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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.


Related in: MedlinePlus

eFORGE Analysis of Surrogate Tissue and Multiple Sclerosis EWAS(A) DHS analysis of multiple sclerosis EWAS. Upper panel shows eFORGE blood, spleen, and thymus enrichment in Roadmap Epigenomics data for top 1,000 hypomethylated DMPs (ranked in the study by likelihood ratio test and Fisher’s method FDR q value). Lower panel shows enrichment for macrophages and monocytes in an analysis of the same regions with BLUEPRINT data.(B) Histone mark analysis of multiple sclerosis EWAS. Panel shows enrichment for top 1,000 study hypomethylated DMRs. Cell type-specific scores are colored by FDR q value. Cell types with q values below 0.01 for histone modifications representative of enhancers (H3K4me1) are shown in red, promoters (H3K4me3) are shown in purple, and polycomb-repressed regions (H3K27me3) are shown in green. Cell types with q values between 0.01 and 0.05 for the histone modification representative of promoters (H3K4me3) are shown in light purple. Cell types with q values above 0.01 for all other histone modifications are shown in blue. H3K36me3 (transcribed regions) and H3K9me3 (a marker for heterochromatin) did not present any significant cell type-specific enrichment patterns. Analyzed regions show enrichment for H3K4me1 (and, at a lower level, H3K4me3) in blood cells.(C) Analysis of surrogate tissue EWAS: the three panels (ENCODE, BLUEPRINT, and consolidated Roadmap, from top to bottom) show enrichment for monocyte, macrophage, and AML for an ovarian cancer prediction EWAS measured on whole blood. There is no enrichment for any other tissue (including lymphoid cells, ovarian tissue, and, interestingly, megakaryocytes). This supports a myeloid-lineage-specific DHS enrichment for top regions from this EWAS. By discarding enrichment in megakaryocyte regions, and showing enrichment for acute promyelocytic leukemia cell lines (NB4 and HL-60), the lineage-specific component of this tissue-specific signal points to a divergence that occurs after differentiation from the common myeloid progenitor and is suggestive of an event during myeloblastic differentiation. This DHS enrichment pattern extends to the myeloblast branch of the myeloid lineage, pointing to these regions being active in the myeloblast, which would be the cell of origin of this tissue-specific signal. This enrichment pattern shows cell types that drive the proposed myeloid/lymphoid imbalance causing the methylation signal observed (Teschendorff et al., 2009, Houseman et al., 2012, Li et al., 2014).
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fig6: eFORGE Analysis of Surrogate Tissue and Multiple Sclerosis EWAS(A) DHS analysis of multiple sclerosis EWAS. Upper panel shows eFORGE blood, spleen, and thymus enrichment in Roadmap Epigenomics data for top 1,000 hypomethylated DMPs (ranked in the study by likelihood ratio test and Fisher’s method FDR q value). Lower panel shows enrichment for macrophages and monocytes in an analysis of the same regions with BLUEPRINT data.(B) Histone mark analysis of multiple sclerosis EWAS. Panel shows enrichment for top 1,000 study hypomethylated DMRs. Cell type-specific scores are colored by FDR q value. Cell types with q values below 0.01 for histone modifications representative of enhancers (H3K4me1) are shown in red, promoters (H3K4me3) are shown in purple, and polycomb-repressed regions (H3K27me3) are shown in green. Cell types with q values between 0.01 and 0.05 for the histone modification representative of promoters (H3K4me3) are shown in light purple. Cell types with q values above 0.01 for all other histone modifications are shown in blue. H3K36me3 (transcribed regions) and H3K9me3 (a marker for heterochromatin) did not present any significant cell type-specific enrichment patterns. Analyzed regions show enrichment for H3K4me1 (and, at a lower level, H3K4me3) in blood cells.(C) Analysis of surrogate tissue EWAS: the three panels (ENCODE, BLUEPRINT, and consolidated Roadmap, from top to bottom) show enrichment for monocyte, macrophage, and AML for an ovarian cancer prediction EWAS measured on whole blood. There is no enrichment for any other tissue (including lymphoid cells, ovarian tissue, and, interestingly, megakaryocytes). This supports a myeloid-lineage-specific DHS enrichment for top regions from this EWAS. By discarding enrichment in megakaryocyte regions, and showing enrichment for acute promyelocytic leukemia cell lines (NB4 and HL-60), the lineage-specific component of this tissue-specific signal points to a divergence that occurs after differentiation from the common myeloid progenitor and is suggestive of an event during myeloblastic differentiation. This DHS enrichment pattern extends to the myeloblast branch of the myeloid lineage, pointing to these regions being active in the myeloblast, which would be the cell of origin of this tissue-specific signal. This enrichment pattern shows cell types that drive the proposed myeloid/lymphoid imbalance causing the methylation signal observed (Teschendorff et al., 2009, Houseman et al., 2012, Li et al., 2014).

