️ 60: Epi-PRS — A New Frontier in Polygenic Risk Prediction with Epigenomic Features

️ Episode 60: Epi-PRS — A New Frontier in Polygenic Risk Prediction with Epigenomic Features In this episode of Base by Base, we explore a breakthrough in genetic risk modeling presented by Zeng et al. (2025) in PNAS. The study introduces Epi-PRS, an innovative framework that enhances polygenic risk prediction from whole-genome sequencing (WGS) data by leveraging predicted epigenomic features. By using large language models trained on extensive reference epigenomic datasets, Epi-PRS transforms personal genome sequences into biologically meaningful molecular features that act as intermediaries between genotype and phenotype. Study highlights:Epi-PRS employs genomic large language models (LLMs) to infer tissue-specific epigenomic profiles from phased diploid genomes, enabling the integration of both common and rare variants into a unified risk model.The framework captures nonlinear relationships and regulatory effects that are often missed by traditional linear PRS methods, allowing more precise modeling of variant impact.Through extensive simulations, Epi-PRS demonstrated superior performance over leading PRS tools, particularly when phenotypic traits are influenced by rare variants or regulatory mechanisms.When applied to UK Biobank data, Epi-PRS achieved substantial improvements in risk prediction for breast cancer and type 2 diabetes, even when using a limited set of LD blocks compared to genome-wide methods.The model also proved robust under population stratification scenarios and identified disease-relevant regulatory regions enriched in tissue-specific epigenomic signals such as enhancers and promoters. Conclusion:Epi-PRS represents a significant step forward in polygenic risk prediction by integrating predicted regulatory features into disease modeling. Its ability to combine WGS data, epigenomic context, and deep learning frameworks offers a powerful and interpretable tool to advance personalized medicine and our understanding of complex genetic traits. Reference:Zeng, W., Guo, H., Liu, Q., & Wong, W.H. (2025). Improving polygenic prediction from whole-genome sequencing data by leveraging predicted epigenomic features. Proceedings of the National Academy of Sciences, 122(24), e2419202122. https://doi.org/10.1073/pnas.2419202122 License:This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

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