Distinguishing Active Versus Passive DNA Demethylation Using Illumina MethylationEPIC BeadChip Microarrays.
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ABSTRACT:
SUBMITTER: Tiedemann RL
PROVIDER: S-EPMC8265207 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The 5-carbon position on cytosine nucleotides preceding guanines in genomic DNA (CpG) are common targets for DNA methylation (5mC). Genomic locations enriched for 5mC contribute to the regulation of chromatin structure and gene expression. While largely stable, massive waves of DNA demethylation are observed in key developmental windows in mammals. Additionally, DNA methylation is therapeutically targeted in cancer via DNA methyltransferase inhibition, in part to reverse abnormal silencing of tumor suppressor genes. DNA methylation removal can occur through both active and passive mechanisms. Ten-eleven translocation enzymes (TETs) oxidize 5mC in a stepwise manner to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC). These oxidized cytosine forms have reported function in gene regulation and also serve as intermediates in an active 5mC removal process that involves the base excision repair pathway. 5mC can also be removed passively through sequential cell divisions in the absence of DNA methylation maintenance. Distinguishing active versus passive 5mC removal on a genome-wide scale remains a challenge. In this chapter, we describe approaches that couple TET-assisted bisulfite (TAB) and oxidative bisulfite (OxBS) conversion to the Illumina MethylationEPIC BeadChIP (EPIC array) and show how these technologies can be used to distinguish active versus passive DNA demethylation. We also describe integrative bioinformatics pipelines to facilitate this analysis.
2021-01-31 | GSE145113 | GEO
Project description:Distinguishing active versus passive DNA demethylation using Illumina MethylationEPIC BeadChip microarrays
| PRJNA606056 | ENA
Project description:DNA methylation (DNAm) is commonly assayed using the Illumina Infinium MethylationEPIC BeadChip, but there is currently little published evidence to define the lower limits of the amount of DNA that can be used whilst preserving data quality. Such evidence is valuable for analyses utilizing precious or limited DNA sources. We used a single pooled sample of DNA in quadruplicate at three dilutions to define replicability and noise, and an independent population dataset of 328 individuals (from a community-based study including US-born non-Hispanic Black and white persons) to assess the impact of total DNA input on the quality of data generated using the Illumina Infinium MethylationEPIC BeadChip. We found that data are less reliable and more noisy as DNA input decreases to 40ng, with clear reductions in data quality; and that low DNA input is associated with a reduction in power to detect EWAS associations, requiring larger sample sizes. We conclude that DNA input as low as 40ng can be used with the Illumina Infinium MethylationEPIC BeadChip, provided quality checks and sensitivity analyses are undertaken.
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Project description:ObjectivesGenomic DNA (gDNA) is the optimal source of DNA for methylation analysis. This study compared methylation patterns in gDNA derived from blood with cell-line derived DNA (clDNA) from the same individuals. The clDNA had been generated via an Epstein-Barr virus transformation of the participant's lymphocytes. This analysis sought to determine whether clDNA has the potential to be utilised in lieu of finite/unavailable gDNA in methylation analyses using Illumina Infinium MethylationEPIC BeadChip arrays that assess 862,927 CpG sites.ResultsDNA samples were divided into two groups with eight gDNA and eight matched clDNA samples compared in each group (n = 16 individuals with 32 samples in total). Methylation patterns for gDNA samples generated for both groups were compared to the clDNA equivalent samples using Partek® Genomics Suite® to assess whether the significantly different CpG sites were consistent between both groups. In total, 28,632 CpG sites with significantly different levels of methylation (p < ×10-8) were common to both groups while 828,072 CpG sites assessed by the MethylationEPIC array were not significantly different in either group. This indicates that there is potential for clDNA to be used as a replacement for finite gDNA samples when absolutely necessary in DNA methylation studies.
