Learning Hierarchical Bayesian Networks for Genome-Wide Association Studies

Raphaël Mourad (), Christine Sinoquet () and Philippe Leray ()
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Raphaël Mourad: Ecole Polytechnique de l’Université de Nantes, LINA, UMR CNRS 6241
Christine Sinoquet: Université de Nantes, LINA, UMR CNRS 6241
Philippe Leray: Ecole Polytechnique de l’Université de Nantes, LINA, UMR CNRS 6241

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 549-556 from Springer

Abstract: Abstract We describe a novel probabilistic graphical model customized to represent the statistical dependencies between genetic markers, in the Human genome. Our proposal relies on a forest of hierarchical latent class models. The motivation is to reduce the dimension of the data to be further submitted to statistical association tests with respect to diseased/non diseased status. A generic algorithm, CFHLC, has been designed to tackle the learning of both forest structure and probability distributions. A first implementation has been shown to be tractable on benchmarks describing 105 variables for 2000 individuals.

Keywords: Bayesian networks; hierarchical latent class model; data dimensionality reduction; genetic marker dependency modelling (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-3-7908-2604-3_56

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