Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Bayesian QTL mapping using skewed Student-t distributions | Genet Sei. Evol. 34 2002 1-21 INRA EDP Sciences 2002 DOI gse 2001001 1 Original article BaYesian QTL mapping using skewed Student-Í distributions Peter von Rohra b Ina Hoeschelea a Departments of Daily Science and Statistics Virginia Polytechnic Institute and State University Blacksburg VA 24061-0315 USA b Institute of Animal Sciences Animal Breeding Swiss Federal Institute of Technology ETH Zurich Switzerland Received 23 April 2001 accepted 17 September 2001 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-i 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-i distribution is evaluated with simulated data sets under five different scenarios of residual error distributions and QTL effects. Bayesian QTL mapping skewed student-i distribution Metropolis-Hastings sampling 1. introduction 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. Eisen and coworkers 3 7 10 presented alternative models for QTL detection in livestock populations. In a collection of papers these authors used heteroskedastic models .
