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| Registros recuperados: 19 | |
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| COSTA, J. A. da; AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; NASCIMENTO, A. C. C.. |
| multicollinearity and high dimensionality problems, making it impossible to obtain stable estimates through the traditional method of estimation based on ordinary least squares. To overcome such challenges, dimensionality reduction methods have been proposed, because of their simple theory and easy application. We compared three dimensionality reduction methods: Principal Components Regression (PCR), Partial Least Squares (PLS), and Independent Components Regression (ICR). An important step for dimensionality reduction and prediction is selecting the number of components, as it affects the linear combinations of the explanatory variables. The linear combinations are inserted into the model to predict the response based on a reduced number of parameters. We... |
| Tipo: Artigo de periódico |
Palavras-chave: Regression analysis; Genomics. |
| Ano: 2021 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139234 |
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| COSTA, J. A. da; AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F.; RESENDE, M. D. V. de; NASCIMENTO, A. C. C.. |
| The principal component regression (PCR) and the independent component regression (ICR) are dimensionality reduction methods and extremely important in genomic prediction. These methods require the choice of the number of components to be inserted into the model. For PCR, there are formal criteria; however, for ICR, the adopted criterion chooses the number of independent components (ICs) associated to greater accuracy and requires high computational time. In this study, seven criteria based on the number of principal components (PCs) and methods of variable selection to guide this choice in ICR are proposed and evaluated in simulated and real data. For both datasets, the most efficient criterion and that drastically reduced computational time determined... |
| Tipo: Artigo de periódico |
Palavras-chave: Melhoramento Genético Vegetal; Produtividade; Arroz; Genomics; Plant breeding; Yields; Rice. |
| Ano: 2022 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139185 |
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| OLIVEIRA, G. F.; MIRANDA, T. L. R.; NASCIMENTO, A. C. C.; NASCIMENTO, M.; CAIXETA, E. T.; SILVA, L. de F.; ALKIMIM, E. R.; SILVA, F. L. da. |
| Coffee growing is one of the most important agricultural activities in the world market. Among the commercially relevant species, there is Coffea canephora,which can be divided into the varietal groups Conilon and Robusta. These varietal groups have complementary agronomic interests. Because of this, hybrids are obtained through the crosses between these groups. Given the difficulty in differentiating between two varietal groups genotypes in the field, the correct discrimination is essential for the definition of crosses in breeding programs. In this context, the objective was to apply a discriminant analysis (DA) to define functions to differentiate between varietal groups and hybrids of canephora, as well as to identify the most relevant phenotypic... |
| Tipo: Artigo de periódico |
Palavras-chave: Café Robusta; Discriminant analysis; Coffea; Multivariate analysis. |
| Ano: 2021 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139353 |
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| TEIXEIRA, F. R. F.; NASCIMENTO, M.; NASCIMENTO, A. C. C.; SILVA, F. F. e; CRUZ, C. D.; AZEVEDO, C. F.; PAIXÃO, D. M.; BARROSO, L. M. A.; VERARDO, L. L.; RESENDE, M. D. V. de; GUIMARÃES, S. E. F.; LOPES, P. S.. |
| The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genome-wide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: ?weight?, ?fat?, ?loin?, and ?performance?. These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS... |
| Tipo: Artigo de periódico |
Palavras-chave: Genome enabled prediction; SNP effects; Análise multivariada.; Melhoramento genético animal; Estatística; Seleção genética.; Animal breeding; Multivariate analysis. |
| Ano: 2016 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1047516 |
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| SUELA, M. M.; AZEVEDO, C. F.; NASCIMENTO, A. C. C.; MOMEN, M.; OLIVEIRA, A. C. B. de; CAIXETA, E. T.; MOROTA, G.; NASCIMENTO, M.. |
| Yield is one of the most important traits of arabica coffee. Plant breeders seek to maximize yield directly or indirectly, using other related traits. The standard multi-trait genome-wide association study (MTM-GWAS) does not accommodate the network structure of phenotypes, therefore, does not address how traits are interrelated. We applied structural equation modeling (SEM) to GWAS to explore interrelated dependencies between phenotypes related to morphology (fruit size and number of reproductive nodes), physiology (vegetative vigor), and productivity (yield) traits using 195 Coffea arábica individuals genotyped with 21,211 single-nucleotide polymorphism markers. We inferred the probabilistic phenotypic network by the Hill-Climbing algorithm to estimate... |
| Tipo: Artigo de periódico |
Palavras-chave: Structural equation modeling; Genome-wide association study; Single nucleotide polymorphism; Coffea arabica var. arabica. |