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Project description:Genome-wide analysis of DNA methylation has now become a relatively inexpensive technique thanks to array-based methylation profiling technologies. The recently developed Illumina Infinium MethylationEPIC BeadChip interrogates methylation at over 850,000 sites across the human genome, covering 99% of RefSeq genes. This array supersedes the widely used Infinium HumanMethylation450 BeadChip, which has permitted insights into the relationship between DNA methylation and a wide range of conditions and traits. Previous research has identified issues with certain probes on both the HumanMethylation450 BeadChip and its predecessor, the Infinium HumanMethylation27 BeadChip, which were predicted to affect array performance. These issues concerned probe-binding specificity and the presence of polymorphisms at target sites. Using in silico methods, we have identified probes on the Infinium MethylationEPIC BeadChip that are predicted to (i) measure methylation at polymorphic sites and (ii) hybridise to multiple genomic regions. We intend these resources to be used for quality control procedures when analysing data derived from this platform.
| S-EPMC4909830 | biostudies-literature
Project description:A newly-developed platform, the Illumina TruSeq Methyl Capture EPIC library prep (TruSeq EPIC), builds on the content of the Infinium MethylationEPIC Beadchip Microarray (EPIC-array) and leverages the power of next-generation sequencing for targeted bisulphite sequencing. We empirically examined the performance of TruSeq EPIC and EPIC-array in assessing genome-wide DNA methylation in breast tissue samples. TruSeq EPIC provided data with a much higher density in the regions when compared to EPIC-array (~2.74 million CpGs with at least 10X coverage vs ~752 K CpGs, respectively). Approximately 398 K CpGs were common and measured across the two platforms in every sample. Overall, there was high concordance in methylation levels between the two platforms (Pearson correlation r = 0.98, P < 0.0001). However, we observed that TruSeq EPIC measurements provided a wider dynamic range and likely a higher quantitative sensitivity for CpGs that were either hypo- or hyper-methylated (β close to 0 or 1, respectively). In addition, when comparing different breast tissue types TruSeq EPIC identified more differentially methylated CpGs than EPIC-array, not only out of additional sites interrogated by TruSeq EPIC alone, but also out of common sites interrogated by both platforms. Our results suggest that both platforms show high reproducibility and reliability in genome-wide DNA methylation profiling, while TruSeq EPIC had a significant improvement over EPIC-array regarding genomic resolution and coverage. The wider dynamic range and likely higher precision of the estimates by the TruSeq EPIC may lead to the identification of novel differentially methylated markers that are associated with disease risk.
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Project description:The advent of array-based genome-wide DNA methylation methods has enabled quantitative measurement of single CpG methylation status at relatively low cost and sample input. Whereas the use of Infinium Human Methylation BeadChips has shown great utility in clinical studies, no equivalent tool is available for rodent animal samples. We examined the feasibility of using the new Infinium MethylationEPIC BeadChip for studying DNA methylation in mouse.In silico, we identified 19,420 EPIC probes (referred as mEPIC probes), which align with a unique best alignment score to the bisulfite converted reference mouse genome mm10. Further annotation revealed that 85% of mEPIC probes overlapped with mm10.refSeq genes at different genomic features including promoters (TSS1500 and TSS200), 1st exons, 5'UTRs, 3'UTRs, CpG islands, shores, shelves, open seas and FANTOM5 enhancers. Hybridization of mouse samples to Infinium Human MethylationEPIC BeadChips showed successful measurement of mEPIC probes and reproducibility between inter-array biological replicates. Finally, we demonstrated the utility of mEPIC probes for data exploration such as hierarchical clustering.Given the absence of cost and labor convenient genome-wide technologies in the murine system, our findings show that the Infinium MethylationEPIC BeadChip platform is suitable for investigation of the mouse methylome. Furthermore, we provide the "mEPICmanifest" with genomic features, available to users of Infinium Human MethylationEPIC arrays for mouse samples.
