The necessity of incorporating non-genetic risk factors into polygenic risk score models

Abstract

The growing public interest in genetic risk scores for various health conditions may inspire preventive health action. However, these risk scores can be deceiving as they do not consider other, easily attainable risk factors, such as sex, BMI, age, smoking habits, parental disease status and physical activity. We show improved performance in identifying the 10% most at-risk individuals for type 2 diabetes (T2D) and coronary artery disease (CAD) by including these common risk factors. Incidence in the highest risk group increases from 3.2- and 4.2-fold to 5.8 for T2D, when comparing the genetics-based model, common risk factor-based model and combined model, respectively. Similarly, we see an increase from 2.9- and 2.4-fold to 5.0-fold risk for CAD. Importantly, we show that genetic and common risk factors capture different risks. As such, it is paramount that all these variables are considered when reporting risk, unlike current practice with available genetic tests.