Linkage disequilibrium analysis of allelic heterogeneity in DNA methylation

Saito, Daisuke; Suyama, Mikita

doi:10.1080/15592294.2015.1115176

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…Therefore, methylation LD decays over much shorter distance in tens to hundreds of bases, with the exception of imprinting regions. Even if longer-read sequencing methods were used, we do not expect a radical change of the block-like pattern presented in this work, which is supported by another recent study 33 . Nonetheless, these short and punctuated blocks capture discrete entities of epigenetic regulation in individual cells widespread in the human genome.…”

Section: Discussionsupporting

confidence: 89%

Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA

Guo¹,

Diep²,

et al. 2017

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Adjacent CpG sites in mammalian genomes can be co-methylated due to the processivity of methyltransferases or demethylases. Yet discordant methylation patterns have also been observed, and found related to stochastic or uncoordinated molecular processes. We focused on a systematic search and investigation of regions in the full human genome that exhibit highly coordinated methylation. We defined 147,888 blocks of tightly coupled CpG sites, called methylation haplotype blocks (MHBs) with 61 sets of whole genome bisulfite sequencing (WGBS) data, and further validated with 101 sets of reduced representation bisulfite sequencing (RRBS) data and 637 sets of methylation array data. Using a metric called methylation haplotype load (MHL), we performed tissue-specific methylation analysis at the block level. Subsets of informative blocks were further identified for deconvolution of heterogeneous samples. Finally, we demonstrated quantitative estimation of tumor load and tissue-of-origin mapping in the circulating cell-free DNA of 59 cancer patients using methylation haplotypes.

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Section: Discussionsupporting

confidence: 89%

Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA

Guo¹,

Diep²,

et al. 2017

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“…Previous studies have identified multiple markers that are differentially regulated in LUAD with improved accuracy. This research has shown that the evaluation of a DNA methylation panel is a highly sensitive method for detecting cancer ( Diaz-Lagares et al, 2016 ; Kim & Kim, 2015 ; Saito & Suyama, 2015 ; Shimizu et al, 2013 ). Lehmann-Werman et al (2016) also demonstrated a superior sensitivity to multiple adjacent CpGs when detecting tissue-specific signatures based on the genome’s methylation status.…”

Section: Discussionmentioning

confidence: 99%

Predicting lung adenocarcinoma disease progression using methylation-correlated blocks and ensemble machine learning classifiers

Yang

Wang

et al. 2021

PeerJ

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Applying the knowledge that methyltransferases and demethylases can modify adjacent cytosine-phosphorothioate-guanine (CpG) sites in the same DNA strand, we found that combining multiple CpGs into a single block may improve cancer diagnosis. However, survival prediction remains a challenge. In this study, we developed a pipeline named “stacked ensemble of machine learning models for methylation-correlated blocks” (EnMCB) that combined Cox regression, support vector regression (SVR), and elastic-net models to construct signatures based on DNA methylation-correlated blocks for lung adenocarcinoma (LUAD) survival prediction. We used methylation profiles from the Cancer Genome Atlas (TCGA) as the training set, and profiles from the Gene Expression Omnibus (GEO) as validation and testing sets. First, we partitioned the genome into blocks of tightly co-methylated CpG sites, which we termed methylation-correlated blocks (MCBs). After partitioning and feature selection, we observed different diagnostic capacities for predicting patient survival across the models. We combined the multiple models into a single stacking ensemble model. The stacking ensemble model based on the top-ranked block had the area under the receiver operating characteristic curve of 0.622 in the TCGA training set, 0.773 in the validation set, and 0.698 in the testing set. When stratified by clinicopathological risk factors, the risk score predicted by the top-ranked MCB was an independent prognostic factor. Our results showed that our pipeline was a reliable tool that may facilitate MCB selection and survival prediction.

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“…Currently, DNA methylation is mainly considered a cooperative process, meaning that the higher the number of 5mCs in a region, the higher the probability is of a cytosine being methylated. In support of this hypothesis, it has been reported that nearby CpGs often share a similar methylation status [ 7 , 8 , 9 , 10 , 11 ]. This pattern of co-methylation has been attributed to the processivity of DNMT3s that, by binding to the DNA as oligomers, simultaneously methylate different nearby cytosines.…”

Section: Introductionmentioning

confidence: 87%

Modeling DNA Methylation Profiles through a Dynamic Equilibrium between Methylation and Demethylation

et al. 2020

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DNA methylation is a heritable epigenetic mark that plays a key role in regulating gene expression. Mathematical modeling has been extensively applied to unravel the regulatory mechanisms of this process. In this study, we aimed to investigate DNA methylation by performing a high-depth analysis of particular loci, and by subsequent modeling of the experimental results. In particular, we performed an in-deep DNA methylation profiling of two genomic loci surrounding the transcription start site of the D-Aspartate Oxidase and the D-Serine Oxidase genes in different samples (n = 51). We found evidence of cell-to-cell differences in DNA methylation status. However, these cell differences were maintained between different individuals, which indeed showed very similar DNA methylation profiles. Therefore, we hypothesized that the observed pattern of DNA methylation was the result of a dynamic balance between DNA methylation and demethylation, and that this balance was identical between individuals. We hence developed a simple mathematical model to test this hypothesis. Our model reliably captured the characteristics of the experimental data, suggesting that DNA methylation and demethylation work together in determining the methylation state of a locus. Furthermore, our model suggested that the methylation status of neighboring cytosines plays an important role in this balance.

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Linkage disequilibrium analysis of allelic heterogeneity in DNA methylation

Cited by 10 publications

References 25 publications

Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA

Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA

Predicting lung adenocarcinoma disease progression using methylation-correlated blocks and ensemble machine learning classifiers

Modeling DNA Methylation Profiles through a Dynamic Equilibrium between Methylation and Demethylation

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