EPIC DNA Methylation Array
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ABSTRACT:
PROVIDER: EGAD00010001642 | EGA |
REPOSITORIES: EGA
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Project description:BackgroundThere has been a steady increase in the number of studies aiming to identify DNA methylation differences associated with complex phenotypes. Many of the challenges of epigenetic epidemiology regarding study design and interpretation have been discussed in detail, however there are analytical concerns that are outstanding and require further exploration. In this study we seek to address three analytical issues. First, we quantify the multiple testing burden and propose a standard statistical significance threshold for identifying DNA methylation sites that are associated with an outcome. Second, we establish whether linear regression, the chosen statistical tool for the majority of studies, is appropriate and whether it is biased by the underlying distribution of DNA methylation data. Finally, we assess the sample size required for adequately powered DNA methylation association studies.ResultsWe quantified DNA methylation in the Understanding Society cohort (n = 1175), a large population based study, using the Illumina EPIC array to assess the statistical properties of DNA methylation association analyses. By simulating null DNA methylation studies, we generated the distribution of p-values expected by chance and calculated the 5% family-wise error for EPIC array studies to be 9 × 10- 8. Next, we tested whether the assumptions of linear regression are violated by DNA methylation data and found that the majority of sites do not satisfy the assumption of normal residuals. Nevertheless, we found no evidence that this bias influences analyses by increasing the likelihood of affected sites to be false positives. Finally, we performed power calculations for EPIC based DNA methylation studies, demonstrating that existing studies with data on ~ 1000 samples are adequately powered to detect small differences at the majority of sites.ConclusionWe propose that a significance threshold of P < 9 × 10- 8 adequately controls the false positive rate for EPIC array DNA methylation studies. Moreover, our results indicate that linear regression is a valid statistical methodology for DNA methylation studies, despite the fact that the data do not always satisfy the assumptions of this test. These findings have implications for epidemiological-based studies of DNA methylation and provide a framework for the interpretation of findings from current and future studies.
| S-EPMC6518823 | biostudies-literature
Project description:BackgroundSex differences are known to play a role in disease aetiology, progression and outcome. Previous studies have revealed autosomal epigenetic differences between males and females in some tissues, including differences in DNA methylation patterns. Here, we report for the first time an analysis of autosomal sex differences in DNAme using the Illumina EPIC array in human whole blood by performing a discovery (n = 1171) and validation (n = 2471) analysis.ResultsWe identified and validated 396 sex-associated differentially methylated CpG sites (saDMPs) with the majority found to be female-biased CpGs (74%). These saDMP's are enriched in CpG islands and CpG shores and located preferentially at 5'UTRs, 3'UTRs and enhancers. Additionally, we identified 266 significant sex-associated differentially methylated regions overlapping genes, which have previously been shown to exhibit epigenetic sex differences, and novel genes. Transcription factor binding site enrichment revealed enrichment of transcription factors related to critical developmental processes and sex determination such as SRY and ESR1.ConclusionOur study reports a reliable catalogue of sex-associated CpG sites and elucidates several characteristics of these sites using large-scale discovery and validation data sets. This resource will benefit future studies aiming to investigate sex specific epigenetic signatures and further our understanding of the role of DNA methylation in sex differences in human whole blood.
| S-EPMC9107695 | biostudies-literature
Project description:The level of dNA methylation in BRE80-BRE80-T5 and T47D cells expressing active and inctive DNMT3A was quantified using EPIC array across more than 850,000 CpGs
2024-10-16 | GSE249125 | GEO
Project description:DNA methylation analysis in oropharyngeal squamous carcinoma (OPSCC) samples and oropharyngeal non-cancerous mucosa samples. Infinium MethylationEPIC BeadChip Kit was used to obtain DNA methylation profiles across more than 850,000 CpG sites. Total samples included 89 OPSCC samples and 5 non-cancerous mucosa samples.
2020-01-20 | GSE124632 | GEO
Project description:Quantification of global DNA methylation using EPIC array
| PRJNA1047525 | ENA
Project description:DNA methylation analysis in oropharyngeal cancer [EPIC array]
| PRJNA512879 | ENA
Project description:Comprehensive DNA methylation analysis in malignant melanoma clinical samples and primary melanocyte culture. Infinium MethylationEPIC BeadChip was used to obtain DNA methylation profiles. Samples included 8 malignant melanoma cases and primary melanocyte culture.
2020-10-22 | GSE140170 | GEO
Project description:DNA methylation is one of the major epigenetic modifications and has frequently demonstrated its suitability as diagnostic and prognostic biomarker. In addition to chip and sequencing based epigenome wide methylation profiling methods, targeted bisulfite sequencing (TBS) has been established as a cost-effective approach for routine diagnostics and target validation applications. Yet, an easy-to-use tool for the analysis of TBS data in combination with array-based methylation results has been missing. Consequently, we have developed EPIC-TABSAT, a user-friendly web-based application for the analysis of targeted sequencing data that additionally allows the integration of array-based methylation results. The tool can handle multiple targets as well as multiple sequencing files in parallel and covers the complete data analysis workflow from calculation of quality metrics to methylation calling and interactive result presentation. The graphical user interface offers an unprecedented way to interpret TBS data alone or in combination with array-based methylation studies. Together with the computation of target-specific epialleles it is useful in validation, research, and routine diagnostic environments. EPIC-TABSAT is freely accessible to all users at https://tabsat.ait.ac.at/.
| S-EPMC6602470 | biostudies-literature
Project description:In human, the 39 coding HOX genes and 18 referenced non-coding antisense transcripts are arranged in four genomic clusters named HOXA, B, C, and D. This highly conserved family belongs to the homeobox class of genes that encode transcription factors required for normal development. Therefore, HOX gene deregulation might contribute to the development of many cancer types. Here, we study HOX gene deregulation in adult glioma, a common type of primary brain tumor. We performed extensive molecular analysis of tumor samples, classified according to their isocitrate dehydrogenase (IDH1) gene mutation status, and of glioma stem cells. We found widespread expression of sense and antisense HOX transcripts only in aggressive (IDHwt) glioma samples, although the four HOX clusters displayed DNA hypermethylation. Integrative analysis of expression-, DNA methylation- and histone modification signatures along the clusters revealed that HOX gene upregulation relies on canonical and alternative bivalent CpG island promoters that escape hypermethylation. H3K27me3 loss at these promoters emerges as the main cause of widespread HOX gene upregulation in IDHwt glioma cell lines and tumors. Our study provides the first comprehensive description of the epigenetic changes at HOX clusters and their contribution to the transcriptional changes observed in adult glioma. It also identified putative "master" HOX proteins that might contribute to the tumorigenic potential of glioma stem cells.
2021-03-09 | GSE161175 | GEO