Navegando por Autor "Azevedo, Camila Ferreira"
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Item Bayesian model combining linkage and linkage disequilibrium analysis for low density-based genomic selection in animal breeding(Journal of Applied Animal Research, 2018) Silva, Fabyano Fonseca; Jerez, Elcer Albenis Zamora; Resende, Marcos Deon Vilela de; Viana, José Marcelo Soriano; Azevedo, Camila Ferreira; Lopes, Paulo Sávio; Nascimento, Moysés; Lima, Rodrigo Oliveira de; Guimarães, Simone Eliza FacioniWe combined linkage (LA) and linkage disequilibrium (LDA) analyses (emerging the term ‘LALDA’) for genomic selection (GS) purposes. The models were fitted to a simulated dataset and to a real data of feed conversion ratio in pigs. Firstly, the significant QTLs (quantitative trait locus) were identified through LA-based mixed models considering the QTL-genotypes as random effects by means of genotypic identity by descent matrix. This matrix was calculated at the positions of significant QTLs (based on LA) allowing to include the QTL-genotype effects additionally to SNP (single nucleotide polymorphism) markers (based on LDA) and additive polygenic effects in several GS models (Bayesian Ridge Regression – BRR; Bayes A – BA; Bayes B – BB; Bayes C – BC and Bayesian LASSO – BL). These models combing all mentioned effects were denominated LALDA. Goodness-of-fit and predictive ability analyses were performed to evaluate the efficiency of these models. For the real data, although slightly, the superiority of the LALDA models was verified in comparison to traditional LDA models for GS. For the simulated dataset, the models presented similar results. For both LDA and LALDA frameworks, BA showed the best fitting through Deviance Information Criterion and higher predictive ability in the simulated and real datasets.Item Classificação multivariada de curvas de progresso da requeima do tomateiro entre acessos do banco de germaplasma de hortaliças da UFV(Ciência Rural, 2011-12-03) Azevedo, Camila Ferreira; Silva, Fabyano Fonseca e; Ribeiro, Natália Barbosa; Silva, Derly Jose Henriques da; Cecon, Paulo Roberto; Barili, Leiri Daiane; Pinheiro, Valeria RosadoO objetivo deste trabalho foi apresentar uma metodologia de análise de experimentos em fitopatologia que considera a comparação de curvas de progressos de doenças na presença de um grande número de tratamentos por meio da análise de cluster. Foram cultivados 42 acessos do Banco de Germoplasma de Hortaliças (BGH) da Universidade Federal de Viçosa (UFV). Ajustou-se o modelo exponencial aos dados de percentagem de severidade de requeima, e as estimativas obtidas quanto à incidência inicial da doença (yo) e taxa de progresso da doença (r) foram submetidas à análise de variância multivariada (Manova), seguindo o delineamento de blocos casualizados. As médias ajustadas foram submetidas à análise de agrupamento hierárquico, o método centroide. Observou-se um número ótimo de seis grupos distintos.Item Divergência genética e índice de seleção via BLUP em acessos de algodoeiro para características tecnológicas da fibra(Pesquisa Agropecuária Tropical, 2014-09) Resende, Marcos Deon Vilela de; Resende, Maria Aparecida Vilela de; Freitas, Joelson André de; Lanza, Marcelo Abreu; Azevedo, Camila FerreiraAs características tecnológicas da fibra do algodoeiro são determinantes da qualidade de seus produtos e de sua remuneração. Este trabalho objetivou estimar a divergência genética entre acessos de algodoeiro e ordenar os melhores, com base em um índice de seleção combinando todas essas características de interesse. Foram avaliados 248 acessos de algodoeiro, empregando-se análise multivariada (distância de Mahalanobis e agrupamento de Tocher) e índice de seleção baseado em rank médio via metodologia de modelos mistos (REML/BLUP). A análise de agrupamento de Tocher permitiu a estruturação populacional dos acessos, resumindo-os em 14 grupos divergentes. As acurácias seletivas foram altas para todas as características avaliadas, variando de 0,89 a 0,94, indicando situação favorável à seleção. As correlações entre as seis variáveis apresentaram magnitudes moderadas a baixas, não sendo possível o melhoramento de uma característica, via seleção indireta, em outra. A seleção simultânea para as características de fibra, com base no índice de seleção de Mulamba e Mock, mostrou- se promissora. Os melhores acessos para as