O uso de modelos gráficos para investigar redes fenotípicas envolvendo características poligênicas

Understanding the causal architecture underlying complex systems biology has a great value in agriculture production for the development of optimal management strategies and selective breeding. So far, most studies in this area use only prior knowledge to propose causal networks and/or do not consider the possible genetic confounding factors on the structure search, which may hide important relationships among phenotypes and also bias the resulting inferred causal network. In this dissertation, we explore many structural learning algorithms and present a new one, called PolyMaGNet (Polygenic traits with Major Genes Network analysis), to search for recursive causal structures involving complex phenotypic traits with polygenic inheritance and also allowing the possibility of major genes affecting the traits. Briefly, a multiple-trait animal mixed model is fitted using a Bayesian approach considering major genes as covariates. Next, posterior samples of the residual covariance matrix are used as input for the Inductive Causation algorithm to search for putative causal structures, which are compared to each other using the Akaike information criterion. The performance of PolyMaGNet was evaluated and compared with another widely used approach in a simulated study considering a QTL mapping population. Results showed that, in the presence of major genes, our method recovered the true skeleton structure as well as the causal directions with a higher rate of true positives. The PolyMaGNet approach was also applied to a real dataset of an F2 Duroc × Pietrain pig resource population to recover the causal structure underlying on carcass, meat quality and chemical composition traits. Results corroborated with the literature regarding the cause-effect relationships between these traits and also provided new insights about phenotypic causal networks and its genetic architectures in complex systems biology.Compreender a arquitetura causal subjacente à sistemas biológicos complexos é de grande valia na produção agrícola para o desenvolvimento de estratégias de manejo e seleção genética. Até o momento, a maior parte dos estudos neste contexto utiliza apenas conhecimento prévio para propor redes causais e/ou não considera fatores de confundimento genético na busca de estruturas, fato que pode ocultar relações importantes entre os fenótipos e viesar inferências sobre a rede causal. Nesta tese, exploramos alguns algoritmos de aprendizagem de estruturas e apresentamos um novo, chamado PolyMaGNet (do inglês, Polygenic traits with Major Genes Network analysis), para buscar estruturas causais recursivas entre características fenotípicas poligênicas complexas e permitindo, também, a possibilidade de efeitos de genes maiores que as afetam. Resumidamente, um modelo misto de múltiplas características é ajustado usando abordagem Bayesiana considerando os genes maiores como covariáveis no modelo. Em seguida, amostras posteriores da matriz de covariância residual são usadas como entrada para o algoritmo de causação indutiva para pesquisar estruturas causais putativas, as quais são comparadas usando o critério de informação de Akaike. O desempenho do PolyMaGNet foi avaliado e comparado com outra abordagem bastante utilizada por meio de um estudo simulado considerando uma população de mapeamento de QTL. Os resultados mostraram que, na presença de genes maiores, o método PolyMaGNet recuperou a verdadeira estrutura do esqueleto, bem como as direções causais, com uma taxa de efetividade maior. O método é ilustrado também utilizando-se um conjunto de dados reais de uma população de suínos F2 Duroc × Pietrain para recuperar a estrutura causal subjacente à características fenotípicas relacionadas a qualidade da carcaça, carne e composição química. Os resultados corroboraram com a literatura sobre as relações de causa-efeito entre os fenótipos e também forneceram novos conhecimentos sobre a rede fenotípica e sua arquitetura genética

Genotype selection of Pouteria sapota (Jacq.) H.E. Moore & Stearn, under a multivariate framework

The pouteria sapota, also popularly known as sapote or mamey sapote, is a fruit tree of sapotaceae family originally from the tropical region of Central America with a great importance due to the almost complete utilization of the tree (fruit, seeds and wood) by industries. Thus, the study of its features becomes indispensable for selecting the most promising genotypes to increase the profitability of its production. In this study, it was used a dataset of 63 sapote trees placed in the botanical garden of Centro Agronómico Tropical de Investigación y Enseñanza (CATIE), located in Turrialba, Costa Rica. 17 quantitative characteristics were measured from trees, in order to evaluate the yield potential through the application of two multivariate statistical techniques: factor analysis (FA) and cluster analysis (CA). Firstly, the FA was performed and the 17 initial characteristics were reduced to four common factors that might describe particular characteristics like “fruit”, “seed”, “wood” and “leaf”. Thereafter, a CA was performed, with scores of FA, allowing the formation of five groups of trees with different traits. This methodology revealed the most promising trees in the economic point of view for every industry that uses the tree as raw material

