A Bayesian Framework To Detect Differentially Methylated Loci in Both Mean And Variability with Next Generation Sequencing

Abstract

DNA methylation at CpG loci is the best known epigenetic process involved in many complex diseases including cancer. In recent years, next-generation sequencing (NGS) has been widely used to generate genome-wide DNA methylation data. Although substantial evidence indicates that di erence in mean methylation proportion between normal and disease is meaningful, it has recently been proposed that it may be important to consider DNA methylation variability underlying common complex disease and cancer. We introduce a robust hierarchical Bayesian framework with a Latent Gaussian model which incorporates both mean and variance to detect di erentially methylated loci for NGS data. To identify methylation loci which are associated with disease, we consider Bayesian statistical hypotheses testing for methylation mean and methylation variance using a twodimensional highest posterior density region. To improve computational e ciency, we use Integrated Nested Laplace Approximation (INLA), which combines Laplace approximations and numerical integration in a very e cient manner for deriving marginal posterior distributions. We performed simulations to compare our proposed method to other alternative methods. The simulation results illustrate that our proposed approach is more powerful in that it detects less false positives and it has true positive rate comparable to the other methods.