Eltaher Shamseldeen, Baenziger P Stephen, Belamkar Vikas, Emara Hamdy A, Nower Ahmed A, Salem Khaled F M, Alqudah Ahmad M, Sallam Ahmed
Department of Agronomy & Horticulture, University of Nebraska-Lincoln, Lincoln, USA.
Department of Plant Biotechnology, Genetic Engineering and Biotechnology Research Institute (GEBRI), University of Sadat City (USC), Sadat City, Egypt.
BMC Genomics. 2021 Jan 2;22(1):2. doi: 10.1186/s12864-020-07308-0.
Improving grain yield in cereals especially in wheat is a main objective for plant breeders. One of the main constrains for improving this trait is the G × E interaction (GEI) which affects the performance of wheat genotypes in different environments. Selecting high yielding genotypes that can be used for a target set of environments is needed. Phenotypic selection can be misleading due to the environmental conditions. Incorporating information from phenotypic and genomic analyses can be useful in selecting the higher yielding genotypes for a group of environments.
A set of 270 F wheat genotypes in the Nebraska winter wheat breeding program was tested for grain yield in nine environments. High genetic variation for grain yield was found among the genotypes. G × E interaction was also highly significant. The highest yielding genotype differed in each environment. The correlation for grain yield among the nine environments was low (0 to 0.43). Genome-wide association study revealed 70 marker traits association (MTAs) associated with increased grain yield. The analysis of linkage disequilibrium revealed 16 genomic regions with a highly significant linkage disequilibrium (LD). The candidate parents' genotypes for improving grain yield in a group of environments were selected based on three criteria; number of alleles associated with increased grain yield in each selected genotype, genetic distance among the selected genotypes, and number of different alleles between each two selected parents.
Although G × E interaction was present, the advances in DNA technology provided very useful tools and analyzes. Such features helped to genetically select the highest yielding genotypes that can be used to cross grain production in a group of environments.
提高谷类作物尤其是小麦的产量是植物育种者的主要目标。改善这一性状的主要限制因素之一是基因型与环境互作（GEI），它会影响小麦基因型在不同环境中的表现。需要选择能够用于一组目标环境的高产基因型。由于环境条件的影响，表型选择可能会产生误导。整合表型和基因组分析的信息有助于为一组环境选择更高产的基因型。
在内布拉斯加州冬小麦育种计划中，对一组270个F小麦基因型在9种环境下的籽粒产量进行了测试。在这些基因型中发现了籽粒产量的高遗传变异。基因型与环境互作也非常显著。在每个环境中，最高产的基因型都不同。9种环境之间籽粒产量的相关性较低（0至0.43）。全基因组关联研究揭示了70个与籽粒产量增加相关的标记性状关联（MTA）。连锁不平衡分析揭示了16个具有高度显著连锁不平衡（LD）的基因组区域。根据三个标准选择了用于在一组环境中提高籽粒产量的候选亲本基因型；每个选定基因型中与籽粒产量增加相关的等位基因数量、选定基因型之间的遗传距离以及每两个选定亲本之间不同等位基因的数量。
尽管存在基因型与环境互作，但DNA技术的进步提供了非常有用的工具和分析方法。这些特性有助于从基因上选择最高产的基因型，可用于在一组环境中进行谷物杂交生产。
