Dataset Information

Enlarged leukocyte referent libraries can explain additional variance in blood-based epigenome-wide association studies.

ABSTRACT:

Aim

We examined whether variation in blood-based epigenome-wide association studies could be more completely explained by augmenting existing reference DNA methylation libraries.

Materials & methods

We compared existing and enhanced libraries in predicting variability in three publicly available 450K methylation datasets that collected whole-blood samples. Models were fit separately to each CpG site and used to estimate the additional variability when adjustments for cell composition were made with each library.

Results

Calculation of the mean difference in the CpG-specific residual sums of squares error between models for an arthritis, aging and metabolic syndrome dataset, indicated that an enhanced library explained significantly more variation across all three datasets (p < 10(-3)).

Conclusion

Pathologically important immune cell subtypes can explain important variability in epigenome-wide association studies done in blood.

SUBMITTER: Kim S

PROVIDER: S-EPMC5072420 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Publications

Enlarged leukocyte referent libraries can explain additional variance in blood-based epigenome-wide association studies.

Kim Stephanie S Eliot Melissa M Koestler Devin C DC Houseman Eugene A EA Wetmur James G JG Wiencke John K JK Kelsey Karl T KT

Epigenomics 20160816 9

<h4>Aim</h4>We examined whether variation in blood-based epigenome-wide association studies could be more completely explained by augmenting existing reference DNA methylation libraries.<h4>Materials & methods</h4>We compared existing and enhanced libraries in predicting variability in three publicly available 450K methylation datasets that collected whole-blood samples. Models were fit separately to each CpG site and used to estimate the additional variability when adjustments for cell composit ...[more]

PMID: 27529193

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Project description:BackgroundIn DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both.ResultsWe present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results.ConclusionsThe usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference.

Dataset Information

Enlarged leukocyte referent libraries can explain additional variance in blood-based epigenome-wide association studies.

Aim

Materials & methods

Results

Conclusion

Publications

Enlarged leukocyte referent libraries can explain additional variance in blood-based epigenome-wide association studies.

Similar Datasets

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