Genetic diversity among progenies of Crambe abyssinica Hochst for seed traits

Crambe is an important biofuel crop and its oil has unique traits such as high erucic acid content which can be used as industrial lubricant, corrosion inhibitor as well as ingredient in synthetic rubber manufacturing. Genetic diversity among 70 progenies of Crambe abyssinica Hochst selected from a population of FMS Brilhante cultivar was quantified by multivariate analysis for traits related to germination, thousand grain weight and oil content. There were significant differences among progenies for all traits studied. Estimation of genetic variance and heritability coefficients showed that the variability found in the progeny is more genetic than environmental which enables genetic gains with selection. Heritability coefficient varied from 68 to 79%, except for oil content and number of dead seedlings. Simple correlation analysis showed that germination and vigor were positively correlated, and thousand grain weight and oil content were not correlated with any of the seed traits. Based on multivariate analysis, the progenies could be grouped into 26 clusters. Clusters 1, 2 and 3 had the highest number of progeny with 7, 8 and 6 lineages, respectively. Clusters 21-26 had higher dissimilarity within the cluster with one in each progeny. The trait that most contributed to the cluster was the germination (36.2%) and less contributed was the number of seedlings killed (1.1%). The progenies indicate genetic diversity for seed traits and the selection of superior progenies is possible considering the studied traits. © 2013.

Estimativas de parâmetros genéticos para pesos do nascimento aos dois anos de idade para bovinos da raça Brahman utilizando modelos de regressão aleatória

Pós-graduação em Zootecnia - FCAV

Modelos genéticos para a produção no dia do controle em bovinos Gir Leiteiro (Bos indicus)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelos de regressão aleatória para a estimação de parâmetros genéticos da produção e constituintes do leite de búfalas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Efeito da interação genótipo ambiente, sob a forma de heterogeneidade de variâncias entre rebanhos

Foram simuladas estruturas de dados em modelos mistos representando o teste de 100 reprodutores, sendo cada reprodutor acasalado com 10 matrizes (total de 1000 matrizes), originando em cada acasalamento 2 proles, totalizando 2000 proles (vinte proles por reprodutor). De cada combinação reprodutor e matriz, dez proles tiveram seu fenótipo expresso no ambiente de baixa produção (Estrato 1) e, a outra metade, no ambiente de alta produção (Estrato 2). A simulação foi realizada de forma a representar diferentes situações de presença de heterogeneidade de variâncias, combinando-se as origens da heterogeneidade, de natureza genética e ambiental. Na presença de heterogeneidade residual, o valor estimado para o componente de variância residual, considerando homogeneidade de variâncias se aproximou do valor médio das variâncias entre os estratos. Houve superestimação, também, do componente de variância genético aditivo. Ao simular heterogeneidade de variância de origem genética, observou-se que a estimação desse componente situou-se em valor intermediário aos simulados. Nessa situação, o componente de variância residual estimado foi próximo do valor simulado, indicando que a heterogeneidade de variâncias quando proveniente de fatores genéticos, não interfere, substancialmente, sobre e estimação do componente de variância residual. Na simulação de dados com presença de heterogeneidade tanto de origem genética quanto ambiental (estrutura de dados 4), conduziu a estimação de componentes de variâncias intermediários aos valores simulados em cada estrato. Assim, observa-se que, mesmo quando os reprodutores apresentam proles bem distribuídas em ambos os estratos, a heterogeneidade de variância proveniente de fatores não genético provoca distorções sobre a estimação da variância genética aditiva. Mas por outro lado, quando a heterogeneidade de variância é decorrente de fatores genéticos, não há grande interferência sobre a estimativa da variância residual, tal comportamento pode ser explicado pela incorporação da matriz de parentesco na estimação do componente de variância genético aditivo, possibilitando discriminar melhor a origem da diferenças entre variâncias. Na estrutura onde a variância residual foi heterogênea a estimativa de herdabilidade foi menor em relação à estrutura de homogeneidade de variâncias. Por outro lado, quando somente a variância genética aditiva foi heterogênea, a estimativa de herdabilidade, considerando-se apenas o estrato de alta variabilidade genética, foi inflacionada pela superestimação da variância genética aditiva. No entanto, a estimativa de herdabilidade obtida, desconsiderando essa fonte de heterogeneidade de variância, foi próxima à situação de homogeneidade de variância, indicando que, quando os reprodutores possuem boa distribuição de proles em diferentes ambientes, as estimativas relacionadas ao efeito genético são ponderadas pelo desempenho dos animais em cada ambiente. As correlações de Spearman e de Pearson entre os valores genéticos preditos dos reprodutores, para todas as situações, foram maiores que 0,90. O resultado indica que, mesmo havendo presença de heterogeneidade de variância genética e/ou ambiental, se os reprodutores possuem proles bem distribuídas entre os ambientes (estratos heterogêneos) a classificação do mérito genético não se altera, o que era esperado, pois em análises unicarácter, quando ocorre uma fonte de viés na avaliação genética, ela é comum a todos os indivíduos. Na situação em que foi imposta a estrutura de dados à presença de heterogeneidade de variância residual com número de número desigual de proles por reprodutor nos estratos, provocou superestimação dos componentes de variância. Porém mesmo havendo alteração na magnitude dos valores genéticos preditos para os reprodutores, a heterogeneidade de variância não alterou a classificação entre os reprodutores todas as correlações de ordem foram próximas à unidade. O efeito da heterogeneidade de variância, oriunda de fatores ambientais, ocasiona em maiores distorções sobre a avaliação genética animal, em relação, quando a mesma é proveniente de causas genéticas. A conexidade genética entre diferentes ambientes, dilui o efeito da heterogeneidade de variância, tanto de origem genética, quanto ambiental, na predição de valores genéticos dos reprodutores.

