We develop a factorial HMM based algorithm for computing genomewide IBD-sharing. The algorithm accepts as input SNP data of measured individuals and estimates the probability of IBD at each locus for every pair of individuals. For two g-degree relatives, when g>2, the computation yields a precision of IBD tagging at least 10% higher than previous methods for the same level of recall (>=95%).
Our algorithm uses a first order Markovian model for the LD process and employs a reduction of the state space of the inheritance vector from being exponential in g to quadratic. The higher accuracy along with the reduced time complexity marks our method as a feasible means for IBD mapping in practical scenarios. In continuation of the results presented in the paper, we performed additional experiments showing the increased performance of our method. A software implementation, called IBDmap, is freely available.

The open source code of the updated version will be made available shortly.