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Learning Hierarchical Bayesian Networks for Genome-Wide Association Studies

In: Proceedings of COMPSTAT'2010

Author

Listed:
  • 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)

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.

Suggested Citation

  • Raphaël Mourad & Christine Sinoquet & Philippe Leray, 2010. "Learning Hierarchical Bayesian Networks for Genome-Wide Association Studies," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 549-556, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_56
    DOI: 10.1007/978-3-7908-2604-3_56
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