Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: A generalized estimating equations approach to quantitative trait locus detection of non-normal traits | 257 Genet Sei. Evol. 35 2003 257-280 INRA EDP Sciences 2003 DOI gse 2003008 Original article a generalized estimating equations approach to quantitative trait locus detection of non-normal traits Peter c. Thomson BiometTY Unit Faculty of Agriculture Food and Natural Resources and Centre for Advanced Technologies in Animal Genetics and Reproduction ReproGen The University of Sydney PMB 3 Camden NSW 2570 Australia Received 12 February 2002 accepted 22 January 2003 Abstract - To date most statistical developments in QTL detection methodology have been directed at continuous traits with an underlying normal distribution. This paper presents a method for QTL analysis of non-normal traits using a generalized linear mixed model approach. Development of this method has been motivated by a backcross experiment involving two inbred lines of mice that was conducted in order to locate a QTL for litter size. A Poisson regression form is used to model litter size with allowances made for under- as well as over-dispersion as suggested by the experimental data. In addition to fixed parity effects random animal effects have also been included in the model. However the method is not fully parametric as the model is specified only in terms of means variances and covariances and not as a full probability model. Consequently a generalized estimating equations GEE approach is used to fit the model. For statistical inferences permutation tests and bootstrap procedures are used. This method is illustrated with simulated as well as experimental mouse data. Overall the method is found to be quite reliable and with modification can be used for QTL detection for a range of other non-normally distributed traits. QTL non-normal traits generalized estimation equation litter size mice 1. introduction Various methods have been developed to detect a quantitative trait locus ranging from the simpler regression based and method of moments to maximum likelihood and Markov Chain Monte Carlo .
