TY - JOUR T1 - Cell type-specific signal analysis in EWAS JF - bioRxiv DO - 10.1101/2021.05.21.445209 SP - 2021.05.21.445209 AU - Charles E. Breeze Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/05/23/2021.05.21.445209.abstract N2 - Hundreds of epigenome-wide association studies (EWAS) have been performed, successfully identifying replicated epigenomic signals in processes such as ageing 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.Competing Interest StatementThe authors have declared no competing interest. ER -