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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...

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

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Exploring the correlation between DNA methylation and biological age using an interpretable machine learning

Sheng Zhou¹, Jing Chen², Shanshan Wei¹

¹Department of Public Health and Health, Guizhou Medical University, Guizhou Province, China.

Scientific Reports

|October 15, 2024

Summary

DNA methylation changes systematically with age and can predict biological age. Machine learning identified key methylation sites, like cg23995914, and explored their biological significance.

Keywords:

Biological age DNA methylation GO enrichment analysis Interpretable machine learning Shapley Additive exPlanations XGBoost

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Area of Science:

Epigenetics and Genomics
Computational Biology
Aging Research

Background:

DNA methylation is crucial for gene regulation and changes predictably with age.
These age-related methylation patterns offer potential for biological age prediction.
Understanding these epigenetic markers is key to aging research.

Purpose of the Study:

To identify specific DNA methylation sites correlated with biological age.
To develop a predictive model for biological age using machine learning.
To explore the biological functions of age-associated methylation markers.

Main Methods:

Utilized human methylation data, undergoing preprocessing and feature selection.
Applied machine learning algorithms (XGBoost, LightGBM, CatBoost) for model construction.
Conducted in-depth analysis using SHAP, Gene Ontology (GO) enrichment, and KEGG pathway analysis on 15 datasets.

Main Results:

Identified 15 distinct groups of methylation sites associated with biological age.
The cg23995914 locus was pinpointed as the most influential predictor of biological age via SHAP values.
GO and KEGG analyses provided initial insights into the biological roles of these methylated loci.

Conclusions:

Established a robust machine learning model for biological age prediction based on DNA methylation.
Highlighted the significance of specific methylation sites, particularly cg23995914, in aging.
Demonstrated the utility of epigenetic markers for understanding the aging process and its molecular underpinnings.