GxE PRS: Genotype-environment interaction in polygenic risk score models for quantitative and binary traits

The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual’s genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing GxE PRS models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose a novel GxE PRS model that correctly incorporates the GxE component to analyze complex traits and diseases. Through extensive simulations, we demonstrate that our proposed model outperforms existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed model to real data, and report significant GxE effects. Specifically, we highlight the impact of our model on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index (BMI) by alcohol intake frequency (ALC). In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension (HYP) by waist-to-hip ratio (WHR). These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxE PRS, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, while providing a practical tool to support further research in this area.