Transfer learning enhances clinical utility of polygenic scores with small, phenotypically refined cohorts.

The clinical utility of PRS may be hindered by reliance on large, heterogeneous data sets for generation that dilute phenotypic specificity. Meanwhile, small, well-defined clinical cohorts (target) are ubiquitous but insufficient for PRS development. We propose an external-PRS (ePRS) framework borrowing from the transfer learning literature that integrates genetic evidence from target cohorts, incorporating continuous evidence measures and genetic correlation for robust predictions. Simulation indicates superior performance of ePRS across varying genetic correlations between the source and target phenotypes. ePRS refines an idiopathic generalized epilepsy (IGE) PRS to improve differentiation between juvenile myoclonic epilepsy (JME) and other IGE subtypes and, leveraging a large attention deficit hyperactivity disorder GWAS, enhances predictions for impulsivity in JME. Finally, to address concerns about potential cross-platform artifacts, we train and evaluate ePRS in the Canadian Cystic Fibrosis (CF) Gene Modifier Study cohort to predict CF-related diabetes using UK Biobank type 2 diabetes (T2D) summary statistics as the external source phenotype. ePRS continues to improve prediction accuracy in this single-cohort, harmonized-QC setting and offers more precise risk stratification and personalized care across complex traits.