Haplotypes and informative SNP selection algorithms: don't block out information
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Methods for Inferring Block-Wise Ancestral History from Haploid Sequences
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
Finding Founder Sequences from a Set of Recombinants
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Imputation-Based Local Ancestry Inference in Admixed Populations
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
Genotype error detection using hidden Markov models of haplotype diversity
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
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There is considerable interest in computational methods to assist in the use of genetic polymorphism data for locating disease-related genes. Haplotypes, contiguous sets of correlated variants, may provide a means of reducing the difficulty of the data analysis problems involved. The field to date has been dominated by methods based on the "haplotype block" hypothesis, which assumes discrete population-wide boundaries between conserved genetic segments, but there is strong reason to believe that haplotype blocks do not fully capture true haplotype conservation patterns. In this paper, we address the computational challenges of using a more flexible, block-free representation of haplotype structure called the "haplotype motif" model for downstream analysis problems. We develope algorithms for htSNP selection and missing data inference using this more generalized model of sequence conservation. Application to a dataset from the literature demonstrates the practical value of these block-free methods.