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A Bayesian network approach to model local dependencies among SNPs

Abstract : In this preliminary work, we investigate a method to model linkage disequilibrium among SNPs (Single Nucleotide Polymorphisms) in the genome. The genetic data such as SNPs is characterized by a typical block-like structure along the genome. Graphical models such as Bayesian networks can provide a fine and biologically relevant modeling of dependencies for both haplotypical and genotypical SNP data. We applied a MWST-based algorithm (Maximum Weighted Spanning Tree) to construct a Bayesian network, relying on the underlying local dependencies.
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Contributor : Christine Sinoquet <>
Submitted on : Tuesday, April 6, 2010 - 5:56:58 PM
Last modification on : Thursday, February 7, 2019 - 3:26:55 PM
Long-term archiving on: : Friday, October 19, 2012 - 11:20:39 AM


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  • HAL Id : hal-00470528, version 1



Raphaël Mourad, Christine Sinoquet, Philippe Leray. A Bayesian network approach to model local dependencies among SNPs. MODGRAPH 2009 Probabilistic graphical models for integration of complex data and discovery of causal models in biology, satellite meeting of JOBIM 2009, Jun 2009, Nantes, France. ⟨hal-00470528⟩



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