AltumAge: A Pan-Tissue DNA-Methylation Epigenetic Clock Based on Deep Learning

Abstract

Several age predictors based on DNA methylation, dubbed epigenetic clocks, have been created in recent years, with the vast majority based on regularized linear regression. This study explores the improvement in the performance and interpretation of epigenetic clocks using deep learning. First, we gathered 143 publicly available data sets from several human tissues to develop AltumAge, a neural network framework that is a highly accurate and precise age predictor. Compared to ElasticNet, AltumAge performs better for within-data set and cross-data set age prediction, being particularly more generalizable in older ages and new tissue types. We then used deep learning interpretation methods to learn which methylation sites contributed to the final model predictions. We observe that while most important CpG sites are linearly related to age, some highly-interacting CpG sites can influence the relevance of such relationships. Using chromatin annotations, we show that the CpG sites with the highest contribution to the model predictions were related to gene regulatory regions in the genome, including proximity to CTCF binding sites. We also found age-related KEGG pathways for genes containing these CpG sites. Lastly, we perform downstream analyses of AltumAge to explore its applicability and compare its age acceleration with Horvaths model. We show that our neural network approach predicts higher age acceleration for tumors and for cells that exhibit age-related changes in vitro, such as replicative senescence and mitochondrial dysfunction. Altogether, our neural network approach provides significant improvement and flexibility to current epigenetic clocks for both performance and model interpretability.