eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data.

Area of Science:

• Genomics
• Epigenetics
• Computational Biology

Background:

• Epigenome-wide association studies (EWAS) investigate non-genetic factors like DNA methylation in human diseases.
• Interpreting complex EWAS data requires sophisticated analytical approaches.

Purpose of the Study:

• To develop and present eFORGE, a novel tool for the analysis and interpretation of EWAS data.
• To determine the cell type-specific regulatory component of differentially methylated positions identified by EWAS.

Main Methods:

• eFORGE detects enrichment of overlap between EWAS-identified differentially methylated positions and DNase I hypersensitive sites.
• The analysis utilizes data from 454 samples across ENCODE, Roadmap Epigenomics, and BLUEPRINT projects.
• The tool is available as a standalone and web-based application.

Main Results:

• Application of eFORGE to 20 public EWAS datasets identified disease-relevant cell types for common diseases.
• A stem cell-like signature was detected in cancer datasets.
• eFORGE demonstrated the ability to detect cell-composition effects in EWAS from heterogeneous tissues.

Conclusions:

• eFORGE effectively bridges large-scale epigenomics data with EWAS findings for target selection.
• The tool provides insights into disease etiology by pinpointing specific cell types and regulatory mechanisms.
• eFORGE enhances the interpretation of EWAS, advancing our understanding of complex diseases.