Genomic landscape of human allele-specific DNA methylation

Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, California, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 04/2012; 109(19):7332-7. DOI: 10.1073/pnas.1201310109
Source: PubMed


DNA methylation mediates imprinted gene expression by passing an epigenomic state across generations and differentially marking specific regulatory regions on maternal and paternal alleles. Imprinting has been tied to the evolution of the placenta in mammals and defects of imprinting have been associated with human diseases. Although recent advances in genome sequencing have revolutionized the study of DNA methylation, existing methylome data remain largely untapped in the study of imprinting. We present a statistical model to describe allele-specific methylation (ASM) in data from high-throughput short-read bisulfite sequencing. Simulation results indicate technical specifications of existing methylome data, such as read length and coverage, are sufficient for full-genome ASM profiling based on our model. We used our model to analyze methylomes for a diverse set of human cell types, including cultured and uncultured differentiated cells, embryonic stem cells and induced pluripotent stem cells. Regions of ASM identified most consistently across methylomes are tightly connected with known imprinted genes and precisely delineate the boundaries of several known imprinting control regions. Predicted regions of ASM common to multiple cell types frequently mark noncoding RNA promoters and represent promising starting points for targeted validation. More generally, our model provides the analytical complement to cutting-edge experimental technologies for surveying ASM in specific cell types and across species.

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Available from: Antoine Molaro, Oct 14, 2015
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    • "This implies that a consensus methylation estimate of 50% could mean 50% of the alleles are methylated in all cells (e.g., allele-specific methylation) or 50% of the cells are fully methylated (e.g., mixtures of cell types), or any combination thereof. Only BS-seq data can properly decompose this information, and at the same time infer allele-specific patterns, using the methylation status from individual DNA fragments (Fang et al., 2012; Statham et al., 2012; Song et al., 2013). But, there are limits: since small fragments are observed, it remains challenging to relate the allelic methylation status at one loci to another genomically distant loci without additional haplotype information (Kuleshov et al., 2014). "
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    ABSTRACT: DNA methylation, the reversible addition of methyl groups at CpG dinucleotides, represents an important regulatory layer associated with gene expression. Changed methylation status has been noted across diverse pathological states, including cancer. The rapid development and uptake of microarrays and large scale DNA sequencing has prompted an explosion of data analytic methods for processing and discovering changes in DNA methylation across varied data types. In this mini-review, we present a compact and accessible discussion of many of the salient challenges, such as experimental design, statistical methods for differential methylation detection, critical considerations such as cell type composition and the potential confounding that can arise from batch effects. From a statistical perspective, our main interests include the use of empirical Bayes or hierarchical models, which have proved immensely powerful in genomics, and the procedures by which false discovery control is achieved.
    Frontiers in Genetics 09/2014; 5:324. DOI:10.3389/fgene.2014.00324
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    • "One type of study relies on methylation quantitative trait locus (mQTL) mapping, which identifies genomic polymorphisms associated with variation of CpG methylation in a cis-regulatory manner [8], [9], [10], [11]. An alternative approach involves characterizing allele-specific methylation, in which a change in a specific polymorphism leads to the direct loss or gain of DNA methylation [2], [3], [12], [13], [14], [15]. While an increasingly large number of associations between SNPs and CpG sites have been reported in these recent efforts, it remains unclear whether mQTL and ASM analyses are truly uncovering the full extent of genome-methylome interactions. "
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    ABSTRACT: Genetic polymorphisms can shape the global landscape of DNA methylation, by either changing substrates for DNA methyltransferases or altering the DNA binding affinity of cis-regulatory proteins. The interactions between CpG methylation and genetic polymorphisms have been previously investigated by methylation quantitative trait loci (mQTL) and allele-specific methylation (ASM) analysis. However, it remains unclear whether these approaches can effectively and comprehensively identify all genetic variants that contribute to the inter-individual variation of DNA methylation levels. Here we used three independent approaches to systematically investigate the influence of genetic polymorphisms on variability in DNA methylation by characterizing the methylation state of 96 whole blood samples in 52 parent-child trios from 22 nuclear pedigrees. We performed targeted bisulfite sequencing with padlock probes to quantify the absolute DNA methylation levels at a set of 411,800 CpG sites in the human genome. With mid-parent offspring analysis (MPO), we identified 10,593 CpG sites that exhibited heritable methylation patterns, among which 70.1% were SNPs directly present in methylated CpG dinucleotides. We determined the mQTL analysis identified 49.9% of heritable CpG sites for which regulation occurred in a distal cis-regulatory manner, and that ASM analysis was only able to identify 5%. Finally, we identified hundreds of clusters in the human genome for which the degree of variation of CpG methylation, as opposed to whether or not CpG sites were methylated, was associated with genetic polymorphisms, supporting a recent hypothesis on the genetic influence of phenotypic plasticity. These results show that cis-regulatory SNPs identified by mQTL do not comprise the full extent of heritable CpG methylation, and that ASM appears overall unreliable. Overall, the extent of genome-methylome interactions is well beyond what is detectible with the commonly used mQTL and ASM approaches, and is likely to include effects on plasticity.
    PLoS ONE 07/2014; 9(7):e99313. DOI:10.1371/journal.pone.0099313 · 3.23 Impact Factor
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    • "Recently, increasing numbers of studies related to epigenetic modifications (e.g. DNA methylation) have revealed that epigenetic modifications are regulators of allele-specific expression of imprinted genes (Court et al., 2014; Fang et al., 2012; Shoemaker et al., 2010). In addition, some studies focused on the imprinting mechanisms in the development and related functions of imprinted genes (Chang et al., 2013). "
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    ABSTRACT: Genomic imprinting is a complex genetic and epigenetic phenomenon that plays important roles in mammalian development and diseases. Mammalian imprinted genes have been identified widely by experimental strategies or predicted using computational methods. Systematic information for these genes would be necessary for the identification of novel imprinted genes and the analysis of their regulatory mechanisms and functions. Here, a well-designed information repository, MetaImprint ( MetaImprint), is presented, which focuses on the collection of information concerning mammalian imprinted genes. The current version of MetaImprint incorporates 539 imprinted genes, including 255 experimentally confirmed genes, and their detailed research courses from eight mammalian species. MetaImprint also hosts genome-wide genetic and epigenetic information of imprinted genes, including imprinting control regions, single nucleotide polymorphisms, non-coding RNAs, DNA methylation and histone modifications. Information related to human diseases and functional annotation was also integrated into MetaImprint. To facilitate data extraction, MetaImprint supports multiple search options, such as by gene ID and disease name. Moreover, a configurable Imprinted Gene Browser was developed to visualize the information on imprinted genes in a genomic context. In addition, an Epigenetic Changes Analysis Tool is provided for online analysis of DNA methylation and histone modification differences of imprinted genes among multiple tissues and cell types. MetaImprint provides a comprehensive information repository of imprinted genes, allowing researchers to investigate systematically the genetic and epigenetic regulatory mechanisms of imprinted genes and their functions in development and diseases.
    Development 06/2014; 141(12):2516-23. DOI:10.1242/dev.105320 · 6.46 Impact Factor
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