Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases

Abstract

Early prediction of risk of cardiovascular disease (CVD), including stroke, is a cornerstone of disease prevention. Clinical risk scores have been widely used for predicting CVD risk from known risk factors. Most CVDs have a substantial genetic component, which also has been confirmed for stroke in recent gene discovery efforts. However, the role of genetics in prediction of risk of CVD, including stroke, has been limited to testing for highly penetrant monogenic disorders. In contrast, the importance of polygenic variation, the aggregated effect of many common genetic variants across the genome with individually small effects, has become more apparent in the last 5 to 10 years, and powerful polygenic risk scores for CVD have been developed. Here we review the current state of the field of polygenic risk scores for CVD including stroke, and their potential to improve CVD risk prediction. We present findings and lessons from diseases such as coronary artery disease as these will likely be useful to inform future research in stroke polygenic risk prediction.

Keywords: cardiovascular disease; genetics; myocardial infarction; risk assessment; stroke.

Figure 1. Distribution of typical current and future PRS in the population.

(A) Stratification of risk for typical current CVD PRSs (explaining 10% of heritability), comparing the top bins in the populations versus the middle PRS category (45–55%) in terms of odds ratio (OR). (B) Distributions of PRS in disease and non-disease individuals for a typical current CVD PRS, as well as receiver-operating characteristic curves (ROC; AUC=0.645). (C) Discrimination for a hypothetical future PRS explaining all CVD heritability (AUC=0.914). Based on simulations of the liability threshold model assuming a CVD population prevalence of 3% and heritability of 50%.