Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Different models of genetic variation and their effect on genomic evaluation | Clark et al. Genetics Selection Evolution 2011 43 18 http content 43 1 18 Ge n et i cs Selection Evolution RESEARCH Open Access Different models of genetic variation and their effect on genomic evaluation Samuel A Clark1 2 John M Hickey 1 and Julius HJ van der Werf1 2 Abstract Background The theory of genomic selection is based on the prediction of the effects of quantitative trait loci QTL in linkage disequilibrium LD with markers. However there is increasing evidence that genomic selection also relies on relationships between individuals to accurately predict genetic values. Therefore a better understanding of what genomic selection actually predicts is relevant so that appropriate methods of analysis are used in genomic evaluations. Methods Simulation was used to compare the performance of estimates of breeding values based on pedigree relationships Best Linear Unbiased Prediction BLUP genomic relationships gBLUP and based on a Bayesian variable selection model Bayes B to estimate breeding values under a range of different underlying models of genetic variation. The effects of different marker densities and varying animal relationships were also examined. Results This study shows that genomic selection methods can predict a proportion of the additive genetic value when genetic variation is controlled by common quantitative trait loci QTL model rare loci rare variant model all loci infinitesimal model and a random association a polygenic model . The Bayes B method was able to estimate breeding values more accurately than gBLUP under the QTL and rare variant models for the alternative marker densities and reference populations. The Bayes B and gBLUP methods had similar accuracies under the infinitesimal model. Conclusions Our results suggest that Bayes B is superior to gBLUP to estimate breeding values from genomic data. The underlying model of genetic variation greatly affects the predictive ability of genomic selection methods and the .
