A data-driven approach to preprocessing Illumina 450K methylation array data
Ruth Pidsley,Chloe C. Y. Wong,Manuela Volta,Katie Lunnon,Jonathan Mill,Jonathan Mill,Leonard C. Schalkwyk +6 more
Reads0
Chats0
TLDR
It is demonstrated that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics, and that 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.Abstract:
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.read more
Citations
More filters
Journal ArticleDOI
Genetic impacts on DNA methylation: research findings and future perspectives.
Sergio Villicaña,Jordana T. Bell +1 more
TL;DR: A review of recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs, can be found in this paper.
Journal ArticleDOI
Epigenetic dysregulation in the developing Down syndrome cortex
Nady El Hajj,Marcus Dittrich,Julia Böck,Theo F. J. Kraus,Indrajit Nanda,Tobias Müller,Larissa Seidmann,Tim Tralau,Danuta Galetzka,Eberhard Schneider,Thomas Haaf +10 more
TL;DR: Bisulfite pyrosequencing and targeted RNA sequencing showed that several genes of PCDHG subfamilies A and B are hypermethylated and transcriptionally downregulated in fetal DS cortex, which is expected to reduce dendrite arborization and growth in cortical neurons.
Journal ArticleDOI
Prenatal unhealthy diet, insulin-like growth factor 2 gene (IGF2) methylation, and attention deficit hyperactivity disorder symptoms in youth with early-onset conduct problems
Jolien Rijlaarsdam,Charlotte A.M. Cecil,Esther Walton,Maurissa Sydney Chapman Mesirow,Caroline L Relton,Tom R. Gaunt,Wendy L. McArdle,Edward D. Barker +7 more
TL;DR: Preventing ‘unhealthy diet’ in pregnancy might reduce the risk of ADHD symptoms in EOP youth via lower offspring IGF2 methylation.
Journal ArticleDOI
Epigenetic signatures of starting and stopping smoking.
Daniel L. McCartney,Anna J. Stevenson,Robert F. Hillary,Rosie M. Walker,Mairead L. Bermingham,Stewart W. Morris,Toni-Kim Clarke,Archie Campbell,Alison D. Murray,Heather C. Whalley,David J. Porteous,Peter M. Visscher,Andrew M. McIntosh,Kathryn L. Evans,Ian J. Deary,Riccardo E. Marioni +15 more
TL;DR: The findings suggest that smoking–associated DNA methylation changes are a result of prolonged exposure to cigarette smoke, and can be reversed following cessation, and may provide an additional criterion on which to stratify risk.
Journal ArticleDOI
Genome-wide mapping of genetic determinants influencing DNA methylation and gene expression in human hippocampus.
Herbert Schulz,Ann-Kathrin Ruppert,Stefan Herms,Christiane Wolf,Nazanin Mirza-Schreiber,Oliver Stegle,Darina Czamara,Andreas J. Forstner,Andreas J. Forstner,Sugirthan Sivalingam,Susanne Schoch,Susanne Moebus,Benno Pütz,Axel M. Hillmer,Nadine Fricker,Hartmut Vatter,Bertram Müller-Myhsok,Bertram Müller-Myhsok,Markus M. Nöthen,Albert J. Becker,Per Hoffmann,Thomas Sander,Sven Cichon +22 more
TL;DR: The present genome-wide quantitative trait loci analyses explore the cis-regulatory effects of single-nucleotide polymorphisms (SNPs) on DNA methylation and gene expression (eQTL) in 110 human hippocampal biopsies to provide a resource for the functional interpretation of SNPs in brain disorders.
References
More filters
Journal ArticleDOI
Bioconductor: open software development for computational biology and bioinformatics
Robert Gentleman,Vincent J. Carey,Douglas M. Bates,Benjamin M. Bolstad,Marcel Dettling,Sandrine Dudoit,Byron Ellis,Laurent Gautier,Yongchao Ge,Jeff Gentry,Kurt Hornik,Torsten Hothorn,Wolfgang Huber,Stefano Maria Iacus,Rafael A. Irizarry,Friedrich Leisch,Cheng Li,Martin Maechler,A. J. Rossini,Günther Sawitzki,Colin A. Smith,Gordon K. Smyth,Luke Tierney,Jean Yang,Jianhua Zhang +24 more
TL;DR: Details of the aims and methods of Bioconductor, the collaborative creation of extensible software for computational biology and bioinformatics, and current challenges are described.
Journal ArticleDOI
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
Rafael A. Irizarry,Bridget G. Hobbs,Francois Collin,Yasmin Beazer-Barclay,Kristen J. Antonellis,Uwe Scherf,Terence P. Speed +6 more
TL;DR: There is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities, and the exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values.
Book ChapterDOI
limma: Linear Models for Microarray Data
TL;DR: This chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments with technical as well as biological replication.
Book
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
TL;DR: In this article, the authors present a detailed case study of R algorithms with publicly available data, and a major section of the book is devoted to fully worked case studies, with a companion website where readers can reproduce every number, figure and table on their own computers.
Journal ArticleDOI
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
TL;DR: In this article, the authors present a Bioinformatics and Computational Biology Solutions Using R and Bioconductor (BIBOS) using R and BIBOS, which is a combination of R and CRF.