Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: The impact of genetic relationship information on genomic breeding values in German Holstein cattle | Habier et al. Genetics Selection Evolution 2010 42 5 http content 42 1 5 GSE Ge n et i cs Selection Evolution RESEARCH Open Access The impact of genetic relationship information on genomic breeding values in German Holstein cattle 1 1 2 3 1 David Habier Jens Tetens Franz-Reinhold Seefried Peter Lichtner Georg Thaller Abstract Background The impact of additive-genetic relationships captured by single nucleotide polymorphisms SNPs on the accuracy of genomic breeding values GEBVs has been demonstrated but recent studies on data obtained from Holstein populations have ignored this fact. However this impact and the accuracy of GEBVs due to linkage disequilibrium LD which is fairly persistent over generations must be known to implement future breeding programs. Materials and methods The data set used to investigate these questions consisted of 3 863 German Holstein bulls genotyped for 54 001 SNPs their pedigree and daughter yield deviations for milk yield fat yield protein yield and somatic cell score. A cross-validation methodology was applied where the maximum additive-genetic relationship amax between bulls in training and validation was controlled. GEBVs were estimated by a Bayesian model averaging approach BayesB and an animal model using the genomic relationship matrix G-BLUP . The accuracy of GEBVs due to LD was estimated by a regression approach using accuracy of GEBVs and accuracy of pedigree-based BLUP-EBVs. Results Accuracy of GEBVs obtained by both BayesB and G-BLUP decreased with decreasing amax for all traits analyzed. The decay of accuracy tended to be larger for G-BLUP and with smaller training size. Differences between BayesB and G-BLUP became evident for the accuracy due to LD where BayesB clearly outperformed G-BLUP with increasing training size. Conclusions GEBV accuracy of current selection candidates varies due to different additive-genetic relationships relative to the training data. Accuracy of future candidates can be lower .
