Conference Agenda

Polygenic scores (PGS) quantify genetic predisposition to complex traits and clinical outcomes based on genotype data. This talk addresses recent computational and biostatistical challenges in PGS modelling, including their integration with environmental risk factors. First, training PGS models on high-dimensional and large-scale genotype data with hundreds of thousands of genetic variants and individuals requires scalable yet interpretable statistical learning methods. Second, the transferability of PGS models to diverse populations with different ancestries remains limited, as models are typically trained on cohorts predominantly of European ancestry. Third, the evaluation of predictive performance is complicated by different and sometimes conflicting definitions of the commonly used R-squared measure on test data. To address these challenges, scalable statistical learning approaches for PGS modelling based on individual-level genotype data are presented, including boosting and anchor regression. Finally, open problems and directions for future research are highlighted, with the aim of improving robustness, interpretability and gene–environment integration in personalized medicine.