Abstract
Diagnosis and prediction of health outcomes using machine learning has shown major advances over the last few years. One of the major challenges remaining is the sparsity of electronic health records data, which often requires an imputation step. Genomic data can potentially be used to improve our ability for imputation; indeed, polygenic risk scores using genetic data have been heavily studied in the past few years. In this talk, I will present approaches for imputation using DNA methylation, and compare those predictions to polygenic risk scores and to traditional EHR imputation approaches.