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Related Concept Videos

Epigenetic Regulation01:37

Epigenetic Regulation

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Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...
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Epigenetic Regulation01:46

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Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
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Histone Modification02:32

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The histone proteins have a flexible N-terminal tail extending out from the nucleosome. These histone tails are often subjected to post-translational modifications such as acetylation, methylation, phosphorylation, and ubiquitination. Particular combinations of these modifications form “histone codes” that influence the chromatin folding and tissue-specific gene expression.
Acetylation
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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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Adjusting for Cell Type Composition in DNA Methylation Data Using a Regression-Based Approach.

Meaghan J Jones1, Sumaiya A Islam1, Rachel D Edgar1

  • 1Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, University of British Columbia, 950 West 28th Avenue, Vancouver, BC, Canada, V5Z 4H4.

Methods in Molecular Biology (Clifton, N.J.)
|July 2, 2015
PubMed
Summary
This summary is machine-generated.

DNA methylation analysis can reveal gene-environment interactions and disease markers. Adjusting for cell type composition is crucial for accurate DNA methylation signatures in population studies.

Keywords:
Cell typeDNA methylationIllumina Infinium HumanMethylation450 BeadChipR statistical softwareStatistical adjustment

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Area of Science:

  • Epigenetics
  • Population Genomics
  • Bioinformatics

Background:

  • DNA methylation patterns are influenced by tissue origin and cell type composition.
  • These factors can confound the identification of gene-environment interactions and disease markers.
  • Accurate analysis requires controlling for cellular heterogeneity.

Purpose of the Study:

  • To describe a regression method for adjusting DNA methylation data for cell type composition.
  • To provide detailed instructions for performing cell type adjustment on high-dimensional data.
  • To highlight the importance of accounting for cellular differences in population studies.

Main Methods:

  • Utilized a regression-based approach to adjust DNA methylation data.
  • Detailed the necessary information required for cell type adjustment.
  • Applied the method to high-dimensional DNA methylation datasets, including Illumina 450K data.

Main Results:

  • A robust method for adjusting DNA methylation data for cell type composition was developed.
  • The method can be adapted for various high-throughput sequencing and array-based technologies.
  • Demonstrated the feasibility of correcting for cellular heterogeneity in epigenome-wide association studies.

Conclusions:

  • Adjusting for cell type composition is vital for accurate DNA methylation analysis in population studies.
  • This method enhances the discovery of true gene-environment interactions and disease associations.
  • The approach is applicable to diverse epigenomic datasets and technologies.