UCLA

DNA methylation patterns change with increasing age, therefore contributing to some age related diseases. Epigenetic drift over time might result in measurable differences between biological and chronological age, so epigenetic changes are shown to be reflective of an individual's lifestyle. Consequently, the estimation of the epigenetic state can help with medical and biological research, and may act as functional biomarkers of disease before clinical threshold is reached. A variety of epigenetic clocks have been constructed with regression models to approach age estimation from individuals’ DNA methylation patterns. This study involves the use of the Epigenetic Pacemaker Model (EPM), an implementation of a fast conditional expectation maximization algorithm to model non-linear epigenetic trait associations directly without transformation of the phenotype of interest. EPM models the initial methylation value as well as the rate of change at each locus, therefore allows an intuitive interpretation of the selected sites. Since EPM is computationally heavy, it will be useful to build a succinct model for the users who would like to use EPM in their studies. Therefore, the aim of the project is to make EPM more accessible for users to analyze their epigenetic data, at the same time conserving the accuracy in the epigenetic data prediction. The project is primarily based on python code, and uses the R shiny to develop a website app in order for users from other places to upload their data and implement the EPM graph displaying the logarithmic trend of the relationship between epigenetic state and chronological age.