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 100 000 variables for 2000 individuals.