Abstract
Epigenetic observations are represented by the total number of reads from a given pool of cells and the number of methylated reads, making it reasonable to model this data by a binomial distribution. There are numerous factors that can influence the probability of success in a particular region. Moreover, there is a strong spatial (alongside the genome) dependence of these probabilities. We incorporate dependence on the covariates and the spatial dependence of the methylation probability for observations from a pool of cells by means of a binomial regression model with a latent Gaussian field and a logit link function. We apply a Bayesian approach including prior specifications on model configurations. We run a mode jumping Markov chain Monte Carlo algorithm (MJMCMC) across different choices of covariates in order to obtain the joint posterior distribution of parameters and models. This also allows finding the best set of covariates to model methylation probability within the genomic region of interest and individual marginal inclusion probabilities of the covariates.
Highlights
Epigenetic modifications contribute to the generation of phenotypic plasticity, but the understanding of its contribution to phenotypic alterations and how the genome influences epigenetic variants requires further investigation (Schmitz, Schultz, Urich, Nery, Pelizzola, Libiger, Alix, McCosh, Chen, Schork et al 2013)
Highthroughput epigenetics experiments have enabled researchers to measure genome-wide epigenetic profiles. This allows performing Epigenome-wide association studies (EWAS), which hold promise for the detection of new regulatory mechanisms that may be susceptible to modification by environmental and lifestyle factors (Michels, Binder, Dedeurwaerder, Epstein, Greally, Gut, Houseman, Izzi, Kelsey, Meissner et al 2013)
We limit ourselves to finding a pattern of signals appearing along the single genome that significantly influences the methylation probability of the corresponding organism. This is done by means of applying a binomial regression model with latent Gaussian variables, which take into account both spatial dependence and variability that can not be explained by the exogenous variables alone
Summary
Epigenetic modifications contribute to the generation of phenotypic plasticity, but the understanding of its contribution to phenotypic alterations and how the genome influences epigenetic variants requires further investigation (Schmitz, Schultz, Urich, Nery, Pelizzola, Libiger, Alix, McCosh, Chen, Schork et al 2013). Becker, Hagmann, Muller, Koenig, Stegle, Borgwardt, and Weigel (2011) previously analysed Arabidopsis data consisting of epigenetic observations on a set of 10 lines, which were separately propagated in a common environment for 30 generations. These were compared with two independent lines propagated for only three generations. We limit ourselves to finding a pattern of signals appearing along the single genome that significantly influences the methylation probability of the corresponding organism This is done by means of applying a binomial regression model with latent Gaussian variables, which take into account both spatial dependence and variability that can not be explained by the exogenous variables alone. Our approach allows to generate a model-averaged classification of the methylation status at different locations and make imputations for those locations that do not have enough observations, whilst the currently used approach is to ignore these locations
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