Bernardeli Arthur, Guilhen José Henrique Soler, Oliveira Isadora Cristina Martins, Guimarães Lauro José Moreira, Borém Aluízio, Jarquin Diego, Pastina Maria Marta
Department of Agronomy, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
Plant Genome. 2025 Jun;18(2):e70030. doi: 10.1002/tpg2.70030.
Maize (Zea mays L.) is a staple crop and the most cultivated cereal worldwide. The expansion of this crop was possible due to efforts in management and breeding. From the breeding standpoint, advances were achieved through field experimental design and analyses, establishing heterotic patterns, and releasing heterotic hybrids. Over the last decade, data analyses have benefited from the surge of genome-based approaches. However, it lacks optimization regarding marker dimensionality, proper selection of tested lines and/or environments, and an indication of promising inbred lines for crosses. This study aimed to convert a high-density single nucleotide polymorphism marker dataset into a low-density dataset and perform genomic selection of maize hybrids tested in drought stress and well-watered environments for grain yield and secondary traits. Single nucleotide polymorphism markers were ranked and selected based on effects from a genome-wide association study. For genomic selection, methods containing general and specific combining abilities (GCA and SCA, respectively) and interaction effects were compared in cross-validation schemes. Accuracies using selected markers were similar to complete marker dataset for all traits under drought nand well-watered conditions. For genomic selection, the model containing the main effects of GCA for inbred lines and testers, SCA for hybrids, and the interaction of GCA and SCA with environments (Model 7) performed better for all traits when information about all environments was included. The model without interaction effects (Model 6) performed better when information about environments was missing.
玉米（Zea mays L.）是一种主食作物，也是全球种植最广泛的谷类作物。由于管理和育种方面的努力，这种作物得以扩大种植。从育种角度来看，通过田间试验设计与分析、建立杂种优势模式以及推出杂种优势杂交种取得了进展。在过去十年中，数据分析受益于基于基因组方法的兴起。然而，在标记维度优化、测试品系和/或环境的恰当选择以及杂交有前景自交系的指示方面仍存在不足。本研究旨在将高密度单核苷酸多态性标记数据集转化为低密度数据集，并对在干旱胁迫和充分灌溉环境下测试的玉米杂交种的籽粒产量和次要性状进行基因组选择。基于全基因组关联研究的效应，对单核苷酸多态性标记进行排序和选择。对于基因组选择，在交叉验证方案中比较了包含一般配合力和特殊配合力（分别为GCA和SCA）以及互作效应的方法。在干旱和充分灌溉条件下，使用选定标记的准确性与完整标记数据集对所有性状的准确性相似。对于基因组选择，当纳入所有环境信息时，包含自交系和测验种的GCA主效应、杂交种的SCA以及GCA和SCA与环境互作的模型（模型7）对所有性状表现更好。当缺少环境信息时，无互作效应的模型（模型6）表现更好。
