Abstract:
|
In epigenetic studies of human diseases, it has been common to compare DNA methylation levels between cancer tissues and normal tissues to identify cancer-related genetic sites. For case-control association studies with high-dimensional DNA methylation data, a network-based penalized logistic regression has been proposed in our earlier article. Network regularization is very efficient for analysis of highly correlated methylation data. However, recent studies found that the methylation levels of the cancer and normal tissues could differ not only in means but also in variances. Penalized logistic regression has a limitation to detect any differences in variances. In this article, we introduce a penalized exponential tilt model using network-based regularization and demonstrate that it can identify differentially methylated loci between cancer and normal tissues when their methylation levels are different in means only, variances only or in both means and variances. We also applied the proposed method to a real methylation data from an ovarian cancer study where methylation levels over 20,000 CpG sites generated from Illumina Infinium HumanMethylation27K Beadchip.
|
ASA Meetings Department
732 North Washington Street, Alexandria, VA 22314
(703) 684-1221 • meetings@amstat.org
Copyright © American Statistical Association.