Modeling dependence in methylation patterns with application to ovarian carcinomas

Changes in cytosine methylation at CpG nucleotides are observed in many cancers and offer great potential for translational research. Diseases such as ovarian cancer that are especially challenging to diagnose and treat are of particular interest, and abnormal methylation in the tandem repeats Sat2 and NBL2 has been observed in a collection of ovarian carcinomas. In earlier analyses of double-stranded methylation patterns in 0.2 kb regions of Sat2 and NBL2, we detected clusters of identically methylated sites in close proximity. These clusters could not be explained by random variation, and our findings suggested a high degree of site-to-site dependence. However, previously developed stochastic models for methylation change have either treated CpG sites independently or employed a context dependent approach to adjust model parameters according to regional methylation levels. In this paper, we introduce a novel neighboring sites model as an alternative methodology for considering dependence in methylation patterns, and we compare the three models in their ability to generate simulated sequences statistically similar to our Sat2 and NBL2 carcinoma samples.