Bayesian Modeling of Epigenetic Variation in Multiple Human Cell Types

With massive amount of sequencing data generated for many epigenetic features in a variety of cell and tissue types, the chief challenges are to build effective and quantitative models explaining how the dynamics in multiple epigenomes lead to differential gene expression and diverse phenotypes. We developed a unified Bayesian framework for jointly annotating multiple epigenomes and detecting differential regulation among multiple tissues and cell types over regions of varying sizes. Our method, called IDEAS ( i ntegrative and d iscriminative e pigenome a nnotation s ystem), achieves superior power and accuracy over existing methods by modeling both position and cell type specific regulatory activities. Using 84 ENCODE epigenetic data sets in 6 cell types, we identified epigenetic variation of different sizes that are strongly associated with differential gene expression. The detected regions are significantly enriched in genetic variants associated with complex phenotypes. Our results yielded much stronger enrichment scores than achievable by existing approaches, and the enriched phenotypes are highly relevant to the corresponding cell types. IDEAS is a powerful statistical tool for integrative annotation of regulatory element...Continue Reading