Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy（http://www.genetics.org/content/192/4/1513.abstract.html?etoc）

Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) genomic selection does not take advantage of this information. Multivariate genomic selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesCπ) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesCπ to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the prediction accuracy for a low-heritability trait could be significantly increased by multivariate genomic selection when a correlated high-heritability trait was available. Further, multiple-trait genomic selection had higher prediction accuracy than single-trait genomic selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait genomic selection were explored.

基因组选择（genomic selection），应该是分子标记辅助育种的发展方向。以前的分子标记辅助育种一般是这样的，先进行基因或QTL作图，然后获得标记进行分子标记辅助选择。定位和QTL分析从某种角度说是走上邪路了，用来产生论文或垃圾论文了，而真正的应用比较少。GS方法，会直接给出一个育种值，这个育种值是根据全基因组的分子标记来估计的，育种值可以作为育种的参考，这样的话也许要回到正道来了。现在基因型鉴定的成本在不断降低，所以得到全基因组的基因型数据会越来越容易，怎样将这些数据用于指导育种是个值得研究的问题。 而GS的不断探索是解决问题的一种尝试。