosome 3 (8.72 Mb) for QT interval, which has
previously been reported for a largely overlapping sample
using microsatellite markers [9]; 2.52 on chromosome 17(74.21 Mb) for QT interval; 2.98 on chromosome 4(124.75 Mb) for PR interval; 2.39 on chromosome 15(75.51 Mb) for LF/HF, and 2.19 on chromosome 11 (4.40
Mb) for TP (Table 4).
Candidate genes and QT, RR interval traits
Because use of a genome-wide p-value threshold may beoverly conservative for candidate genes directly implicated in cardiovascular physiology, we conducted a sec-

Table 2: Proportion of association results at different p-value thresholds

p < 0.0001
p < 0.001
p > 0.10

QT

RR

PR

0.000127
0.001650.891

0.000197
0.001490.886

0.000169
0.00156
0.882

LF/HF
0.000281
0.00143
0.895

Tot Power
0.000197
0.001720.895

SDNN
0.0001120.00145
0.893

Average
0.0001800.00155
0.890

Shown are the proportion of p-values for single SNP association tests using GEE which are more extreme than 0.0001, 0.001 and less extreme than
0.10 for the six electrocardiographic and heart rate variability traits, after excluding SNPs with call rate <80%, minor allele frequency <10%, HardyWeinberg equilibrium p-value < 0.001. The proportions observed closely approximate the proportions expected for a null distribution – 0.0001,
0.001 and 0.90.

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Table 3: Top GEE association results for electrocardiographic phenotypes
Trait

SNP rank

rs ID

Chromosome

Physical Position

GEE p-value

FBAT p-value

Gene region (within 60 kb)

13
11
8
5
12
19
7
2

26777526
108252434
31564949
150527337
114038413
15858564
28739796
12153871

8.4 × 10-6
4.2 × 10-5
4.7 × 10-5
4.9 × 10-5
7.7 × 10-5
8.2 × 10-5
8.5 × 10-5
8.7 × 10-5

0.665
0.865
0.033
0.104
1.3 × 10-4
6.0 × 10-5
0.002
0.001

RPL21
DDX10
NRG1
ANXA6

14
15
7
8
10
22
3
1

53945264
50105438
77959200
53668308
80016392
25706391
165861395
112905406

1.7 × 10-5
1.9 × 10-5
2.6 × 10-5
3.6 × 10-5
4.5 × 10-5
4.9 × 10-5
5.2 × 10-5
5.7 × 10-5

0.032
0.033
0.018
0.571
0.032
0.227
2.5 × 10-4
0.232

2
15
16
11
21
4
11
1

136809987
53131579
9516007102296609
28922444
82325666
41032137
96957067

3.2 × 10-7
1.1 × 10-5
3.0 × 10-5
3.1 × 10-5
3.1 × 10-5
5.1 × 10-5
5.4 × 10-5
6.2 × 10-5

0.133
0.062
0.001
0.031
0.011
0.014
0.008
0.086

4
12
13
13
2
8
7
10

178693340
40596176
88369006
88515431
11643262
13674417
128485610
97884521

6.9 × 10-6
1.3 × 10-5
1.6 × 10-5
1.9 × 10-5
2.0 × 10-5
2.1 × 10-5
3.0 × 10-5
4.2 × 10-5

0.034
0.002
0.003
0.003
0.113
0.015
0.026
0.864

13
4
2
13
7
5
1
2

35561302
77285607
35366992
106192232
125719004
74135390
184794336
47318190

7.7 × 10-6
1.5 × 10-5
2.2 × 10-5
2.4 × 10-5
3.0 × 10-5
3.8 × 10-5
4.0 × 10-5
5.5 × 10-5

0.031
0.004
0.001
0.016
0.042
0.113
0.042
0.040

5
5
8
5
2
18
8
8

109411118
107430837
52963944
109419129
27247237
4117583108356753
52740967

2.0 × 10-5
2.1 × 10-5
4.0 × 10-5
4.5 × 10-5
5.3 × 10-5
5.9 × 10-5
6.4 × 10-5
8.4 × 10-5

0.024
4.1 × 10-5
0.018
0.110
0.405
0.036
0.587
0.007

Top 8 QT interval SNPs
QT
QT
QT
QT
QT
QT
QT
QT

1
2
3
4
5
6
7
8

rs10507380
rs2726920
rs763552
rs366307
rs3858646
rs1558139
rs1695508
rs10495588

CYP4F2

Top 8 RR interval SNPs
RR
RR
RR
RR
RR
RR
RR
RR

1
2
3
4
5
6
7
8

rs2179896
rs10518674
rs321967
rs4319121
rs844429
rs4345013
rs2643191
rs2932529

MAPK6
MAGI2

CAPZA1

Top 8 PR interval SNPs
PR
PR
PR
PR
PR
PR
PR
PR

1
2
3
4
5
6
7
8

rs882300
rs10518795
rs7201988
rs2096767
rs2831936
rs10516736
rs4488182
rs10489798

