Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Sensitivity of methods for estimating breeding values using genetic markers to the number of QTL and distribution of QTL variance | Coster et al. Genetics Selection Evolution 2010 42 9 http content 42 1 9 Ge n et i cs Selection Evolution RESEARCH Open Access Sensitivity of methods for estimating breeding values using genetic markers to the number of QTL and distribution of QTL variance 1 12 1 1 Albart Coster John WM Bastiaansen Mario PL Calus Johan AM van Arendonk Henk Bovenhuis Abstract The objective of this simulation study was to compare the effect of the number of QTL and distribution of QTL variance on the accuracy of breeding values estimated with genomewide markers MEBV . Three distinct methods were used to calculate MEBV a Bayesian Method BM Least Angle Regression LARS and Partial Least Square Regression PLSR . The accuracy of MEBV calculated with BM and LARS decreased when the number of simulated QTL increased. The accuracy decreased more when QTL had different variance values than when all QTL had an equal variance. The accuracy of MEBV calculated with PLSR was affected neither by the number of QTL nor by the distribution of QTL variance. Additional simulations and analyses showed that these conclusions were not affected by the number of individuals in the training population by the number of markers and by the heritability of the trait. Results of this study show that the effect of the number of QTL and distribution of QTL variance on the accuracy of MEBV depends on the method that is used to calculate MEBV. Background In current breeding programs estimation of breeding values is based on phenotypes of selection candidates and their relatives often measured after animals reach to a certain age. This leads to a moderate to long generation interval substantial costs and complex logistics for phenotypic recording 1 . Comparatively breeding values estimated with genomewide distributed markers MEBV will increase annual genetic gain due to a reduced generation interval and improved accuracy at lower costs 2 1 . Calculation of MEBV requires a population with information .
