UCLA

Various epigenetic clocks have been constructed using DNA methylation data, using regression models to estimate age from DNA methylation patterns. To overcome the constraints imposed by these epigenetic clocks, the Epigenetic Pacemaker (EPM) clock is built to predict an individual’s epigenetic state in an unbiased non-linear manner. The EPM clock models the initial methylation value and rate of change in methylation at each methylation locus, enabling an intuitive interpretation of coefficients of selected sites. Since the EPM model is computationally heavy, selecting informative loci in the preprocessing step is necessary. We selected model sites using either a novel randomized ridge regression selection method or the Pearson Correlation Coefficient (PCC) method. The PCC metric achieved higher performance and was used as the site selection method when applying EPM clock to a schizophrenia data set. In this data set, age acceleration predicted by EPM model was positively correlated with schizophrenia status and sex as a male. By experimenting with different EPM models, we conclude that a full model using all samples to build an EPM model generates stable epigenetic state predictions. Building EPM model using more sites with higher PCC values correlated with phenotype traits is more informative.