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A statistical model for the analysis of beta values in DNA methylation studies
Leonie Weinhold1, Simone Wahl2, Sonali Pechlivanis3
1Department of Medical Biometry, Informatics and Epidemiology, University of Bonn, Sigmund-Freud-Str. 25, Bonn, D-53127, Germany. weinhold@imbie.uni-bonn.de.
BMC Bioinformatics
|November 24, 2016
Summary
We developed a new statistical model for analyzing DNA methylation beta values. This model better fits the data than traditional methods, improving the identification of associations in epigenome-wide association studies.
Area of Science:
- Genetics
- Biostatistics
Background:
- DNA methylation analysis is crucial for personalized medicine.
- Beta values, derived from Illumina's 450k BeadChip array, are commonly used to measure DNA methylation.
- Traditional statistical methods for beta value analysis have limitations due to strong regularity assumptions.
Purpose of the Study:
- To develop a novel statistical model for analyzing DNA methylation beta values.
- To address the limitations of existing methods like beta regression and M-value regression.
Main Methods:
- A statistical model was derived from a bivariate gamma distribution for signal intensities M and U.
- The model accounts for potential correlations between M and U, reflecting the data-generating process.
- The model's performance was evaluated using simulated data and real DNA methylation data.
Main Results:
- The proposed model demonstrated a significantly better fit to the data compared to beta regression and M-value regression.
- The model successfully incorporates the underlying data-generating process of beta values.
Conclusions:
- The new model enhances the identification of associations between beta values and covariates in epigenome-wide association studies.
- The proposed model is as user-friendly as existing methods for beta value analysis.
