Comparison of genetic risk prediction models to improve prediction of coronary heart disease in two large cohorts of the MONICA/KORA study

Abstract

It is still unclear how genetic information, provided as single‐nucleotide polymorphisms (SNPs), can be most effectively integrated into risk prediction models for coronary heart disease (CHD) to add significant predictive value beyond clinical risk models. For the present study, a population‐based case‐cohort was used as a trainingset (451 incident cases, 1488 noncases) and an independent cohort as testset (160 incident cases, 2749 noncases). The following strategies to quantify genetic information were compared: A weighted genetic risk score including Metabochip SNPs associated with CHD in the literature (GRSMetabo); selection of the most predictive SNPs among these literature‐confirmed variants using priority‐Lasso (PLMetabo); validation of two comprehensive polygenic risk scores: GRSGola based on Metabochip data, and GRSKhera (available in the testset only) based on cross‐validated genome‐wide genotyping data. We used Cox regression to assess associations with incident CHD. C‐index, category‐free net reclassification index (cfNRI) and relative integrated discrimination improvement (IDIrel) were used to quantify the predictive performance of genetic information beyond Framingham risk score variables. In contrast to GRSMetabo and PLMetabo, GRSGola significantly improved the prediction (delta C‐index [95% confidence interval]: 0.0087 [0.0044, 0.0130]; IDIrel: 0.0509 [0.0131, 0.0894]; cfNRI improved only in cases: 0.1761 [0.0253, 0.3219]). GRSKhera yielded slightly worse prediction results than GRSGola.