A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies.

Raphaël Mourad (1) , Christine Sinoquet (1) , Philippe Leray (1)

1 LINA - Laboratoire d'Informatique de Nantes Atlantique

The forest of hierarchical latent class models offers several advantages for genome-wide association studies: accurate modeling of linkage disequilibrium, flexible data dimensionality reduction and biological meaning borne by latent variables.

Mots clés: linkage disequilibrium; probabilistic graphical model; hierarchical latent class models; genome-wide association studies

Domaines: Génétique humaine; Bio-informatique [q-bio.QM]; Bio-Informatique, Biologie Systémique [q-bio.QM]; Autres [stat.ML]

Raphaël Mourad, Christine Sinoquet, Philippe Leray. A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies.. BMC Bioinformatics, 2011, 12 (1), pp.16. ⟨10.1186/1471-2105-12-16⟩. ⟨hal-00567988⟩