Constrained hidden Markov models for population-based haplotyping.
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.


CONCLUSIONS
Relatively simple probabilistic approaches for haplotype reconstruction based on structured hidden Markov models are competitive with more complex, well-established techniques in this field.
Landwehr
Niels
N
Machine Learning Lab, Department of Computer Science, Albert-Ludwigs-University Freiburg, Germany. landwehr@informatik.uni-freiburg.de
Mielikäinen
Taneli
T
Eronen
Lauri
L
Toivonen
Hannu
H
Mannila
Heikki
H
D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D004252:DNA Mutational Analysis / Q000379:methods*; D008040:Genetic Linkage / Q000235:genetics; D005828:Genetics, Population; D006239:Haplotypes; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods*