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Dataset results
41 results for “Epigenome-Wide Association Studies”
Crossreactive probes on Illumina DNA methylation arrays: a large study on ALS shows that a cautionary approach is warranted in interpreting epigenome-wide association studies
<p>Data corresponding to the paper "Crossreactive probes on Illumina DNA methylation arrays: a large study on ALS shows that a cautionary approach is warranted in interpreting epigenome-wide association studies."<br> <br> Corresponding scripts can be found at: <a href="https://github.com/pjhop/dnamarray_crossreactivity">https://github.com/pjhop/dnamarray_crossreactivity</a><br> All downstream analyses in <a href="https://github.com/pjhop/dnamarray_crossreactivity/blob/master/analysis/c9_analysis.Rmd">c9_analysis.Rmd</a> and in<a href="https://github.com/pjhop/dnamarray_crossreactivity/blob/master/analysis/supplementary_note.Rmd"> supplementary_note.Rmd</a> can be reproduced using the deposited data as follows:</p> <ul> <li>Clone the dnamarray_crossreactivity repository: < git clone https://github.com/pjhop/dnamarray_crossreactivity.git ></li> <li>Download the data ('data.zip') and place it in the 'dnamarray_crossreactivity' folder.</li> <li>Unzip the data.zip folder</li> </ul> <p>Scripts used to generate the data in each subdirectory can be found at:</p> <ul> <li>data/processed/c9_matches/: <a href="https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/c9_matches">https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/c9_matches</a></li> <li>data/output/ewas/: <a href="https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/ewas">https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/ewas</a></li> <li>data/output/figs/: empty folder, running 'c9_analysis.Rmd' will save figures here.</li> <li>data/misc/: <a href="https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/other">https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/other</a></li> <li>data/extdata: <ul> <li>Zhou <em>et al.</em> annotations (EPIC.hg19.manifest.tsv.gz, HM450.hg19.manifest.pop.tsv.gz, HM450.hg19.manifest.tsv.gz) were downloaded from: <a href="https://zwdzwd.github.io/InfiniumAnnotation">https://zwdzwd.github.io/InfiniumAnnotation</a> (downloaded at 17/09/2020)</li> <li>Naeem <em>et al.</em><em> </em>data (12864_2013_7006_MOESM2_ESM.csv) was downloaded from: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3943510/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3943510/</a></li> <li>Chen <em>et al.</em> data (48639-non-specific-probes-Illumina450k.xlsx) was downloaded from <a href="https://github.com/Jfortin1/funnorm_repro/blob/master/bad_probes/48639-non-specific-probes-Illumina450k.xlsx">https://github.com/Jfortin1/funnorm_repro/blob/master/bad_probes/48639-non-specific-probes-Illumina450k.xlsx</a></li> <li>The anno_450k.txt.gz and anno_EPIC.txt.gz are subsets of the annotation files included in the following package respectively: <a href="https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylation450kanno.ilmn12.hg19.html">https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylation450kanno.ilmn12.hg19.html</a> and <a href="https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylationEPICanno.ilm10b2.hg19.html">https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylationEPICanno.ilm10b2.hg19.html</a></li> </ul> </li> <li> data/genome_bs: Scripts used to generate these data can be found at <a href="https://github.com/pjhop/DNAmCrosshyb/blob/master/data-raw/bisulfite_convert_hg19.R">https://github.com/pjhop/DNAmCrosshyb/blob/master/data-raw/bisulfite_convert_hg19.R</a> and <a href="https://github.com/pjhop/DNAmCrosshyb/blob/master/data-raw/bisulfite_convert_hg38.R">https://github.com/pjhop/DNAmCrosshyb/blob/master/data-raw/bisulfite_convert_hg38.R</a> .</li> <li> data/raw: Individual-level data is available upon access at: <a href="https://ega-archive.org/studies/EGAS00001004587">https://ega-archive.org/studies/EGAS00001004587</a></li> </ul>
Meta-analysis results of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight
<p>Birthweight is associated with health outcomes across the life course, DNA methylation may be an underlying mechanism. In this meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from -183 to 178 grams per 10% increase in methylation (P<sub>Bonferroni</sub><1.06x10<sup>-7</sup>).</p>
Epigenome-wide association study of kidney function identifies trans-ethnic and ethnic-specific loci
