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Activity Number: 414
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #313596
Title: A Bayesian Framework to Detect Differentially Methylated Loci in Both Mean and Variability with Next-Generation Sequencing
Author(s): Shuang Li*+ and Varghese George and Duchwan Ryu and Xiaoling Wang and Shaoyong Su and Huidong Shi and Robert H. Podolsky and Hongyan Xu
Companies: and Georgia Regents University and Georgia Regents University and Georgia Regents University and Georgia Regents University and Georgia Regents University and Wayne State University and Georgia Regents University
Keywords: Generation Sequencing ; Methylated Loci ; Bayesian
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 difference 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 differentially methylated loci for NGS data. To identify methylation loci which are associated with disease, we consider Bayesian statistical hypotheses testing for mean, variance and a joint statistical test for both mean and variance. To improve computational efficiency, we use Integrated Nested Laplace Approximation (INLA), which combines Laplace approximations and numerical integration in a very efficient manner for deriving marginal posterior distributions. We performed simulations to compare our proposed method to a score test. The simulation results


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