DOI


Introduction

Here we provide a complete workflow for the preprocessing and analysis of DNA methylation array data. The workflow combines best practices in the field with in-house developed methodology, and is geared towards large-scale studies, including Epigenome-wide Association Studies (EWAS). Its development was informed by our research analysing BIOS consortium data, which contains multiomics measures from 6 Dutch biobanks comprising ~4000 individuals (Dekkers et al. 2016, R. C. Slieker et al. (2016), Bonder et al. (2017), Luijk et al. (2018)) .

The DNAmArray-package contains a series of convenient functions for the quality control, normalization, and analysis of methylation array data. The workflow has been thoroughly tested for the Illumina 450k array but is similarly applicable to the newer 850k EPIC array.

It is worth noting that this workflow makes extensive use of other BioConductor packages. For example, the DNAmArray function read.metharray.exp.par() converts IDAT files to an RGset by harnessing functions from minfi (Aryee et al. 2014) and combining them with BiocParallel. Usually the required packages are installed automatically, but otherwise please refer to the relevant package’s documentation.

Furthermore, we have also developed packages used by this workflow. MethylAid (Iterson et al. 2014) provides a web application to assist in performing interactive sample quality control, and bacon (Iterson et al. 2017) corrects for bias and inflation in ome-wide association studies, such as EWAS.


Example Data

The example data (Cobben et al. 2019) used in this workflow is available from the NCBI Gene Expression Omnibus (GEO), a public repository of microarray data. It contains genome-wide DNA methylation data from whole blood obtained using the Illumina 450k microarray. The participants consist of 46 fetal alcohol spectrum disorder (FASD) cases and 92 controls from both a discovery and replication cohort.


Installation

The DNAmArray-package can be installed in several ways, and has been successfully for >= R-3.2.0 on various Linux-builds and for >= R-3.5.3 on Windows.

Install using the devtools-package

First install the package devtools, then use the install_github() function to fetch the DNAmArray package.

library(devtools)
install_github("molepi/DNAmArray")

Install from source using git/R

Using git, you can git clone our repository and then install the package, changing _x.y.z. to the version you cloned.

git clone git@github.com/molepi/DNAmArray.git
R CMD build DNAmArray
R CMD INSTALL DNAmArray_x.y.z.tar.gz

References

Aryee, Martin J., Andrew E. Jaffe, Hector Corrada-Bravo, Christine Ladd-Acosta, Andrew P. Feinberg, Kasper D. Hansen, and Rafael A. Irizarry. 2014. “Minfi: A flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays.” Bioinformatics 30 (10). Oxford University Press: 1363–9. doi:10.1093/bioinformatics/btu049.

Bonder, Marc Jan, René Luijk, Daria V Zhernakova, Matthijs Moed, Patrick Deelen, Martijn Vermaat, Maarten van Iterson, et al. 2017. “Disease variants alter transcription factor levels and methylation of their binding sites.” Nature Genetics 49 (1). Nature Publishing Group: 131–38. doi:10.1038/ng.3721.

Cobben, Jan M., Izabela M. Krzyzewska, Andrea Venema, Adri N. Mul, Abeltje Polstra, Alex V. Postma, Robert Smigiel, et al. 2019. “DNA-methylation abundantly associates with fetal alcohol spectrum disorder and its subphenotypes.” Epigenomics, March. Future Science Ltd London, UK, epi–2018–0221. doi:10.2217/epi-2018-0221.

Dekkers, Koen F., Maarten van Iterson, Roderick C. Slieker, Matthijs H. Moed, Marc Jan Bonder, Michiel van Galen, Hailiang Mei, et al. 2016. “Blood lipids influence DNA methylation in circulating cells.” Genome Biology 17 (1). BioMed Central: 138. doi:10.1186/s13059-016-1000-6.

Iterson, Maarten van, Elmar W. Tobi, Roderick C. Slieker, Wouter Den Hollander, René Luijk, P. Eline Slagboom, and Bastiaan T. Heijmans. 2014. “MethylAid: Visual and interactive quality control of large Illumina 450k datasets.” Bioinformatics 30 (23). Oxford University Press: 3435–7. doi:10.1093/bioinformatics/btu566.

Iterson, Maarten van, Erik W. van Zwet, Bastiaan T. Heijmans, Peter A.C. ’t Hoen, Joyce van Meurs, Rick Jansen, Lude Franke, et al. 2017. “Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution.” Genome Biology 18 (1). BioMed Central: 19. doi:10.1186/s13059-016-1131-9.

Luijk, René, Haoyu Wu, Cavin K Ward-Caviness, Eilis Hannon, Elena Carnero-Montoro, Josine L. Min, Pooja Mandaviya, et al. 2018. “Autosomal genetic variation is associated with DNA methylation in regions variably escaping X-chromosome inactivation.” Nature Communications 9. Nature Publishing Group. doi:10.1038/S41467-018-05714-3.

Slieker, Roderick C, Maarten van Iterson, René Luijk, Marian Beekman, Daria V Zhernakova, Matthijs H Moed, Hailiang Mei, et al. 2016. “Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms.” Genome Biology 17 (1). BioMed Central: 191. doi:10.1186/s13059-016-1053-6.