Enhancing the performance and interpretability of epigenetic clocks

This Zenodo data release provides the datasets required to reproduce the analyses and conclusions of our study on the regulatory mechanisms underlying epigenetic clocks. The data are designed to be used together with the complete analysis workflow and code available in the associated GitHub repository: https://github.com/Hoffmann-Lab/Enhancing_the_performance_and_interpretability_of_epigenetic_clocks. Epigenetic clocks based on DNA methylation (DNAm) accurately predict chronological age, yet their biological underpinnings remain poorly understood. We investigated whether age-predictive CpGs exert regulatory effects by overlapping transcription factor binding sites (TFBS). Our analyses show that most CpGs used in established epigenetic clocks do not overlap known TFBS, indicating that clock performance is not primarily driven by transcription factor binding dynamics. However, CpGs located within TFBS highlight transcription factors potentially involved in aging, including ZBED1, NFE2, and CEBPB, while RELA, IKZF1, and STAT3 are protected from age-associated methylation changes. Using TFBS-associated CpGs combined with noise-stabilizing feature engineering, we developed the "TFMethyl Clock", which outperforms existing models in age prediction. CpGs selected by this model are enriched for genes involved in interleukin-1β production and fatty-acid metabolism and preferentially localize to NR2C2 binding sites. Together, this dataset and the accompanying codebase enable full reproducibility and demonstrate that integrating regulatory information into epigenetic clocks improves predictive performance while providing mechanistic insight into aging.