<p>Trans-ethnic meta-analysis results for eGFR EWAS</p> <p>Description of variables:</p> <p>CpG CPG name<br> chr Chromosome <br> pos_hg38 Position hg38 <br> Beta Effect <br> SE Standard error <br> P P value <br> totalsamplesize number of participants <br> direction direction of effect for studies <br> hetpval P value for heterogeneity <br> hetdf Degree of freedom <br> gene Gene location</p> <p>Upload of this dataset was completed by The EWAS Catalog team. The data can be queried along with hundreds of other EWAS at ewascatalog.org. To upload your EWAS summary statistics and have a zenodo DOI generated for you go to ewascatalog.org/upload. </p>
Maternal Glycemic Dysregulation During Pregnancy and Neonatal Blood DNA Methylation: Meta-analyses of Epigenome-Wide Association Studies
<p>Maternal glycemic dysregulation during pregnancy increases the risk of adverse health outcomes in her offspring, a risk thought to be linearly related to maternal hyperglycemia. It is hypothesized that changes in offspring DNA methylation (DNAm) underline these associations. To address this hypothesis, we conducted fixed-effects meta-analyses of epigenome-wide association study (EWAS) results from eight birth cohorts investigating relationships between cord blood DNAm and fetal exposure to maternal glucose (Nmaximum= 3,503), insulin (Nmaximum= 2,062), and area under the curve of glucose (AUCgluc) following oral glucose tolerance tests (Nmaximum = 1,505). Inflation and bias was addressed with the bacon Bioconductor package for individual cohorts and the meta-analysis results and reported model estimates and standard errors are adjusted. Greater maternal AUCgluc during pregnancy was associated with lower cord blood DNAm at neighboring CpGs cg26974062 and cg02988288 in TXNIP. Follow-up analyses show that these changes may have functional and later-life consequences and warrant further mediation and causal analysis. </p> <p>The corresponding paper can be found here: https://doi.org/10.2337/dc21-1701.</p> <p>Upload of this dataset was completed by The EWAS Catalog team. The data can be queried along with hundreds of other EWAS at ewascatalog.org. To upload your EWAS summary statistics and have a Zenodo DOI generated for you go to ewascatalog.org/upload.</p>
A multi-ethnic epigenome-wide association study of leukocyte DNA methylation and blood lipids
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Epigenome-Wide Association Study of DNA Methylation and Adult Asthma in the Agricultural Lung Health Study
<p>In an epigenome-wide association study of blood DNA methylation in adults conducted in the Agricultural Lung Health Study, 524 differentially methylated CpG sites were associated with non-atopic asthma (FDR<0.05) and 1,086 differentially methylated CpGs were associated with atopic asthma. No CpG sites were differentially methylated in relation to atopy alone. </p>
Linking the gut microbiome to host DNA methylation by a discovery and replication epigenome-wide association study
<p>BACKGROUND: The datafiles deposited here are products of the research project "<strong>Linking the gut microbiome to host DNA methylation by a discovery and replication epigenome-wide association study"</strong></p><p>Authors: Ayşe Demirkan1,2, Jenny van Dongen3,4, Casey T. Finnicum5, Harm-Jan Westra1, Soesma Jankipersadsing1, Gonneke Willemsen3,4, Richard G. Ijzerman6, Dorret I. Boomsma3,4, Erik A. Ehli5, Marc Jan Bonder1, Jingyuan Fu,1,7 Lude Franke1, Cisca Wijmenga1, Eco J.C. de Geus3,4, Alexander Kurilshikov1, Alexandra Zhernakova1</p><p>1 Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands</p><p>2 Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine & People-Centered AI institute University of Surrey, Guildford, United Kingdom</p><p>3 Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands</p><p>4 Amsterdam Public Health Research Institute, Amsterdam, the Netherlands</p><p>5 Avera Institute of Human Genetics, Avera McKennan Hospital & University Health Center, Sioux Falls, SD, USA</p><p>6 Department of Endocrinology, Amsterdam University Medical Center, location VUMC, Amsterdam, the Netherlands</p><p>7 Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands</p><p><strong>Corresponding Authors: </strong>Alexandra Zhernakova; Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands</p><p>Ayse Demirkan; Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine & People-Centered AI institute University of Surrey, Guildford, United Kingdom.