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
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
Algorithms for Association Study Design Using a Generalized Model of Haplotype Conservation
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
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The promise of plentiful data on common human geneticvariations has given hope that we will be able to uncovergenetic factors behind common diseases that have provendifficult to locate by prior methods. Much recent interestin this problem has focused on using haplotypes (contiguousregions of correlated genetic variations), instead of theisolated variations, in order to reduce the size of the statisticalanalysis problem. In order to most effectively usesuch variation data, we will need a better understandingof haplotype structure, including both the general principlesunderlying haplotype structure in the human populationand the specific structures found in particular geneticregions or sub-populations. This paper presents a probabilisticmodel for analyzing haplotype structure in a populationusing conserved motifs found in statistically significantsub-populations. It describes the model and computationalmethods for deriving the predicted motif set and haplotypestructure for a population. It further presents results on simulateddata, in order to validate the method, and on two realdatasets from the literature, in order to illustrate its practicalapplication.