In order to evaluate the population impact of putative causal genetic variants over the life course of disease, we extended the static estimation of population-attributable risk fraction and developed a novel tool to evaluate how the population impact changes over time using the Framingham Heart Study Offspring Cohort data provided to the Genetic Analysis Workshop 16, Problem 2. A set of population-attributable risk fractions based on survival functions were estimated under the proportional hazards models. The development of this novel measure of population impact creates a more comprehensive estimate of population impact over the life course of disease, which may help us to better understand genetic susceptibility at the population level

Danyu Lin

Kari E North

Nora Franceschini

Yijuan Hu

Yu Yan

Springer - Publisher Connector

BMC Proceedings
Proceedings
Evaluation of population impact of candidate polymorphisms
for coronary heart disease in the Framingham Heart Study
Offspring Cohort
Yu Yan1, Yijuan Hu2, Kari E North*1,3, Nora Franceschini1 and Danyu Lin2
Addresses: 1Department of Epidemiology, University of North Carolina, 2101 McGavran-Greenberg Hall, Chapel Hill, North Carolina 27599,
USA, 2Department of Biostatistics, University of North Carolina, 3101 McGavran-Greenberg Hall, Chapel Hill, North Carolina 27599, USA and
3Carolina Center for Genome Sciences, University of North Carolina, 5009 Genetic Medicine Building, Chapel Hill, North Carolina 27599, USA
E-mail: Yu Yan - yuyan.unc@gmail.com; Yijuan Hu - yhu@bios.unc.edu; Kari E North* - kari_north@unc.edu;
Nora Franceschini - noraf@unc.edu; Danyu Lin - lin@bios.unc.edu
*Corresponding author
from Genetic Analysis Workshop 16
St Louis, MO, USA 17-20 September 2009
Published: 15 December 2009
BMC Proceedings 2009, 3(Suppl 7):S118 doi: 10.1186/1753-6561-3-S7-S118
This article is available from: http://www.biomedcentral.com/1753-6561/3/S7/S118
© 2009 Yan 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
In order to evaluate the population impact of putative causal genetic variants over the life course of
disease, we extended the static estimation of population-attributable risk fraction and developed a
novel tool to evaluate how the population impact changes over time using the Framingham Heart
Study Offspring Cohort data provided to the Genetic Analysis Workshop 16, Problem 2. A set of
population-attributable risk fractions based on survival functions were estimated under the
proportional hazards models. The development of this novel measure of population impact creates
a more comprehensive estimate of population impact over the life course of disease, which may
help us to better understand genetic susceptibility at the population level.
Background
The ongoing discovery of new genetic markers from
genome-wide association studies presents opportunities
and challenges for scientists to evaluate these new
biomarkers. One of the critical questions that has been
raised is how to evaluate the potential population
impact of these new markers. First proposed by Levin
in 1953 [1], the primary measure of impact is the
population-attributable risk fraction (PAF, also known as
the population-attributable risk proportion). The PAF,
determined by the prevalence of exposure and the
magnitude of association, measures the proportion of
disease risk in the total population associated with one
or multiple exposures; thus the PAF is useful in
evaluating the impact of different exposures at the
population level. However, the current PAF estimation
does not account for age of onset data (i.e., time-to-event
data). In this study, we developed methodological
approaches to estimate the population impact of genetic
