Improvement in Prediction of Coronary Heart Disease Risk over Conventional Risk Factors Using SNPs Identified in Genome-Wide Association Studies

Objective: We examined whether a panel of SNPs, systematically selected from genome-wide association studies (GWAS), could improve risk prediction of coronary heart disease (CHD), over-and-above conventional risk factors. These SNPs have already demonstrated reproducible associations with CHD; here we examined their use in long-term risk prediction. Study Design and Setting: SNPs identified from meta-analyses of GWAS of CHD were tested in 840 men and women aged 55–75 from the Edinburgh Artery Study, a prospective, population-based study with 15 years of follow-up. Cox proportional hazards models were used to evaluate the addition of SNPs to conventional risk factors in prediction of CHD risk. CHD was classified as myocardial infarction (MI), coronary intervention (angioplasty, or coronary artery bypass surgery), angina and/or unspecified ischaemic heart disease as a cause of death; additional analyses were limited to MI or coronary intervention. Model performance was assessed by changes in discrimination and net reclassification improvement (NRI). Results: There were significant improvements with addition of 27 SNPs to conventional risk factors for prediction of CHD (NRI of 54%, P