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      DNA methylation profiling in human lung tissue identifies genes associated with COPD

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          Abstract

          <p id="d8563605e367">Chronic obstructive pulmonary disease (COPD) is a smoking-related disease characterized by genetic and phenotypic heterogeneity. Although association studies have identified multiple genomic regions with replicated associations to COPD, genetic variation only partially explains the susceptibility to lung disease, and suggests the relevance of epigenetic investigations. We performed genome-wide DNA methylation profiling in homogenized lung tissue samples from 46 control subjects with normal lung function and 114 subjects with COPD, all former smokers. The differentially methylated loci were integrated with previous genome-wide association study results. The top 535 differentially methylated sites, filtered for a minimum mean methylation difference of 5% between cases and controls, were enriched for CpG shelves and shores. Pathway analysis revealed enrichment for transcription factors. The top differentially methylated sites from the intersection with previous GWAS were in <i>CHRM1, GLT1D1</i>, and <i>C10orf11</i>; sorted by GWAS <i>P</i>-value, the top sites included <i>FRMD4A, THSD4</i>, and <i>C10orf11</i>. Epigenetic association studies complement genetic association studies to identify genes potentially involved in COPD pathogenesis. Enrichment for genes implicated in asthma and lung function and for transcription factors suggests the potential pathogenic relevance of genes identified through differential methylation and the intersection with a broader range of GWAS associations. </p>

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          Most cited references25

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          High density DNA methylation array with single CpG site resolution.

          We have developed a new generation of genome-wide DNA methylation BeadChip which allows high-throughput methylation profiling of the human genome. The new high density BeadChip can assay over 480K CpG sites and analyze twelve samples in parallel. The innovative content includes coverage of 99% of RefSeq genes with multiple probes per gene, 96% of CpG islands from the UCSC database, CpG island shores and additional content selected from whole-genome bisulfite sequencing data and input from DNA methylation experts. The well-characterized Infinium® Assay is used for analysis of CpG methylation using bisulfite-converted genomic DNA. We applied this technology to analyze DNA methylation in normal and tumor DNA samples and compared results with whole-genome bisulfite sequencing (WGBS) data obtained for the same samples. Highly comparable DNA methylation profiles were generated by the array and sequencing methods (average R2 of 0.95). The ability to determine genome-wide methylation patterns will rapidly advance methylation research. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Repurposing the CRISPR-Cas9 system for targeted DNA methylation

            Epigenetic studies relied so far on correlations between epigenetic marks and gene expression pattern. Technologies developed for epigenome editing now enable direct study of functional relevance of precise epigenetic modifications and gene regulation. The reversible nature of epigenetic modifications, including DNA methylation, has been already exploited in cancer therapy for remodeling the aberrant epigenetic landscape. However, this was achieved non-selectively using epigenetic inhibitors. Epigenetic editing at specific loci represents a novel approach that might selectively and heritably alter gene expression. Here, we developed a CRISPR-Cas9-based tool for specific DNA methylation consisting of deactivated Cas9 (dCas9) nuclease and catalytic domain of the DNA methyltransferase DNMT3A targeted by co–expression of a guide RNA to any 20 bp DNA sequence followed by the NGG trinucleotide. We demonstrated targeted CpG methylation in a ∼35 bp wide region by the fusion protein. We also showed that multiple guide RNAs could target the dCas9-DNMT3A construct to multiple adjacent sites, which enabled methylation of a larger part of the promoter. DNA methylation activity was specific for the targeted region and heritable across mitotic divisions. Finally, we demonstrated that directed DNA methylation of a wider promoter region of the target loci IL6ST and BACH2 decreased their expression.
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              Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

              During the past 5 years, high-throughput technologies have been successfully used by epidemiology studies, but almost all have focused on sequence variation through genome-wide association studies (GWAS). Today, the study of other genomic events is becoming more common in large-scale epidemiological studies. Many of these, unlike the single-nucleotide polymorphism studied in GWAS, are continuous measures. In this context, the exercise of searching for regions of interest for disease is akin to the problems described in the statistical 'bump hunting' literature. New statistical challenges arise when the measurements are continuous rather than categorical, when they are measured with uncertainty, and when both biological signal, and measurement errors are characterized by spatial correlation along the genome. Perhaps the most challenging complication is that continuous genomic data from large studies are measured throughout long periods, making them susceptible to 'batch effects'. An example that combines all three characteristics is genome-wide DNA methylation measurements. Here, we present a data analysis pipeline that effectively models measurement error, removes batch effects, detects regions of interest and attaches statistical uncertainty to identified regions. We illustrate the usefulness of our approach by detecting genomic regions of DNA methylation associated with a continuous trait in a well-characterized population of newborns. Additionally, we show that addressing unexplained heterogeneity like batch effects reduces the number of false-positive regions. Our framework offers a comprehensive yet flexible approach for identifying genomic regions of biological interest in large epidemiological studies using quantitative high-throughput methods.
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                Author and article information

                Journal
                Epigenetics
                Epigenetics
                Informa UK Limited
                1559-2294
                1559-2308
                August 11 2016
                August 26 2016
                : 11
                : 10
                : 730-739
                Article
                10.1080/15592294.2016.1226451
                5094634
                27564456
                62ba278f-f286-4024-883d-d82186fdff46
                © 2016
                History

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