</p><p>FILES: </p><p>1-merged_lld16s.Rata: Epigenome-wide association of 16s microbial abundances in LifeLines-Deep (LLD, n = 616, 450k methylation array) </p><p>2-lld_mgs.Rdata: Epigenome-wide association ofshotgun metagenomic sequencing derived taxa relative abundances (n = 683, 450k methylation array))</p><p>3-lld_<i>mgs_</i>pathways. Rdata: Epigenome-wide association ofshotgun metagenomic sequencing derived bacterial pathway relative abundances (n = 683, 450k methylation array)</p><p>FUNDING: The Lifelines initiative has been made possible by subsidy from the Dutch Ministry of Health, Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen (UMCG), Groningen University and the Provinces in the North of the Netherlands (Drenthe, Friesland, Groningen). The Netherlands Twin Register acknowledges funding from the Netherlands Organization for Scientific Research (NWO): (NWO 911–09–032; NWO 480-04-004; 480-15-001/674, NWO 916-130-82), Biobanking and Biomolecular Research Infrastructure (184.033.111), and the BBRMI-NL-financed BIOS Consortium (NWO 184.021.007), NWO Large Scale infrastructures X-Omics (184.034.019), Genotype/phenotype database for behaviour genetic and genetic epidemiological studies (ZonMw Middelgroot 911-09-032); Netherlands Twin Registry Repository: researching the interplay between genome and environment (NWO-Groot 480-15-001/674); the Avera Institute, Sioux Falls (USA), the European Research Council (Genetics of Mental Illness 230374), the European Research Council (Genetics of Mental Illness 230374), and INRA-Pfizer. Pfizer provided support for data collection, but did not have any additional role in the study design, data analysis, decision to publish, or preparation of the manuscript.</p><p> </p>
Data from: Epigenome-wide association study of lung function level and its change
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Leukocyte-adjusted epigenome-wide association studies of blood from solid tumor patients
GEO Series GSE50409. Homo sapiens. 428 samples. Type: Methylation profiling by array.
Epigenome-wide association study on peripheral blood DNA of bronchopulmonary dysplasia in preterm infants
GEO Series GSE225313. Homo sapiens. 175 samples. Type: Methylation profiling by array.
Epigenome-Wide Association Study for Transgenerational Disease Epimutation Sperm Biomarkers following Ancestral Exposure to Jet Fuel Hydrocarbons
GEO Series GSE155922. Rattus norvegicus. 41 samples. Type: Methylation profiling by high throughput sequencing.
An epigenome-wide association study of Alzheimer’s disease blood highlights robust DNA hypermethylation in the HOXB6 gene.
GEO Series GSE144858. Homo sapiens. 300 samples. Type: Methylation profiling by genome tiling array.
Exploring Epigenetic Drift and Rare Epivariations in Amyotrophic Lateral Sclerosis by an Epigenome-Wide Association Study
GEO Series GSE239901. Homo sapiens. 122 samples. Type: Methylation profiling by genome tiling array.
Study of the DNA methylation in the stroke outcome: an Epigenome-Wide Association Study
GEO Series GSE203399. Homo sapiens. 121 samples. Type: Methylation profiling by array.
A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies
GEO Series GSE55763. Homo sapiens. 2711 samples. Type: Methylation profiling by array.
Epigenome-wide association study of Prostate Cancer in African American Men identified differentially methylated genes
GEO Series GSE269244. Homo sapiens. 238 samples. Type: Methylation profiling by genome tiling array.
DNA METHYLATION SIGNATURES OF CERVICAL PRE-INVASIVE AND INVASIVE DISEASE: AN EPIGENOME-WIDE ASSOCIATION STUDY
GEO Series GSE287994. Homo sapiens. 241 samples. Type: Methylation profiling by genome tiling array.
Epigenome-Wide Association Study in multiple system atrophy (MSA)
GEO Series GSE143157. Homo sapiens. 156 samples. Type: Methylation profiling by genome tiling array.
Epigenome-wide Association Study of Obesity Susceptibility in Human Twins
GEO Series GSE218363. Homo sapiens. 44 samples. Type: Methylation profiling by high throughput sequencing.
Buccals are likely to be a more informative surrogate tissue than blood for epigenome-wide association studies [Illumina tiling]
GEO Series GSE46573. Homo sapiens. 22 samples. Type: Methylation profiling by genome tiling array.
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International Brain Laboratory public data
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OpenNeuro
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