中文摘要：

利用高密度分子标记，在基因组水平上预测育种值已经在动植物遗传育种研究中得到应用，但是高密度标记也带来许多统计和计算上的问题．为了解决这些垫因组选择中的问题．产生了很多不同的方法，包括RR—BLUEGBLUP，BayesA，BayesB，BayesCπ和Bayesian LASSO等．本文将这些方法用于一组小麦数据集的分析．同时模拟了不同数目QTL和不同遗传率情况下各种方法分析结果的差异．研究结果表明：在确定基因组选择方法时，要充分考虑所研究性状的遗传结构．如果确认某种性状由较少的大效应QTL控制时，各种方法预测能力的差异较大，应选择BayesCπ．如果QTL数目中等，各种方法预测能力的绝对差异较小，但是仍然发现BayesA优于其他方法．如果性状由大量的徽效基因决定，各种方法之间几乎找不到显著的差异，不过此时无论是在模拟分析还是在小麦实际产量的预测中，RR—BLUP都略优于其他方法，说明在这种情况下RR-BLUP是有效的方法．

英文摘要：

Recent advances in molecular genetics techniques have made dense marker maps available, and the prediction of breeding value at the genome level has been employed in genetics research. However, an increasingly large number of markers raise both statistical and computational issues in genomic selection （GS）, and many methods have been developed for genomic prediction to address these problems, including ridge regression-best linear unbiased prediction （RR-BLUP）, genomic best linear unbiased prediction, BayesA, BayesB, BayesCπ, and Bayesian LASSO. In this paper, these methods were compared regarding inference under different conditions, using real data from a wheat data set and simulated scenarios with a small number of quantitative trait loci （QTL） （20）, a moderate number of QTL （60, 180） and an extreme number of QTL （540）. This study showed that the genetic architecture of a trait should be fully considered when a GS method is chosen. If a small amount of loci had a large effect on a trait, great differences were found between the predictive ability of various methods and BayesCπ was recommended. Although there was almost no significant difference between the predictive ability of BayesCπ andBayesB, BayesCπ is more feasible than BayesB for real data analysis. If a trait was controlled by a moderate number of genes, the absolute differences between the various methods were small, but BayesA was also found to be the most accurate method. Furthermore, BayesA was widely adaptable and could perform well with different numbers of QTL. If a trait was controlled by an extreme number of minor genes, almost no significant differences were detected between the predictive ability of various methods, but RR-BLUP slightly outperformed the others in both simulated scenarios and real data analysis, thus demonstrating its robustness and indicating that it was quite effective in this case.