The Epigenetic Pacemaker: modeling epigenetic states under an evolutionary framework

Abstract

Summary: Epigenetic rates of change, much as evolutionary mutation rate along a lineage, vary during lifetime. Accurate estimation of the epigenetic state has vast medical and biological implications. To account for these non-linear epigenetic changes with age, we recently developed a formalism inspired by the Pacemaker model of evolution that accounts for varying rates of mutations with time. Here, we present a python implementation of the Epigenetic Pacemaker (EPM), a conditional expectation maximization algorithm that estimates epigenetic landscapes and the state of individuals and may be used to study non-linear epigenetic aging.

Availability and implementation: The EPM is available at https://pypi.org/project/EpigeneticPacemaker/ under the MIT license. The EPM is compatible with python version 3.6 and above.

Fig. 1.

Epigenetic state predictions for ($n=405$) test samples compared to the chronological age of each sample with a line of best fit for the EPM (A) and linear regression (B) models. The non-linear trend observed in the EPM model better captures the observed aging trend and reduces observed error as measured by mean absolute error (MAE). (C) Epigenetic state predictions made for whole blood samples using the EPM and (D) linear model