Gráficos de controle para monitoramento de processos multivariados

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Female fertility in a Guzerat dairy subpopulation: Heterogeneity of variance components for calving intervals

The objectives of the present study were to determine if variance components of calving intervals varied with age at calving and if considering calving intervals as a longitudinal trait would be a useful approach for fertility analysis of Zebu dairy herds. With these purposes, calving records from females born from 1940 to 2006 in a Guzerat dairy subpopulation in Brazil were analyzed. The fixed effects of contemporary groups, formed by year and farm at birth or at calving, and the regressions of age at calving, equivalent inbreeding coefficient and day of the year on the studied traits were considered in the statistical models. In one approach, calving intervals (Cl) were analyzed as a single trait, by fitting a statistical model on which both animal and permanent environment effects were adjusted for the effect of age at calving by random regression. In a second approach, a four-trait analysis was conducted, including age at first calving (AFC) and three different female categories for the calving intervals: first calving females; young females (less than 80 months old, but not first calving); or mature females (80 months old or more). Finally, a two-trait analysis was performed, also including AFC and Cl, but calving intervals were regarded as a single trait in a repeatability model. Additionally, the ranking of sires was compared among approaches. Calving intervals decreased with age until females were about 80 months old, remaining nearly constant after that age. A quasi-linear increase of 11.5 days on the calving intervals was observed for each 10% increase in the female's equivalent inbreeding coefficient. The heritability of AFC was 0.37. For Cl. the genetic-phenotypic variance ratios ranged from 0.064 to 0.141, depending on the approach and on ages at calving. Differences among genetic variance components for calving intervals were observed along the animal's lifetime. Those differences confirmed the longitudinal aspect of that trait, indicating the importance of such consideration when accessing fertility of Zebu dairy females, especially in situations where the available information relies on their calving intervals. Spearman rank correlations among approaches ranged from 0.90 to 0.95, and changes observed in the ranking of sires suggested that the genetic progress of the population could be affected by the approach chosen for the analysis of calving intervals. (C) 2012 Elsevier ay. All rights reserved.

Genetic analysis of kernel oil content in tropical maize with design III and QTL mapping

Oil content and grain yield in maize are negatively correlated, and so far the development of high-oil high-yielding hybrids has not been accomplished. Then a fully understand of the inheritance of the kernel oil content is necessary to implement a breeding program to improve both traits simultaneously. Conventional and molecular marker analyses of the design III were carried out from a reference population developed from two tropical inbred lines divergent for kernel oil content. The results showed that additive variance was quite larger than the dominance variance, and the heritability coefficient was very high. Sixteen QTL were mapped, they were not evenly distributed along the chromosomes, and accounted for 30.91% of the genetic variance. The average level of dominance computed from both conventional and QTL analysis was partial dominance. The overall results indicated that the additive effects were more important than the dominance effects, the latter were not unidirectional and then heterosis could not be exploited in crosses. Most of the favorable alleles of the QTL were in the high-oil parental inbred, which could be transferred to other inbreds via marker-assisted backcross selection. Our results coupled with reported information indicated that the development of high-oil hybrids with acceptable yields could be accomplished by using marker-assisted selection involving oil content, grain yield and its components. Finally, to exploit the xenia effect to increase even more the oil content, these hybrids should be used in the Top Cross((TM)) procedure.

