Fast Bayesian Haplotype Inference Via Context Tree Weighting
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Identification and Frequency Estimation of Inversion Polymorphisms from Haplotype Data
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
A mixed integer programming model for the parsimonious loss of heterozygosity problem
ISBRA'12 Proceedings of the 8th international conference on Bioinformatics Research and Applications
An Integer Programming Formulation of the Parsimonious Loss of Heterozygosity Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Numerous efforts are underway to catalog genetic variation in human populations. While the majority of studies of genetic variation have focused on single base pair differences between individuals, i.e. single nucleotide polymorphisms (SNPs), several recent studies have demonstrated that larger scale structural variation including copy number polymorphisms and inversion polymorphisms are also common. However, direct techniques for detection and validation of structural variants are generally much more expensive than detection and validation of SNPs. For some types of structural variation, in particular deletions, the polymorphism produces a distinct signature in the SNP data. In this paper, we describe a new probabilistic method for detecting deletion polymorphisms from SNP data. The key idea in our method is that we estimate the frequency of the haplotypes in a region of the genome both with and without the possibility of a deletion in the region and apply a generalized likelihood ratio test to assess the significance of a deletion. Application of our method to the HapMap Phase I data revealed 319 candidate deletions, 142 of these overlap with variants identified in earlier studies, while 177 are novel. Using Phase II HapMap data we predict 6730 deletions.