Marginal role for 53 common genetic variants in cardiovascular disease prediction

Objective: We investigated discrimination and calibration of cardiovascular disease (CVD) risk scores when genotypic was added to phenotypic information. The potential of genetic information for those at intermediate risk by a phenotype-based risk score was assessed.

Methods: Data were from seven prospective studies including 11,851 individuals initially free of CVD or diabetes, with 1,444 incident CVD events over 10 years’ follow-up. We calculated a score from 53 CVD-related single nucleotide polymorphisms (SNPs) and an established CVD risk equation “QRISK-2” comprising phenotypic measures. The area under the receiver operating characteristic curve (AUROC), detection rate for given false positive rate (FPR), and net reclassification index (NRI) were estimated for gene scores alone and in addition to the QRISK-2 CVD risk score. We also evaluated use of genetic information only for those at intermediate risk according to QRISK-2.

Results: The AUROC was 0.635 for QRISK-2 alone, and 0.623 with addition of the gene score. The detection rate for 5% FPR improved from 11.9% to 12.0% when adding the gene score. For a 10-year CVD risk cut-off point of 10%, the NRI was 0.25% when the gene score was added to QRISK-2. Applying the genetic risk score only to those with QRISK-2 risk of 10-<20%, and prescribing statins where risk exceeded 20%, suggested genetic information could prevent one additional event for every 462 people screened.

Conclusions: The gene score produced minimal incremental population-wide utility over phenotypic risk prediction of CVD. Tailored prediction using genetic information for those at intermediate risk may have clinical utility.