Chinese Academy of Agricultural Sciences Institute of Animal Science.
Tianjin Agricultural University.
Brief Bioinform. 2023 Mar 19;24(2). doi: 10.1093/bib/bbad043.
Incorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only. The prediction ability of MAK was significantly more robust than the genomic best linear unbiased prediction, BayesB, BayesRR and the multi trait Bayesian method in the four real animal and plant datasets, and the computational efficiency of MAK was roughly 100 times faster than BayesB and BayesRR.
将相关性状的基因型和表型纳入多性状模型中，可以显著提高动物和植物育种以及人类遗传学中目标性状的预测准确性。然而，在大多数情况下，待评估个体的相关性状和目标性状的表型信息同时为零，特别是对于新生儿。因此，我们提出了一种机器学习框架 MAK，通过构建多目标集成回归链并自动选择辅助性状，仅使用基因型信息来预测目标性状的基因组估计育种值，从而提高目标性状的预测准确性。在四个真实的动植物数据集上，MAK 的预测能力明显比基因组最佳线性无偏预测、BayesB、BayesRR 和多性状贝叶斯方法更稳健，并且 MAK 的计算效率比 BayesB 和 BayesRR 快约 100 倍。
