Pervasive interactions between exposures and polygenic risk can inform more effective clinical and behavioral interventions

Key Points:

• Recent studies emphasize the challenges and advancements in applying polygenic risk scores (PRS) across diverse populations, highlighting the need to manage differential performance to ensure equitable clinical implementation (Lewis et al., 2024; Lennon et al., 2024; Wang et al., 2024).
• Research frameworks have been developed to estimate disease incidence stratified by polygenic risk and to improve PRS calibration and prediction accuracy across various ancestries and contexts, addressing biases and variability in PRS performance (Jermy et al., 2024; Hou et al., 2024; Ding et al., 2023).
• Integration of polygenic risk scores with clinical risk factors has shown improved prediction for cardiovascular diseases and other chronic conditions, with implications for personalized prevention and treatment strategies (Samani et al., 2024; Natarajan et al., 2017; Fahed & Natarajan, 2023).
• Gene–environment interactions and their impact on health are increasingly recognized as critical for understanding complex traits and refining polygenic risk models, with efforts to mitigate statistical challenges such as type 1 error inflation in interaction studies (Herrera-Luis et al., 2024; Jayasinghe et al., 2024; Westerman & Sofer, 2024).
• Large-scale genomic initiatives like the All of Us Research Program and advances in statistical methods and tools (e.g., PLINK, Bayesian regression) are enhancing the diversity and scalability of genomic data, improving polygenic prediction particularly for under-represented populations (Bick et al., 2024; Tsuo et al., 2024; Purcell et al., 2007; Ge et al., 2019).