A New Statistical Method for Haplotype Inference from Genotype Data

J.-H. Zhang, L.-Y. Wu, J. Chen, and X.-S. Zhang (PRC)


haplotype, genotype, SNP, Markov chain, dynamic pro gramming.


This paper proposes a new statistical method for the population-based haplotype inference problem. The de signed method does not assume haplotype blocks in the population and allows each individual haplotype to have its own structure, and thus is able to accommodate recom bination and obtains higher adaptivity to the genotype data. The method presents a general Markov chain framework for haplotype inference problem. Based on this frame work, a dynamic programming algorithm is developed to find its maximum likelihood solution. The algorithm is theoretically guaranteed to find exact globally optimal so lutions within polynomial running time. Through extensive computational experiments on simulated and real genotype data, the proposed method is shown to be efficient, and out performs previous methods especially in the case of long marker maps.

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