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

Polygenic risk scores can be used to model the individual genetic liability for human traits. Current methods primarily focus on modeling the mean of a phenotype while neglecting the variance. However, genetic variants associated with phenotypic variance can provide important insights into gene–environment interaction studies. We propose snpboostlss, a cyclical gradient boosting algorithm for a Gaussian location-scale model to jointly derive sparse polygenic models for both the mean and the variance of a quantitative phenotype. To improve computational efficiency on high-dimensional and large-scale genotype data (large <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>n</mml:mi> </mml:math> and large <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>p</mml:mi> </mml:math> ), we only consider a batch of most relevant variants in each boosting step. We investigate the effect of statins therapy (the environmental factor) on low-density lipoprotein in the UK Biobank cohort using the snpboostlss algorithm. We find evidence of an interaction between statins usage and the polygenic risk scores for phenotypic variance in both cross-sectional and longitudinal analyses. Particularly, following the spirit of target trial emulation, we observe that the treatment effect of statins was more substantial in people with higher polygenic risk scores for phenotypic variance, indicating gene–environment interaction. When applying to body mass index, the newly constructed polygenic risk scores for variance show significant interaction with physical activity and sedentary behavior. Therefore, the polygenic risk scores for phenotypic variance derived by snpboostlss have potential to identify individuals that could benefit more from environmental changes (e.g. medical intervention and lifestyle changes).