Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Further insights of the variance component method for detecting QTL in livestock and aquacultural species: relaxing the assumption of additive effects | Genet. Sel. Evol. 40 2008 585-606 INRA EDP Sciences 2008 DOI gse 2008028 Available online at Original article Further insights of the variance component method for detecting QTL in livestock and aquacultural species relaxing the assumption of additive effects Victor Martinez Faculty of Veterinary Sciences Universidad de Chile Avda Santa Rosa 11735 Santiago Chile Received 4 December 2007 accepted 1st August 2008 Abstract - Complex traits may show some degree of dominance at the gene level that may influence the statistical power of simple models . assuming only additive effects to detect quantitative trait loci QTL using the variance component method. Little has been published on this topic even in species where relatively large family sizes can be obtained such as poultry pigs and aquacultural species. This is important when the idea is to select regions likely to be harbouring dominant QTL or in marker assisted selection. In this work we investigated the empirical power and accuracy to both detect and localise dominant QTL with or without incorporating dominance effects explicitly in the model of analysis. For this purpose populations with variable family sizes and constant population size and different values for dominance variance were simulated. The results show that when using only additive effects there was little loss in power to detect QTL and estimates of position using or not using dominance were empirically unbiased. Further there was little gain in accuracy of positioning the QTL with most scenarios except when simulating an overdominant QTL. QTL additive effect dominance power REML 1. INTRODUCTION Quantitative trait loci QTL detection using mixed linear models is one of the preferred methods for estimating the contribution of a particular chromosomal segment to the observed variance in general pedigrees from outbred populations 2 19 . This method infers QTL segregation using as a covariance structure the number of alleles
