Abstract
A deeper understanding of how environmental factors influence genetic risks is crucial for exploring their combined effects on health outcomes. This can be effectively achieved by incorporating genotype-environment (GxE) interactions in polygenic risk score (PRS) models. We applied our recently developed GxEprs model to a wide range of obesity-related complex traits and diseases, leveraging data from the UK Biobank, to capture significant GxE signals. This work represents the first application of the "GxEprs" method, designed to minimize issues with spurious GxE signals and model misspecification. We identified significant GxE signals especially in quantitative phenotypes such as body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF) and waist circumference (WC) and our results indicated a significant enhancement in prediction accuracy in most traits, highlighting the importance of the GxE component. This study demonstrated the potential of incorporating GxE interactions in PRS models, offering a broad understanding of genetic risks and laying foundation in applying these insights in personalized medicine.