MMP13
BMP3
PTBP2

Top 8 LF/HF SNPs
LF/HF
LF/HF
LF/HF
LF/HF
LF/HF
LF/HF
LF/HF
LF/HF

1
2
3
4
5
6
7
8

rs1395479
rs2215456
rs1336938
rs796184
rs4669749
rs1871841
rs721691
rs10509700

NEIL3

GREB1

Top 8 Total Power SNPs
TP
TP
TP
TP
TP
TP
TP
TP

1
2
3
4
5
6
7
8

rs9315385
rs2276886
rs726698
rs1330948
rs2283064
rs10515199
rs1407709
rs1723482

DCAMKL1
CXCL9

GRM8

Top 8 SDNN SNPs
SDNN
SDNN
SDNN
SDNN
SDNN
SDNN
SDNN
SDNN

1
2
3
4
5
6
7
8

rs2966762
rs286751
rs1013621
rs1378506
rs1866559
rs2049161
rs9297393
rs7012655

FBXL17

CGREF1ANGPT1

The top 8 association results for ECG phenotypes QT, RR and PR interval and for HRV phenotypes low frequency to high frequency power (LF/HF), total power (TP) andstandard deviation of normal RR intervals (SDNN) are shown from a total 70,987 SNPs tested in additive genetic models using GEE. Chromosome and physical position using
NCBI build 35 reference sequence (hg17) are shown. The corresponding FBAT p-value is shown (note: the number of informative families varies substantially). Genes within
60 kb of a SNP are shown.

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Table 4: Suggestive linkage results for ECG and HRV phenotypes

phenotypemaximum LOD

SNP

chromosome

position (Mb)

1.5 LOD CI

ECG
PR
QT
QT

2.98
2.52
2.50

rs1011725
rs10512617
rs1644624
173

124.7574.21
8.72

113.07 – 138.58
66.43 – 76.22
5.49 – 13.81

2.39
2.19

rs10519165
rs10500600

15
11

75.51
4.40

67.57 – 79.33
0.80 – 11.77

HRV
LF/HF
TP

Shown are the linkage results exceeding a maximum multipoint LOD score of 2.0 for electrocardiographic QT, RR and PR intervals and the ratio of
low frequency to high frequency power, total power and the standard deviation of normal RR intervals. The SNP at the maximum LOD score isshown as well as the confidence interval spanning the 1.5 LOD drop from the maximum.
Mb = megabase; CI = confidence interval; ECG = electrocardiogram; PR = PR interval adjusted for age, sex, RR interval; QT = QT interval adjusted
for age, sex, RR interval; HRV = heart rate variability; LF/HF = ratio of low frequency to high frequency power; TP = total power.

ondary analysis of SNPs within 60 kb of candidate genes
for ECG and HRV traits. Among 88 SNPs (MAF ≥ 0.10, callrate ≥ 0.8, HWE p-value ≥ 0.001) in 9 genes implicated incongenital Long QT Syndromes or QT interval duration,
only SNPs in NOS1AP had p < 0.05 for association with
QT interval duration (Table 5). Among 35 SNPs in 8
adrenergic receptor genes, SNPs with nominal p < 0.05
included two for RR interval, one for PR interval, none for
LF/HF power, two for total power and 7 for SDNN (Table
5).

Discussion
We confirmed the heritability of electrocardiographic RR
interval (inverse heart rate) and PR interval and reproduced our previously demonstrated findings that QTinterval and HRV traits are heritable in our communitybased European ancestry sample [9,16]. We have tested
70,987 common genetic variants (MAF ≥ 10%) for association with six heritable electrocardiographic and heart
rate variability phenotypes, which have been shown to be
associated with adverse cardiovascular outcomes, including sudden cardiac death. No result attained a genomewide significance threshold, such as p < 1 × 10-7 requiredfor Bonferroni correction for six traits and 70,987 SNPs.
The failure to achieve a genome-wide p-value thresholdreflects the massive penalty incurred by testing so many
hypotheses and the limited power to achieve such a pvalue given the modest effects of common variants that
contribute to complex traits. However, the presence
among the test results with nominal p < 0.001 for association with QT interval duration of common variants at the
NOS1AP locus, a recently identified and multiply replicated myocardial repolarization gene [20], supports thepresence of some true positive results among the mostly
false positive findings.

Strengths of our study include the moderate-sized, wellcharacterized community-based sample, without ascertainment on phenotype, and the precision of the ECG and
HRV measurements as supported by their substantial heritability. Some limitations pertain. Our study had limited
power to detect modest genetic effects in a single stage.
The Affymetrix 100K GeneChip, a first generation genotyping array, has patchy coverage of common genetic variation, leaving many regions untested. Our sample isalmost exclusively of European ancestry, potentially limiting extension to other populations with different linkage
disequilibrium patterns or environmental exposures.