seis variáveis, simultaneamente, foram 4S180, C96480, Giza75, 196Lasani11, Brown Egyptian, Early Fluff 316, C268-80 e 207MG-82607.Item Genotypic variation and relationships among traits for root morphology in a panel of tropical maize inbred lines under contrasting nitrogen levels(Euphytica, 2019-03) Torres, Lívia Gomes; Caixeta, Diego Gonçalves; Rezende, Wemerson Mendonça; Schuster, Andreia; Azevedo, Camila Ferreira; Silva, Fabyano Fonseca e; Lima, Rodrigo Oliveira DeA strategy to increase nitrogen (N) use efficiency in maize is the genetic improvement of N acquisition through root morphology. Here, we quantified the genetic variation of 150 tropical maize inbred lines for root morphology and shoot traits and investigated the relationships among traits. We evaluated the inbred lines at the seedling stage in a greenhouse experiment under two treatments: high N and low N supply. A mixed model approach was used to estimate variance components. Canonical correlations were estimated between root- and shoot-related groups of traits, and the genetic diversity among inbred lines was determined. Our inbred line panel showed huge genetic variability for all traits and presented large genetic diversity under both N levels. Root dry weight (RDW) was associated with shoot dry weight (SDW) at high N, and RDW and total root length (TRL) were positively associated with SDW at low N. Based on SDW, RDW and TRL, we selected a set of the top 15 maize inbred lines to be used in maize breeding programs focusing on N-use efficiency. We therefore concluded that there is a significant diversity in tropical maize inbred lines, which have the genetic potential to produce N-efficient hybrids and maize breeding populations for N stress conditions.Item Mapeamento de QTL para características de crescimento de suínos por meio de modelos de regressão aleatória(Pesquisa Agropecuária Brasileira, 2013-01-29) Pinheiro, Valeria Rosado; Silva, Fabyano Fonseca e; Guimarães, Simone Eliza Facioni; Resende, Marcos Deon Vilela de; Lopes, Paulo Sávio; Cruz, Cosme Damião; Azevedo, Camila FerreiraO objetivo deste trabalho foi avaliar eficiência de modelos de regressão aleatória (MRA) para detectar locus de características quantitativas (QTL) para características de crescimento, em suínos. Utilizou-se uma população divergente F2 Piau x Comercial. A eficiência da metodologia proposta na detecção de QTL foi comparada à da metodologia tradicional de regressão por intervalo de mapeamento. Para tanto, utilizaram-se MRA com efeitos aleatórios poligênicos, de ambiente permanente e de QTL, tendo-se utilizado o enfoque de matriz de covariância "identical‑by‑descent" associada aos efeitos de QTL. Testou-se a significância dos efeitos de QTL mediante a razão de verossimilhanças, tendo-se considerado o modelo como completo quando houve efeito de QTL, ou nulo, quando não. A comparação entre os modelos foi feita nas posições dos marcadores (seis marcadores microssatélites) e nas intermediárias, entre os marcadores. O MRA detectou QTL significativo na posição 65 cM do cromossomo 7 e, portanto, foi mais eficiente que a metodologia tradicional, que não detectou QTL significativo em nenhum dos fenótipos avaliados. A metodologia proposta possibilitou a detecção de QTL com efeito sobre toda a trajetória de crescimento, dentro da amplitude de idade considerada (do nascimento aos 150 dias).Item Métodos de redução de dimensionalidade aplicados na seleção genômica para características de carcaça em suínos(Universidade Federal de Viçosa, 2012-07-26) Azevedo, Camila Ferreira; Peternelli, Luiz Alexandre; http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4723301Z7; Resende, Marcos Deon Vilela de; http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4709374E4; Silva, Fabyano Fonseca e; http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4766260Z2; http://lattes.cnpq.br/8861113007032888; Nascimento, Carlos Souza do; http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4734058H3; Lopes, Paulo Sávio; http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4783377H1A principal contribuição da genética molecular no melhoramento animal é a utilização direta das informações de DNA no processo de identificação de animais geneticamente superiores. Sob esse enfoque, a seleção genômica ampla (Genome Wide Selection GWS), a qual consiste na análise de um grande número de marcadores SNPs (Single