Identification and analysis of methylation call differences between bisulfite microarray and bisulfite sequencing data with statistical learning techniques

Döring, Matthias; Gasparoni, Gilles; Gries, Jasmin; Nordström, Karl; Lutsik, Pavlo; Walter, Jörn; Pfeifer, Nico

doi:10.1186/1471-2105-16-s3-a7

Cited by 2 publications

(2 citation statements)

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“…The development of statistical models to analyze DNA methylation is the subject of intense and ongoing research [19,33,7,8]. In this paper, we proposed a likelihood-based approach to analyze and infer the associations between covariates and methylation levels in Illumina 450k data.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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show abstract

“…Döring et al . [ 9 ] modeled the differences in sequence bias that exist for methylation determination through microarray hybridization and bisulfite sequencing.…”

Section: Student Presentationsmentioning

confidence: 99%