Mentions: Third, we assessed whether eFORGE can uncover patterns in published EWAS data that would inform the functional interpretation of the statistical findings. Using the top 1,000 hypomethylated regions for an EWAS on multiple sclerosis (MS) (Huynh et al., 2014), we generated eFORGE plots for several DHS reference sets (Figure 6A). We observed a tissue-specific enrichment in immune cells, which is unexpected for a study performed on pathology-free brain tissue. To support this observation, we carried out additional eFORGE analysis using histone marks (Kundaje et al., 2015) that showed an enhancer-specific signature (H3K4me1) underlying this DHS enrichment (Figure 6B). We then intersected the top 1,235 hypomethylated regions from the study that gave rise to the observed immune signal with the locations of active enhancers (n = 1,158) previously identified in microglial cells (Lavin et al., 2014). These immune cells constitute up to 15% of all cells in the mammalian CNS (Xavier et al., 2014). A Fisher’s exact test confirmed significant co-localization of the microglial-specific active enhancers (p value: 2.70e-07, odds ratio [OR]: 5.88, 95% confidence interval [CI]: 3.19–9.96), suggesting that microglial enhancers may be potential drivers of the MS EWAS signal. In conclusion, eFORGE analysis of published MS EWAS data uncovered tissue-specific patterns, suggesting potential molecular mechanisms relevant to the etiology of the disease.


eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data
eFORGE Analysis of Surrogate Tissue and Multiple Sclerosis EWAS(A) DHS analysis of multiple sclerosis EWAS. Upper panel shows eFORGE blood, spleen, and thymus enrichment in Roadmap Epigenomics data for top 1,000 hypomethylated DMPs (ranked in the study by likelihood ratio test and Fisher’s method FDR q value). Lower panel shows enrichment for macrophages and monocytes in an analysis of the same regions with BLUEPRINT data.(B) Histone mark analysis of multiple sclerosis EWAS. Panel shows enrichment for top 1,000 study hypomethylated DMRs. Cell type-specific scores are colored by FDR q value. Cell types with q values below 0.01 for histone modifications representative of enhancers (H3K4me1) are shown in red, promoters (H3K4me3) are shown in purple, and polycomb-repressed regions (H3K27me3) are shown in green. Cell types with q values between 0.01 and 0.05 for the histone modification representative of promoters (H3K4me3) are shown in light purple. Cell types with q values above 0.01 for all other histone modifications are shown in blue. H3K36me3 (transcribed regions) and H3K9me3 (a marker for heterochromatin) did not present any significant cell type-specific enrichment patterns. Analyzed regions show enrichment for H3K4me1 (and, at a lower level, H3K4me3) in blood cells.(C) Analysis of surrogate tissue EWAS: the three panels (ENCODE, BLUEPRINT, and consolidated Roadmap, from top to bottom) show enrichment for monocyte, macrophage, and AML for an ovarian cancer prediction EWAS measured on whole blood. There is no enrichment for any other tissue (including lymphoid cells, ovarian tissue, and, interestingly, megakaryocytes). This supports a myeloid-lineage-specific DHS enrichment for top regions from this EWAS. By discarding enrichment in megakaryocyte regions, and showing enrichment for acute promyelocytic leukemia cell lines (NB4 and HL-60), the lineage-specific component of this tissue-specific signal points to a divergence that occurs after differentiation from the common myeloid progenitor and is suggestive of an event during myeloblastic differentiation. This DHS enrichment pattern extends to the myeloblast branch of the myeloid lineage, pointing to these regions being active in the myeloblast, which would be the cell of origin of this tissue-specific signal. This enrichment pattern shows cell types that drive the proposed myeloid/lymphoid imbalance causing the methylation signal observed (Teschendorff et al., 2009, Houseman et al., 2012, Li et al., 2014).
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fig6: eFORGE Analysis of Surrogate Tissue and Multiple Sclerosis EWAS(A) DHS analysis of multiple sclerosis EWAS. Upper panel shows eFORGE blood, spleen, and thymus enrichment in Roadmap Epigenomics data for top 1,000 hypomethylated DMPs (ranked in the study by likelihood ratio test and Fisher’s method FDR q value). Lower panel shows enrichment for macrophages and monocytes in an analysis of the same regions with BLUEPRINT data.(B) Histone mark analysis of multiple sclerosis EWAS. Panel shows enrichment for top 1,000 study hypomethylated DMRs. Cell type-specific scores are colored by FDR q value. Cell types with q values below 0.01 for histone modifications representative of enhancers (H3K4me1) are shown in red, promoters (H3K4me3) are shown in purple, and polycomb-repressed regions (H3K27me3) are shown in green. Cell types with q values between 0.01 and 0.05 for the histone modification representative of promoters (H3K4me3) are shown in light purple. Cell types with q values above 0.01 for all other histone modifications are shown in blue. H3K36me3 (transcribed regions) and H3K9me3 (a marker for heterochromatin) did not present any significant cell type-specific enrichment patterns. Analyzed regions show enrichment for H3K4me1 (and, at a lower level, H3K4me3) in blood cells.(C) Analysis of surrogate tissue EWAS: the three panels (ENCODE, BLUEPRINT, and consolidated Roadmap, from top to bottom) show enrichment for monocyte, macrophage, and AML for an ovarian cancer prediction EWAS measured on whole blood. There is no enrichment for any other tissue (including lymphoid cells, ovarian tissue, and, interestingly, megakaryocytes). This supports a myeloid-lineage-specific DHS enrichment for top regions from this EWAS. By discarding enrichment in megakaryocyte regions, and showing enrichment for acute promyelocytic leukemia cell lines (NB4 and HL-60), the lineage-specific component of this tissue-specific signal points to a divergence that occurs after differentiation from the common myeloid progenitor and is suggestive of an event during myeloblastic differentiation. This DHS enrichment pattern extends to the myeloblast branch of the myeloid lineage, pointing to these regions being active in the myeloblast, which would be the cell of origin of this tissue-specific signal. This enrichment pattern shows cell types that drive the proposed myeloid/lymphoid imbalance causing the methylation signal observed (Teschendorff et al., 2009, Houseman et al., 2012, Li et al., 2014).
Mentions: Third, we assessed whether eFORGE can uncover patterns in published EWAS data that would inform the functional interpretation of the statistical findings. Using the top 1,000 hypomethylated regions for an EWAS on multiple sclerosis (MS) (Huynh et al., 2014), we generated eFORGE plots for several DHS reference sets (Figure 6A). We observed a tissue-specific enrichment in immune cells, which is unexpected for a study performed on pathology-free brain tissue. To support this observation, we carried out additional eFORGE analysis using histone marks (Kundaje et al., 2015) that showed an enhancer-specific signature (H3K4me1) underlying this DHS enrichment (Figure 6B). We then intersected the top 1,235 hypomethylated regions from the study that gave rise to the observed immune signal with the locations of active enhancers (n = 1,158) previously identified in microglial cells (Lavin et al., 2014). These immune cells constitute up to 15% of all cells in the mammalian CNS (Xavier et al., 2014). A Fisher’s exact test confirmed significant co-localization of the microglial-specific active enhancers (p value: 2.70e-07, odds ratio [OR]: 5.88, 95% confidence interval [CI]: 3.19–9.96), suggesting that microglial enhancers may be potential drivers of the MS EWAS signal. In conclusion, eFORGE analysis of published MS EWAS data uncovered tissue-specific patterns, suggesting potential molecular mechanisms relevant to the etiology of the disease.

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.


Related in: MedlinePlus