DNA methylation provides a rich epigenetic signal that reflects both genetic and environmental influences and can potentially be leveraged in multiple ways in medicine. In this talk, I will discuss two complementary directions: using methylation risk scores for disease prediction and for imputing missing phenotypes from electronic health records, and using methylation data for association analysis, where signals of interest are often obscured by tissue heterogeneity. I will describe how dimensionality reduction and deconvolution techniques enable the identification of cell-type–specific disease signals from bulk methylation measurements, and conclude by highlighting open computational questions at the intersection of prediction, interpretability, and heterogeneous biological data.