A statistical model for the analysis of beta values in DNA methylation studies. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Weinhold, Leonie; Wahl, Simone; Pechlivanis, Sonali; Hoffmann, Per; Schmid, Matthias
• Is Part Of: BMC bioinformatics. Volume 17:Issue 1(2016)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-12
• Subjects: Bounded response variables -- DNA methylation -- Gamma Regression -- Gradient Boosting -- HumanMethylation450k BeadChip; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-016-1347-4
• Languages: English
• ISSNs: 1471-2105

Abstract

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 formM /(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 intensitiesM andU . By allowing for possible correlations betweenM andU, the proposed model explicitly takes into account the data-generating process underlying the calculation of beta values. Using simulated data and a real sample of DNA methylation data from the Heinz Nixdorf Recall cohort study, we demonstrate that the proposed model fits our data significantly better than beta regression and M-value regression. Conclusion The proposed model contributes to an improved 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.