A revised multi-tissue, multi-platform epigenetic clock model for methylation array data

Abstract

Epigenetic changes have long been investigated in association with the process of aging in humans. DNA methylation has been extensively used as a surrogate measure of biological age and correlations between “DNA methylation age” and chronological age have been established. A wide variety of epigenetic clocks has been designed to predict age in different tissues and on data obtained from different methylation platforms. We aimed to extend the scope of one of the most used epigenetic age predictors, the Horvath pan-tissue epigenetic clock, to improve its accuracy on data acquired from the latest Illumina methylation platform (BeadChip EPIC). We present three models trained on close to 6,000 samples of various source tissues and platforms and demonstrate their superior performance (Pearson correlation (r) = 0.917–0.921 and median absolute error (MAE) = 3.60–3.85 years) compared to the original model (r = 0.880 and MAE = 5.13 years) on a test set of more than 4,000 samples. The gain in accuracy was especially pronounced on EPIC array data (r = 0.89, MAE = 3.54 years vs. r = 0.83, MAE = 6.09 years), which was not available at the time when the original model was created. Our updated epigenetic clocks predict chronological age with great precision in an independent test cohort of samples on multiple tissue types and data platforms. Two of the three presented models exclusively use the covariates of the original epigenetic clock, albeit with different coefficients, allowing for straightforward adaptation for prefiltered datasets previously processed with the original predictor.