| S-EPMC5688710 | biostudies-literature
Project description:Differentially methylated regions (DMRs) are genomic regions with methylation patterns across multiple CpG sites that are associated with a phenotype. In this study, we proposed a Principal Component (PC) based DMR analysis method for use with data generated using the Illumina Infinium MethylationEPIC BeadChip (EPIC) array. We obtained methylation residuals by regressing the M-values of CpGs within a region on covariates, extracted PCs of the residuals, and then combined association information across PCs to obtain regional significance. Simulation-based genome-wide false positive (GFP) rates and true positive rates were estimated under a variety of conditions before determining the final version of our method, which we have named DMRPC. Then, DMRPC and another DMR method, coMethDMR, were used to perform epigenome-wide analyses of several phenotypes known to have multiple associated methylation loci (age, sex, and smoking) in a discovery and a replication cohort. Among regions that were analysed by both methods, DMRPC identified 50% more genome-wide significant age-associated DMRs than coMethDMR. The replication rate for the loci that were identified by only DMRPC was higher than the rate for those that were identified by only coMethDMR (90% for DMRPC vs. 76% for coMethDMR). Furthermore, DMRPC identified replicable associations in regions of moderate between-CpG correlation which are typically not analysed by coMethDMR. For the analyses of sex and smoking, the advantage of DMRPC was less clear. In conclusion, DMRPC is a new powerful DMR discovery tool that retains power in genomic regions with moderate correlation across CpGs.
| S-EPMC10193914 | biostudies-literature
Project description:DNA methylation age (DNAm age) has become a widely utilized epigenetic biomarker for the aging process. The Horvath method for determining DNAm age is perhaps the most widely utilized and validated DNA methylation age assessment measure. Horvath DNAm age is calculated based on methylation measurements at 353 loci, present on Illumina's 450k and 27k DNA methylation microarrays. With increasing use of the more recently developed Illumina MethylationEPIC (850k) microarray, it is worth revisiting this aging measure to evaluate estimation differences due to array design. Of the requisite 353 loci, 17 are missing from the 850k microarray. Similarly, an alternate, 71 loci DNA methylation age assessment measure created by Hannum et al. is missing 6 requisite loci. Using 17 datasets with 27k, 450k, and/or 850k methylation data, we compared each sample's epigenetic age estimated from all 353 loci required by the Horvath DNAm age calculator, and using only the 336 loci available on the 850k array. In 450k/27k data, removing loci not on the 850k array resulted in underestimation of Horvath's DNAm age. Underestimation of Horvath DNAm age increased from ages 0 to ~20, remaining stable thereafter (mean deviation = -3.46 y, SD = 1.13 for individuals ≥20 years). Underestimation of Horvath's DNAm age by the reduced 450k/27k data was similar to the underestimation observed in the 850k data indicating it is driven by missing probes. In analogous examination of Hannum's DNAm age, the magnitude and direction of epigenetic age misestimation varied with chronological age. In conclusion, inter-array deviations in DNAm age estimations may be largely driven by missing probes between arrays, despite default probe imputation procedures. Though correlations and associations based on Horvath's DNAm age may be unaffected, researchers should exercise caution when interpreting results based on absolute differences in DNAm age or when mixing samples assayed on different arrays.
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Project description:Illumina BeadChips are widely utilized in epigenome-wide association studies (EWAS). Several studies have reported that many probes on these arrays have poor reliability. Here, we compare different pre-processing methods to improve intra-class correlation coefficients (ICC). We describe the characteristics of ICC across the genome, within and between studies, and across different array platforms. Using technical duplicates from 128 subjects, we find that with raw data only 22.5% of the CpGs on 450 K array have 'acceptable' ICCs (>0.5). Data preprocessing steps, such as background correction and dye bias correction, can reduce technical noise and improve the percentage to 38.5%. Similar to previous studies, we found that ICC is associated with CpG methylation level such that 83% of CpGs with intermediate methylation (0.1< beta-value <0.9) have acceptable ICCs, whereas only 21% of CpGs with low or high methylation (beta-value <0.1 or >0.9) have acceptable ICCs. ICC is also correlated with CpG methylation variance; after mutual adjustment for beta-value and variance, only variance remains correlated. Many CpGs with poor ICCs (<0.5) are located in biologically important regulatory regions, including gene promoters and CpG islands. Poor ICC at these sites appears to be a consequence of low biologic variation among individuals rather than increased technical measurement variation. ICCs quality classifications are highly concordant across different array platforms and across different studies. We find that ICC can be reliably estimated with 30 pairs of duplicate samples. CpGs with acceptable ICC have higher study power and are more commonly reported in published epigenome-wide studies.
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