Conclusion
Staged GWAS designs in which a modest number ofresults from GWAS in the first stage are tested in a second
independent sample and the combined statistical evidence considered, are powerful and efficient approaches
to identify common variants associated with complex
traits [29,30]. Such approaches may be warranted for follow up of findings from this study. Through suchapproaches we hope to find additional genetic factors that
could improve our understanding of human biology,
serve as targets for novel therapeutics or contribute toimproved identification of individuals at high risk for cardiovascular events. Our report and the web posting of all
results from 70,987 SNPs tested for association with ECG
and HRV phenotypes in the Framingham Heart Study represents a first step in this endeavor.

Abbreviations
ECG = electrocardiogram; FBAT = family-based association test; GEE = generalized estimating equations; GWAS
= genome wide association study; HRV = heart rate variability; LF/HF = ratio of low frequency to high frequency
power; LOD = log-likelihood ratio; SNP = single nucleotide polymorphism; TP = total power

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Table 5: Candidate gene associations with electrocardiographic and heart rate variability traits

Candidate Gene

SNP

Chrom

rs10494365
rs10494366
rs6683968
rs347311
rs945713
rs1932933

1
1
1
1
1
1

rs10503797
rs10515621

Physical position

Minor allele frequency

Phenotype

GEE P-value

158815647
158817343
158923070
159035565
158867328
158849704

0.11
0.39
0.32
0.28
0.42
0.39

QT
QT
QT
QT
QT
QT

0.01
9.0 × 10-4
1.0 × 10-4
0.01
2.4 × 10-4
4.3 × 10-48
526659190
148226737

0.11
0.14

RR
RR

0.04
0.04

rs520180826765839

0.25

PR

0.04

rs2195926
rs9325124

55159255501
148229011

0.31
0.44

TP
TP

0.01
0.03

rs10503791
rs2195926
rs10503788
rs10503789
rs10503790
rs10503794
rs7000280

8
5
8
8
8
8
8

26611431
159255501
26611063
26611248
26611360
26612682
26611857

0.17
0.31
0.18
0.18
0.18
0.18
0.18

SDNN
SDNN
SDNN
SDNN
SDNN
SDNN
SDNN

0.02
0.03
0.01
0.01
0.01
0.01
0.01

QT interval
NOS1APNOS1APNOS1APNOS1APNOS1APNOS1AP
RR interval
ADRA1AADRB2
PR interval
ADRA1A
LF/HF power
NA
Total power
ADRA1B
ADRB2
SDNN
ADRA1A
ADRA1BADRA1A
ADRA1A
ADRA1A
ADRA1A
ADRA1A

Associations with QT interval of SNPs within 60 kb of candidate genes implicated in congenital Long QT Syndrome or QT duration and with RR
and PR interval, low frequency to high frequency power (LF/HF), total power, and the standard deviation of normal RR intervals (SDNN) of SNPs
within 60 kb of adrenergic receptor genes at a nominal p < 0.05 are shown.**SNPs with minor allele frequency <10%, call rate <80% or Hardy-Weinberg p-value < 0.001 were excluded. Candidate gene loci tested for
association with QT interval duration include 88 SNPs within 60 kb of the following genes: KCNQ1, KCNH2, SCN5A, ANK2, KCNE1, KCNE2, KCNJ2,
CACNA1C and NOS1AP. Candidate gene loci tested for association with RR, PR, LF/HF, total power, and SDNN include 35 SNPs within 60 kb of thefollowing genes: ADRB1, ADRB2, ADRA1A, ADRA1B, ADRA1D, ADRA2A, ADRA2B, and ADRA2C.

Competing interests
The authors declare that they have no competing interests.

Authors' contributions
Drs. Larson and Newton-Cheh had full access to all of thedata in the study and take responsibility for the integrityof the data and the accuracy of the data analysis.

Study concept and design: Newton-Cheh, Levy, O'Donnell,
Larson; Acquisition of data: Levy, O'Donnell, NewtonCheh; Analysis and interpretation of data: Newton-Cheh,
Larson, O'Donnell, Levy, Guo; Drafting of the manuscript:
Newton-Cheh; Critical revision of the manuscript for important intellectual content: Larson, Levy, O'Donnell, Wang;
Statistical expertise: Guo, Larson; Administrative, technical,

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BMC Medical Genetics 2007, 8(Suppl 1):S7

or material support: Levy, O'Donnell; Study supervision:
Newton-Cheh,
Larson,
Levy,
O'Donnell.

http://www.biomedcentral.com/1471-2350/8/S1/S7

14.
15.

Acknowledgements
The investigators would like to express their gratitude to the FHS participants and key collaborators: Emelia J. Benjamin and Ramachandran S. Vasan.
ECG intervals were measured by eResearchTechnology, Inc. The coreexaminations were funded by N01-HC25195. Dr. Newton-Cheh is supported by the NIH (K23) and the Doris Duke Charitable Foundation Clinical Scientist Development Award. Electrocardiographic measurements
were supported by an unrestricted grant from Pfizer. A portion of theresearch was conducted using the BU Linux Cluster for Genetic Analysis(LinGA) funded by the NIH NCRR (National Center for Research
Resources) Shared Instrumentation grant (1S10RR163736-01A1).

16.
17.

18.

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 areavailable online at http://www.biomedcentral.com/1471-2350/8?issue=S1.

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