Incorporating Genomic Annotation into a Hidden Markov Model for DNA Methylation Tiling Array Data

DNA methylation is an epigenetic modification that plays an important role in many biological processes. To better understand this epigenetic mechanism, genome-wide investigations of DNA methylation can be conducted via tiling array technology. In such DNA methylation profiling studies, statistical methods are employed to estimate the DNA methylation status of each probe represented on the tiling array. Although many of the current methods account for the inherent dependency between neighboring probes, they do not effectively utilize the wealth of information available in genomic annotation databases. Since previous studies indicate that DNA methylation patterns of some organisms differ by genomic element (e.g., gene, intergenic region), such genomic annotation information may be useful in predicting DNA methylation status. In this work, a novel statistical model is proposed within the hidden Markov model framework to incorporate genomic annotation information in the prediction of DNA methylation status. The advantages of incorporating genomic annotation via the proposed method are investigated through a simulation study and real data applications.