Development of Bayesian Graphical Models for Next-Generation Genetic Studies

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Graphical models have been one of the most efficient statistical tools used in the last twenty years for the analysis of complex structured high-dimensional data (Friedman, 2004). Graphical models provide a probabilistic framework for making inference and representing the knowledge that we have about these complex structured data. In biological research and more particularly in the emerging ‘omics disciplines such as genomics, proteomics, metabolomics, transcriptomics, data are often generated from complex high-throughput experiments and from complex experimental designs. Graphical models can represent these complex biological problems, leading to relatively simple and tractable computational algorithms to obtain the quantities of interest. Despite its obvious advantages the use of graphical models under either the frequentist or Bayesian framework remains relatively rare in biological research. In ‘omics disciplines, this could be partly explained by the computational difficulties in fitting graphical models to high-dimensional data and also the lack of a general theoretical framework for Bayesian graphical models.