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Artificial Neural Networks in the Prediction of Genetic Merit to Flowering Traits in Bean Cultivars

2020; Multidisciplinary Digital Publishing Institute; Volume: 10; Issue: 12 Linguagem: Inglês

10.3390/agriculture10120638

2077-0472

Renato Domiciano Silva Rosado, Cosme Damião Cruz, Leiri Daiane Barili, José Eustáquio de Souza Carneiro, Pedro Crescêncio Souza Carneiro, Vinícius Quintão Carneiro, Jackson Tavela da Silva, Moysés Nascimento,

Genetic Mapping and Diversity in Plants and Animals

Flowering is an important agronomic trait that presents non-additive gene action. Genome-enabled prediction allow incorporating molecular information into the prediction of individual genetic merit. Artificial neural networks (ANN) recognize patterns of data and represent an alternative as a universal approximation of complex functions. In a Genomic Selection (GS) context, the ANN allows automatically to capture complicated factors such as epistasis and dominance. The objectives of this study were to predict the individual genetic merits of the traits associated with the flowering time in the common bean using the ANN approach, and to compare the predictive abilities obtained for ANN and Ridge Regression Best Linear Unbiased Predictor (RR-BLUP). We used a set of 80 bean cultivars and genotyping was performed with a set of 384 SNPs. The higher accuracy of the selective process of phenotypic values based on ANN output values resulted in a greater efficacy of the genomic estimated breeding value (GEBV). Through the root mean square error computational intelligence approaches via ANN, GEBV were shown to have greater efficacy than GS via RR-BLUP.