| Ano: 2023 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1157822 |
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| TEIXEIRA, F. R. F.; NASCIMENTO, M.; CECON, P. R.; CRUZ, C. D.; SILVA, F. F. e; NASCIMENTO, A. C. C.; AZEVEDO, C. F.; MARQUES, D. B. D.; SILVA, M. V. G. B.; CARNEIRO, A. P. S.; PAIXAO, D. M.. |
| Knowledge of lactation curves in dairy cattle is essential for understanding the animal production in milk production systems. Genomic prediction of lactation curves represents the genetic pattern of milk production of the animals in the herd. In this context, we made genomic predictions of lactation curves through genome-wide selection (GWS) to characterize the genetic pattern of lactation traits in Girolando cattle based on parameters estimated by nonlinear mixed effects (NLME) models. Data of 1,822 milk control records from 226 Girolando animals genotyped for 37,673 single nucleotide polymorphisms were analyzed. Nine NLME models were compared to identify the equation with the best fit. The lactation traits estimated by the best model were submitted to... |
| Tipo: Artigo de periódico |
Palavras-chave: Previsão genômica; Bovino; Gado Leiteiro; Curva de Lactação; Heritability; Genome; Girolando. |
| Ano: 2021 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1133535 |
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| SOUSA, I. C. de; NASCIMENTO, M.; SILVA, G. N.; NASCIMENTO, A. C. C.; CRUZ, C. D.; SILVA, F. F. e; ALMEIDA, D. P. de; PESTANA, K. N.; AZEVEDO, C. F.; ZAMBOLIM, L.; CAIXETA, E. T.. |
| Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and... |
| Tipo: Artigo de periódico |
Palavras-chave: Statistical learning; Hemileia Vastatrix; Plant breeding; Artificial intelligence. |
| Ano: 2020 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1125524 |
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| SOUSA, I. C. de; NASCIMENTO, M.; SANT’ANNA, I. de C.; CAIXETA, E. T.; AZEVEDO, C. F.; CRUZ, C. D.; SILVA, F. L. da; ALKIMIM, E. R.; NASCIMENTO, A. C. C.; SERÃO, N. V. L.. |
| Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions about the relationships between inputs and the output allowing great flexibility to handle different types of complex non-additive effects, such as dominance and epistasis. Despite this advantage, the biological interpretability of ANNs is still limited. The aim of this research was to estimate the heritability and markers effects for two traits in Coffea canephora using an additive-dominance architecture ANN and to compare it with genomic best linear unbiased prediction (GBLUP). The data used consists of 51... |
| Tipo: Artigo de periódico |
Palavras-chave: Rede neural artificial; Marcador Genético; Coffea Canephora; Neural networks; Dominance (genetics). |
| Ano: 2022 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1143026 |
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| VOLPATO, L.; ALVES, R. S.; TEODORO, P. E.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; LUDKE, W. H.; SILVA, F. L. da; BORÉM, A.. |
| At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data... |
| Tipo: Artigo de periódico |
Palavras-chave: Bayesian-inference; Genomic selection; Breeding values; Seed protein; Mixed models; Inferência Bayesian; Modelo misto; Seleção genômica; Soja; Soybeans; Agronomic traits; Prediction. |
| Ano: 2019 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1110400 |
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| OLIVEIRA, G. F.; NASCIMENTO, A. C. C.; NASCIMENTO, M.; SANT'ANNA, I. de C.; ROMERO, J. V.; AZEVEDO, C. F.; BHERING, L. L.; CAIXETA, E. T.. |
| This study assessed the efficiency of Genomic selection (GS) or genome‐wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In... |
| Tipo: Artigo de periódico |
Palavras-chave: Regressão Linear; Seleção Genótipa; Genomics; Plant selection guides; Plant breeding. |
| Ano: 2021 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139325 |
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| NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; CRUZ, C. D.; NASCIMENTO, A. C. C.; VIANA, J. M. S.; AZEVEDO, C. F.; BARROSO, L. M. A.. |
| Genomic 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... |
| Tipo: Artigo de periódico |
Palavras-chave: Seleção genômica; Genomic selection; Regularized regression; SNP effects; Estatística; Marker-assisted selection; Simulation models; Statistics. |
| Ano: 2017 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1084109 |
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| BARROSO, L. M. A.; NASCIMENTO, M.; NASCIMENTO, A. C. C.; SILVA, F. F.; SERÃO, N. V. L.; CRUZ, C. D.; RESENDE, M. D. V. de; SILVA, F. L.; AZEVEDO, C. F.; LOPES, P. S.; GUIMARÃES, S. E. F.. |
| Background: Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). Results: The regularized quantile regression (RQR) enabled the discovery, at different levels of interest... |
| Tipo: Artigo de periódico |
Palavras-chave: Genome association; Pig; Regularized quantile regression; QTL; Growth curves.; Melhoramento genético animal; Porco; Suíno; Swine.. |
| Ano: 2017 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1084057 |
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| Registros recuperados: 19 | |
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