Genomic selection has become a reality in plant breeding programs with the reduction in genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for predicting hybrid performance. The dynamics of a commercial breeding program involve the evaluation of several traits simultaneously in a large set of target environments. Therefore, multi-trait multi-environment (MTME) genomic prediction models can leverage these datasets by exploring the correlation between traits and Genotype-by-Environment (G×E) interaction. Herein, we assess predictive abilities of univariate and multivariate genomic prediction models in a maize breeding program. To this end, we used data from 415 maize hybrids evaluated in 4 years of second season field trials for the traits grain yield, number of ears, and grain moisture. Genotypes of these hybrids were inferred in silico based on their parental inbred lines using single nucleotide polymorphisms (SNPs) markers obtained via genotyping-by-sequencing (GBS). Because genotypic information was available for only 257 hybrids, we used the genomic and pedigree relationship matrices to obtain the H matrix for all 415 hybrids. Our results demonstrated that in the single-environment context the use of multi-trait models was always superior in comparison to their univariate counterparts. Besides that, although MTME models were not particularly successful in predicting hybrid performance in untested years, they improved the ability to predict the performance of hybrids that had not been evaluated in any environment. However, the computational requirements of this kind of model could represent a limitation to its practical implementation and further investigation is necessary.

中文翻译：

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基因组预测应用于第二季玉米杂交种的多种性状和环境

随着基因分型成本的降低，基因组选择已成为植物育种计划中的现实。特别是在玉米育种计划中，它成为预测杂交性能的有前途的工具。商业育种计划的动态涉及在大量目标环境中同时评估几个性状。因此，多性状多环境 (MTME) 基因组预测模型可以通过探索性状与环境基因型 (G×E) 相互作用之间的相关性来利用这些数据集。在此，我们评估了玉米育种计划中单变量和多变量基因组预测模型的预测能力。为此，我们使用了 4 年第二季田间试验中评估的 415 个玉米杂交种的数据，用于测定谷物产量、穗数和谷物水分的性状。使用通过基因分型测序 (GBS) 获得的单核苷酸多态性 (SNP) 标记，基于它们的亲本近交系在计算机上推断这些杂种的基因型。因为只有 257 个杂种的基因型信息可用，我们使用基因组和谱系关系矩阵来获得所有 415 个杂种的 H 矩阵。我们的结果表明，在单一环境背景下，与单变量模型相比，多特征模型的使用始终优于单变量模型。除此之外，尽管 MTME 模型在预测未经测试的年份中的混合动力性能方面并不是特别成功，但它们提高了预测尚未在任何环境中评估的混合动力性能的能力。然而，