variants over the life course of disease using the
longitudinal Framingham Heart Study Offspring Cohort
and incident coronary heart disease (CHD) events.
Page 1 of 5
(page number not for citation purposes)
BioMed Central
Open Access
Methods
Population and phenotype
We used the Framingham Heart Study Offspring Cohort
data provided to the Genetic Analysis Workshop (GAW)
16, Problem 2, for the analyses. The Framingham Heart
Study is a longitudinal community-based cohort of
cardiovascular disease and its risk factors that began in
1948 with the recruitment of the Original Cohort [2].
Between 1971 and 1975, 5124 children or spouses of the
Original Cohort were enrolled into the Offspring Cohort
[3]. The Offspring Cohort has undergone eight examina-
tions every 4 to 8 years. The present study is composed of
unrelated Offspring participants. Of 2760 Offspring
participants who gave informed consent for data collected
to be used by anyone, we excluded those biologically
related participants (n = 813), participants without
genotyping data (n = 211), and those with prevalent
CHD at baseline (n = 2). After these exclusions, a total of
1734 unrelated Offspring participants were available for
analysis. The Framingham Heart Study Offspring Cohort
study protocol was approved by Boston University
Medical Center Institutional Review Board and this
investigation was approved by University of North
Carolina at Chapel Hill Institutional Review Board.
A CHD event was defined as any of the following:
recognized myocardial infarction diagnosed through an
electrocardiogram or enzymes, coronary insufficiency, or
death attributed to CHD.
Genotyping methods and single-nucleotide polymorphism
(SNP) selection
The Affymetric 500 k chip was used to genotype
individual participant DNA. SNPs selected for this
study were based on published candidate gene studies
and genome-wide association studies. A total of 23 SNPs
associated with major CHD or major cardiovascular
disease were included in this investigation.
Statistical analyses
To assess whether genotype distributions departed from
Hardy-Weinberg equilibrium, a c2 goodness-of-fit test
was used. We used Cox proportional hazards to estimate
the hazard ratios and 95% confidence intervals of
incident CHD. The hazard function was formulated on
the age scale using the age at onset of CHD obtained as
part of the GAW 16 Problem 2 data release. Covariates,
including sex, smoking, diabetes, systolic blood pressure,
anti-hypertensive treatment, total cholesterol levels,
high-density lipoprotein cholesterol, and body mass
index, were included in the models to reduce the residual
variance. The association was considered to be signifi-
cant if the p-value was less than 0.05. Assuming additive
inheritance, a variable taking on the values 0 for
reference genotype, 1 for heterozygous genotype, and 2
for homozygous genotype was used to test genetic effects
for each SNP.
Significant associations between three SNPs (rs1333049,
rs618675, and rs1376251) and increased risk of incident
CHD were noted. We further explored the association
with the risk score, which was constructed by summing
the number of risk alleles across these three CHD
susceptibility SNPs. The distribution ranged from zero
to six alleles. Because very few participants have zero
(n = 27) or six (n = 8) risk alleles, these participants were
included into the closest group (e.g., zero was grouped
with one risk allele).
Our methodological approach integrates multiple PAF
estimates at multiple ages for a single variant in an
attempt to create a comprehensive estimate of popula-
tion impact.
For a binary disease status D and a binary exposure
indicator E, the PAF is defined as
A
D D E
D
= = − = =
=
