Artigos
URI permanente para esta coleçãohttps://locus.ufv.br/handle/123456789/11796
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Item Delineamentos aumentados no melhoramento de plantas em condições de restrições de recursos(Ciência Rural, 2009-12) Peternelli, Luiz Alexandre; Barbosa, Márcio Henrique Pereira; Carvalho, Melissa Pisaroglo de; Souza, Emanuel Fernando Maia deEste trabalho teve por objetivo comparar a eficiência na seleção de genótipos e a qualidade das estimativas dos componentes de variância e da herdabilidade, empregando o delineamento em blocos aumentados (DBA), o delineamento em blocos aumentados duplicados (DBAD) e o grupo de experimentos em blocos casualizados com tratamentos comuns (EBCTC). Para a comparação, foi imposta a condição de que o número de genótipos sob seleção era maior do que o número de unidades experimentais disponíveis para os delineamentos com repetição. Quatro cenários foram compostos da combinação de diferentes valores de herdabilidade e do coeficiente de variação residual paramétricos. Das 2.400 simulações por cenário, o DBA apresentou a maior eficiência de seleção. Os delineamentos com repetição (DBAD e EBCTC) apresentaram eficiência de seleção semelhante entre si. É possível concluir que a não realização de repetições, avaliando-se um maior número de genótipos, pode trazer melhores resultados ao programa de melhoramento de plantas.Item Neural networks for predicting breeding values and genetic gains(Scientia Agricola, 2014-04-16) Silva, Gabi Nunes; Tomaz, Rafael Simões; Sant'Anna, Isabela de Castro; Nascimento, Moysés; Bhering, Leonardo Lopes; Cruz, Cosme DamiãoAnalysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.Item Evaluation of the efficiency of artificial neural networks for genetic value prediction(Genetics and Molecular Research, 2016-03-28) Silva, G.N.; Tomaz, R.S.; Sant’Anna, I.C.; Carneiro, V.Q.; Cruz, C.D.; Nascimento, M.Artificial neural networks have shown great potential when applied to breeding programs. In this study, we propose the use of artificial neural networks as a viable alternative to conventional prediction methods. We conduct a thorough evaluation of the efficiency of these networks with respect to the prediction of breeding values. Therefore, we considered eight simulated scenarios, and for the purpose of genetic value prediction, seven statistical parameters in addition to the phenotypic mean in a network designed as a multilayer perceptron. After an evaluation of different network configurations, the results demonstrated the superiority of neural networks compared to estimation procedures based on linear models, and indicated high predictive accuracy and network efficiency.Item Regularized quantile regression applied to genome-enabled prediction of quantitative traits(Genetics and Molecular Research, 2017-03-22) Nascimento, M; E Silva, FF; de Resende, MD; CD, Cruz; Nascimento, AC; Viana, JM; Azevedo, CF; Barroso, LMGenomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qτ(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that "best" represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively.