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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.

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Karyotype View of EWAS Hits and Bar Chart of EWAS Tissues(A) This karyotype view was obtained taking top ten study hits from each of the 20 EWAS with eFORGE signal (taken from Michels et al., 2013) and performed using ensembl KaryoView (http://www.ensembl.org/Homo_sapiens/Location/Genome). Many EWAS exclude probes from sex chromosomes as part of study analysis, and therefore there is an absence of top hits in these chromosomes on the graph. Apart from this, there seems to be no strong bias in the distribution of EWAS hits along the genome.(B) Bar chart indicating analyzed tissue for 20 EWAS with eFORGE signal from Michels et al. (2013). As is to be expected for an easily accessible tissue, blood is the most analyzed category, with ten studies.See also Table S7.
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fig4: Karyotype View of EWAS Hits and Bar Chart of EWAS Tissues(A) This karyotype view was obtained taking top ten study hits from each of the 20 EWAS with eFORGE signal (taken from Michels et al., 2013) and performed using ensembl KaryoView (http://www.ensembl.org/Homo_sapiens/Location/Genome). Many EWAS exclude probes from sex chromosomes as part of study analysis, and therefore there is an absence of top hits in these chromosomes on the graph. Apart from this, there seems to be no strong bias in the distribution of EWAS hits along the genome.(B) Bar chart indicating analyzed tissue for 20 EWAS with eFORGE signal from Michels et al. (2013). As is to be expected for an easily accessible tissue, blood is the most analyzed category, with ten studies.See also Table S7.

Mentions: Next, we applied eFORGE to analyze published EWAS data. First, we considered all EWAS compiled in a review article (Michels et al., 2013) that analyzed at least 100 samples using Illumina Infinium BeadChips. This qualified 44 datasets for eFORGE analysis, of which 20 showed eFORGE signal (q value <0.05). 14 studies showed an enrichment pattern specific to particular tissues. For instance, we observed blood-specific patterns for six blood-based EWAS, and stem cell-specific patterns for five cancer and aging EWAS (Figure 3). The genome-wide distribution of hits from these studies is shown in Figure 4. In addition, we found a larger average sample size for studies that present eFORGE signal (average n = 527) compared to those studies that did not (average n = 191). Taken together, these results suggest that tissue-specific enrichment patterns are widespread among EWAS and that eFORGE demonstrates the capacity to detect these patterns.


eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data
Karyotype View of EWAS Hits and Bar Chart of EWAS Tissues(A) This karyotype view was obtained taking top ten study hits from each of the 20 EWAS with eFORGE signal (taken from Michels et al., 2013) and performed using ensembl KaryoView (http://www.ensembl.org/Homo_sapiens/Location/Genome). Many EWAS exclude probes from sex chromosomes as part of study analysis, and therefore there is an absence of top hits in these chromosomes on the graph. Apart from this, there seems to be no strong bias in the distribution of EWAS hits along the genome.(B) Bar chart indicating analyzed tissue for 20 EWAS with eFORGE signal from Michels et al. (2013). As is to be expected for an easily accessible tissue, blood is the most analyzed category, with ten studies.See also Table S7.
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Related In: Results  -  Collection

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fig4: Karyotype View of EWAS Hits and Bar Chart of EWAS Tissues(A) This karyotype view was obtained taking top ten study hits from each of the 20 EWAS with eFORGE signal (taken from Michels et al., 2013) and performed using ensembl KaryoView (http://www.ensembl.org/Homo_sapiens/Location/Genome). Many EWAS exclude probes from sex chromosomes as part of study analysis, and therefore there is an absence of top hits in these chromosomes on the graph. Apart from this, there seems to be no strong bias in the distribution of EWAS hits along the genome.(B) Bar chart indicating analyzed tissue for 20 EWAS with eFORGE signal from Michels et al. (2013). As is to be expected for an easily accessible tissue, blood is the most analyzed category, with ten studies.See also Table S7.
Mentions: Next, we applied eFORGE to analyze published EWAS data. First, we considered all EWAS compiled in a review article (Michels et al., 2013) that analyzed at least 100 samples using Illumina Infinium BeadChips. This qualified 44 datasets for eFORGE analysis, of which 20 showed eFORGE signal (q value <0.05). 14 studies showed an enrichment pattern specific to particular tissues. For instance, we observed blood-specific patterns for six blood-based EWAS, and stem cell-specific patterns for five cancer and aging EWAS (Figure 3). The genome-wide distribution of hits from these studies is shown in Figure 4. In addition, we found a larger average sample size for studies that present eFORGE signal (average n = 527) compared to those studies that did not (average n = 191). Taken together, these results suggest that tissue-specific enrichment patterns are widespread among EWAS and that eFORGE demonstrates the capacity to detect these patterns.

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&nbsp;type-specific regulatory component of a set of&nbsp;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&nbsp;the ENCODE, Roadmap Epigenomics, and BLUEPRINT projects. Application of eFORGE to&nbsp;20&nbsp;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