Pr( ) Pr( | )
Pr( )
1 1 0
1
[1]. We extended this static measure to the age-of-onset
data with potentially multiple risk factors
A t
T t T t X
T t
( )
Pr( ) Pr( | )
Pr( )
,= ≤ − ≤ =
≤
0
where T denotes the time to disease and X denotes a set
of potential genetic factors. We can rewrite this measure
in terms of survival function
A t
S t S t
S t
( )
( ) ( )
( )
,= −
−
0
1
and S(t) = Pr(T > t) and S0(t) = Pr(T > t | X = 0).
S(t) was estimated by the Kaplan-Meier nonparametric
method. If X pertained to a single genetic variant, then
we estimated S0(t) by the Kaplan-Meier method as well.
If X consisted of several genetic factors, then we
estimated S0(t) under a semiparametric regression
model. All the statistical analyses were performed in
SAS 9.1 (SAS institute, Cary, NC). A PAF plot was
provided to indicate how the population impact
changed over the life course of disease.
Results
A total of 137 incident CHD events were identified. The
allele frequencies for all 23 SNPs analyzed in this study
were in Hardy-Weinberg equilibrium (p > 0.01). Because
BMC Proceedings 2009, 3(Suppl 7):S118 http://www.biomedcentral.com/1753-6561/3/S7/S118
Page 2 of 5
(page number not for citation purposes)
the estimates for SNP effects on incident CHD were
almost identical after adjusting for aforementioned
covariates, only unadjusted hazard ratios with 95%
confidence intervals were reported (Table 1). Three SNPs
(rs1333049 close to the CDKN2A/2B gene, rs618675 in
the GJA4 gene, and rs1376251 in the TAS2R50 gene)
were significantly associated with the incident CHD
(Table 1). Further exploration indicated that the risk
score (p = 0.0004) was significantly associated with
incident CHD risk (Table 1).
The PAF plots for the risk score and three significant
SNPs with and without adjustment for covariates are
shown in Figure 1. The age at onset of CHD ranged from
41 to 81 years old. The PAFs were much higher for the
risk score (PAF = 41% on average) than for each
individual SNP. The unadjusted PAFs showed a subtle
decline with age, whereas the adjusted PAFs slightly
increased over time.
Discussion
Our study replicates the association between CHD risk
and rs1333049 close to the CDKN2A/2B gene, rs618675
in the GJA4 gene, and rs1376251 in the TAS2R50 gene,
in Caucasians. However, the number of events (max-
imum of 137 for CHD) was small. Thus, we had limited
power to detect association for each individual SNP and
our study results need to be validated in different, large
population-based studies.
We assessed here the impact of the known cardiovascular
disease genes/loci on the population burden of CHD
over time, based on data from the Framingham Heart
Study Offspring Cohort. Static PAFs have been
Table 1: Characteristics of selected SNPs, and associations between the CHD incidence and SNPs
HR estimates (95% CI)
Closest
gene
Gene name SNP
[reference]
Chr Physical
position
Risk allele MAF Heterozygous
genotype
Homozygous
genotype
p-Value
GJA4 gap junction protein, alpha 4 rs618675 [4] 1 34922761 C 0.19 1.32 (1.01, 1.72) 1.73 (1.01, 2.97) 0.0451
PCSK9 proprotein convertase
subtilisin/kexin type 9
rs2114580 [4] 1 55167236 A 0.24 1.22 (0.94, 1.59) 1.49 (0.88, 2.54) 0.1376
PSRC1 proline/serine-rich
coiled-coil 1
rs599839 [5] 1 109623689 A 0.22 1.05 (0.79, 1.41) 1.11 (0.62, 1.98) 0.7262
MIA3 melanoma inhibitory activity
family, member 3
rs17465637 [5] 1 220890152 C 0.29 1.07 (0.82, 1.39) 1.14 (0.67, 1.93) 0.6282
FMN2 formin 2 rs17672135 [6] 1 238512219 C 0.11 1.20 (0.83, 1.73) 1.44 (0.69, 2.99) 0.3286
OR13G1 olfactory receptor, family 13,
subfamily G, member 1
rs1151640 [7] 1 245902573 C 0.43 0.98 (0.77, 1.25) 0.96 (0.59, 1.56) 0.8628
rs2943634 [5] 2 226776324 C 0.35 1.08 (0.84, 1.39) 1.16 (0.71, 1.92) 0.5508
