Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm (original) (raw)
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Bioinformatics, 2015
Motivation: DNA methylation patterns are well known to vary substantially across cell types or tissues. Hence, existing normalization methods may not be optimal if they do not take this into account. We therefore present a new R package for normalization of data from the Illumina Infinium Human Methylation450 BeadChip (Illumina 450 K) built on the concepts in the recently published funNorm method, and introducing cell-type or tissue-type flexibility. Results: funtooNorm is relevant for data sets containing samples from two or more cell or tissue types. A visual display of cross-validated errors informs the choice of the optimal number of
A DNA methylation atlas of normal human cell types
Nature
DNA methylation is a fundamental epigenetic mark that governs gene expression and chromatin organization, thus providing a window into cellular identity and developmental processes1. Current datasets typically include only a fraction of methylation sites and are often based either on cell lines that underwent massive changes in culture or on tissues containing unspecified mixtures of cells2–5. Here we describe a human methylome atlas, based on deep whole-genome bisulfite sequencing, allowing fragment-level analysis across thousands of unique markers for 39 cell types sorted from 205 healthy tissue samples. Replicates of the same cell type are more than 99.5% identical, demonstrating the robustness of cell identity programmes to environmental perturbation. Unsupervised clustering of the atlas recapitulates key elements of tissue ontogeny and identifies methylation patterns retained since embryonic development. Loci uniquely unmethylated in an individual cell type often reside in tran...
Genomic and phenomic insights from an atlas of genetic effects on DNA methylation
2020
Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. Here we describe results of DNA methylation-quantitative trait loci (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTL of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We reveal that the genetic architecture of DNAm levels is highly polygenic and DNAm exhibits signatures of negative and positive natural selection. Using shared genetic control between distal DNAm sites we construct networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic factors are associated with both blood DNAm levels and complex diseases but in most cases these associations do not refle...
DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns
Genome Biology, 2014
Background: DNA epigenetic modifications, such as methylation, are important regulators of tissue differentiation, contributing to processes of both development and cancer. Profiling the tissue-specific DNA methylome patterns will provide novel insights into normal and pathogenic mechanisms, as well as help in future epigenetic therapies. In this study, 17 somatic tissues from four autopsied humans were subjected to functional genome analysis using the Illumina Infinium HumanMethylation450 BeadChip, covering 486 428 CpG sites. Results: Only 2% of the CpGs analyzed are hypermethylated in all 17 tissue specimens; these permanently methylated CpG sites are located predominantly in gene-body regions. In contrast, 15% of the CpGs are hypomethylated in all specimens and are primarily located in regions proximal to transcription start sites. A vast number of tissue-specific differentially methylated regions are identified and considered likely mediators of tissue-specific gene regulatory mechanisms since the hypomethylated regions are closely related to known functions of the corresponding tissue. Finally, a clear inverse correlation is observed between promoter methylation within CpG islands and gene expression data obtained from publicly available databases.
Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation
Nature Genetics, 2021
Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.
Identification of genes with consistent methylation levels across different human tissues
2014
DNA methylation plays an important role in regulating cell growth and disease development. Methylation profiles are examined by bisulfite conversion; however, the lack of markers for bisulfite conversion efficiency and appropriate internal control genes remains a major challenge. To address these issues, we utilized two bioinformatics approaches, coefficients of variances and resampling tests, to identify probes showing stable methylation levels from several independent microarray datasets. Mass spectrometry validated the consistently high methylation levels of the five probes (N4BP2, EGFL8, CTRB1, TSPAN3, and ZNF690) in 13 human tissue types from 24 cell lines. Linear associations between detected methylation levels and methyl concentrations of DNA samples were further demonstrated in three genes (N4BP2, EGFL8, and CTRB1). To summarize, we identified five genes which may serve as internal controls for methylation studies by analyzing large-scale microarray data, and three of them can be used as markers for evaluating the efficiency of bisulfite conversion.
Human molecular genetics, 2015
DNA methylation is a key functional regulatory mechanism in human genome, which plays critical roles in development, differentiation and many diseases. With rapid progress of large-scale projects (e.g. ENCODE), many DNA methylation data across diverse cell lines have been produced. However, common methylation patterns, cell lineage- and cell line-specific DNA methylation patterns across multiple cell lines have not yet been explored completely. Using the DNA methylation data across 54 human cell lines, we identified 35 276 local DNA methylation regions called local clusters of CpG sites (LCCSs). We constructed an LCCS co-methylation network and investigated the common DNA methylation patterns across all cell lines, which reveal two distinct groups in terms of their methylation level and genomic characteristics. We further detected diverse sets of cell lineage-specific high- and low-methylation patterns, which were depleted in promoter, CpG island (CGI) and repeat regions but enriche...
Epigenetics & Chromatin, 2013
Background: DNA methylation has been recognized as a key mechanism in cell differentiation. Various studies have compared tissues to characterize epigenetically regulated genomic regions, but due to differences in study design and focus there still is no consensus as to the annotation of genomic regions predominantly involved in tissue-specific methylation. We used a new algorithm to identify and annotate tissue-specific differentially methylated regions (tDMRs) from Illumina 450k chip data for four peripheral tissues (blood, saliva, buccal swabs and hair follicles) and six internal tissues (liver, muscle, pancreas, subcutaneous fat, omentum and spleen with matched blood samples).