RT Journal Article
SR Electronic
T1 Accounting for cell lineage and sex effects in the identification of cell-specific DNA methylation using a Bayesian model selection algorithm
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 124826
DO 10.1101/124826
A1 Nicole M. White
A1 Miles C. Benton
A1 Daniel W. Kennedy
A1 Andrew Fox
A1 Lyn R. Griffiths
A1 Rodney A. Lea
A1 Kerrie L. Mengersen
YR 2017
UL http://biorxiv.org/content/early/2017/04/06/124826.abstract
AB 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.