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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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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

Estimating population structure using epigenome-wide methylation data.

Ziqing Wang^1,2, Kent D Taylor³, Jerome I Rotter³

¹CardioVascular Institute, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA 02215, United States.

Briefings in Bioinformatics

|March 28, 2026

Summary

This summary is machine-generated.

Population stratification inflates epigenome-wide association studies (EWAS). We developed methylation population scores (MPSs) to predict genetic principal components (GPCs), effectively capturing population structure and reducing EWAS inflation.

Keywords:

DNA methylation epigenome-wide association study (EWAS)population stratification

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Area of Science:

Genetics
Epigenetics
Population Genetics

Background:

Population stratification is a significant confounder in epigenome-wide association studies (EWAS), potentially leading to inflated results.
Accurate control for population structure is crucial for reliable EWAS findings, especially in multi-ethnic cohorts.

Purpose of the Study:

To develop and validate a novel method, methylation population scores (MPSs), for estimating population structure in EWAS.
To assess the performance of MPSs in capturing genetic principal components (GPCs) and reducing inflation in EWAS.
To provide an alternative to GPCs when genetic data are unavailable.

Main Methods:

Utilized multi-ethnic DNA methylation data from five cohorts (MESA, CARDIA, JHS, ARIC, HCHS/SOL) with Illumina EPIC arrays.
Employed a feature selection approach and two-stage weighted least squares Lasso regression to construct MPSs, predicting GPCs.
Adjusted for covariates including age, sex, smoking, alcohol use, race/ethnicity, BMI, and cell type proportions.

Main Results:

MPSs demonstrated strong correlations with corresponding GPCs in test datasets (R² from 0.27 to 0.98).
MPSs successfully recapitulated GPC patterns in differentiating ethnic groups and outperformed alternative methylation-based principal components.
MPSs showed comparable performance to GPCs in mitigating inflation within EWAS.

Conclusions:

Methylation population scores (MPSs) provide a reliable estimation of population structure using DNA methylation data.
MPSs effectively capture genetic structure across diverse populations and can serve as a valuable complement or alternative to GPCs.
This method enhances the accuracy and reliability of EWAS findings, particularly in genetic data-scarce scenarios.