In multiple studies DNA methylation has proven to be an accurate biomarker of age. To develop these biomarkers, the methylation of multiple CpG sites is typically linearly combined to predict chronological age [1]. By contrast, in this study we apply the Universal PaceMaker (UPM) model to investigate changes in DNA methylation during aging. The UPM was initially developed by us in a separate project to study rate acceleration/deceleration during genome evolution. Here, rather than identifying which linear combinations of sites predicts age as in the traditional approach, the new framework, the Epigenetic PaceMaker (EPM) models the rates of change of multiple CpG sites, as well as their starting methylation levels, and estimates the age of each individual to optimize the model fit [2]. We refer to the estimated age as the “epigenetic age”, which is in contrast to the known chronological age of each individual. We construct a statistical framework and devise an algorithm to determine whether a genomic pacemaker is in effect (i.e. rates of change vary with age). The decision is made by comparing two competing likelihood-based models, the molecular clock (MC) and UPM. As a separate task, we have devised a conditional expectation maximization procedure that relies on the intrinsic property of the mathematical formulation of the EPM, allowing us to entirely replace the cumbersome linear algebra steps by a closed form rational function in the input parameters, leading to a speed up of several orders of magnitudes [3].

A follow up question to the initial MC/EPM question of [2], is to decide whether there are trends in population aging. To address this question, we have devised a two-stage procedure leveraging our epigenetic universal pacemaker to infer in an unbiased manner the aging trends over the entire lifespan of a population. By applying this procedure to datasets spanning broad age ranges, including one from before birth to old age, and over several tissue types, we have shown unambiguously the existence of a universal logarithmic trend in epigenetic aging [4].  We believe this is a significant result, and will impact future studies that use epigenetics to elucidate human aging and longevity.