Brain areas involved in medial temporal lobe seizures: a principal component analysis of ictal SPECT data

The study describes brain areas involved in medial temporal lobe (mTL) seizures of 12 patients. All patients showed so-called oro-alimentary behavior within the first 20 s of clinical seizure manifestation characteristic of mTL seizures. Single photon emission computed tomography (SPECT) images of regional cerebral blood flow (rCBF) were acquired from the patients in ictal and interictal phases and from normal volunteers. Image analysis employed categorical comparisons with statistical parametric mapping and principal component analysis (PCA) to assess functional connectivity. PCA supplemented the findings of the categorical analysis by decomposing the covariance matrix containing images of patients and healthy subjects into distinct component images of independent variance, including areas not identified by the categorical analysis. Two principal components (PCs) discriminated the subject groups: patients with right or left mTL seizures and normal volunteers, indicating distinct neuronal networks implicated by the seizure. Both PCs were correlated with seizure duration, one positively and the other negatively, confirming their physiological significance. The independence of the two PCs yielded a clear clustering of subject groups. The local pattern within the temporal lobe describes critical relay nodes which are the counterpart of oro-alimentary behavior: (1) right mesial temporal zone and ipsilateral anterior insula in right mTL seizures, and (2) temporal poles on both sides that are densely interconnected by the anterior commissure. Regions remote from the temporal lobe may be related to seizure propagation and include positively and negatively loaded areas. These patterns, the covarying areas of the temporal pole and occipito-basal visual association cortices, for example, are related to known anatomic paths.

On the Behaviour of Marginal and Conditional Akaike Information Criteria in Linear Mixed Models

In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion (AIC) have been used, based either on the marginal or on the conditional distribution. We show that the marginal AIC is no longer an asymptotically unbiased estimator of the Akaike information, and in fact favours smaller models without random effects. For the conditional AIC, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that leads to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional AIC, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in an R package is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.

A cladistic analysis of apolipoprotein B gene variation and plasma lipid and apolipoprotein B levels, using familial data

Any functionally important mutation is embedded in an evolutionary matrix of other mutations. Cladistic analysis, based on this, is a method of investigating gene effects using a haplotype phylogeny to define a set of tests which localize causal mutations to branches of the phylogeny. Previous implementations of cladistic analysis have not addressed the issue of analyzing data from related individuals, though in human studies, family data are usually needed to obtain unambiguous haplotypes. In this study, a method of cladistic analysis is described in which haplotype effects are parameterized in a linear model which accounts for familial correlations. The method was used to study the effect of apolipoprotein (Apo) B gene variation on total-, LDL-, and HDL-cholesterol, triglyceride, and Apo B levels in 121 French families. Five polymorphisms defined Apo B haplotypes: the signal peptide Insertion/deletion, Bsp 1286I, XbaI, MspI, and EcoRI. Eleven haplotypes were found, and a haplotype phylogeny was constructed and used to define a set of tests of haplotype effects on lipid and apo B levels.^ This new method of cladistic analysis, the parametric method, found significant effects for single haplotypes for all variables. For HDL-cholesterol, 3 clusters of evolutionarily-related haplotypes affecting levels were found. Haplotype effects accounted for about 10% of the genetic variance of triglyceride and HDL-cholesterol levels. The results of the parametric method were compared to those of a method of cladistic analysis based on permutational testing. The permutational method detected fewer haplotype effects, even when modified to account for correlations within families. Simulation studies exploring these differences found evidence of systematic errors in the permutational method due to the process by which haplotype groups were selected for testing.^ The applicability of cladistic analysis to human data was shown. The parametric method is suggested as an improvement over the permutational method. This study has identified candidate haplotypes for sequence comparisons in order to locate the functional mutations in the Apo B gene which may influence plasma lipid levels. ^

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

Computación evolutiva de bosques de expansión mínimos con restricción de grado y de rol

