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

Renato Domiciano Silva Rosado, Cosme Damião Cruz, Leiri Daiane Barili, José Eustáquio de Souza Carneiro, Pedro Crescêncio Souza Carneiro, Vinicius Quintão Carneiro, Jackson Tavela da Silva and Moyses Nascimento
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Renato Domiciano Silva Rosado: Department of Statistics, Graduate Program in Applied Statistics and Biometry, Federal University of Viçosa (UFV), Viçosa 36570-900, Brazil
Cosme Damião Cruz: Department of Statistics, Graduate Program in Applied Statistics and Biometry, Federal University of Viçosa (UFV), Viçosa 36570-900, Brazil
Leiri Daiane Barili: Faculdade Centro Mato Grossense (FACEM), Sorriso 78890-000, Brazil
José Eustáquio de Souza Carneiro: Department of General Biology, Graduate Program in Genetics and Breeding, UFV, Viçosa 36570-900, Brazil
Pedro Crescêncio Souza Carneiro: Department of General Biology, Graduate Program in Genetics and Breeding, UFV, Viçosa 36570-900, Brazil
Vinicius Quintão Carneiro: Department of Biology, Graduate Program in Genetics and Plant Breeding, Federal University of Lavras (UFLA), Lavras 37200-900, Brazil
Jackson Tavela da Silva: Department of General Biology, Graduate Program in Genetics and Breeding, UFV, Viçosa 36570-900, Brazil
Moyses Nascimento: Department of Statistics, Graduate Program in Applied Statistics and Biometry, Federal University of Viçosa (UFV), Viçosa 36570-900, Brazil

Agriculture, 2020, vol. 10, issue 12, 1-11

Abstract: 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.

Keywords: common beans; multilayer perceptron; radial basis function network; genomic prediction (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
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