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A Bayesian approach for constructing genetic maps when markers are miscoded

  • Guilherme JM RosaEmail author
  • Brian S Yandell
  • Daniel Gianola
Open Access
Research

Abstract

The advent of molecular markers has created opportunities for a better understanding of quantitative inheritance and for developing novel strategies for genetic improvement of agricultural species, using information on quantitative trait loci (QTL). A QTL analysis relies on accurate genetic marker maps. At present, most statistical methods used for map construction ignore the fact that molecular data may be read with error. Often, however, there is ambiguity about some marker genotypes. A Bayesian MCMC approach for inferences about a genetic marker map when random miscoding of genotypes occurs is presented, and simulated and real data sets are analyzed. The results suggest that unless there is strong reason to believe that genotypes are ascertained without error, the proposed approach provides more reliable inference on the genetic map.

Keywords

genetic map construction miscoded genotypes Bayesian inference 

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Copyright information

© INRA, EDP Sciences 2002

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Guilherme JM Rosa
    • 1
    Email author
  • Brian S Yandell
    • 2
  • Daniel Gianola
    • 3
  1. 1.Department of Biostatistics, UNESPBotucatu, SPBrazil
  2. 2.Departments of Statistics and of HorticultureUniversity of WinconsinMadisonUSA
  3. 3.Departments of Animal Science and of Biostatistics & Medical InformaticsUniversity of WisconsinMadisonUSA

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