Nucleotide Polymorphisms) amplamente distribuídos no genoma, foi idealizada. A utilização dessas informações é um desafio, uma vez que o número de marcadores é muito maior que o número de animais genotipados (alta dimensionalidade) e tais marcadores são altamente correlacionados (multicolinearidade). No entanto, o sucesso da seleção genômica ampla deve-se a escolha de metodologias que contemplem essas adversidades. Diante do exposto, o presente trabalho teve por objetivo propor a aplicação dos métodos de regressão via Componentes Independentes (Independent Component Regression ICR), regressão via componentes principais (Principal Component Regression PCR), regressão via Quadrados Mínimos Parciais (Partial Least Squares PLSR) e RR-BLUP, considerando características de carcaça em uma população F2 de suínos proveniente do cruzamento de dois varrões da raça naturalizada brasileira Piau com 18 fêmeas de linhagem comercial (Landrace × Large White × Pietrain), desenvolvida na Universidade Federal de Viçosa. Os objetivos específicos foram estimar Valores Genéticos Genômicos (Genomic Breeding Values GBV) para cada indivíduo avaliado e estimar efeitos de marcadores SNPs, visando a comparação dos métodos. Os resultados indicaram que o método ICR se mostrou mais eficiente, uma vez que este proporcionou maiores valores de acurácia na estimação do GBV para a maioria das características de carcaça.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 Multi-trait multi-environment Bayesian model reveals G x E interaction for nitrogen use efficiency components in tropical maize(Plos One, 2018) Torres, Lívia Gomes; Rodrigues, Mateus Cupertino; Lima, Nathan Lamounier; Trindade, Tatiane Freitas Horta; Silva, Fabyano Fonseca e; Azevedo, Camila Ferreira; DeLima, Rodrigo OliveiraIdentifying maize inbred lines that are more efficient in nitrogen (N) use is an important strategy and a necessity in the context of environmental and economic impacts attributed to the excessive N fertilization. N-uptake efficiency (NUpE) and N-utilization efficiency (NUtE) are components of N-use efficiency (NUE). Despite the most maize breeding data have a multitrait structure, they are often analyzed under a single-trait framework. We aimed to estimate the genetic parameters for NUpE and NUtE in contrasting N levels, in order to identify superior maize inbred lines, and to propose a Bayesian multi-trait multi-environment (MTME) model. Sixty-four tropical maize inbred lines were evaluated in two experiments: at high (HN) and low N (LN) levels. The MTME model was compared to single-trait multi-environment (STME) models. Based on deviance information criteria (DIC), both multi- and single- trait models revealed genotypes x environments (G x E) interaction. In the MTME model, NUpE was found to be weakly heritable with posterior modes of heritability of 0.016 and 0.023 under HN and LN, respectively. NUtE at HN was found to be highly heritable (0.490), whereas under LN condition it was moderately heritable (0.215). We adopted the MTME model, since combined analysis often presents more accurate breeding values than single models. Superior inbred lines for NUpE and NUtE were identified and this information can be used to plan crosses to obtain maize hybrids that have superior nitrogen use efficiency.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 Quadrados mínimos parciais uni e multivariado aplicados na seleção genômica para características de carcaça em suínos(Ciência Rural, 2013-03-31) Azevedo, Camila Ferreira; Silva, Fabyano Fonseca e; Peternelli, Luiz Alexandre; Guimarães, Simone Eliza Facione; Lopes, Paulo Sávio; Rezende, Marcos Deon Vilela deA principal contribuição da genética molecular é a utilização direta das informações de DNA no processo de identificação de indivíduos geneticamente superiores. Sob esse enfoque, idealizou-se a seleção genômica ampla (Genome Wide Selection - GWS), a qual consiste na análise de marcadores SNPs (Single Nucleotide Polymorphisms) amplamente distribuídos no genoma. Devido a esse grande número de SNPs, geralmente maior que o número de indivíduos genotipados, e à alta colinearidade entre eles, métodos de redução de dimensionalidade são requeridos. Dentre estes, destaca-se o método de regressão via Quadrados Mínimos Parciais (Partial Least Squares - PLS), que além de solucionar tais problemas, permite uma abordagem multivariada, considerando