Epigenetic Pacemaker: A fast conditional expectation maximization algorithm for modeling epigenetic states
DNA methylation is widely used to model physiological phenotypes, such as aging1 and type II diabetes2. The Epigenetic Pacemaker, EPM, is an implementation of a fast conditional expectation maximization algorithm that models epigenetic states under and evolutionary framework3. The EPM was first introduced by Snir et al. 4 as an extension of the Universal Pacemaker (UPM) model of genome evolution. In contrast to regression bases approaches, the EPM does not assume a linear relationship between the epigenetic state and a trait of interest. As a result the EPM can model non-linear epigenetic trait associations directly without transformation of the phenotype of interest5. The software implementation of the Epigenetic Pacemaker is described our publication The Epigenetic Pacemaker - modeling epigenetic states under an evolutionary framework6.
pip3 install EpigeneticPacemaker
- Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).
- Orozco, L. D. et al. Epigenome-wide association in adipose tissue from the METSIM cohort. Hum. Mol. Genet. 0, 223495 (2018).
- Snir, S. & Pellegrini, M. An epigenetic pacemaker is detected via a fast conditional expectation maximization algorithm. 10, 695–706 (2018).
- Snir, S., vonHoldt, B. M. & Pellegrini, M. A Statistical Framework to Identify Deviation from Time Linearity in Epigenetic Aging. PLoS Comput. Biol. 12, 1–15 (2016).
- Snir, S., Farrell, C. & Pellegrini, M. Human epigenetic ageing is logarithmic with time across the entire lifespan. Epigenetics (2019). doi:10.1080/15592294.2019.1623634
- Colin Farrell, Sagi Snir, Matteo Pellegrini, The Epigenetic Pacemaker - modeling epigenetic states under an evolutionary framework, Bioinformatics, , btaa585, https://doi.org/10.1093/bioinformatics/btaa585