Abstract: Variety selection in livestock breeding occupies an important position. Genomic selection, as a novel technology in livestock breeding, has raised considerable concern. It can shorten the generation interval, speed up the genetic progress, and it can select the candidate individuals as breeding stock without phenotypic data. In 2001, Meuviwisen proposed the concept of genomic selection, which was first applied in dairy cattle. Until August 2014, there were 34 member countries of Interbull organization that had applicated genomic selection in their national dairy cattle breeding group. With the popularization and continuous promotion of genomic selection, some problems of the accuracy of genomic estimated breeding value need to be solved. Various methods of genomic selection have been proposed and more efficient models are being developed. So it has great practical significance to exploit better models and algorithm to improve the accuracy of genomic estimated breeding value. So far, there were 17 Bayesian methods that have been successively proposed. This thesis briefly introduced the classical BayesA and BayesB methods for genomic selection. BayesA assumed that all loca have effect, while BayesB supposed that a small part of locus have effect, and the percentage was extremely small. Therefore, BayesA and BayesB had different models and algorithms. After Meuviwisen proposed classic Bayesian methods, other methods were like mushrooms springing up. New Bayesian methods were based on the classical Bayesian methods, which was optimized by improving the hypothetical model and algorithm. For example, BayesC method, which was based on BayesB, optimized the π value in the model. BayesCπ and BayesDπ were the improvement of BayesC, and these two approaches assumed that marker effect variance of each locus had the same value, whereas BayesC assumed that its marker effect variance of each locus was different. BayesDπ, which was based on BayesCπ, optimized the scale parameter of inverse chi-square distribution. Bayes Lasso had the same idea with BayesA. However, its marker effects were assumed to be another distribution for Laplace, so its posterior distributions of marker effects were also changed. BayesRS method assumed that the variances of marker effect were allocated in different percentage of total genetic variance. In order to find proper hypothesis model and parameters, other Bayesian methods were also based on predecessors' research through changing the prior assumption and improving parameters of the model. At present, commonly used methods for genomic selection are classic Bayesian methods and BayesCπ, which have stabile calculation results and high accuracy of genomic estimated breeding value. In the three Bayesian algorithms, the accuracy is generally arranged into BayesB > BayesCπ > BayesA, but accuracy of genomic estimated breeding value of some traits is not the case. Compared with classical Bayesian method, parameter optimization can improve the accuracy of genome estimated breeding value to some extent. In a word, on the basis of the classical Bayesian method，for the purpose of improving the accuracy of genomic estimated breeding value, the extension of bayesian methods and its parameters optimization strategy seeked for the optimal model and parameters optimization through biological genetic algorithm combined with actual population situation. They enriched and expanded the genomic selection algorithm, and can make the genomic breeding value more reference significance. As the animal breeding process is far from the foreign breeding process in China, genomic selection can cultivate new breed, enrich the genetic resources of China and accelerate the pace of livestock and poultry breeding process. Meanwhile, the algorithm study of genomic slection and its application in China was introduced. In face of the advantages of genomic selection, whole genomic selection breeding technology is imperative. Furthermore, the main problems in current researches and the key points in future studies were also proposed, in hope of providing reference for obtaining more reliable and faster algorithm of genomic selection.

Key words: genomic selection, Bayesian method, parameter optimization