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A hidden Markov-model for gene mapping based on whole-genome next generation sequencing data

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  • Claesen Jürgen

    (Interuniversity Institute of Biostatistics and Statistical Bioinformatics, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium)

  • Burzykowski Tomasz

    (I-Biostat, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium)

Abstract

The analysis of polygenic, phenotypic characteristics such as quantitative traits or inheritable diseases requires reliable scoring of many genetic markers covering the entire genome. The advent of high-throughput sequencing technologies provides a new way to evaluate large numbers of single nucleotide polymorphisms as genetic markers. Combining the technologies with pooling of segregants, as performed in bulk segregant analysis, should, in principle, allow the simultaneous mapping of multiple genetic loci present throughout the genome. We propose a hidden Markov-model to analyze the marker data obtained by the bulk segregant next generation sequencing. The model includes several states, each associated with a different probability of observing the same/different nucleotide in an offspring as compared to the parent. The transitions between the molecular markers imply transitions between the states of the model. After estimating the transition probabilities and state-related probabilities of nucleotide (dis)similarity, the most probable state for each SNP is selected. The most probable states can then be used to indicate which genomic regions may be likely to contain trait-related genes. The application of the model is illustrated on the data from a study of ethanol tolerance in yeast. Software is written in R. R-functions, R-scripts and documentation are available on www.ibiostat.be/software/bioinformatics.

Suggested Citation

  • Claesen Jürgen & Burzykowski Tomasz, 2015. "A hidden Markov-model for gene mapping based on whole-genome next generation sequencing data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(1), pages 21-34, February.
    • Handle: RePEc:bpj:sagmbi:v:14:y:2015:i:1:p:21-34:n:3
    DOI: 10.1515/sagmb-2014-0007
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    References listed on IDEAS

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    1. Héctor Corrada Bravo & Rafael A. Irizarry, 2010. "Model-Based Quality Assessment and Base-Calling for Second-Generation Sequencing Data," Biometrics, The International Biometric Society, vol. 66(3), pages 665-674, September.
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