Abstract
Hundreds of epigenome-wide association studies (EWAS) have been performed, successfully identifying replicated epigenomic signals in processes such as aging and smoking. Despite this progress, it remains a major challenge in EWAS to detect both cell type-specific and cell type confounding effects impacting study results. One way to identify these effects is through eFORGE (experimentally derived Functional element Overlap analysis of ReGions from EWAS), a published tool that uses 815 datasets from large-scale mapping studies to detect enriched tissues, cell types, and genomic regions. Here, I show that eFORGE analysis can be extended to EWAS differentially variable positions (DVPs), identifying target cell types and tissues. In addition, I also show that eFORGE tissue-specific enrichment can be detected for sites below EWAS significance threshold. I develop on these and other analysis examples, extending our knowledge of eFORGE cell type- and tissue-specific enrichment results for different EWAS.
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Breeze, C.E. (2022). Cell Type-Specific Signal Analysis in Epigenome-Wide Association Studies. In: Guan, W. (eds) Epigenome-Wide Association Studies. Methods in Molecular Biology, vol 2432. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1994-0_5
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DOI: https://doi.org/10.1007/978-1-0716-1994-0_5
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