A data-driven approach to preprocessing Illumina 450K methylation array data

By Ruth Pidsley, Chloe C Y Wong, M. Volta, K. Lunnon, Jonathan Mill, Leonard C. Schalkwyk. BMC Genomics, 2013

1,058 citations recorded by the source corpus.

As 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. The standard index of DNA methylation at any specific CpG site is β = M/(M + U + 100) where M and U are methylated and unmethylated signal intensities, respectively. Betas (βs) 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. Careful 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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d6780662d62ce843992ea64f68c71fc50c1a2c37

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Citations in the corpus

Functional normalization of 450k methylation array data improves replication in large cancer studies (2014, background): “, 2012] and dasen [Pidsley et al., 2013].”
An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease (2016, methodology): “The R package wateRmelon [70] was used for β-value normalization and quality control.”
A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking (2021, methodology): “We examined 34 individuals with two scans each in cerebellum 36 .”
RnBeads 2.0: comprehensive analysis of DNA methylation data (2019, methodology): “html), watermelon [38], and ChAMP [39], and with the methyKit [40] package for analyzing bisulfite sequencing data.”
De novo identification of differentially methylated regions in the human genome (2015, methodology): “For each comparison, samples were normalised separately using the dasen method from the R package wateRmelon [23].”
ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip (2015, methodology): “proposed background adjustment instead of background correction: adding the offset between Infinium I and II probe intensity values back to Infinium I probe intensities (5).”
Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma (2017, positive): “Data was preprocessed using the “minfi” (73) and “wateRmelon” (74) R packages.”
Epigenetic Signatures of Cigarette Smoking (2016, background): “Despite the decline in the prevalence of smoking in many countries, it remains the leading preventable cause of death in the world, accounting for ≈6 million deaths each year.(2)”