Classification using a scale of visual notes is a strategy used to select erect bean plants in order to improve bean plant architectures. Use of morphological traits associated with the phenotypic expression of bean architecture in classification procedures may enhance selection. The objective of this study was to evaluate the potential of artificial neural networks (ANNs) as auxiliary tools in the improvement of bean plant architecture. Data from 19 lines were evaluated for 22 traits, in 2007 and 2009 winter crops. Hypocotyl diameter and plant height were selected for analysis through ANNs. For classification purposes, these lines were separated into two groups, determined by the plant architecture notes. The predictive ability of ANNs was evaluated according to two scenarios to predict the plant architecture - training with 2007 data and validating in 2009 data (scenario 1), and vice versa (scenario 2). For this, ANNs were trained and validated using data from replicates of the evaluated lines for hypocotyl diameter individually, or together with the mean height of plants in the plot. In each scenario, the use of data from replicates or line means was evaluated for prediction through previously trained and validated ANNs. In both scenarios, ANNs based on hypocotyl diameter and mean height of plants were superior, since the error rates obtained were lower than those obtained using hypocotyl diameter only. Lower apparent error rates were verified in both scenarios for prediction when data on the means of the evaluated traits were submitted to better trained and validated ANNs.
The study of complex traits using large databases of molecular markers has reshaped genetic breeding programs as it allows the direct incorporation of information from a large number of molecular markers for the prediction of genomic values. However, the large number of markers can lead to problems of computational demand, multicollinearity, and dimensionality. We evaluated the use of Multilayer Perceptron Neural networks to resolve this problem and propose a new dimensionality reduction method called Probe Subset Selection Methodology, for the prediction of genetic values, in Genome Wide Selection studies. We used a simulated F1 population for 12 quantitative traits, including different modeling structures, average degrees of dominance and heritability. The Multilayer Perceptron Neural Networks, together with the proposed Probe ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 21 (1): gmr18982 G.N. Silva et al. 2 Subset Selection Methodology, provided more accurate predictions than the RR-BLUP methodology and reduced the root mean square error from 577.249 to values below 24. The use of computational intelligence in breeding programs is a promising tool for prediction purposes, since epistasis and dominance were not limiting factors for the proposed Multilayer Perceptron Neural Network method.
Rice (Oryza sativa) is crop that adapts well to diverse soil and climate conditions; breeding programs have generally been committed to identifying and selecting genotypes that are stable and have high productivity in various environments. In this sense, studies of adaptability and stability are of paramount importance to aid in the recommendation of cultivars, since it allows growers to obtain detailed information about the behavior of the genotypes in each region. We evaluatde the adaptability and stability of irrigated rice genotypes grown with continuous flooding, for the selection and recommendation of cultivars for crops or breeding programs. Eighteen genotypes were evaluated for grain yield in four agricultural years at three sites, covering 12 environments. The adaptability and stability were assessed by the methods of Eberhart and Russell, multiple centroids and GGE biplot. Genotypes behaved differently regarding stability and adaptability in the different environments. Both methodologies identified BRA 02691 and MGI 0607-1 as promising to be released as cultivars; however, classification inconsistencies occurred, such as for the line BRA 031001. Multiple centroid and GGE biplot methods demonstrated greater sensitivity ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 19 (3): gmr18434 A.C. Silva Junior et al. 2 than the Eberhart and Russell method. Using the methods simultaneously provides an innovative approach to the interpretation of GxE interactions and is a viable alternative for genotype classification. The genotype MGI 0607-1 showed promising behavior independent of the methodology used.
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