múltiplos fenótipos. Diante do exposto, objetivou-se aplicar e comparar a regressão PLS univariada (UPLS) e multivariada (MPLS) na GWS para características de carcaça em uma população F2 de suínos Piau×Comercial. Os resultados evidenciaram a superioridade do método MPLS, uma vez que este proporcionou maiores valores de acurácia em relação à abordagem univariada.Item Regional heritability mapping and genome-wide association identify loci for complex growth, wood and disease resistance traits in Eucalyptus(New Phytologist, 2016-09-08) Resende, Rafael Tassinari; Resende, Marcos Deon Vilela; Silva, Fabyano Fonseca; Azevedo, Camila Ferreira; Takahashi, Elizabete Keiko; Silva-Junior, Orzenil Bonfim; Grattapaglia, DarioAlthough genome-wide association studies (GWAS) have provided valuable insights into the decoding of the relationships between sequence variation and complex phenotypes, they have explained little heritability. Regional heritability mapping (RHM) provides heritability estimates for genomic segments containing both common and rare allelic effects that individually contribute too little variance to be detected by GWAS. We carried out GWAS and RHM for seven growth, wood and disease resistance traits in a breeding population of 768 Eucalyptus hybrid trees using EuCHIP60K. Total genomic heritabilities accounted for large proportions (64–89%) of pedigree-based trait heritabilities, providing additional evidence that complex traits in eucalypts are controlled by many sequence variants across the frequency spectrum, each with small contributions to the phenotypic variance. RHM detected 26 quantitative trait loci (QTLs) encompassing 2191 single nucleotide polymorphisms (SNPs), whereas GWAS detected 13 single SNP–trait associations. RHM and GWAS QTLs individually explained 5–15% and 4–6% of the genomic heritability, respectively. RHM was superior to GWAS in capturing larger proportions of genomic heritability. Equated to previously mapped QTLs, our results highlighted genomic regions for further examination towards gene discovery. RHM-QTLs bearing a combination of common and rare variants could be useful enhancements to incorporate prior knowledge of the underlying genetic architecture in genomic prediction models.Item Regressão via componentes independentes aplicada à seleção genômica para características de carcaça em suínos(Pesquisa Agropecuária Brasileira, 2013-05-06) Azevedo, Camila Ferreira; Silva, Fabyano Fonseca e; Lopes, Paulo Sávio; Guimarães, Simone Eliza Facioni; Resende, Marcos Deon Vilela deO objetivo deste trabalho foi avaliar a eficiência do método de regressão via componentes independentes (ICR) na estimação de valores genéticos genômicos e dos efeitos de marcadores SNP para características de carcaça de uma população F2 de suínos (Piau x linhagem comercial). Os métodos foram avaliados por meio da concordância entre os valores genéticos preditos e os fenótipos corrigidos, observados por validação cruzada, e também foram comparados com outros métodos geralmente utilizados para os mesmos propósitos, tais como RR-BLUP, PCR e PLS. Os métodos ICR e PCR apresentam resultados similares, mas o método ICR apresenta maiores valores de acurácia.Item Ridge, Lasso and Bayesian additive-dominance genomic models(BMC Genetics, 2015-08-25) Azevedo, Camila Ferreira; Resende, Marcos Deon Vilela de; Silva, Fabyano Fonseca e; Viana, José Marcelo Soriano; Valente, Magno Sávio Ferreira; Resende Jr, Márcio Fernando Ribeiro; Muñoz, PatricioA complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close. Amongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (−2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.Item Ridge, lasso and bayesian additive-dominance genomic models and new estimators for the experimental accuracy of genome selection(Universidade Federal de Viçosa, 2015-10-26) Azevedo, Camila Ferreira; Resende, Marcos Deon Vilela de; http://lattes.cnpq.br/8861113007032888The main contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. Under this approach, genome-wide selection (GWS) can be used with this purpose. GWS consists in analyzing of a large number of SNP markers widely distributed in the genome. This simulation work presents a complete approach for genomic selection by using adequate genetic models including dominance effects, which are essential for selecting crosses and clones as well as for improving the estimation