Artigos
URI permanente para esta coleçãohttps://locus.ufv.br/handle/123456789/11796
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Item Artificial neural networks and linear discriminant analysis in early selection among sugarcane families(Crop Breeding and Applied Biotechnology, 2017-10) Peternelli, Luiz Alexandre; Moreira, Édimo Fernando Alves; Nascimento, Moysés; Cruz, Cosme DamiãoOne of the major challenges in sugarcane breeding programs is an efficient selection of genotypes in the initial phase. The purpose of this study was to compare modelling by artificial neural networks (ANN) and linear discriminant analysis (LDA) as alternatives for the selection of promising sugarcane families based on the indirect traits number of sugarcane stalks (NS), stalk diameter (SD) and stalk height (SH). The analysis focused on two models, a full one with all predictors, and a reduced one, from which the variable SH was excluded. To compare and assess the applied methods, the apparent error rate (AER) and true positive rate (TPR) were used, derived from the confusion matrix. Modeling with ANN and LDA can be used successfully for selection among sugarcane families. The reduced model may be preferable, for having a low AER, high TPR and being easier to obtain in operational terms.Item New insights into genomic selection through population-based non-parametric prediction methods(Scientia Agricola, 2019-07) Lima, Leísa Pires; Azevedo, Camila Ferreira; Resende, Marcos Deon Vilela de; Silva, Fabyano Fonseca e; Suela, Matheus Massariol; Nascimento, Moysés; Viana, José Marcelo SorianoGenome-wide selection (GWS) is based on a large number of markers widely distributed throughout the genome. Genome-wide selection provides for the estimation of the effect of each molecular marker on the phenotype, thereby allowing for the capture of all genes affecting the quantitative traits of interest. The main statistical tools applied to GWS are based on random regression or dimensionality reduction methods. In this study a new non-parametric method, called Delta-p was proposed, which was then compared to the Genomic Best Linear Unbiased Predictor (G-BLUP) method. Furthermore, a new selection index combining the genetic values obtained by the G-BLUP and Delta-p, named Delta-p/G-BLUP methods, was proposed. The efficiency of the proposed methods was evaluated through both simulation and real studies. The simulated data consisted of eight scenarios comprising a combination of two levels of heritability, two genetic architectures and two dominance status (absence and complete dominance). Each scenario was simulated ten times. All methods were applied to a real dataset of Asian rice (Oryza sativa) aiming to increase the efficiency of a current breeding program. The methods were compared as regards accuracy of prediction (simulation data) or predictive ability (real dataset), bias and recovery of the true genomic heritability. The results indicated that the proposed Delta-p/G-BLUP index outperformed the other methods in both prediction accuracy and predictive ability.Item Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee(Pesquisa Agropecuária Brasileira, 2017-03) Silva, Gabi Nunes; Nascimento, Moysés; Sant’Anna, Isabela de Castro; Cruz, Cosme Damião; Caixeta, Eveline Teixeira; Carneiro, Pedro Crescêncio Souza; Rosado, Renato Domiciano Silva; Pestana, Kátia Nogueira; Almeida, Dênia Pires de; Oliveira, Marciane da SilvaThe objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.Item Adaptability and stability assessment of bean cultivars of the carioca commercial group by a Bayesian approach(Acta Scientiarum. Agronomy, 2018-07) Nascimento, Moysés; Euzebio, Milena Pierotti; Fonseca, Inês Cristina de Batista; Fonseca Júnior, Nelson da Silva; Giordani, Willian; Gonçalves, Leandro Simões AzeredoTo develop new bean commercial cultivars, a series of experiments called Value for Cultivation and Use (VCU) assays are necessary. Bayesian analysis using information on prior VCU trials is an alternative to obtain greater precision during genotype selection. The objective of the present work was to select, under a Bayesian perspective, genotypes of the carioca bean from the state of Paraná that combine high adaptability and phenotypic stability, using information from previous VCU assays. This study used data from six experiments conducted in a randomized block design, in which the grain yield of 18 genotypes was assayed. To represent weakly informative prior distributions, the study used probability distributions with high variance; to