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LEARNING SNP DEPENDENCIES USING EMBEDDED BAYESIAN NETWORKS

This paper describes an efficient, scalable method for learning nucleotide dependencies around SNP sites using Bayesian networks. The complexity of the network is reduced by introducing a set of hidden nodes that do not appear in the original model. The resulting model is an embedded Bayesian network that encodes both local and global dependencies between the observed variables and allows for structure and parameter learning on parallel machines from distributed data. The results of the proposed learning method are reported on both synthetic data and DNA sequences of the human genome.