DNA methylation is a representative epigenetic change that occurs in our body and plays an essential role in regulating gene expression as well as in cancer progression. Identification of differentially methylated genes between two biological conditions has been popularly studied in epigenetic association studies. However, most of statistical methods aim to detect differences in mean methylation levels between two conditions. So, they are limited to identify differences in methylation variances which have been recently observed in cancer research. Moreover, they often fail to identify genes containing both differentially methylated CpG sites and neutral sites due to weak group association signals. In this article, we propose a new statistical method based on a group-penalized exponential tilt model that essentially combines an exponential tilt model and group lasso, regrading each gene as a group of multiple CpG sites. The proposed method is able to detect differentially methylated genes, capturing both mean and variance association signals. In our extensive simulation study, we demonstrated that the proposed method has superior selection performance, compared with the existing statistical methods developed for detection of differentially methylated genes. We also applied it to 450K DNA methylation data of The Cancer Genome Atlas Breast Invasive Carcinoma Collection. We were able to identify potentially cancer-related genes.
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