Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses

by Luanluan Sun, Lisa Pennells, Stephen Kaptoge, Christopher P. Nelson, Scott C. Ritchie, Gad Abraham, Matthew Arnold, Steven Bell, Thomas Bolton, Stephen Burgess, Frank Dudbridge, Qi Guo, Eleni Sofianopoulou, David Stevens, John R. Thompson, Adam S. Butterworth, Angela Wood, John Danesh, Nilesh J. Samani, Michael Inouye, Emanuele Di Angelantonio BackgroundPolygenic risk scores (PRSs) can stratify populations into cardiovascular disease (CVD) risk groups. We aimed to quantify the potential advantage of adding information on PRSs to conventional risk factors in the primary prevention of CVD. Methods and findingsUsing data from UK Biobank on 306,654 individuals without a history of CVD and not on lipid-lowering treatments (mean age [SD]: 56.0 [8.0] years; females: 57%; median follow-up: 8.1 years), we calculated measures of risk discrimination and reclassification upon addition of PRSs to risk factors in a conventional risk prediction model (i.e., age, sex, systolic blood pressure, smoking status, history of diabetes, and total and high-density lipoprotein cholesterol). We then modelled the implications of initiating guideline-recommended statin therapy in a primary care setting using incidence rates from 2.1 million individuals from the Clinical Practice Research Datalink. The C-index, a measure of risk discrimination, was 0.710 (95% CI 0.703 –0.717) for a CVD prediction model containing conventional risk predictors alone. Addition of information on PRSs increased the C-inde...
Source: PLoS Medicine - Category: Internal Medicine Authors: Source Type: research