Encontrar el árbol de expansión mínimo con restricción de grado de un grafo (DCMST por sus siglas en inglés) es un problema NP-complejo ampliamente estudiado. Una de sus aplicaciones más importantes es el dise~no de redes. Aquí nosotros tratamos una nueva variante del problema DCMST, que consiste en encontrar el árbol de expansión mínimo no solo con restricciones de grado, sino también con restricciones de rol (DRCMST), es decir, a~nadimos restricciones para restringir el rol que los nodos tienen en el árbol. Estos roles pueden ser nodo raíz, nodo intermedio o nodo hoja. Por otra parte, no limitamos el número de nodos raíz a uno, por lo que, en general, construiremos bosques de DRCMSTs. El modelado en los problemas de dise~no de redes puede beneficiarse de la posibilidad de generar más de un árbol y determinar el rol de los nodos en la red. Proponemos una nueva representación basada en permutaciones para codificar los bosques de DRCMSTs. En esta nueva representación, una permutación codifica simultáneamente todos los árboles que se construirán. Nosotros simulamos una amplia variedad de problemas DRCMST que optimizamos utilizando ocho algoritmos de computación evolutiva diferentes que codifican los individuos de la población utilizando la representación propuesta. Los algoritmos que utilizamos son: algoritmo de estimación de distribuciones (EDA), algoritmo genético generacional (gGA), algoritmo genético de estado estacionario (ssGA), estrategia evolutiva basada en la matriz de covarianzas (CMAES), evolución diferencial (DE), estrategia evolutiva elitista (ElitistES), estrategia evolutiva no elitista (NonElitistES) y optimización por enjambre de partículas (PSO). Los mejores resultados fueron para el algoritmo de estimación de distribuciones utilizado y ambos tipos de algoritmos genéticos, aunque los algoritmos genéticos fueron significativamente más rápidos.---ABSTRACT---Finding the degree-constrained minimum spanning tree (DCMST) of a graph is a widely studied NP-hard problem. One of its most important applications is network design. Here we deal with a new variant of the DCMST problem, which consists of finding not only the degree- but also the role-constrained minimum spanning tree (DRCMST), i.e., we add constraints to restrict the role of the nodes in the tree to root, intermediate or leaf node. Furthermore, we do not limit the number of root nodes to one, thereby, generally, building a forest of DRCMSTs. The modeling of network design problems can benefit from the possibility of generating more than one tree and determining the role of the nodes in the network. We propose a novel permutation-based representation to encode the forest of DRCMSTs. In this new representation, one permutation simultaneously encodes all the trees to be built. We simulate a wide variety of DRCMST problems which we optimize using eight diferent evolutionary computation algorithms encoding individuals of the population using the proposed representation. The algorithms we use are: estimation of distribution algorithm (EDA), generational genetic algorithm (gGA), steady-state genetic algorithm (ssGA), covariance matrix adaptation evolution strategy (CMAES), diferential evolution (DE), elitist evolution strategy (ElististES), non-elitist evolution strategy (NonElististES) and particle swarm optimization (PSO). The best results are for the estimation of distribution algorithm and both types of genetic algorithms, although the genetic algorithms are significantly faster. iv

Divergent environments and population bottlenecks fail to generate premating isolation in Drosophila pseudoobscura

While the feasibility of bottleneck-induced speciation is in doubt, population bottlenecks may still affect the speciation process by interacting with divergent selection. To explore this possibility, I conducted a laboratory speciation experiment using Drosophila pseudoobscura involving 78 replicate populations assigned in a two-way factorial design to both bottleneck (present vs. absent) and environment (ancestral vs. novel) treatments. Populations independently evolved under these treatments and were then tested for assortative mating and male mating success against their common ancestor. Bottlenecks alone did not generate any premating isolation, despite an experimental design that was conducive to bottleneck-induced speciation. Premating isolation also did not evolve in the novel environment treatment, neither in the presence nor absence of bottlenecks. However, male mating success was significantly reduced in the novel environment treatment, both as a plastic response to this environment and as a result of environment-dependent inbreeding effects in the bottlenecked populations. Reduced mating success of derived males will hamper speciation by enhancing the mating success of immigrant, ancestral males. Novel environments are generally thought to promote ecological speciation by generating divergent natural selection. In the current experiment, however, the novel environment did not cause the evolution of any premating isolation and it reduced the likelihood of speciation through its effects on male mating success.

Genetic Simplex Modeling of Eysenck's Dimensions of Personality in a Sample of Young Australian Twins

The relative stability and magnitude of genetic and environmental effects underlying major dimensions of adolescent personality across time were investigated. The Junior Eysenck Personality Questionnaire was administered to over 540 twin pairs at ages 12, 14 and 16 years. Their personality scores were analyzed using genetic simplex modeling which explicitly took into account the longitudinal nature of the data. With the exception of the dimension lie, multivariate model fitting results revealed that familial aggregation was entirely explained by additive genetic effects. Results from simplex model fitting suggest that large proportions of the additive genetic variance observed at ages 14 and 16 years could be explained by genetic effects present at the age of 12 years. There was also evidence for smaller but significant genetic innovations at 14 and 16 years of age for male and female neuroticism, at 14 years for male extraversion, at 14 and 16 years for female psychoticism, and at 14 years for male psychoticism.