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

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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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Aggregated Enrichment Statistics for Studies with eFORGE Signal from a Recent ReviewStudies were obtained from the review by Michels et al. (2013). This heatmap shows the enrichment statistics (presented as –log10(binomial p value)) for an unbiased selection of EWAS (n = 20 studies, each with at least 100 samples). Many of these studies show an enrichment pattern specific to particular tissues, such as blood (blue box, seven studies) and stem cells (red box, five studies). In addition, one ccRCC study shows a kidney specific enrichment and one CLL study presents a lung-specific enrichment (lung tissue and IMR90). Other patterns are more mixed (yellow box, six studies). Of the seven blood-enriched studies, six were performed in blood and one was performed in breast cancer tissue, which may contain immune cells. All five studies that show a stem cell-specific enrichment are exclusively cancer or aging EWAS. Of the six studies that show a mixed enrichment, there is evidence of different components underlying variation. For example, the EWAS on child maltreatment performed on salivary DNA, despite showing enrichment for many tissues, has blood cell types as the highest categories. Work remains to be done to refine these mixed signals and define the components that are driving enrichment for several different tissue types. See also Table S7.
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fig3: Aggregated Enrichment Statistics for Studies with eFORGE Signal from a Recent ReviewStudies were obtained from the review by Michels et al. (2013). This heatmap shows the enrichment statistics (presented as –log10(binomial p value)) for an unbiased selection of EWAS (n = 20 studies, each with at least 100 samples). Many of these studies show an enrichment pattern specific to particular tissues, such as blood (blue box, seven studies) and stem cells (red box, five studies). In addition, one ccRCC study shows a kidney specific enrichment and one CLL study presents a lung-specific enrichment (lung tissue and IMR90). Other patterns are more mixed (yellow box, six studies). Of the seven blood-enriched studies, six were performed in blood and one was performed in breast cancer tissue, which may contain immune cells. All five studies that show a stem cell-specific enrichment are exclusively cancer or aging EWAS. Of the six studies that show a mixed enrichment, there is evidence of different components underlying variation. For example, the EWAS on child maltreatment performed on salivary DNA, despite showing enrichment for many tissues, has blood cell types as the highest categories. Work remains to be done to refine these mixed signals and define the components that are driving enrichment for several different tissue types. 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
Aggregated Enrichment Statistics for Studies with eFORGE Signal from a Recent ReviewStudies were obtained from the review by Michels et al. (2013). This heatmap shows the enrichment statistics (presented as –log10(binomial p value)) for an unbiased selection of EWAS (n = 20 studies, each with at least 100 samples). Many of these studies show an enrichment pattern specific to particular tissues, such as blood (blue box, seven studies) and stem cells (red box, five studies). In addition, one ccRCC study shows a kidney specific enrichment and one CLL study presents a lung-specific enrichment (lung tissue and IMR90). Other patterns are more mixed (yellow box, six studies). Of the seven blood-enriched studies, six were performed in blood and one was performed in breast cancer tissue, which may contain immune cells. All five studies that show a stem cell-specific enrichment are exclusively cancer or aging EWAS. Of the six studies that show a mixed enrichment, there is evidence of different components underlying variation. For example, the EWAS on child maltreatment performed on salivary DNA, despite showing enrichment for many tissues, has blood cell types as the highest categories. Work remains to be done to refine these mixed signals and define the components that are driving enrichment for several different tissue types. See also Table S7.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5120369&req=5

fig3: Aggregated Enrichment Statistics for Studies with eFORGE Signal from a Recent ReviewStudies were obtained from the review by Michels et al. (2013). This heatmap shows the enrichment statistics (presented as –log10(binomial p value)) for an unbiased selection of EWAS (n = 20 studies, each with at least 100 samples). Many of these studies show an enrichment pattern specific to particular tissues, such as blood (blue box, seven studies) and stem cells (red box, five studies). In addition, one ccRCC study shows a kidney specific enrichment and one CLL study presents a lung-specific enrichment (lung tissue and IMR90). Other patterns are more mixed (yellow box, six studies). Of the seven blood-enriched studies, six were performed in blood and one was performed in breast cancer tissue, which may contain immune cells. All five studies that show a stem cell-specific enrichment are exclusively cancer or aging EWAS. Of the six studies that show a mixed enrichment, there is evidence of different components underlying variation. For example, the EWAS on child maltreatment performed on salivary DNA, despite showing enrichment for many tissues, has blood cell types as the highest categories. Work remains to be done to refine these mixed signals and define the components that are driving enrichment for several different tissue types. 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