Comparison of DNA methylation measured by Illumina 450K and EPIC BeadChips in blood of newborns and 14-year-old children

Solomon, Olivia; MacIsaac, Julie L.; Quach, Hong; Tindula, Gwen; Kobor, Michæl S.; Huen, Karen; Meaney, Michael J.; Eskenazi, Brenda; Barcellos, Lisa F.; Holland, Nina

doi:10.1080/15592294.2018.1497386

Cited by 72 publications

(83 citation statements)

References 20 publications

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“…To investigate the correlation between the arrays, the Pearson correlation coefficient was calculated per sample for the 370,699 probes common to both arrays and passing quality filters. Corroborating previous studies [3,4,6,8], we found that the per sample correlation of methylation -value was high. A 450K vs EPIC scatter plot of methylation -value for one cartilage sample illustrates that the overall correlation was high (Pearson's r 0.99) although many CpGs show greater than 5% difference between arrays (Fig.…”

Section: Overall Sample Correlation Between 450k and Epic Arrayssupporting

confidence: 89%

“…We also observed that CpG site methylation variance was generally low across our cartilage samples ( Fig.2A). Poor correlation CpG sites (r < 0.2) had a significantly lower β value variance than high correlation (r > 0.8) sites (median variance of 0.0018 and 0.0064 respectively, p<2.2x10 -16 , Fligner-Killeen non-parametric variance test) This confirms that the pattern of low variance sites and poor correlation observed in whole blood [3,4] and placenta [5] is also seen in cartilage.…”

Section: Variance and Correlation Of Cpg Sites By Probe Typesupporting

confidence: 73%

“…The Guthrie card whole blood dataset showed similar median (r = 0.285) and mean (r = 0.221) individual CpG correlations to cartilage ( Supplemental Table 1A) and other whole blood comparisons [3,4], with only 16.68% of probes having Pearson correlations above 0.8 ( Fig. 3A, Supplemental Table 1A).…”

Section: Differences In Variance and Correlation Between Tissue Typesmentioning

confidence: 72%

“…As ongoing studies and meta-analyses may use data from both array platforms, previous studies have assessed the concordance of the two by correlating methylation levels of the same samples assayed on both arrays. Studies comparing the two arrays have been reported in tumour, whole blood and placental samples [3][4][5][6]. Whilst high overall per sample correlations (Pearson's r > 0.9) were reported, two of the blood studies found that many individual sites in whole blood showed poor correlations (r < 0.2) [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…The studies in whole blood suggested that CpG sites with low variance and that are either completely methylated or un-methylated tend to exhibit poor reproducibility between 450K and EPIC arrays. Furthermore, several studies have highlighted sites with low reproducibility in blood and placental tissue that they have recommended be removed from the analysis when performing meta-analysis of 450K and EPIC datasets [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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Correlation of Infinium HumanMethylation450K and MethylationEPIC BeadChip arrays in cartilage

Cheung

Burgers

Young

et al. 2019

Preprint

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BackgroundDNA methylation of CpG sites is commonly measured using Illumina Infinium BeadChip platforms. The Infinium MethylationEPIC array has replaced the Infinium Methylation450K array. The two arrays use the same technology, with the EPIC array assaying 865859 CpG sites, almost double the number of sites present on the 450K array. In this study, we compare DNA methylation values of shared CpGs of the same human cartilage samples assayed using both platforms. Methods DNA methylation was measured in 21 human cartilage samples using the Illumina InfiniumMethylation450K BeadChip and the Infinium methylationEPIC array. Additional matched 450K and EPIC data in whole tumour and whole blood were downloaded from GEO GSE92580 and GSE86833 respectively. Data were processed using the Bioconductor package Minfi. Additionally, DNA methylation of six CpG sites was validated for the same 21 cartilage samples by use of pyrosequencing. ResultsIn cartilage samples, overall sample correlations between methylation values generated by the two arrays were high (Pearson correlation coefficient r > 0.96). However, 50.5% of CpG sites showed poor correlation (r < 0.2) between arrays. Sites with limited variance and with either very high or very low methylation levels in cartilage exhibited lower correlation values, corroborating prior studies in whole blood. Bisulfite pyrosequencing did not highlight one array as generating more accurate methylation values that the other. For a specific CpG site, the array methylation correlation coefficient differed between cartilage, tumour and whole blood, reflecting the difference in methylation variance between cell types. These patterns can be observed across different tissues with different CpG site variances. When performing differential methylation analysis, the mean probe correlation co-efficient increased with increasing Δβ threshold used. ConclusionCpG sites with low variability within a tissue showed poor reproducibility between arrays.However, variance and thus reproducibility differs across different tissue types. Therefore, researchers should be cautious when analysing methylation of CpG sites that show low methylation variance within the cell type of interest, regardless of platform or method used to assay methylation.

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Section: Overall Sample Correlation Between 450k and Epic Arrayssupporting

confidence: 89%

Section: Variance and Correlation Of Cpg Sites By Probe Typesupporting

confidence: 73%

Section: Differences In Variance and Correlation Between Tissue Typesmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Correlation of Infinium HumanMethylation450K and MethylationEPIC BeadChip arrays in cartilage

Cheung

Burgers

Young

et al. 2019

Preprint

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“Epigenetic clocks”: Theory and applications in human biology

Ryan

2020

American J Hum Biol

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All humans age, but how we age—and how fast—differs considerably from person to person. This deviation between apparent age and chronological age is often referred to as “biological age” (BA) and until recently robust tools for studying BA have been scarce. “Epigenetic clocks” are starting to change this. Epigenetic clocks use predictable changes in the epigenome, usually DNA methylation, to estimate chronological age with unprecedented accuracy. More importantly, deviations between epigenetic age and chronological age predict a broad range of health outcomes and mortality risks better than chronological age alone. Thus, epigenetic clocks appear to capture fundamental molecular processes tied to BA and can serve as powerful tools for studying health, development, and aging across the lifespan. In this article, I review epigenetic clocks, especially as they relate to key theoretical and applied issues in human biology. I first provide an overview of how epigenetic clocks are constructed and what we know about them. I then discuss emerging applications of particular relevance to human biologists—those related to reproduction, life‐history, stress, and the environment. I conclude with an overview of the methods necessary for implementing epigenetic clocks, including considerations of study design, sample collection, and technical considerations for processing and interpreting epigenetic clocks. The goal of this review is to highlight some of the ways that epigenetic clocks can inform questions in human biology, and vice versa, and to provide human biologists with the foundational knowledge necessary to successfully incorporate epigenetic clocks into their research.

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