In perennial ryegrass (Lolium perenne L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations (TP1, n = 463 and TP2, n = 517) phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait (r_g = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait (r_g = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor (r_p = −0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (r_p = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.

在多年生黑麦草（黑麦草L），年度和季节性干物质产量（DMY）和牧草的营养品质是旨在通过选择性育种进行改良的高优先性状。基因组预测（GP）已被证明是用于改善复杂性状的有价值的工具，并且可以通过使用多性状（MT）预测模型来进一步增强。在这项研究中，我们使用在多个位置表型的两个不同的训练群体（TP1，n = 463和TP2，n = 517）对MT预测模型的相对性能进行了评估，以提高对DMY和关键营养品质性状的预测能力。MT模型在DMY方面的表现优于单性状（ST）模型，在营养品质性状（例如低，高和总WSC）方面，其表现优于24％至59％，而67％至105％当训练和测试集（MT-CV2）或单独的测试集（MT-CV3）中都包含相关的第二性状时（性状辅助基因组选择）。但是，当训练集中包括次要特征而不是测试集（MT-CV1）时，与ST模型相比，预测能力没有统计学意义（p> 0.05）。我们评估了使用MT-CV2模型时训练集大小的影响。使用高度相关的特征（[R _{g ^}= 0.88）作为MT-CV2模型的次要特征，即使将训练集缩小到其原始大小的50％，DMY的预测能力也不会损失。相反，使用弱相关的次要特征（[R _{g ^}= 0.56）在MT-CV2模型中，当训练集大小仅比其原始大小减少11％时，预测能力开始下降。使用ST模型，与训练集无关的人群的基因组预测能力较差（[R _p= -0.06）。但是，使用MT-CV2模型时，预测能力是肯定的，并且很高（[R _p= 0.76）。我们的结果证明了对饲草物种MT模型的首次评估，并说明了在饲草和其他异种植物物种中使用MT基因组选择来加速复杂农艺性状（如DMY和营养品质特性）的遗传收获的前景。