On the Trail of Thyroid Hormone Receptor Epigenetics

Sarkar, Pradip

doi:10.1002/9783527697274.ch17

Cited by 1 publication

(1 citation statement)

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“…The analysis of DNA methylation has become of considerable interest in biomedical research, as epigenetic studies have shown numerous associations between methylation levels and diseases such as cancer and cardiovascular disease [2,27,24,30,32]. Today, most research focuses on the cytosineguanine dinucleotide ("CpG") sites of the DNA, which are the locations where methylation is primarily found in humans [20].…”

Section: Introductionmentioning

confidence: 99%

A statistical model for the analysis of beta values in DNA methylation studies

Weinhold

Wahl

Pechlivanis³

et al. 2016

BMC Bioinformatics

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BackgroundThe 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.ResultsWe 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. 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.ConclusionThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1347-4) contains supplementary material, which is available to authorized users.

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