Dataset Information

The emerging landscape of dynamic DNA methylation in early childhood.

ABSTRACT: DNA methylation has been found to associate with disease, aging and environmental exposure, but it is unknown how genome, environment and disease influence DNA methylation dynamics in childhood.By analysing 538 paired DNA blood samples from children at birth and at 4-5 years old and 726 paired samples from children at 4 and 8 years old from four European birth cohorts using the Illumina Infinium Human Methylation 450 k chip, we have identified 14,150 consistent age-differential methylation sites (a-DMSs) at epigenome-wide significance of p?

SUBMITTER: Xu CJ

PROVIDER: S-EPMC5217260 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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The emerging landscape of dynamic DNA methylation in early childhood.

Xu Cheng-Jian CJ Bonder Marc Jan MJ Söderhäll Cilla C Bustamante Mariona M Baïz Nour N Gehring Ulrike U Jankipersadsing Soesma A SA van der Vlies Pieter P van Diemen Cleo C CC van Rijkom Bianca B Just Jocelyne J Kull Inger I Kere Juha J Antó Josep Maria JM Bousquet Jean J Zhernakova Alexandra A Wijmenga Cisca C Annesi-Maesano Isabella I Sunyer Jordi J Melén Erik E Li Yang Y Postma Dirkje S DS Koppelman Gerard H GH

BMC genomics 20170105 1

<h4>Background</h4>DNA methylation has been found to associate with disease, aging and environmental exposure, but it is unknown how genome, environment and disease influence DNA methylation dynamics in childhood.<h4>Results</h4>By analysing 538 paired DNA blood samples from children at birth and at 4-5 years old and 726 paired samples from children at 4 and 8 years old from four European birth cohorts using the Illumina Infinium Human Methylation 450 k chip, we have identified 14,150 consistent ...[more]

PMID: 28056824

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Project description:DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development. Most cell types, except germ cells and pre-implantation embryos, display relatively stable DNA methylation patterns, with 70-80% of all CpGs being methylated. Despite recent advances we still have a too limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in depth analysis of 42 whole genome bisulfite sequencing (WGBS) data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8% of autosomal CpGs within a normal developmental context, a majority of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription factor binding sites (TFBS), which allow identification of key lineage specific regulators. In addition, differentially methylated regions (DMRs) often harbor SNPs associated with cell type related diseases as determined by GWAS. The results also highlight the general inefficiency of WGBS as 70-80% of the sequencing reads across these data sets provided little or no relevant information regarding CpG methylation. To further demonstrate the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results confirm that only a fraction does so as part of coordinated regulatory programs. Therefore our selected DMRs can serve as a starting point to help guide novel, more effective reduced representation approaches to capture the most informative fraction of CpGs as well as further pinpoint putative regulatory elements. Analysis of 42 human WGBS libraries comprising 30 distinct primary cell lines, tissues, in vitro derived cell types and cell lines. BiSeq raw sequencing reads were aligned using maq in bisulfite mode (Li et al. 2008) or bsmap 2.7 (Xi et al. 2009) against human genome version hg19/GRCh37, discarding duplicate reads. DNA methylation calling was performed based on an extended custom software pipeline published previously for RRBS (Gu et al., 2010). The bed files contain all seen CpGs within the given library. The number of methylated reads/number of total reads is listed in the score column. This study includes a re-analysis of Samples from the NIH Roadmap Epigenomics Mapping Consortium (REMC; GSE16256, GSE17312), Hodges et al. 2011 (GSE31971), Molaro et al. 2011 (GSE30340), and Xi et al. 2013. WARNING: This submission is incomplete pending further deposits of raw data files from Meissner via EDACC.

Dataset Information

The emerging landscape of dynamic DNA methylation in early childhood.

Publications

The emerging landscape of dynamic DNA methylation in early childhood.

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