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.
Highlights
Most of the methods currently used in statistical mapping of quantitative trait loci (QTL) share the common assumption of normally distributed phenotypic observations
The objective of this study was to incorporate the approach developed by Fernandez and Steel [4] into a Bayesian QTL mapping method, and to implement it with a Metropolis Hastings algorithm, instead of a Gibbs sampler with data augmentation, for better mixing of the Markov chain
A robust Bayesian QTL mapping method was implemented, which allows for non-normal, continuous distributions of phenotypes within QTL genotypes, via skewed Student-t distributions of residual phenotypes in the analysis
Summary
Most of the methods currently used in statistical mapping of quantitative trait loci (QTL) share the common assumption of normally distributed phenotypic observations. According to Coppieters et al [2], these approaches are not suitable for analysis of phenotypes, which are known to violate the normality assumption. Deviations from normality are likely to affect the accuracy of QTL detection with conventional methods. A nonparametric QTL interval mapping approach had been developed for experimental crosses (Kruglyak and Lander [8]) which was extended by Coppieters et al [2] for half-sib pedigrees in outbred populations. Elsen and coworkers ([3,7,10]) presented alternative models for QTL detection in livestock populations. In a collection of papers these authors used heteroskedastic models
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