An epigenetic pacemaker is detected via a fast conditional expectation maximization algorithm

Abstract

Aim: DNA methylation has proven to be a remarkably accurate biomarker for human age, allowing the prediction of chronological age to within a couple of years. Recently, we proposed that the Universal PaceMaker (UPM), a flexible paradigm for modeling evolution, could be applied to epigenetic aging. Nevertheless, application to real data was restricted to small datasets for technical limitations. Materials & methods: We partition the set of variables into to two subsets and optimize the likelihood function on each set separately. This yields an extremely efficient Conditional Expectation Maximization algorithm, alternating between the two sets while increasing the overall likelihood. Results: Using the technique, we could reanalyze datasets of larger magnitude and show significant advantage to the UPM approach. Conclusion: The UPM more faithfully models epigenetic aging than the time linear approach while methylated sites accelerate and decelerate jointly.