An alternative strategy is to use an unsupervised approach , which defines relevant regions across the genome first , independently of any phenotype information , and then tests methylation levels in these predefined regions against a phenotype ( 19,20,26 ) . In this study , we propose a new unsupervised approach for testing differential methylation in regions against continuous phenotype such as age , tumor size or marker protein concentrations . Note that in the proposed mixed effects model ( Supplementary Text , Section 1 ) , the phenotype variable is included as an independent variable and the methylation values as the outcome variable . Therefore , no distributional assumptions ( e.g. normal distribution ) are made for the continuous phenotypes . Table 1 lists several previously proposed unsupervised methods . A challenge with unsupervised approaches is their lack of specificity . Unlike gene expression data , the regional boundary of DNA methylation is often not well defined . Therefore , currently available approaches that summarize methylation levels in a region using mean or median methylation levels of the CpGs within the region may have results that vary depending on the boundaries of the region . In addition , when testing associations between phenotype and the summarized methylation levels in a genomic region , the spatial correlations between CpG sites within the region is ignored .