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Guthrie card methylomics identifies temporally stable epialleles that are present at birth in humans

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

OTHER RELATED OMICS DATASETS IN: PRJNA167304PRJNA167303PRJNA167305

ORGANISM(S): Homo sapiens

PROVIDER: GSE38128 | GEO | 2012/08/27

SECONDARY ACCESSION(S): PRJNA167303

REPOSITORIES: GEO

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Guthrie card methylomics identifies temporally stable epialleles that are present at birth in humans.

Project description: Not available

| S-EPMC3483543 | biostudies-literature

Guthrie card methylomics identifies temporally stable epialleles that are present at birth in humans (Bisulfite-Seq)

Project description:A major concern in common disease epigenomics is distinguishing causal from consequential epigenetic variation. One means of addressing this issue is to identify the temporal origins of epigenetic variants via longitudinal analyses. However, prospective birth-cohort studies are expensive and time-consuming. Here we report DNA methylomics of archived Guthrie cards for the retrospective longitudinal analyses of in utero-derived DNA methylation variation. We first validate two methodologies for generating comprehensive DNA methylomes from Guthrie cards. Then, using an integrated epigenomic/genomic analysis of Guthrie cards and follow-up samplings, we identify inter-individual DNA methylation variation that is present both at birth and three years later. These findings suggest that disease-relevant epigenetic variation could be detected at birth i.e. before overt clinical disease. Guthrie card methylomics offers a potentially powerful and cost-effective strategy for studying the dynamics of inter-individual epigenomic variation in a range of common human diseases.

2012-08-27 | GSE38127 | GEO

Guthrie card methylomics identifies temporally stable epialleles that are present at birth in humans (Methylation BeadChip)

2012-08-27 | GSE37966 | GEO

Guthrie card methylomics identifies temporally stable epialleles that are present at birth in humans (Methylation BeadChip)

2012-08-27 | E-GEOD-37966 | biostudies-arrayexpress

Guthrie card methylomics identifies temporally stable epialleles that are present at birth in humans (Bisulfite-Seq)

2012-08-27 | E-GEOD-38127 | biostudies-arrayexpress

Stable Inheritance of Spontaneous Epialleles in Arabidopsis thaliana

Project description:This research uses consecutive generations of two independent mutation accumulation (MA) lines in model organism A. thaliana to understand transgenerational stability of epialleles via self-fertilization. With whole-genome bisulfite sequencing, regions of instability were identified and quantified. The vast majority of the methylated genome is stably inherited to offspring and the identified unstable regions do not change frequently between generations. Additionally, an epigenetic cross of two MA lines was created to understand inheritance patterns of epialleles via outcrossing in the absence of genetic variation. Whole-genome bisulfite sequencing was used to predict epigenotype of the offspring without single nucleotide polymorphisms. In regions of differential methylation between the parents, about half of regions show predictable inheritance.

2017-07-24 | GSE94298 | GEO

Meiotically stable natural epialleles of Sadhu, a novel Arabidopsis retroposon.

Project description: Not available

| S-EPMC1401498 | biostudies-literature

Quartet Methylomics

Project description:The Quartet Project aims to provide resources for QC of multiple types of omic technologies and the effective integration of diverse datasets from various scenarios. Large quantities of multi-omics materials, datasets, and best practices for their QC utilities were developed for whole process QC of large-scale, multi-center, and longitudinal multi-omics profiling.

2023-09-04 | GSE241900 | GEO

Winter is coming: How humans forage in a temporally structured environment.

Project description: Not available

| S-EPMC4948597 | biostudies-literature

Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data [Methylomics]

Project description:We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.

2019-07-18 | GSE134052 | GEO

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