Bayesian Modeling of Epigenetic Variation in Multiple Human Cell Types
Yu Zhang , Feng Yue , Ross C. Hardison
With high-throughput sequencing data generated for multiple epigenetic features in many cell types, a chief challenge is to explain the dynamics in multiple epigenomes that lead to differential regulation and phenotypes. We introduce a Bayesian framework for jointly annotating multiple epigenomes and detecting differential regulation among multiple cell types. Our method, IDEAS (integrative and discriminative epigenome annotation system), achieves superior power by modeling both position and cell type specific epigenetic activities. Using ENCODE data sets in 6 cell types, we identified epigenetic variation strongly associated with differential gene expression. The detected regions are significantly enriched in disease genetic variants with much stronger enrichment scores than achievable by existing methods, and the enriched phenotypes are highly relevant to the corresponding cell types. IDEAS is a powerful tool for integrative epigenome annotation and detection of variation, which could be of important utility in elucidating the interplay between genetics, gene regulation and diseases.