Discover the genetic basis of common genetic diseases represents a public health issue. However this task presents several difficulties such as high data dimensionality and identification of the causal mutations. For this purpose, the modelling of dependencies between genetic markers using hierarchical bayesian networks offers several possibilities: data dimension reduction through latent variables and identification of causal markers through the conditional independence property.