PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Constrained hidden Markov models for population-based haplotyping
Niels Landwehr, Taneli Mielikäinen, Lauri Eronen, Hannu Toivonen and Heikki Mannila
BMC Bioinformatics Volume 8, Number S2, 2007.

Abstract

Background. Haplotype Reconstruction is the problem of resolving the hidden phase information in genotype data obtained from laboratory measurements. Solving this problem is an important intermediate step in gene association studies, which seek to uncover the genetic basis of complex diseases. We propose a novel approach for haplotype reconstruction based on constrained hidden Markov models. Models are constructed by incrementally refining and regularizing the structure of a simple generative model for genotype data under Hardy-Weinberg equilibrium. Results. The proposed method is evaluated on real-world and simulated population data. Results show that it is competitive with other recently proposed methods in terms of reconstruction accuracy, while offering a particularly good trade-off between computational costs and quality of results for large datasets. Conclusion. Relatively simple probabilistic approaches for haplotype reconstruction based on structured hidden Markov models are competitive with more complex, well-established techniques in this field.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:3014
Deposited By:Taneli Mielikäinen
Deposited On:03 July 2007