rs10516882 [4] 4 92127599 C 0.17 1.19 (0.85, 1.67) 1.41 (0.72, 2.78) 0.3170
THBS4 thrombospondin 4 rs264986 [4] 5 79206180 T 0.27 1.01 (0.77, 1.32) 1.01 (0.59, 1.74) 0.9656
MTHFD1L methylenetetrahydrofolate
dehydrogenase 1-like
rs6922269 [5] 6 151294678 A 0.27 1.13 (0.87, 1.48) 1.29 (0.76, 2.18) 0.3506
PHACTR1 phosphatase and actin
regulator 1
rs1512411 [4] 6 13439076 C 0.34 1.01 (0.78, 1.31) 1.03 (0.61, 1.71) 0.9235
WNT2 wingless-type MMTV
integration site family
member 2
rs39312 [4] 7 116742021 A 0.35 1.06 (0.83, 1.35) 1.12 (0.68, 1.83) 0.6554
CDKN2A/2B cyclin-dependent kinase
inhibitor 2A/2B
rs1333049 [6] 9 22115503 C 0.48 1.28 (1.01, 1.63) 1.64 (1.02, 2.66) 0.0433
rs501120 [5] 10 44073873 C 0.14 0.99 (0.71, 1.39) 0.99 (0.50, 1.94) 0.9681
KIAA0528 KIAA0528 rs10505879 [4] 12 22539123 G 0.22 1.13 (0.86, 1.50) 1.28 (0.74, 2.24) 0.3753
TAS2R50 taste receptor, type 2,
member 50
rs1376251 [7] 12 11030119 C 0.33 1.31 (1.00, 1.71) 1.71 (1.01, 2.92) 0.0476
ALOX5AP arachidonate 5-lipoxygenase-
activating protein
rs7984952 [4] 13 30129806 T 0.43 1.24 (0.97, 1.58) 1.54 (0.95, 2.51) 0.0817
rs7995384 [4] 13 30177259 T 0.29 1.20 (0.92, 1.57) 1.44 (0.84, 2.48) 0.1830
rs117395 [4] 13 30365911 T 0.13 1.09 (0.75, 1.59) 1.19 (0.56, 2.52) 0.6459
SMAD3 SMAD family member 3 rs17228212 [5] 15 65245693 C 0.29 1.04 (0.80, 1.35) 1.08 (0.64, 1.81) 0.7781
rs2549513 [4] 16 78108228 C 0.13 1.03 (0.73, 1.47) 1.06 (0.53, 2.15) 0.8617
CDH13 cadherin 13, H-cadherin rs8055236 [6] 16 81769899 T 0.19 1.26 (0.95, 1.67) 1.59 (0.91, 2.78) 0.1045
SEZ6L seizure related 6 homolog rs688034 [6] 22 25019635 C 0.33 1.10 (0.84, 1.43) 1.20 (0.71, 2.04) 0.4971
Risk Scorea 1.32 (1.13, 1.54) 0.0004
aThe HR estimated the hazard of having incident CHD for one unit increase in the risk score.
BMC Proceedings 2009, 3(Suppl 7):S118 http://www.biomedcentral.com/1753-6561/3/S7/S118
Page 3 of 5
(page number not for citation purposes)
extensively used to rank risk factors and to assess the
prospective gains in disease prevention. In this study, we
extended the static estimation of PAFs and evaluated
how the population impact of genetic variants changed
over the life course of CHD, as shown in the PAF plot
(Figure 1). The unadjusted PAFs associated with genetic
variants slightly decreased as age advanced, whereas
adjusted PAFs showed a subtle increase with age, which
may be due to the small number of events in these data,
especially in the early and late age groups. For example,
only six CHD events occurred before the age of 45,
whereas eight events occurred after 75. However, we
observed much higher PAFs for the risk score compared
with each individual SNP, suggesting the importance of
evaluating multiple genetic variants for the population
impact analysis.
While the use of the risk score summary metric was
useful in this population in which no single SNP
achieved a large PAF, these estimates should be inter-
preted with caution because any time we combine SNP
effects based on statistical significance and effect size, we
will automatically obtain an improved effect estimate
and p-value.
Conclusion
Our development of the novel tool for population
impact extends the current PAF analyses and creates a
more comprehensive estimate of population impact over
the life course of disease, which may improve the
understanding of genetic risk factors at the population
level.
List of abbreviations used
CHD: Coronary heart disease; GAW: Genetic Analysis
Workshop; HR: Hazard ratio; MAF: Minor allele
frequency; PAF: Population-attributable risk fraction;
SNP: Single-nucleotide polymorphism.
Competing interests
The authors declare that they have no competing
interests.
Authors’ contributions
YY and KEN conceived of the study, performed the
