Moysés Nascimento

Neural networks for predicting breeding values and genetic gains

Analysis 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.

Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes

The purpose of this work was to evaluate a methodology of adaptability and phenotypic stability of alfalfa genotypes based on the training of an artificial neural network considering the methodology of Eberhart and Russell. Data from an experiment on dry matter production of 92 alfalfa genotypes (Medicago sativa L.) were used. The experimental design constituted of randomized blocks, with two repetitions. The genotypes were submitted to 20 cuttings, in the growing season of November 2004 to June 2006. Each cutting was considered an environment. The artificial neural network was able to satisfactorily classify the genotypes. In addition, the analysis presented high agreement rates, compared with the results obtained by the methodology of Eberhart and Russell.

Adaptabilidade e estabilidade via regressão não paramétrica em genótipos de café

The objective of this work was to evaluate a methodology of phenotypic adaptability and stability analyses of coffee genotypes based on nonparametric regression. The technique used differs from other techniques because it reduces the influence of extreme points resulting from the presence of genotypes whose answers to a certain environment are too different on the estimation of the adaptability parameter. Data from an experiment studying the average yield of 40 coffee (Coffea canephora) genotypes in a randomized block design with six replicates were used to evaluate the method. The genotypes were evaluated along five years (1996, 1998, 1999, 2000 and 2001) in two locations (Sooretama and Marilândia, ES, Brazil), in a total of ten environments. The methodology proposed proved adequate and efficient, since it eliminates the disproportionate effects induced by the presence of extreme points and avoids misleading recommendations of genotypes in terms of adaptability.

Avaliação do coeficiente de variação experimental para caracteres de frutos de pimenteiras

O objetivo deste estudo foi formular uma classificacao para coeficientes de variacao, de experimentos com pimentas do genero Capsicum, para utilizacao em seis variaveis morfologicas do fruto. Foram selecionados 38 experimentos com resultados publicados, em que se constaram dados de caracterizacoes morfologicas do fruto e coeficientes de variacao (CV) das seguintes variaveis respostas: peso medio do fruto, comprimento do fruto, comprimento do pedunculo, maior e menor diâmetro do fruto e espessura do pericarpo. As faixas de classificacao dos CV foram baseadas na metodologia proposta por Garcia (1989), que considera a propriedade da distribuicao normal do CV. Pelo teste de Lilliefors, a 5% de probabilidade, todas as variaveis satisfizeram a pressuposicao de distribuicao normal do CV. As faixas de classificacao do CV, obtidas para essas seis variaveis, foram distintas daquelas da classificacao geral, proposta por Gomes (2000). Ademais, constatou-se que as classificacoes dos coeficientes de variacao de variaveis morfologicas de pimenteiras do genero Capsicum dependem da variavel resposta, sendo que as maiores faixas de classificacao de CV ocorreram para menor diâmetro do fruto, espessura do pericarpo e peso medio do fruto.

Abordagem bayesiana para avaliação da adaptabilidade e estabilidade de genótipos de alfafa

The objective of this work was to propose a Bayesian approach for the Eberhart & Russell method to evaluate the phenotypic adaptability and stability of alfafa (Medicago sativa) genotypes, as well as to evaluate the efficiency of the use of prior informative and noninformative distributions. Data from a randomized block design experiment evaluating the forage dry weight of 92 genotypes were used. The Bayesian methodology proposed was implemented in the free software R by the MCMCregress function of the MCMCpack package. To represent the noninformative prior distributions, a probability distribution with high variance was used; and, to represent the informative prior, a meta‑analysis concept was adopted, characterized by the use of information provided by previous studies. The comparison between the prior distributions was done using the Bayes Factor, with the BayesFactor function of the MCMCpack package, which indicated that an informative prior is more appropriate under the conditions of this study.

Eficiência técnica da atividade leiteira em Minas Gerais: uma aplicação de regressão quantílica

O objetivo principal neste estudo foi analisar a influencia de variaveis tecnicas e economicas sobre os indices de eficiencia tecnica de produtores de leite de Minas Gerais ao longo de pontos distintos da distribuicao dos indices de eficiencia utilizando-se a tecnica de regressao quantilica. Os indices de eficiencia tecnica foram estimados com base em um modelo de fronteira estocastica utilizando-se dados de 875 produtores de leite do estado de Minas Gerais coletados no ano de 2005. Os principais resultados revelaram, na fronteira de producao, que possivelmente esta havendo utilizacao extensiva do fator terra. De modo geral, a variavel percentual de vacas em lactacao foi a mais relevante na explicacao da eficiencia tecnica em todos os quantis estudados, enquanto o percentual de mao-de-obra familiar utilizado foi importante para explicar apenas os menores niveis de eficiencia. Alem disso, foi encontrada diferenca significativa entre os coeficientes estimados dos quantis em estudo, o que mostra que as variaveis explicativas nao tem o mesmo impacto no aumento da eficiencia em todos os pontos da distribuicao.

Redes neurais artificiais para identificar genótipos de feijão‑caupi semiprostrado com alta adaptabilidade e estabilidade fenotípicas

O objetivo deste trabalho foi verificar a concordância entre as redes neurais artificiais (RNAs) e o metodo de Eberhart & Russel na identificacao de genotipos de feijao-caupi (Vigna unguiculata) com alta adaptabilidade e estabilidade fenotipicas. Utilizou-se o delineamento experimental de blocos ao acaso com quatro repeticoes. Os tratamentos consistiram de 18 linhagens experimentais e duas cultivares de feijao-caupi. Foram conduzidos quatro ensaios de valor de cultivo e uso nos municipios de Aquidauana, Chapadao do Sul e Dourados, no estado do Mato Grosso do Sul. Os dados de produtividade de graos foram submetidos as analises de variância individual e conjunta. Em seguida, os dados foram submetidos as analises de adaptabilidade e estabilidade por meio dos metodos de Eberhart & Russell e de RNAs. Houve elevada concordância entre os metodos avaliados quanto a discriminacao da adaptabilidade fenotipica dos genotipos de feijao-caupi semiprostrado, o que indica que as RNAs podem ser utilizadas em programas de melhoramento genetico. Em ambos os metodos avaliados, os genotipos BRS Xiquexique, TE97-304G-12 e MNC99-542F-5 sao recomendados para ambientes desfavoraveis, gerais e favoraveis, respectivamente, por apresentarem produtividade de graos acima da media geral dos ambientes e alta estabilidade fenotipica.

Superiority of artificial neural networks for a genetic classification procedure

The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.

Genomic Sequence of the Yeast Kluyveromyces marxianus CCT 7735 (UFV-3), a Highly Lactose-Fermenting Yeast Isolated from the Brazilian Dairy Industry

ABSTRACT Here, we present the draft genome sequence of Kluyveromyces marxianus CCT 7735 (UFV-3), including the eight chromosomes and the mitochondrial genomic sequences.

Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.

A significant contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. With this approach, genome-wide selection (GWS) can be used for this purpose. GWS consists of analyzing a large number of single nucleotide polymorphism markers widely distributed in the genome; however, because the number of markers is much larger than the number of genotyped individuals, and such markers are highly correlated, special statistical methods are widely required. Among these methods, independent component regression, principal component regression, partial least squares, and partial principal components stand out. Thus, the aim of this study was to propose an application of the methods of dimensionality reduction to GWS of carcass traits in an F2 (Piau x commercial line) pig population. The results show similarities between the principal and the independent component methods and provided the most accurate genomic breeding estimates for most carcass traits in pigs.