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Related Experiment Video

Updated: Jun 13, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
14:56

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

Published on: May 6, 2022

A statistical method for excluding non-variable CpG sites in high-throughput DNA methylation profiling.

Hailong Meng1, Andrew R Joyce, Daniel E Adkins

  • 1Altria Client Services, Research Development & Engineering, Richmond, VA 23219, USA. hlmeng@yahoo.com

BMC Bioinformatics
|May 6, 2010
PubMed
Summary
This summary is machine-generated.

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Identifying and removing non-variable CpG sites from DNA methylation array analyses improves biomarker discovery. This method enhances statistical power and reduces false discoveries in disease research.

Area of Science:

  • Genomics
  • Epigenetics
  • Biomarker Discovery

Background:

  • High-throughput DNA methylation arrays are crucial for discovering disease biomarkers.
  • A challenge is the presence of non-variable CpG sites, which can lead to false discoveries and reduced statistical power.
  • These non-variable sites do not reflect inter-individual differences relevant to disease outcomes.

Purpose of the Study:

  • To develop and validate a method for identifying and removing non-variable CpG sites from methylation array data.
  • To improve the accuracy and power of methylation biomarker discovery.
  • To enhance the reliability of epigenome-wide association studies.

Main Methods:

  • A novel method was developed to estimate the proportion of non-variable CpG sites.

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Last Updated: Jun 13, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
14:56

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

Published on: May 6, 2022

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
07:50

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

  • The method was applied to DNA methylation data from 311 peripheral blood mononuclear cell samples.
  • Data were analyzed using an array assaying 1505 CpG sites.

    • Main Results:

    • A significant proportion of the assayed CpG sites exhibited no inter-individual variation in methylation.
    • The proposed method successfully identified and allowed for the exclusion of these non-variable sites.
    • Exclusion of non-variable sites led to stronger association signals.

    Conclusions:

    • The developed method substantially improves methylation biomarker discovery by filtering non-variable sites.
    • This approach enhances association signals between methylation sites and outcome variables.
    • The method effectively controls the false discovery rate, increasing the reliability of findings.