Abstract
It is recognized that traditional risk factors do not identify everyone who will develop cardiovascular disease. There is a growing interest in the discovery of novel biomarkers that will augment the predictive potential of traditional cardiovascular risk factors. The era of genome-wide association studies (GWAS) has resulted in the discovery of common genetic polymorphisms associated with a multitude of cardiovascular traits and raises the possibility that these variants can be used in clinical risk prediction. Assessing and evaluating the new genetic risk markers and quantification of the improvement in risk prediction models that incorporate this information is a major challenge. In this paper we discuss the key metrics that are used to assess prediction models—discrimination, calibration, reclassification, and demonstration on how to calculate and interpret these metrics.
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References
Lloyd-Jones DM (2010) Cardiovascular risk prediction: basic concepts, current status, and future directions. Circulation 121:1768–1777
Kannel WB et al (1961) Factors of risk in the development of coronary heart disease—six year follow-up experience. The Framingham Study. Ann Intern Med 55:33–50
Pencina MJ et al (2009) Predicting the 30-year risk of cardiovascular disease: the Framingham heart study. Circulation 119:3078–3084
Hippisley-Cox J et al (2007) Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ 335:136
Aktas MK et al (2004) Global risk scores and exercise testing for predicting all-cause mortality in a preventive medicine program. JAMA 292:1462–1468
Khot UN et al (2003) Prevalence of conventional risk factors in patients with coronary heart disease. JAMA 290:898–904
Brautbar A et al (2009) Impact of adding a single allele in the 9p21 locus to traditional risk factors on reclassification of coronary heart disease risk and implications for lipid-modifying therapy in the Atherosclerosis Risk in Communities study. Circ Cardiovasc Genet 2:279–285
Nambi V et al (2012) The 9p21 genetic variant is additive to carotid intima media thickness and plaque in improving coronary heart disease risk prediction in white participants of the Atherosclerosis Risk in Communities (ARIC) Study. Atherosclerosis 222:135–137
Paynter NP et al (2010) Association between a literature-based genetic risk score and cardiovascular events in women. JAMA 303:631–637
Ioannidis JP (2009) Prediction of cardiovascular disease outcomes and established cardiovascular risk factors by genome-wide association markers. Circ Cardiovasc Genet 2:7–15
Kathiresan S et al (2008) Polymorphisms associated with cholesterol and risk of cardiovascular events. N Engl J Med 358:1240–1249
Ridker PM et al (2007) Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 297:611–619
Scheuner MT et al (2010) General Cardiovascular Risk Profile identifies advanced coronary artery calcium and is improved by family history: the multiethnic study of atherosclerosis. Circ Cardiovasc Genet 3:97–105
Cook NR, Ridker PM (2009) Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med 150:795–802
Pencina MJ et al (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27:157–172
Wang TJ (2011) Assessing the role of circulating, genetic, and imaging biomarkers in cardiovascular risk prediction. Circulation 123:551–565
Janssens AC et al (2011) Strengthening the reporting of genetic risk prediction studies: the GRIPS statement. Circ Cardiovasc Genet 4:206–209
Wilson PW et al (1998) Prediction of coronary heart disease using risk factor categories. Circulation 97:1837–1847
Cook NR (2007) Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 115:928–935
Zollner S, Pritchard JK (2007) Overcoming the winner’s curse: estimating penetrance parameters from case-control data. Am J Hum Genet 80:605–615
Lubin JH, Gail MH (1984) Biased selection of controls for case-control analyses of cohort studies. Biometrics 40:63–75
Thanassoulis G, Vasan RS (2010) Genetic cardiovascular risk prediction: will we get there? Circulation 122:2323–2334
Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387
Janssens AC et al (2006) Predictive testing for complex diseases using multiple genes: fact or fiction? Genet Med 8:395–400
Wray NR et al (2010) The genetic interpretation of area under the ROC curve in genomic profiling. PLoS Genet 6, e1000864
van der Net JB et al (2009) Value of genetic profiling for the prediction of coronary heart disease. Am Heart J 158:105–110
(2001) Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 285: 2486–2497
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Schulz, C., Padmanabhan, S. (2017). Methods to Assess Genetic Risk Prediction. In: Touyz, R., Schiffrin, E. (eds) Hypertension. Methods in Molecular Biology, vol 1527. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6625-7_2
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DOI: https://doi.org/10.1007/978-1-4939-6625-7_2
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