represent informative prior distributions, it adopted the meta-analysis concept used in prior VCU assays (2007/2008, 2008/2009, 2009/2010, 2010/2011, 2011/2012, 2012/2013, and 2013/2014). Bayesian inference provided greater precision in selecting carioca bean genotypes with high adaptability and phenotypical stability through the Eberhart and Russell method. The Bayes factor indicated that the use of a priori information gives more accurate results for genotype selection. According to the study, most genotypes are widely adaptable based on informative priors, except for the Bola Cheia cultivar, which has specific adaptability to favorable environments.Item GenomicLand: Software for genome-wide association studies and genomic prediction(Acta Scientiarum. Agronomy, 2019) Nascimento, Moysés; Fontes, Vitor Cunha; Silva, Fabyano Fonseca e; Resende, Marcos Deon Vilela de; Cruz, Cosme DamiãoGenomicLand is free software intended for prediction and genomic association studies based on the R software. This computational tool has an intuitive interface and supports large genomic databases, without requiring the user to use the command line. GenomicLand is available in English, can be downloaded from the Internet (https://licaeufv.wordpress.com/), and requires the Windows or Linux operating system. The software includes statistical procedures based on mixed models, Bayesian inference, dimensionality reduction and artificial intelligence. Examples of data files that can be processed by GenomicLand are available. The examples are useful to learn about the operation of the modules and statistical procedures.Item Quantile regression of nonlinear models to describe different levels of dry matter accumulation in garlic plants(Ciência Rural, 2018-02-19) Puiatti, Guilherme Alves; Cecon, Paulo Roberto; Nascimento, Moysés; Nascimento, Ana Carolina Campana; Carneiro, Antônio Policarpo Souza; Silva, Fabyano Fonseca e; Puiatti, Mário; Oliveira, Ana Carolina Ribeiro dePlant growth analyses are important because they generate information on the demand and necessary care for each develop- ment stage of a plant. Nonlinear regression models are appropriate for the description of curves of growth, since they include parameters with practical biological interpretation. However, these models present information in terms of the conditional mean, and they are subject to problems in the adjustment caused by possible outliers or asymmetry in the distribution of the data. Quantile regression can solve these problems, and it allows the estimation of different quantiles, generating more complete and robust results. The objective of this research was to adjust a nonlinear quantile regression model for the study of dry matter accumulation in garlic plants (Allium sativum L.) over time, estimating parameters at three different quantiles and classifying each garlic accession according to its growth rate and asymptotic weight. The nonlinear regression model fitted was a Logistic model, and 30 garlic accessions were evaluated. These 30 accessions were divided based on the model with the closest quantile estimates; 12 accessions were classified as of lesser interest for planting, 6 were classified as intermediate, and 12 were classified as of greater interest for planting.Item Modelagem hierárquica Bayesiana na avaliação de curvas de crescimento de suínos genotipados para o gene halotano(Ciência Rural, 2014-10) Macedo, Leandro Roberto de; Silva, Fabyano Fonseca e; Cirillo, Marcelo Ângelo; Nascimento, Moysés; Paixão, Débora Martins; Guimarães, Simone Eliza Facioni; Lopes, Paulo Sávio; Santos, Jussara Aparecida dos; Azevedo, Camila FerreiraPara avaliar a influência do gene halotano sobre a curva de crescimento de suínos, bem como sua interação com o sexo do animal, foi proposta uma modelagem hierárquica Bayesiana. Nesta abordagem, os parâmetros dos modelos não- lineares de crescimento (Logístico, Gompertz e von Bertalanffy) foram estimados conjuntamente com os efeitos de sexo e genótipos do gene halotano. Foram utilizados 344 animais F2(Comercial x Piau) pesados ao nascer, aos 21, 42, 63, 77, 105 e 150 dias. O modelo Logístico foi aquele que apresentou melhor qualidade de ajuste por apresentar menor DIC (Deviance Information Criterion) que os demais. As amostras das distribuições marginais a posteriori para as diferenças entre as estimativas dos parâmetros do modelo Logístico indicaram que o peso dos machos à idade adulta com genótipo heterozigoto (HalNn) foi superior ao dos homozigotos (HalNN). A título de comparação, também foi