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Epigenetic Regulation

Epigenetic Regulation

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...

Epigenetic Regulation

Epigenetic Regulation

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

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Related Experiment Video

Updated: May 23, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

Penalized logistic regression for high-dimensional DNA methylation data with case-control studies.

Hokeun Sun¹, Shuang Wang

¹Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.

Bioinformatics (Oxford, England)

|April 3, 2012

Summary

This study introduces a new penalized logistic regression model for analyzing DNA methylation data, effectively identifying cancer-associated CpG sites by accounting for correlations within genes.

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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Area of Science:

Genomics
Epigenetics
Bioinformatics

Background:

DNA methylation is a critical epigenetic modification regulating gene expression.
CpG hypermethylation in specific regions is a hallmark of cancer.
Limited methodological literature exists for case-control association studies on high-dimensional DNA methylation data.

Purpose of the Study:

To propose a penalized logistic regression model for high-dimensional DNA methylation data with grouped structures.
To develop a method that accounts for correlations among CpG sites within genes.
To enhance the stable and confident selection of methylation CpG sites associated with outcomes.

Main Methods:

A penalized logistic regression model incorporating l(1) and squared l(2) penalties on degree-scaled differences of coefficients.
A stability selection procedure to provide selection probabilities for regression coefficients.
Application to high-dimensional array data, specifically Illumina Infinium HumanMethylation27K Beadchip.

Main Results:

The proposed method outperforms existing regularization techniques like lasso and elastic-net in simulation studies with correlated data.
The method successfully identified important CpG sites and genes associated with ovarian cancer from over 20,000 CpGs.
Several identified genes show potential links to cancer development.

Conclusions:

The developed penalized regression model with stability selection is effective for analyzing correlated DNA methylation data.
This approach offers a robust tool for identifying cancer-related epigenetic markers.
The findings contribute to a better understanding of DNA methylation's role in cancer etiology.