Abstract
We have applied and implemented HMM (Hidden Markov Model) algorithms to calculate QTL genotype probabilities from marker and pedigree data in general population structures. These algorithms have a linear complexity in memory. In nearly all experimental pedigrees they result in more precise genotype estimates than the most commonly used approaches for determining genotypes at non-marker positions in QTL analysis in outbred F 2 line intercrosses [1], which include an exponential complexity factor as well as a data-reducing sampling step [2]. With a proper choice of parameters, the results from the existing methods can also be reproduced exactly. We show how the relative run times differ by a factor of 50 when 24 SNP markers are used, with our run time practically independent of marker count. The new method can also provide multi-generational probability estimates and perform haplotype inference from unphased data, which further improves accuracy and flexibility. An important future application of this method is for computationally efficient QTL genotype estimation in maps based on data from SNP chips containing 1000s of markers with mixed information content, for which there are no other suitable methods available at present.
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Nettelblad, C., Holmgren, S., Crooks, L., Carlborg, Ö. (2009). cnF2freq: Efficient Determination of Genotype and Haplotype Probabilities in Outbred Populations Using Markov Models. In: Rajasekaran, S. (eds) Bioinformatics and Computational Biology. BICoB 2009. Lecture Notes in Computer Science(), vol 5462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00727-9_29
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DOI: https://doi.org/10.1007/978-3-642-00727-9_29
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