Homomorphic encryption enables privacy preserving polygenic risk scores.

Polygenic risk score models (PRSs) are important tools in precision medicine, enabling personalized risk prediction; however, they raise privacy concerns. Fully homomorphic encryption (FHE) provides a potential solution, allowing computation on encrypted genomic data. Here, we develop an open-source implementation of FHE for PRS (HEPRS), available online. HEPRS involves a three party system: clients (clinicians handling sensitive genetic data), modelers developing a PRS (academics), and evaluators (a local hospital running the models while maintaining confidentiality). We apply HEPRS to synthetic datasets and a 110,000 single-nucleotide-polymorphism (SNP) model for schizophrenia and show that encrypted and plaintext PRSs agree closely. We investigate encryption parameters that influence computational accuracy, memory, and time, demonstrating that HEPRS is practical to use on a single CPU. These results show that FHE enables realistic, privacy-preserving PRSs with negligible accuracy loss, supporting secure and scalable genomic analytics.