Homomorphic Encryption: An Application to Polygenic Risk Scores

1BackgroundPolygenic risk scores (PRSs) have emerged as a powerful tool in precision medicine, enabling personalized risk assessments for complex diseases. However, the use of sensitive genomic data in PRS calculations raises concerns about privacy and security. Fully homomorphic encryption (FHE) offers a promising solution by allowing computations on encrypted data, preserving the privacy of both genomic information and PRS models. MethodsHere, we present an application of FHE for encrypted PRS calculations using a particular protocol (CKKS) within the Lattigo library. Our approach involves a three-party system: clients (clinicians handling sensitive genetic data), modelers developing a PRS (academics or companies), and evaluators (a local hospital running the models while maintaining data confidentiality). We demonstrate the feasibility and accuracy of our approach by applying it to synthetic datasets of various sizes and to a robust 110k-single-nucleotide polymorphism (SNP) model for schizophrenia. The complete codebase and a sample dataset are available at https://github.com/gersteinlab/HEPRS. ResultsThe difference between traditional plaintext and encrypted PRS calculation results is negligible: the R2 is 0.999 and the mean squared error is 2.27 x 10-6. Moreover, while the encrypted calculation is roughly 1,000 times slower than conventional non-encrypted ones (when considering only the core PRS calculation), the computation remains feasible on a single-CPU node. For example, processing [~]1,100 individuals with [~]110k SNPs took six minutes and [~]65 GB of memory on a laptop computer. In addition, we investigated the impact of the encryption parameters on the computational time and accuracy in detail, showing the expected slowdown with higher security settings. ConclusionOur approach showcases the applicability and feasibility of using FHE on real-world PRS models. With the pressing need for privacy-preserving solutions in the era of precision medicine, our work serves as a pilot application, offering a simple use case and providing a detailed comparison and evaluation in terms of accuracy, cost, and scalability.