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Statistical approaches for the analysis of DNA methylation microarray data

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Abstract

Following the rapid development and adoption in DNA methylation microarray assays, we are now experiencing a growth in the number of statistical tools to analyze the resulting large-scale data sets. As is the case for other microarray applications, biases caused by technical issues are of concern. Some of these issues are old (e.g., two-color dye bias and probe- and array-specific effects), while others are new (e.g., fragment length bias and bisulfite conversion efficiency). Here, I highlight characteristics of DNA methylation that suggest standard statistical tools developed for other data types may not be directly suitable. I then describe the microarray technologies most commonly in use, along with the methods used for preprocessing and obtaining a summary measure. I finish with a section describing downstream analyses of the data, focusing on methods that model percentage DNA methylation as the outcome, and methods for integrating DNA methylation with gene expression or genotype data.

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Acknowledgments

I would like to thank Dr. Joe Hacia for his comments on an early draft and Dr. Christina Curtis for discussions regarding methods for data integration. I would also like to thank Tim Triche Jr. for his work on Beta Regression and the preprocessing of DNA methylation data from Illumina’s Infinium platform, and Dr. Peter W. Laird for the many helpful discussions over the years. This work was supported by NCI grant number R01 CA097346 and NIEHS grant number P30 ES07048. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

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  1. Department of Preventive Medicine, Keck School of Medicine of USC, 1540 Alcazar Street, CHP 220C, Los Angeles, CA, 90089, USA

    Kimberly D. Siegmund

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Siegmund, K.D. Statistical approaches for the analysis of DNA methylation microarray data. Hum Genet 129, 585–595 (2011). https://doi.org/10.1007/s00439-011-0993-x

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