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

View Article: PubMed Central - PubMed

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. 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.

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

No MeSH data available.


Analysis of the HNR Study data. The figure shows a kernel density plot of the Pearson correlations between the signal intensities M and U across the full set of 429,750 CpG sites
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Fig1: Analysis of the HNR Study data. The figure shows a kernel density plot of the Pearson correlations between the signal intensities M and U across the full set of 429,750 CpG sites

Mentions: After pre-processing, a total of n= 1,118 persons and 429,750 CpG sites remained in the analysis set. The distribution of the 429,750 Pearson correlation coefficients between the signal intensities M and U is shown in Fig. 1. The majority of the coefficients was substantially larger than zero, indicating that the independence assumption for M and U was not justified. More than 99.2 % of the correlation coefficients were positive (mean = 0.452, sd = 0.140).Fig. 1


A statistical model for the analysis of beta values in DNA methylation studies
Analysis of the HNR Study data. The figure shows a kernel density plot of the Pearson correlations between the signal intensities M and U across the full set of 429,750 CpG sites
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5120494&req=5

Fig1: Analysis of the HNR Study data. The figure shows a kernel density plot of the Pearson correlations between the signal intensities M and U across the full set of 429,750 CpG sites
Mentions: After pre-processing, a total of n= 1,118 persons and 429,750 CpG sites remained in the analysis set. The distribution of the 429,750 Pearson correlation coefficients between the signal intensities M and U is shown in Fig. 1. The majority of the coefficients was substantially larger than zero, indicating that the independence assumption for M and U was not justified. More than 99.2 % of the correlation coefficients were positive (mean = 0.452, sd = 0.140).Fig. 1

View Article: PubMed Central - PubMed

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. 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.

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

No MeSH data available.