of additive effects for parent selection. To date, the approaches via Ridge, Lasso and Bayesian additive-dominance models have not been evaluated and compared in the literature.The performance of 10 additive-dominance prediction models (including current ones and proposed modifications) were evaluated. A new modified Bayesian/Lasso method (called BayesA*B* or t-BLASSO) performed best in the prediction of genomic breeding value of individuals, in all the four scenarios (two heritabilities × two genetic architectures). The BayesA*B*-type methods showed better ability for recovering the dominance variance/additive variance ratio. Also, the role of the three quantitative genetics information sources (called linkage disequilibrium, co- segregation and pedigree relationships) in genomic selection were elucidated by decomposing the heritability and accuracy in the three components and showing their relations with the structure of populations and the genetic improvement in the short and long run. Moreover, this simulation work also, we developed the new estimators for the prediction accuracy of genomic selection. The work proposes and evaluates the performance and efficiency of these new estimators called regularized estimator (RE) and hybrid estimator (HE). The regularized estimator takes in consideration both the genomic and trait heritabilities, in addition to the predictive ability. The hybrid estimator (HE), combines both experimental and expected accuracies. The comparisons of the RE and HE with the traditional (TE) were done under four validation procedures. In general, the new estimator presented accuracies closer to the parametric ones, mainly when selecting markers. It was also less biased and more precise, with smaller standard deviations than the traditional estimator. The TE can be used only with independent validation, where it tends to perform better than RE, although overestimating the accuracy. The hybrid estimator (HE) proved to be very effective in the absence of validation. The independent validation showed to be superior over the Jacknife procedures, chasing better the parametric accuracy with or without marker selection. The following inferences can be made according to the accuracy estimator and kind of validation: (i) most probable accuracy: HE without validation; (ii) highest possible accuracy: TE with independent validation; (iii) lowest possible accuracy: RE with independent validation.Item Triple categorical regression for genomic selection: application to cassava breeding(Scientia Agricola, 2019-09) Lima, Leísa Pires; Azevedo, Camila Ferreira; Resende, Marcos Deon Vilela de; Silva, Fabyano Fonseca e; Viana, José Marcelo Soriano; Oliveira, Eder Jorge deGenome-wide selection (GWS) is currently a technique of great importance in plant breeding, since it improves efficiency of genetic evaluations by increasing genetic gains. The process is based on genomic estimated breeding values (GEBVs) obtained through phenotypic and dense marker genomic information. In this context, GEBVs of N individuals are calculated through appropriate models, which estimate the effect of each marker on phenotypes, allowing the early identification of genetically superior individuals. However, GWS leads to statistical challenges, due to high dimensionality and multicollinearity problems. These challenges require the use of statistical methods to approach the regularization of the estimation process. Therefore, we aimed to propose a method denominated as triple categorical regression (TCR) and compare it with the genomic best linear unbiased predictor (G-BLUP) and Bayesian least absolute shrinkage and selection operator (BLASSO) methods that have been widely applied to GWS. The methods were evaluated in simulated populations considering four different scenarios. Additionally, a modification of the G-BLUP method was proposed based on the TCR-estimated (TCR/G-BLUP) results. All methods were applied to real data of cassava (Manihot esculenta) with to increase efficiency of a current breeding program. The methods were compared through independent validation and efficiency measures, such as prediction accuracy, bias, and recovered genomic heritability. The TCR method was suitable to estimate variance components and heritability, and the TCR/G-BLUP method provided efficient GEBV predictions. Thus, the proposed methods provide new insights for GWS.