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

View Article: PubMed Central - PubMed

ABSTRACT

Epigenome-wide association studies (EWAS) provide an alternative approach for studying human disease through consideration of non-genetic variants such as altered DNA methylation. To advance the complex interpretation of EWAS, we developed eFORGE (http://eforge.cs.ucl.ac.uk/), a new standalone and web-based tool for the analysis and interpretation of EWAS data. eFORGE determines the cell type-specific regulatory component of a set of EWAS-identified differentially methylated positions. This is achieved by detecting enrichment of overlap with DNase I hypersensitive sites across 454 samples (tissues, primary cell types, and cell lines) from the ENCODE, Roadmap Epigenomics, and BLUEPRINT projects. Application of eFORGE to 20 publicly available EWAS datasets identified disease-relevant cell types for several common diseases, a stem cell-like signature in cancer, and demonstrated the ability to detect cell-composition effects for EWAS performed on heterogeneous tissues. Our approach bridges the gap between large-scale epigenomics data and EWAS-derived target selection to yield insight into disease etiology.

No MeSH data available.


eFORGE Analysis of tDMPs and cDMPsResults show ability to predict target tissues from known tissue-specific differentially methylated positions (tDMPs) and cell type-specific DMPs (cDMPs): the heatmap is a composite of results for the top 1,000 tDMPs for blood, kidney, and lung (Lowe et al., 2015), and top cDMPs for CD14+, T cells, and NK cells (Jaffe and Irizarry, 2014). With tDMPs and cDMPs, we have the advantage of a known prior tissue- or cell type-specific association. We can thus test whether the eFORGE tool identifies the correct tissue. The color-coded enrichment results show that eFORGE identified the correct tissue or cell type each time, with no false-positives. This confirms the tool can signal when regions are associated by DNAm with a specific cell type. See also Figure S1 and Data S2, S3, S4, and S5.
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fig2: eFORGE Analysis of tDMPs and cDMPsResults show ability to predict target tissues from known tissue-specific differentially methylated positions (tDMPs) and cell type-specific DMPs (cDMPs): the heatmap is a composite of results for the top 1,000 tDMPs for blood, kidney, and lung (Lowe et al., 2015), and top cDMPs for CD14+, T cells, and NK cells (Jaffe and Irizarry, 2014). With tDMPs and cDMPs, we have the advantage of a known prior tissue- or cell type-specific association. We can thus test whether the eFORGE tool identifies the correct tissue. The color-coded enrichment results show that eFORGE identified the correct tissue or cell type each time, with no false-positives. This confirms the tool can signal when regions are associated by DNAm with a specific cell type. See also Figure S1 and Data S2, S3, S4, and S5.

Mentions: As a positive control, we assessed the ability of eFORGE to identify the correct tissues and cell type(s) when tested with probe sets of established tissue and cell-type specificity. In this regard, we analyzed three sets of previously reported tissue-specific DMPs (tDMPs) (Lowe et al., 2015) and three sets of cell type-specific DMPs (cDMPs) (Jaffe and Irizarry, 2014) using consolidated Roadmap DHS data. Figure 2 shows the resulting heatmap, which demonstrates the ability of eFORGE to unambiguously identify the relevant tissues and cell types for each tDMP and cDMP set (i.e., blood, kidney, lung, monocytes, natural killer cells, and T cells). To quantify the level at which eFORGE can detect mixed tissue- and cell type-specific enrichment, we next assessed its performance on mixed tDMP and cDMP probe lists. Figure S1A shows the result for a mixture of tDMPs from lung and kidney tissues (Lowe et al., 2015). Although both tissues were predicted correctly in eFORGE, the tissue-specific signal was lower, due to a lack of specific enrichment for the mixed sets in each of the cell types. Figure S1B shows the corresponding results for mixed cDMPs. Here, sets of 148 B cell-specific and 148 monocyte-specific cDMPs (Jaffe and Irizarry, 2014) were mixed, and again the corresponding cell types were correctly predicted. Taken together, we provided evidence that eFORGE can identify the correct target tissues and cell types from individual and mixed probe sets.


eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data
eFORGE Analysis of tDMPs and cDMPsResults show ability to predict target tissues from known tissue-specific differentially methylated positions (tDMPs) and cell type-specific DMPs (cDMPs): the heatmap is a composite of results for the top 1,000 tDMPs for blood, kidney, and lung (Lowe et al., 2015), and top cDMPs for CD14+, T cells, and NK cells (Jaffe and Irizarry, 2014). With tDMPs and cDMPs, we have the advantage of a known prior tissue- or cell type-specific association. We can thus test whether the eFORGE tool identifies the correct tissue. The color-coded enrichment results show that eFORGE identified the correct tissue or cell type each time, with no false-positives. This confirms the tool can signal when regions are associated by DNAm with a specific cell type. See also Figure S1 and Data S2, S3, S4, and S5.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC5120369&req=5

fig2: eFORGE Analysis of tDMPs and cDMPsResults show ability to predict target tissues from known tissue-specific differentially methylated positions (tDMPs) and cell type-specific DMPs (cDMPs): the heatmap is a composite of results for the top 1,000 tDMPs for blood, kidney, and lung (Lowe et al., 2015), and top cDMPs for CD14+, T cells, and NK cells (Jaffe and Irizarry, 2014). With tDMPs and cDMPs, we have the advantage of a known prior tissue- or cell type-specific association. We can thus test whether the eFORGE tool identifies the correct tissue. The color-coded enrichment results show that eFORGE identified the correct tissue or cell type each time, with no false-positives. This confirms the tool can signal when regions are associated by DNAm with a specific cell type. See also Figure S1 and Data S2, S3, S4, and S5.
Mentions: As a positive control, we assessed the ability of eFORGE to identify the correct tissues and cell type(s) when tested with probe sets of established tissue and cell-type specificity. In this regard, we analyzed three sets of previously reported tissue-specific DMPs (tDMPs) (Lowe et al., 2015) and three sets of cell type-specific DMPs (cDMPs) (Jaffe and Irizarry, 2014) using consolidated Roadmap DHS data. Figure 2 shows the resulting heatmap, which demonstrates the ability of eFORGE to unambiguously identify the relevant tissues and cell types for each tDMP and cDMP set (i.e., blood, kidney, lung, monocytes, natural killer cells, and T cells). To quantify the level at which eFORGE can detect mixed tissue- and cell type-specific enrichment, we next assessed its performance on mixed tDMP and cDMP probe lists. Figure S1A shows the result for a mixture of tDMPs from lung and kidney tissues (Lowe et al., 2015). Although both tissues were predicted correctly in eFORGE, the tissue-specific signal was lower, due to a lack of specific enrichment for the mixed sets in each of the cell types. Figure S1B shows the corresponding results for mixed cDMPs. Here, sets of 148 B cell-specific and 148 monocyte-specific cDMPs (Jaffe and Irizarry, 2014) were mixed, and again the corresponding cell types were correctly predicted. Taken together, we provided evidence that eFORGE can identify the correct target tissues and cell types from individual and mixed probe sets.

View Article: PubMed Central - PubMed

ABSTRACT

Epigenome-wide association studies (EWAS) provide an alternative approach for studying human disease through consideration of non-genetic variants such as altered DNA methylation. To advance the complex interpretation of EWAS, we developed eFORGE (http://eforge.cs.ucl.ac.uk/), a new standalone and web-based tool for the analysis and interpretation of EWAS data. eFORGE determines the cell type-specific regulatory component of a set of EWAS-identified differentially methylated positions. This is achieved by detecting enrichment of overlap with DNase I hypersensitive sites across 454 samples (tissues, primary cell types, and cell lines) from the ENCODE, Roadmap Epigenomics, and BLUEPRINT projects. Application of eFORGE to 20 publicly available EWAS datasets identified disease-relevant cell types for several common diseases, a stem cell-like signature in cancer, and demonstrated the ability to detect cell-composition effects for EWAS performed on heterogeneous tissues. Our approach bridges the gap between large-scale epigenomics data and EWAS-derived target selection to yield insight into disease etiology.

No MeSH data available.