Dataset Information

Comparison of Illumina 450K and EPIC arrays in placental DNA methylation.

ABSTRACT: Illumina HumanMethylation450 BeadChip (450K) has been commonly used to investigate DNA methylation in human tissues. Recently, it has been replaced by Illumina HumanMethylationEPIC BeadChip (EPIC) covering over 850,000 CpGs distributed genome-wide. Many consortia have now datasets coming from both arrays and aspire to analyze the two together. The placenta shows a high number of intermediate methylation levels and is often investigated for obstetric/birth outcomes, and potentially for long-term programming in offspring. We performed a systematic comparison between the two arrays using 108 duplicate placental samples from Gen3G birth cohort. We find that placenta shows a high per-sample correlation between the arrays, and higher median correlations at individual CpGs than those reported for blood. We identify 26,340 probes with absolute difference in per cent methylation >10%. We conclude that EPIC and 450K placental data can be combined, and we provide two lists of CpGs that should be excluded to avoid misleading results.

SUBMITTER: Fernandez-Jimenez N

PROVIDER: S-EPMC6791701 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Comparison of Illumina 450K and EPIC arrays in placental DNA methylation.

Fernandez-Jimenez Nora N Allard Catherine C Bouchard Luigi L Perron Patrice P Bustamante Mariona M Bilbao Jose Ramon JR Hivert Marie-France MF

Epigenetics 20190628 12

Illumina HumanMethylation450 BeadChip (450K) has been commonly used to investigate DNA methylation in human tissues. Recently, it has been replaced by Illumina HumanMethylationEPIC BeadChip (EPIC) covering over 850,000 CpGs distributed genome-wide. Many consortia have now datasets coming from both arrays and aspire to analyze the two together. The placenta shows a high number of intermediate methylation levels and is often investigated for obstetric/birth outcomes, and potentially for long-term ...[more]

PMID: 31250700

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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.

Project description:BackgroundAs the most stable and experimentally accessible epigenetic mark, DNA methylation is of great interest to the research community. The landscape of DNA methylation across tissues, through development and in disease pathogenesis is not yet well characterized. Thus there is a need for rapid and cost effective methods for assessing genome-wide levels of DNA methylation. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a very useful addition to the available methods for DNA methylation analysis but its complex design, incorporating two different assay methods, requires careful consideration. Accordingly, several normalization schemes have been published. We have taken advantage of known DNA methylation patterns associated with genomic imprinting and X-chromosome inactivation (XCI), in addition to the performance of SNP genotyping assays present on the array, to derive three independent metrics which we use to test alternative schemes of correction and normalization. These metrics also have potential utility as quality scores for datasets.ResultsThe standard index of DNA methylation at any specific CpG site is ? = M/(M + U + 100) where M and U are methylated and unmethylated signal intensities, respectively. Betas (?s) calculated from raw signal intensities (the default GenomeStudio behavior) perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. More elaborate manipulation of quantiles proves to be counterproductive.ConclusionsCareful selection of preprocessing steps can minimize variance and thus improve statistical power, especially for the detection of the small absolute DNA methylation changes likely associated with complex disease phenotypes. For the convenience of the research community we have created a user-friendly R software package called wateRmelon, downloadable from bioConductor, compatible with the existing methylumi, minfi and IMA packages, that allows others to utilize the same normalization methods and data quality tests on 450K data.

Dataset Information

Comparison of Illumina 450K and EPIC arrays in placental DNA methylation.

Publications

Comparison of Illumina 450K and EPIC arrays in placental DNA methylation.

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