considerada a abordagem frequentista tradicional, baseada em dois passos distintos, a qual, por apresentar um menor poder de discernimento estatístico, não mostrou diferenças significativas.Item Impact of energy restriction during late gestation on the muscle and blood transcriptome of beef calves after preconditioning(BMC Genomics, 2018) Nascimento, Moysés; Sanglard, Leticia P.; Moriel, Philipe; Sommer, Jeffrey; Ashwell, Melissa; Poore, Matthew H.; Duarte, Márcio de S.; Serão, Nick V. L.Maternal nutrition has been highlighted as one of the main factors affecting intra-uterine environment. The increase in nutritional requirements by beef cows during late gestation can cause nutritional deficiency in the fetus and impact the fetal regulation of genes associated with myogenesis and immune response.Forty days before the expected calving date, cows were assigned to one of two diets: 100% (control) or 70% (restricted group) of the daily energy requirement. Muscle samples were collected from 12 heifers and 12 steers, and blood samples were collected from 12 steers. The objective of this work was to identify and to assess the biological relevance of differentially expressed genes (DEG) in the skeletal muscle and blood of beef calves born from cows that experienced [or not] a 30% energy restriction during the last 40 days of gestation.A total of 160, 164, and 346 DEG (q-value< 0.05) were identified in the skeletal muscle for the effects of diet, sex, and diet-by-sex interaction, respectively. For blood, 452, 1392, and 155 DEG were identified for the effects of diet, time, and diet-by-time interaction, respectively. For skeletal muscle, results based on diet identified genes involved in muscle metabolism. In muscle, from the 10 most DEG down-regulated in the energy-restricted group (REST), we identified 5 genes associated with muscle metabolism and development: SLCO3A1, ATP6V0D1, SLC2A1, GPC4, and RASD2. In blood, among the 10 most DEG, we found genes related to response to stress up-regulated in the REST after weaning, such as SOD3 and INO80D, and to immune response down-regulated in the REST after vaccination, such as OASL, KLRF1, and LOC104968634.In conclusion, maternal energy restriction during late gestation may limit the expression of genes in the muscle and increase expression in the blood of calves. In addition, enrichment analysis showed that a short-term maternal energy restriction during pregnancy affects the expression of genes related to energy metabolism and muscle contraction, and immunity and stress response in the blood. Therefore, alterations in the intra-uterine environment can modify prenatal development with lasting consequences to adult life.Item The Eberhart and Russel’s bayesian method used as an instrument to select maize hybrids(Euphytica, 2018-03-08) Nascimento, Moysés; Oliveira, Tâmara Rebecca Albuquerque de; Carvalho, Hélio Wilson Lemos de; Costa, Emiliano Fernandes Nassau; Amaral Junior, Antonio Teixeira do; Gravina, Geraldo de Amaral; Carvalho Filho, José Luiz Sandes deAdaptability and stability analysis methods that use a priori information allow identifying and selecting potentially productive genotypes with greater accuracy. The aim of the current study is to use the Eberhart and Russel’ Bayesian method as an instrument to analyze the adaptability and stability of hybrid maize cultivars and to assess the efficiency of using the distribution of informative and non-informative priors to select cultivars. Twenty-five (25) hybrid maize cultivars were assessed in 11 environments located in the Brazilian Northeastern region, during 2012 and 2013, according to a complete randomized block design, with two repetitions. The Eberhart and Russel’s methodology was performed in the GENES software, whereas the Bayesian procedure was implemented in the free software R, by using the MCMCregress function of the MCMCpack package. The adaptability and stability parameters values and the credibility intervals have shown that the Eberhart and Russel’s method via Bayesian technique has shown greater stability-estimation accuracy and greater efficiency in recommending cultivars adapted to favorable and unfavorable environments. The Bayesian methods using priories informative (M1) and few informative (M2) distributions have presented the same genotype classifications in the comparison between a priori distributions; however, according to the Bayes Factor, the M1 was the most adequate distribution to help finding more reliable estimates.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.