Multivariate analysis for selecting animals for experimental research

Background Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate

Amazonian trees show increased edge effects due to Atlantic Ocean warming and northward displacement of the Intertropical Convergence Zone since 1980

Recent investigations indicate a warming of Atlantic Ocean surface waters since 1980, probably influenced by anthropic actions, inducing rainfall intensification mainly during the rainy season and slight reductions during the dry season in the Amazon. Under these climate changes, trees in upland forests (terra firme) could benefit from the intensification of the hydrological cycle and could also be affected by the reduction of precipitation during the dry season. Results of dendrochronological analyses, spatial correlations and structural equation models, showed that Scleronema micranthum (Ducke) Ducke (Malvaceae) trees exposed in fragmented areas and to edge effects in Central Amazonian terra firme forest were more sensitive to the increase in the Atlantic Ocean surface temperature and consequent northward displacement of the Intertropical Convergence Zone, mainly during the dry season. Therefore, we proved that in altered and potentially more stressful environments such as edges of fragmented forests, recent anthropogenic climatic changes are exerting pressure on tree growth dynamics, inducing alterations in their performance and, consequently, in essential processes related to ecosystem services. Changes that could affect human well-being, highlighting the need for strategies that reduce edge areas expansion in Amazon forests and anthropic climate changes of the Anthropocene.Fil: Albiero Júnior, Alci. Universidade de Sao Paulo; BrasilFil: Camargo, José Luís Campana. National Institute Of Amazonian Research; BrasilFil: Roig Junent, Fidel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Schöngart, Jochen. National Institute Of Amazonian Research; BrasilFil: Pinto, Renan Mercuri. São Paulo State Technological College; BrasilFil: Venegas González, Alejandro. Universidad Mayor; ChileFil: Tomazello Filho, Mario. Universidade de Sao Paulo; Brasi

Análise multivariada na seleção de animais em pesquisas experimentais

Background: Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective: To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods: The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results: The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion: The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate.Fundamento: Muitos pesquisadores buscam métodos para a seleção de grupos homogêneos de animais em pesquisas experimentais, fato que se justifica por ser a homogeneidade pré-requisito indispensável à casualização de tratamentos. A ausência de métodos robustos, que atendam a princípios estatísticos e biológicos, faz com que os pesquisadores utilizem métodos empíricos ou subjetivos, influenciando seus resultados. Objetivo: Desenvolver modelo estatístico multivariado para a seleção de grupo homogêneo de animais para pesquisas experimentais e elaborar pacote computacional que o operacionalize. Métodos: O conjunto de dados ecocardiográficos de 115 ratos Wistar, machos, com estenose aórtica (EAo) supravalvular foi utilizado para exemplificar o desenvolvimento do modelo. Inicialmente, os dados foram padronizados, tornando-se adimensionais. Em sequência, submeteu-se a matriz de variabilidade do conjunto à análise de componentes principais (ACP) buscando-se reduzir o espaço paramétrico e conservar a variabilidade relevante. Essa técnica estabeleceu um novo sistema cartesiano em que os animais foram alocados e, finalmente, construiu-se a região de confiança (elipsoide) para o perfil de respostas homogêneas dos animais. Os que se situaram no interior do elipsoide foram considerados pertencentes ao grupo homogêneo; caso contrário, espúrios ao grupo. Resultados: A ACP estabeleceu oito eixos descritores que representaram a variabilidade acumulada dos dados em 88,71%. A alocação dos animais no novo sistema e a construção da região de confiança revelou a presença de seis espúrios ao lote homogêneo formado por 109 animais. Conclusão: O critério biométrico proposto mostra-se eficiente, pois considera o animal como um todo, analisando conjuntamente todos os parâmetros mensurados, além de apresentar pequena frequência de descartes

Núcleos de Ensino da Unesp: artigos 2008

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq