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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido


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Volume 14, Issue 1

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

Jürgen Claesen
  • Corresponding author
  • Interuniversity Institute of Biostatistics and Statistical Bioinformatics, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Tomasz Burzykowski
Published Online: 2014-12-05 | DOI: https://doi.org/10.1515/sagmb-2014-0007

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.

This article offers supplementary material which is provided at the end of the article.

Keywords: bulk segregant analysis; hidden Markov-models; next generation sequencing

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About the article

Corresponding author: Jürgen Claesen, Interuniversity Institute of Biostatistics and Statistical Bioinformatics, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium, e-mail:


Published Online: 2014-12-05

Published in Print: 2015-02-01


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 14, Issue 1, Pages 21–34, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2014-0007.

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