HMC (Haplotype Inference Based on Markov Chain)is a new haplotype inference methods based on Markov chain model which do not assume haplotype blocks in the population and allows each individual haplotype to have its own structure, thus are able to accommodate recombination and obtain higher adaptivity to the genotype data, specifically in the case of long marker maps. The proposed method presents a general Markov chain model for haplotype inference problem. A dynamic programming algorithm is developed for the model. The algorithm is theoretically guaranteed to find exact global optimal solutions within polynomial running time. Through extensive computational experiments on simulated and real genotype data, the designed algorithm is shown to be efficient, and outperforms previous methods.
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Ling-Yun Wu, Ji-Hong Zhang, and Raymond Chan.
Improved approach for haplotype inference based on Markov chain.
In Proceedings of 2nd International Symposium on Optimization and Systems Biology, Lecture Notes in Operations Research, Vol. 9, pp. 204–215, World Publishing Corporation, Beijing, 2008.