Haplotyping as perfect phylogeny: conceptual framework and efficient solutions
Proceedings of the sixth annual international conference on Computational biology
Large scale reconstruction of haplotypes from genotype data
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Model-based inference of haplotype block variation
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Maximum likelihood resolution of multi-block genotypes
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Insights on haplotype inference on large genotype datasets
BSB'10 Proceedings of the Advances in bioinformatics and computational biology, and 5th Brazilian conference on Bioinformatics
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With the rapid progress of genotyping techniques, many large-scale, genome-wide disease studies are now under way. One of the challenges of large disease-association studies is developing a fast and accurate computing method for haplotype inference from genotype data. In this paper, a new computing method for population-based haplotype inference problem is proposed. The designed 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 recombination and obtain higher adaptivity to the genotype data, specifically in the case of long marker maps. This method develops a dynamic programming algorithm, which is theoretically guaranteed to find exact maximum likelihood solutions of the variable order Markov chain model for haplotype inference problem within linear running time. Hence, it is fast and, as a result, practicable for large genotype datasets. Through extensive computational experiments on large-scale real genotype data, the proposed method is shown to be fast and efficient.