Candidate gene genotypes, along with conventional risk factor assessment, improve estimation of coronary heart disease risk in healthy UK men.

One of the aims of cardiovascular genetics is to test the efficacy of the use of genetic information to predict cardiovascular risk. We therefore investigated whether inclusion of a set of common variants in candidate genes along with conventional risk factor (CRF) assessment enhanced coronary heart disease (CHD)-risk algorithms. We followed middle-aged men in the prospective Northwick Park Heart Study II (NPHSII) for 10.8 years and analyzed complete trait and genotype information available on 2057 men (183 CHD events). Of the 12 genes previously associated with CHD risk, in stepwise multivariate risk analysis, uncoupling protein 2 (UCP2; P = 0.0001), apolipoprotein E (APOE; P = 0.0003), lipoprotein lipase (LPL; P = 0.007), and apolipoprotein AIV (APOA4; P = 0.04) remained in the model. Their combined area under the ROC curve (A(ROC)) was 0.62 (0.58-0.66) [12.6% detection rate for a 5% false positive rate (DR(5))]. The A(ROC) for the CRFs age, triglyceride, cholesterol, systolic blood pressure, and smoking was 0.66 (0.61-0.70) (DR(5) = 14.2%). Combining CRFs and genotypes significantly improved discrimination (P = 0.001). Inclusion of previously demonstrated interactions of smoking with LPL, interleukin-6 (IL6), and platelet/endothelial cell adhesion molecule (PECAM1) genotypes increased the A(ROC) to 0.72 (0.68-0.76) for a DR(5) of 19.1% (P = 0.01 vs CRF combined with genotypes). For a modest panel of selected genotypes, CHD-risk estimates incorporating CRFs and genotype-risk factor interactions were more effective than risk estimates that used CRFs alone.