Comparative study of estimation methods for genomic breeding values

Estimation of genomic breeding values is important in genomic selection. Bayesian and BLUP methods are the main techniques employed. In this study, we conducted a comparative study of BayesA, BayesB, BayesCπ and GBLUP methods in simulated data and real data of Chinese Holstein cattle. Results showed that, in simulated data, the accuracies of all methods were all similarly elevated with the increase of reference population size, but they made different responses to the changes of marker number or QTL number. In real data of Chinese Holstein cattle, BayesA generated the highest accuracy almost for all six traits, and GBLUP performed as well as BayesA for the traits of milk yield, fat yield and protein yield, while for the trait of fat percentage, protein percentage and somatic cell score, three Bayesian methods showed superior to GBLUP. Comprehensively analyzing above results, it can be speculated that accuracies of the three Bayesian methods are not only influenced by the absolute value of QTL number or marker number, but may also be influenced by the ratio of QTL number to marker number. And there is at least one kind of Bayesian methods performing better than GBLUP, when the ratio of QTL number versus marker number is very small or involving large-effect QTL.