This article is part of the supplement: Probabilistic Modeling and Machine Learning in Structural and Systems Biology
Constrained hidden Markov models for population-based haplotyping
1 Machine Learning Lab, Department of Computer Science, Albert-Ludwigs-University Freiburg, Germany
2 HIIT Basic Research Unit, Department of Computer Science, University of Helsinki, Finland
BMC Bioinformatics 2007, 8(Suppl 2):S9 doi:10.1186/1471-2105-8-S2-S9Published: 3 May 2007
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.
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.
Relatively simple probabilistic approaches for haplotype reconstruction based on structured hidden Markov models are competitive with more complex, well-established techniques in this field.