statistical analysis, and drafted the manuscript. YH
helped performed the statistical analysis and helped to
draft the manuscript. NF participated in the study design
and helped to draft the manuscript. DL participated in
the study design, helped performed the statistical
analysis and helped to draft the manuscript. All authors
read and approved the final manuscript.
Acknowledgements
The Genetic Analysis Workshops are supported by NIH grant R01
GM031575 from the National Institute of General Medical Sciences.
Figure 1
The PAF plot over time for the risk score, rs1333049, rs1376251, and rs618675.
BMC Proceedings 2009, 3(Suppl 7):S118 http://www.biomedcentral.com/1753-6561/3/S7/S118
Page 4 of 5
(page number not for citation purposes)
This article has been published as part of BMC Proceedings Volume 3
Supplement 7, 2009: Genetic Analysis Workshop 16. The full contents of
the supplement are available online at http://www.biomedcentral.com/
1753-6561/3?issue=S7.
References
1. Levin ML: The occurrence of lung cancer in man. Acta Unio Int
Contra Cancrum 1953, 9:531–541.
2. Dawber TR, Meadors GF and Moore FE Jr: Epidemiological
approaches to heart disease: the Framingham Study. Am J
Public Health Nations Health 1951, 41:279–281.
3. Kannel WB, Feinleib M, McNamara PM, Garrison RJ and Castelli WP:
An investigation of coronary heart disease in families. The
Framingham offspring study. Am J Epidemiol 1979, 110:281–290.
4. Larson MG, Atwood LD, Benj amin E J , Cupp les LA,
D’Agostino RB Sr, Fox CS, Govindaraju DR, Guo CY, Heard-
Costa NL, Hwang SJ, Murabito JM, Newton-Cheh C, O’Donnell CJ,
Seshadri S, Vasan RS, Wang TJ, Wolf PA and Levy D: Framingham
Heart Study 100 K project: genome-wide associations
for cardiovascular disease outcomes. BMC Med Genet 2007,
8(Suppl 1):S5.
5. Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M,
Mayer B, Dixon RJ, Meitinger T, Braund P, Wichmann HE, Barrett JH,
König IR, Stevens SE, Szymczak S, Tregouet DA, Iles MM, Pahlke F,
Pollard H, Lieb W, Cambien F, Fischer M, Ouwehand W,
Blankenberg S, Balmforth AJ, Baessler A, Ball SG, Strom TM,
Braenne I, Gieger C, Deloukas P, Tobin MD, Ziegler A,
Thompson JR, Schunkert H and WTCCC and the Cardiogenics
Consortium: Genomewide association analysis of coronary
artery disease. N Engl J Med 2007, 357:443–453.
6. Genome-wide association study of 14,000 cases of seven
common diseases and 3,000 shared controls. Nature 2007,
447:661–678.
7. Shiffman D, Ellis SG, Rowland CM, Malloy MJ, Luke MM,
Iakoubova OA, Pullinger CR, Cassano J, Aouizerat BE,
Fenwick RG, Reitz RE, Catanese JJ, Leong DU, Zellner C,
Sninsky JJ, Topol EJ, Devlin JJ and Kane JP: Identification of four
gene variants associated with myocardial infarction. Am J
Hum Genet 2005, 77:596–605.
Publish with BioMed Central   and  every 
scientist can read your work free of charge
"BioMed Central will be the most significant development for 
disseminating the results of biomedical research in our lifetime."
Sir Paul Nurse, Cancer Research UK
Your research papers will be:
available free of charge to the entire biomedical community
peer reviewed and published immediately upon acceptance
cited in PubMed and archived on PubMed Central 
yours — you keep the copyright
Submit your manuscript here:
http://www.biomedcentral.com/info/publishing_adv.asp
BioMedcentral
BMC Proceedings 2009, 3(Suppl 7):S118 http://www.biomedcentral.com/1753-6561/3/S7/S118
Page 5 of 5
(page number not for citation purposes)


Evaluation of population impact of candidate polymorphisms for coronary heart disease in the Framingham Heart Study Offspring Cohort

Abstract

Similar works

Full text

Available Versions

Springer - Publisher Connector

Carolina Digital Repository