13.04.2026 Neue Publikation: Detecting gene–environment interactions to guide personalized intervention: Boosting distributional regression for polygenic scores

Polygenic risk scores (PRS) combine information across many genetic variants to estimate a person’s predisposition to a trait. While most PRS focus on modeling the mean of a trait, patterns of variability can also be informative because they may indicate that genetic effects differ across environments. We propose a new algorithm that efficiently builds PRS for both the mean and the variance of a trait simultaneously. Investigating low-density lipoprotein (LDL) and body mass index (BMI) in UK Biobank, we found that the variance PRS (vPRS) showed strong interactions with environmental exposures such as statin use, physical activity and sedentary behavior. Individuals with higher LDL vPRS showed larger LDL reductions associated with statin use, while individuals with higher BMI vPRS showed stronger associations with physical activity and sedentary behavior, highlighting a novel application of PRS in gene-environment interactions with potential implications for personalized interventions. Details of the paper can be found here.