Modeling Dependence in Methylation Patterns with Application to Ovarian Carcinomas

Lacey Michelle R and Ehrlich Melanie
Additional contact information
Lacey Michelle R: Tulane University
Ehrlich Melanie: Tulane University

Statistical Applications in Genetics and Molecular Biology, 2009, vol. 8, issue 1, 29

Abstract: 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.

Keywords: methylation; neighboring sites; dependence (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1489 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:40

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.2202/1544-6115.1489

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().