Amaral Lígia de Oliveira, Miranda Glauco Vieira, Val Bruno Henrique Pedroso, Silva Alice Pereira, Moitinho Alyce Carla Rodrigues, Unêda-Trevisoli Sandra Helena
Laboratory of Biotechnology and Plant Breending, Department of Agricultural Sciences, São Paulo State University - UNESP/FCAV, Jaboticabal, Brazil.
Department of Agronomy Coordination, Federal Technological University of Paraná, Curitiba, Brazil.
Front Plant Sci. 2022 Jul 12;13:814046. doi: 10.3389/fpls.2022.814046. eCollection 2022.
Soybean has a recognized narrow genetic base that often makes it difficult to visualize available genetic and phenotypic variability and identify superior genotypes during the selection process. However, the phenotypic expression of soybean plants is highly affected by photoperiod and the cultivation of a given variety is performed in the latitude range that presents ideal conditions for its development based on its relative maturity group (RMG) for the optimization of the phenotypic expression of its genotype. Based on the above, this study aimed to evaluate the efficiency of artificial neural networks (ANNs) as a tool for the correct discrimination and classification of tropical soybean genotypes according to their relative maturity group during the population selection process with the aim of optimizing the phenotypic performance of these selected genotypes. For this purpose, three biparental populations were synthesized, one with a wide genetic variability for the RMG character obtained from the hybridization between genitors of maturity groups RMG 5 (Sub-tropical 23° LS) × RMG 9.4 (Tropical 0° LS) and two populations with a narrow variability obtained between genitors RMG 7.3 (Tropical 20° LS) × RMG 9.4 and RMG 5.3 × RMG 6.7, respectively. Criteria for comparing the developed ANN architecture with Fisher's linear and Anderson's quadratic parametric discriminant methodologies were applied to the data for the discrimination and classification of the genotypes. ANN showed an apparent error rate of less than 8.16% as well as a low influence of environmental factors, correctly classifying the genotypes in the populations even in cases of reduced genetic variability such as in the RMG 5 × RMG 6 population. In contrast, the discriminant functions were inefficient in correctly classifying the genotypes in the populations with genealogical similarity (RMG 5 × RMG 6) and wide genetic variability, with an error rate of more than 50%. Based on the results of this study, ANN can be used for the discrimination of genotypes in the initial generations of selection in breeding programs for the development of high performance cultivars for wide and reduced photoperiod amplitudes, even with fewer selection environments, more efficiently, and with fewer time and resources applied. As a result of similarity between the parents, ANN can correctly classify genotypes from populations with a narrow genetic base, in addition to pure lines and genotypes with a high degree of inbreeding.
大豆具有公认的狭窄遗传基础，这常常使得在选择过程中难以直观地看到可用的遗传和表型变异性，也难以识别优良基因型。然而，大豆植株的表型表达受光周期的影响很大，并且根据其相对成熟组（RMG），在为其发育提供理想条件的纬度范围内种植特定品种，以优化其基因型的表型表达。基于上述情况，本研究旨在评估人工神经网络（ANN）作为一种工具在群体选择过程中根据热带大豆基因型的相对成熟组进行正确判别和分类的效率，目的是优化这些选定基因型的表型表现。为此，合成了三个双亲群体，一个群体具有来自RMG 5（亚热带23°LS）×RMG 9.4（热带0°LS）的亲本杂交获得的RMG性状的广泛遗传变异性，另外两个群体分别具有来自RMG 7.3（热带20°LS）×RMG 9.4和RMG 5.3×RMG 6.7的亲本之间获得的狭窄变异性。将所开发的人工神经网络架构与费舍尔线性判别法和安德森二次参数判别法进行比较的标准应用于基因型判别和分类的数据。人工神经网络显示出低于8.16%的表观错误率以及环境因素的低影响，即使在遗传变异性降低的情况下，如在RMG 5×RMG 6群体中，也能正确地对群体中的基因型进行分类。相比之下，判别函数在正确分类具有谱系相似性（RMG 5×RMG 6）和广泛遗传变异性的群体中的基因型时效率低下，错误率超过50%。基于本研究的结果，人工神经网络可用于育种计划中选择初始世代的基因型判别，以培育适用于广泛和减小光周期幅度的高性能品种，即使在较少的选择环境下，也能更高效地使用更少的时间和资源。由于亲本之间具有相似性，人工神经网络除了能正确分类纯系和高度近交的基因型外，还能对遗传基础狭窄的群体中的基因型进行正确分类。
