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,Moyses Nascimento,Vinicius Quintão Carneiro,Jackson Tavela Da Silva,Pedro Crescêncio Souza Carneiro,José Eustáquio De Souza Carneiro

doi:10.3390/agriculture10120638

Renato Domiciano Silva Rosado, Cosme Damião Cruz + Show 6 more

Open Access

https://doi.org/10.3390/agriculture10120638

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Journal: Agriculture	Publication Date: Dec 16, 2020
Citations: 10	License type: CC BY 4.0

Affiliation: Universidade Federal de Viçosa

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.

Highlights

The development of common bean cultivars contributed significantly to the increase in the mean national yield of 500 kg ha−1 [1] for more than 1331 kg ha−1 in the mean of three planting seasons (first crop or water (1504.50 kg ha−1 ), second crop or drought (1492 kg ha−1 ) and third crop or irrigated (996.5 kg ha−1 ), seeing current Black, Carioca and other grain color patterns of common beans cultivars [2]
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 Artificial neural networks (ANN) approaches, and to compare the predictive abilities obtained for ANN and Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) [8] for predicting genetic merit
The results showed superiority of ANN in the prediction of genomic estimated breeding value (GEBV) in the scenarios with higher and lower density of markers, parallel to higher levels of linkage disequilibrium and greater heritability

Summary

Introduction

The development of common bean cultivars contributed significantly to the increase in the mean national yield of 500 kg ha−1 (in the 1970s) [1] for more than 1331 kg ha−1 (in the 2018/2019 season) in the mean of three planting seasons (first crop or water (1504.50 kg ha−1 ), second crop or drought (1492 kg ha−1 ) and third crop or irrigated (996.5 kg ha−1 ), seeing current Black, Carioca and other grain color patterns of common beans cultivars [2]. The identification of cultivars with an early cycle allows the planning of harvests for periods of less rain, the reduction of water consumption by irrigated crops, and reduction of the time exposed to the risk of plague and disease [5,6,7]. GS has been successfully used to Agriculture 2020, 10, 638; doi:10.3390/agriculture10120638 www.mdpi.com/journal/agriculture

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