A Statistical Model for the Analysis of Beta Values in DNA Methylation Studies

TL;DR

This paper introduces a new statistical model based on a bivariate gamma distribution to analyze DNA methylation beta values, addressing limitations of traditional methods and improving association detection with covariates.

Contribution

A novel bivariate gamma-based model for beta value analysis that accounts for correlation between signal intensities, enhancing epigenome-wide association studies.

Findings

Model explicitly accounts for correlation between M and U

Improves detection of associations with covariates

Comparable ease of use to existing methods

Abstract

Background: The analysis of DNA methylation is a key component in the development of personalized treatment approaches. A common way to measure DNA methylation is the calculation of beta values, which are bounded variables of the form M = (M + U) that are generated by Illumina's 450k BeadChip array. The statistical analysis of beta values is considered to be challenging, as traditional methods for the analysis of bounded variables, such as M-value regression and beta regression, are based on regularity assumptions that are often too strong to adequately describe the distribution of beta values. Results: We develop a statistical model for the analysis of beta values that is derived from a bivariate gamma distribution for the signal intensities M and U. By allowing for possible correlations between M and U, the proposed model explicitly takes into account the data-generating process underlying the calculation of beta values. Conclusion: The proposed model can be used to improve the identification of associations between beta values and covariates such as clinical variables and lifestyle factors in epigenome-wide association studies. It is as easy to apply to a sample of beta values as beta regression and M-value regression.