efficient genome ancestry inference in complex pedigrees with inbreeding motivation highdensity snp data of model animal resources provides opportunities for fineresolution genetic variation studies these genetic resources are generated through a variety of breeding schemes that involve multiple generations of matings derived from a set of founder animals in this article we investigate the problem of inferring the most probable ancestry of resulting genotypes given a set of founder genotypes due to computational difficulty existing methods either handle only small pedigree data or disregard the pedigree structure however large pedigrees of model animal resources often contain repetitive substructures that can be utilized in accelerating computationresults we present an accurate and efficient method that can accept complex pedigrees with inbreeding in inferring genome ancestry inbreeding is a commonly used process in generating genetically diverse and reproducible animals it is often carried out for many generations and can account for most of the computational complexity in realworld model animal pedigrees our method builds a hidden markov model that derives the ancestry probabilities through inbreeding process without explicit modeling in every generation the ancestry inference is accurate and fast independent of the number of generations for model animal resources such as the collaborative cross cc experiments on both simulated and real cc data demonstrate that our method offers comparable accuracy to those methods that build an explicit model of the entire pedigree but much better scalability with respect to the pedigree size