Velazco Julio G, Jordan David R, Mace Emma S, Hunt Colleen H, Malosetti Marcos, van Eeuwijk Fred A
Department of Plant Breeding, EEA Pergamino, National Institute of Agricultural Technology (INTA), Pergamino, Argentina.
Biometris - Mathematical and Statistical Methods, Wageningen University and Research, Wageningen, Netherlands.
Front Plant Sci. 2019 Jul 31;10:997. doi: 10.3389/fpls.2019.00997. eCollection 2019.
Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines , with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum.
籽粒产量和持绿抗旱适应性状是高粱育种中广泛适应多种环境的重要选择目标。联合多性状分析可能会提高这些性状的基因组预测能力。本研究的目的是评估多性状模型利用相关辅助性状信息来改进高粱籽粒产量和持绿性亲本育种值基因组预测的能力，并确定在特定情况下优化预测结果的性状组合。数据集包括2645个测交杂种在26个环境中的表型表现以及它们母本系的基因组和系谱信息。所考虑的性状为籽粒产量（GY）、持绿性（SG）、株高（PH）和开花时间（FT）。我们评估了多性状G-BLUP模型相对于单性状G-BLUP在预测性能上的改进。还探索了使用融合系谱和基因组信息的混合亲缘关系矩阵来优化多性状预测。通过多性状基因组分析利用训练群体的PH信息时，GY的预测能力提高了16%。对于SG预测，只有当预测系也有GY信息时，多性状G-BLUP才能充分发挥优势，预测能力提高了19%。使用组合的系谱-基因组关系矩阵进一步优化了传统多性状G-BLUP预测的预测能力、无偏性和准确性。本研究结果表明，结合常规测量性状的多性状基因组评估可用于改进高粱作物生产力和干旱适应性的预测。
