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Accounting for cell lineage and sex effects in the identification of cell-specific DNA methylation using a Bayesian model selection algorithm

Nicole M. White, Miles C. Benton, Daniel W. Kennedy, Andrew Fox, Lyn R. Griffiths, Rodney A. Lea, Kerrie L. Mengersen
doi: https://doi.org/10.1101/124826
Nicole M. White
1ARC Center of Excellence in Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
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Miles C. Benton
2Genomics Research Center, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
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Daniel W. Kennedy
1ARC Center of Excellence in Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
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Andrew Fox
3Florey Department of Neuroscience and Mental Health, Melbourne, Australia
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Lyn R. Griffiths
2Genomics Research Center, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
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Rodney A. Lea
2Genomics Research Center, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
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Kerrie L. Mengersen
1ARC Center of Excellence in Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
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  • For correspondence: k.mengersen@qut.edu.au
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Abstract

Cell- and sex-specific differences in DNA methylation are major sources of epigenetic variation in whole blood. Failure to account for these confounders may lead to substantial bias in the identification of differentially methylated CpGs and predicted levels of differential methylation. Previous studies have provided evidence of cell-specific methylation, but all of these have been restricted to the detection of differential methylation in a single cell type. We developed a Bayesian model selection algorithm for the identification of cell-specific methylation profiles that incorporates knowledge of shared cell lineage, to accommodate differential methylation in one or more cell types. Under the proposed methodology, sex-specific differences in methylation by cell type are also assessed. Using publicly available cell-sorted methylation data, we show that 51.3% of female CpG markers and 61.4% of male CpG markers identified were associated with differential methylation in more than one cell type. The impact of cell lineage on differential methylation was also highlighted. An evaluation of sex-specific differences revealed marked differences in CD56+NK methylation, within both single and multi-cell dependent methylation patterns. Our findings demonstrate the need to account for cell lineage in studies of differential methylation and associated sex effects.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 06, 2017.
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Accounting for cell lineage and sex effects in the identification of cell-specific DNA methylation using a Bayesian model selection algorithm
Nicole M. White, Miles C. Benton, Daniel W. Kennedy, Andrew Fox, Lyn R. Griffiths, Rodney A. Lea, Kerrie L. Mengersen
bioRxiv 124826; doi: https://doi.org/10.1101/124826
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Accounting for cell lineage and sex effects in the identification of cell-specific DNA methylation using a Bayesian model selection algorithm
Nicole M. White, Miles C. Benton, Daniel W. Kennedy, Andrew Fox, Lyn R. Griffiths, Rodney A. Lea, Kerrie L. Mengersen
bioRxiv 124826; doi: https://doi.org/10.1101/124826

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