Bayesian QTL mapping using skewed Student- t distributions

  • Peter von Rohr1, 2 and

    Affiliated with

    • Ina Hoeschele1Email author

      Affiliated with

      Genetics Selection Evolution200234:1

      DOI: 10.1186/1297-9686-34-1-1

      Received: 23 April 2001

      Accepted: 17 September 2001

      Published: 15 January 2002

      Abstract

      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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      Authors’ Affiliations

      (1)
      Departments of Dairy Science and Statistics, Virginia Polytechnic Institute and State University
      (2)
      Institute of Animal Sciences, Animal Breeding, Swiss Federal Institute of Technology (ETH)

      Copyright

      © INRA, EDP Sciences 2002

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