Epigenetic pacemaker: closed form algebraic solutions

Abstract

Background: DNA methylation is widely used as a biomarker in crucial medical applications as well as for human age prediction of very high accuracy. This biomarker is based on the methylation status of several hundred CpG sites. In a recent line of publications we have adapted a versatile concept from evolutionary biology -the Universal Pacemaker (UPM) -to the setting of epigenetic aging and denoted it the Epigenetic PaceMaker (EPM). The EPM, as opposed to other epigenetic clocks, is not confined to specific pattern of aging, and the epigenetic age of the individual is inferred independently of other individuals. This allows an explicit modeling of aging trends, in particular non linear relationship between chronological and epigenetic age. In one of these recent works, we have presented an algorithmic improvement based on a two-step conditional expectation maximization (CEM) algorithm to arrive at a critical point on the likelihood surface. The algorithm alternates between a time step and a site step while advancing on the likelihood surface. Results: Here we introduce non trivial improvements to these steps that are essential for analyzing data sets of realistic magnitude in a manageable time and space. These structural improvements are based on insights from linear algebra and symbolic algebra tools, providing us greater understanding of the degeneracy of the complex problem space. This understanding in turn, leads to the complete elimination of the bottleneck of cumbersome matrix multiplication and inversion, yielding a fast closed form solution in both steps of the CEM. In the experimental results part, we compare the CEM algorithm over several data sets and demonstrate the speedup obtained by the closed form solutions. Our results support the theoretical analysis of this improvement. Conclusions: These improvements enable us to increase substantially the scale of inputs analyzed by the method, allowing us to apply the new approach to data sets that could not be analyzed before.

The Multi-State Epigenetic Pacemaker enables the identification of combinations of factors that influence DNA methylation

“…As an alternative to penalized regression based methods we previously developed an evolutionary based model for epigenetic dynamics, the Epigenetic Pacemaker (EPM) [13,14]. The EPM attempts to minimize the difference between observed and predicted methylation values amongst a collection of sites through the implementation of a conditional expectation maximization algorithm [15]. Under the EPM the observed methylation status of a collection of sites is modeled linearly with respect to an input factor of interest, such as age.…”