Bayesian QTL mapping using skewed Student-t distributions

  • Peter von Rohr
  • Ina HoescheleEmail author
Open Access


In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may lead to detection of false positive QTL. To improve the robustness of Bayesian QTL mapping methods, the normal distribution for residuals is replaced with a skewed Student-t distribution. The latter distribution is able to account for both heavy tails and skewness, and both components are each controlled by a single parameter. The Bayesian QTL mapping method using a skewed Student-t distribution is evaluated with simulated data sets under five different scenarios of residual error distributions and QTL effects.


Bayesian QTL mapping skewed Student-t distribution Metropolis-Hastings sampling 

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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 (, 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 ( applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Departments of Dairy Science and StatisticsVirginia Polytechnic Institute and State UniversityBlacksburgUSA
  2. 2.Institute of Animal Sciences, Animal BreedingSwiss Federal Institute of Technology (ETH)ZurichSwitzerland

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