Prediction of Coronary Heart Disease Risk using a Genetic Risk Score: The
Atherosclerosis Risk in Communities Study
Alanna C. Morrison1, Lance A. Bare2, Lloyd E. Chambless3, Stephen G. Ellis4, Mary Malloy5,
John P. Kane5, James S. Pankow6, James J. Devlin2, James T. Willerson7, and Eric Boerwinkle1,7
1Human Genetics Center and Division of Epidemiology, University of Texas Health Science Center at Houston, Houston, TX.
2Celera, Alameda, CA.
3Collaborative Studies Coordinating Center, University of North Carolina, Chapel Hill, NC.
4Department of Cardiovascular Medicine, The Cleveland Clinic Foundation, Cleveland, OH.
5Cardiovascular Research Institute, University of California, San Francisco, CA.
6Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN.
7Texas Heart Institute, Houston, TX.
Received for publication May 4, 2006; accepted for publication January 16, 2007.
Recent studies have evaluated whether incorporating nontraditional risk factors improves coronary heart dis-
ease (CHD) prediction models. This 1986–2001 US study aggregated the contribution of multiple single nucleotide
polymorphisms into a genetic risk score (GRS) and assessed whether the GRS plus traditional risk factors predict
CHD better than traditional risk factors alone. The Atherosclerosis Risk in Communities (ARIC) cohort was followed
for a median of 13 years for CHD events (n ¼ 1,452). Individuals were genotyped for 116 single nucleotide
polymorphisms associated with CHD in multiple case-control studies. Single nucleotide polymorphisms nominally
predicting incident CHD in the ARIC study were included in the GRS. The GRS was significantly associated
with incident CHD in Blacks (hazard rate ratio ¼ 1.20, 95% confidence interval: 1.11, 1.29) and Whites (hazard
rate ratio ¼ 1.10, 95% confidence interval: 1.06, 1.14) as well as in each tertile defined by the traditional cardio-
vascular risk score (p ? 0.02). When receiver operating characteristic curves based on traditional risk factors were
recalculated after the GRS was added, the increase in the area under the receiver operating characteristic curve
was statistically significant for Blacks and suggestive of improved CHD prediction for Whites. This study demon-
strates the concept of aggregating information from multiple single nucleotide polymorphisms into a risk score and
indicates that it can improve prediction of incident CHD in the ARIC study.
cardiovascular diseases; genetics; polymorphism, genetic; risk factors
Abbreviations: ACRS, ARIC Cardiovascular Risk Score; ARIC, Atherosclerosis Risk in Communities; AUC, area under the
curve; CHD, coronary heart disease; GRS, genetic risk score; SNP, single nucleotide polymorphism.
Coronary heart disease (CHD) is the leading cause of
mortality in the United States (1), and numerous studies
have focused on identifying risk factors contributing to
CHD. The Framingham Study developed a risk score that
includes major risk factors, such as age, blood pressure,
cigarette smoking, total cholesterol, high density lipoprotein
cholesterol, and diabetes status (2). A similar score was
developed that demonstrates the ability of traditional risk
factors to predict CHD in the Atherosclerosis Risk in Com-
munities (ARIC) study (3).
Recently, studies have evaluated whether emerging risk
factors, such as C-reactive protein, improve prediction of
Correspondence to Dr. Eric Boerwinkle, Human Genetics Center, University of Texas Health Science Center at Houston, 1200 Herman Pressler,
Suite 453E, Houston, TX 77030 (e-mail: Eric.Boerwinkle@uth.tmc.edu).
American Journal of Epidemiology
Copyright ª 2007 by the Johns Hopkins Bloomberg School of Public Health
All rights reserved; printed in U.S.A.
American Journal of Epidemiology Advance Access published April 18, 2007
by guest on June 2, 2013
7. Morrison A, Bray M, Folsom A, et al. ADD1 460W allele
associated with cardiovascular disease in hypertensive indi-
viduals. Hypertension 2002;39:1053–7.
8. McCarthy J, Parker A, Salem R, et al. Large scale association
analysis for identification of genes underlying premature
coronary heart disease: cumulative perspective from
analysis of 111 candidate genes. J Med Genet 2004;41:
9. The Atherosclerosis Risk in Communities (ARIC) Study: de-
sign and objectives. The ARIC Investigators. Am J Epidemiol
10. White A, Folsom A, Chambless L, et al. Community surveil-
lance of coronary heart disease in the Atherosclerosis Risk in
Communities (ARIC) Study: methods and initial two years’
experience. J Clin Epidemiol 1996;49:223–33.
11. Siedel J, Hagele EO, Ziegenhorn J, et al. Reagent for
the enzymatic determination of serum total cholesterol
with improved lipolytic efficiency. Clin Chem 1983;29:
12. Warnick GR, Benderson JM, Albers JJ. Dextran sulfate-Mg2þ
precipitation procedure for quantification of high-density-
lipoprotein cholesterol. Clin Chem 1982;28:1379–88.
13. Iannone M, Taylor J, Chen J, et al. Multiplexed single nucle-
otide polymorphism genotyping by oligonucleotide ligation
and flow cytometry. Cytometry 2000;39:131–40.
14. Li Y, Nowotny P, Holmans P, et al. Association of late-onset
Alzheimer’s disease with genetic variation in multiple mem-
bers of the GAPD gene family. Proc Natl Acad Sci U S A
15. Shiffman D, Ellis S, Rowland C, et al. Identification of four
gene variants associated with myocardial infarction. Am J
Hum Genet 2005;77:596–605.
16. Chambless LE, Diao G. Estimation of time-dependent area
under the ROC curve for long-term risk prediction. Stat Med
17. Barkley R, Chakravarti A, Cooper R, et al. Positional identi-
fication of hypertension susceptibility genes on chromosome
2. Hypertension 2004;43:477–82.
18. Horikawa Y, Oda N, Cox N, et al. Genetic variation in the gene
encoding calpain-10 is associated with type 2 diabetes melli-
tus. Nat Genet 2000;26:163–75.
19. Horvath S, Xu X, Laird N. The family based association test
method: strategies for studying general genotype-phenotype
associations. Eur J Hum Genet 2001;9:301–6.
20. Horne B, Anderson J, Carlquist J, et al. Generating genetic risk
scores from intermediate phenotypes for use in association
studies of clinically significant endpoints. Ann Hum Genet
21. Ortlepp J, Lauscher J, Janssens U, et al. Analysis of several
hundred genetic polymorphisms may improve assessment of
the individual genetic burden for coronary artery disease. Eur J
Intern Med 2002;13:485–92.
22. Aston C, Ralph D, Lalo D, et al. Oligogenic combinations
associated with breast cancer risk in women under 53 years of
age. Hum Genet 2005;116:208–21.
23. Poort S, Rosendaal F, Reitsma P, et al. A common genetic
variation in the 3#-untranslated region of the prothrombin gene
is associated with elevated plasma prothrombin levels and an
increase in venous thrombosis. Blood 1996;88:3698–703.
24. Tommerup N, Vissing H. Isolation and fine mapping of 16
novel human zinc-finger encoding cDNAs identify putative
candidate genes for developmental and malignant disorders.
25. Miki H, Okada Y, Hirokawa N. Analysis of the kinesin su-
perfamily: insights into structure and function. Trends Cell
26. Jones D, Chambless L, Folsom R, et al. Risk factors for cor-
onary heart disease in African Americans: The Atherosclerosis
Risk in Communities Study, 1987–1997. Arch Intern Med
8 Morrison et al.
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