We propose a new hierarchical latent class model devoted to represent statistical dependencies between genetic markers, in the human genome. Our proposal relies on a forest of hierarchical latent class models. The motivation is the reduction of dimension of the data to be further submitted to statistical association tests with respect to diseased/non diseased status. An 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 $10^5$ variables for $2000$ individuals.