Dataset Information

Differential DNA Methylation Analysis without a Reference Genome.

ABSTRACT: Genome-wide DNA methylation mapping uncovers epigenetic changes associated with animal development, environmental adaptation, and species evolution. To address the lack of high-throughput methods for DNA methylation analysis in non-model organisms, we developed an integrated approach for studying DNA methylation differences independent of a reference genome. Experimentally, our method relies on an optimized 96-well protocol for reduced representation bisulfite sequencing (RRBS), which we have validated in nine species (human, mouse, rat, cow, dog, chicken, carp, sea bass, and zebrafish). Bioinformatically, we developed the RefFreeDMA software to deduce ad hoc genomes directly from RRBS reads and to pinpoint differentially methylated regions between samples or groups of individuals (http://RefFreeDMA.computational-epigenetics.org). The identified regions are interpreted using motif enrichment analysis and/or cross-mapping to annotated genomes. We validated our method by reference-free analysis of cell-type-specific DNA methylation in the blood of human, cow, and carp. In summary, we present a cost-effective method for epigenome analysis in ecology and evolution, which enables epigenome-wide association studies in natural populations and species without a reference genome.

SUBMITTER: Klughammer J

PROVIDER: S-EPMC4695333 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Differential DNA Methylation Analysis without a Reference Genome.

Klughammer Johanna J Datlinger Paul P Printz Dieter D Sheffield Nathan C NC Farlik Matthias M Hadler Johanna J Fritsch Gerhard G Bock Christoph C

Cell reports 20151208 11

Genome-wide DNA methylation mapping uncovers epigenetic changes associated with animal development, environmental adaptation, and species evolution. To address the lack of high-throughput methods for DNA methylation analysis in non-model organisms, we developed an integrated approach for studying DNA methylation differences independent of a reference genome. Experimentally, our method relies on an optimized 96-well protocol for reduced representation bisulfite sequencing (RRBS), which we have va ...[more]

PMID: 26673328

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Project description:BackgroundOrganophosphates (OP) are widely used insecticides that acutely inhibit acetylcholinesterase enzyme activity. There is great interest in improving the understanding of molecular mechanisms related to chronic OP exposure induced toxicity. We aim to elucidate epigenetic changes associated with OP exposure, using untargeted analysis of genome-wide DNA methylation data.MethodsIn a population-based case control study of Parkinson's disease (PD), we assessed ambient OP exposure via residential and workplace proximity to commercial applications. We investigated associations between OP exposure and genome-wide DNA methylation (Illumina 450 k) in 580 blood samples (342 PD patients, 238 controls) and 259 saliva samples (128 patients, 131 controls). To identify differential methylation related to OP exposure, we controlled for age, sex, European ancestry, and PD status; in addition, we stratified by disease status.ResultsWe identified 70 genome-wide significant CpGs, including cg01600516 in ALOX12 (cor = 0.27, p = 1.73E-11) and two CpGs in HLA genes, cg01655658 (cor = -0.24, p = 2.80E-09) in HLA-L (pseudogene) and cg15680603 (cor = 0.20, p = 7.94E-07) in HLA-DPA1. Among the 70 CpGs located in 41 genes, 14 were also differentially methylated in saliva samples. The most overrepresented pathway was the nicotinic acetylcholine receptor signaling pathway (fold enrichment = 15.63, p = 1.01E-03, FDR = 1.64E-01). Expanding to a larger number of genes (CpG p < 5E-04, FDR < 2.25E-01; 1077 CpGs, 662 genes), the most enriched pathway shifted to the muscarinic acetylcholine receptor 1 and 3 signaling pathway (p-value = 5.36E-04, FDR = 4.73E-02). When we stratified by PD status, results were similar. Of the 70 significant CpGs, 63 were detected among both patients and controls and 7 were only associated with OP exposure among patients.ConclusionsThis study finds chronic low-level OP exposure is associated with differential DNA methylation in blood and saliva, both in elderly population controls and PD patients. Our study results suggest that long-term sub-acute OP exposure influences methylation in genes enriched for muscarinic and nicotinic acetylcholine receptor pathways.

Project description:BackgroundDNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.MethodsIn this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.ResultsPanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.ConclusionsPanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html .

Dataset Information

Differential DNA Methylation Analysis without a Reference Genome.

Publications

Differential DNA Methylation Analysis without a Reference Genome.

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