Estudo genético de populações de soja com fonte de resistência à Heterodera glycines (raça 3) oriundas de topocruzamentos tipo alimento x tipo grão

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Variabilidade genética e alterações morfológicas em progênies de polinização aberta de Eucalyptus grandis sob o efeito do paclobutrazol

The research objective was to study the genetic diversity of morphological traits in Eucalyptus grandis progenies growing under paclobutrazol regulator effects. The progeny trail was set up through design of randomized blocks. The morphological changes occurred before and during the plant flowering were analyzed. The estimation of genetic parameters were for plant height and stem diameter. The paclobutrazol have caused changes on plant development being strong by the beginning and becoming lightening through the evaluations. The coefficients of variation have shown there is higher genetic diversity within than among progenies for the studied traits. Therefore, it can have high efficiency on selection within progenies in the Eucalyptus grandis breeding program.

Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil

The objective of this paper is to model variations in test-day milk yields of first lactations of Holstein cows by RR using B-spline functions and Bayesian inference in order to fit adequate and parsimonious models for the estimation of genetic parameters. They used 152,145 test day milk yield records from 7317 first lactations of Holstein cows. The model established in this study was additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. Authors modeled the average lactation curve of the population with a fourth-order orthogonal Legendre polynomial. They concluded that a cubic B-spline with seven random regression coefficients for both the additive genetic and permanent environment effects was to be the best according to residual mean square and residual variance estimates. Moreover they urged a lower order model (quadratic B-spline with seven random regression coefficients for both random effects) could be adopted because it yielded practically the same genetic parameter estimates with parsimony. (C) 2012 Elsevier B.V. All rights reserved.

Incorporação de informações genômicas no desenvolvimento de índices econômicos para a seleção de bovinos leiteiros

A eficiência econômica da bovinocultura leiteira está relacionada à utilização de animais que apresentem, concomitantemente, bom desempenho quanto à produção, reprodução, saúde e longevidade. Nisto, o índice de seleção configura-se como ferramenta importante ao aumento da lucratividade nesse sistema, visto que permite a seleção de reprodutores para várias características simultaneamente, considerando a relação entre elas bem como a relevância econômica das mesmas. Com a recente disponibilidade de dados genômicos tornou-se ainda possível expandir a abrangência e acurácia dos índices de seleção por meio do aumento do número e qualidade das informações consideradas. Nesse contexto, dois estudos foram desenvolvidos. No primeiro, o objetivo foi estimar parâmetros genéticos e valores genéticos (VG) para características relacionadas à produção e qualidade do leite incluindo-se a informação genômica na avaliação genética. Foram utilizadas medidas de idade ao primeiro parto (IPP), produção de leite (PROD), teor de gordura (GOR), proteína (PROT), lactose, caseína, escore de células somáticas (ECS) e perfil de ácidos graxos de 4.218 vacas bem como os genótipos de 755 vacas para 57.368 polimorfismos de nucleotídeo único (SNP). Os componentes de variância e VG foram obtidos por meio de um modelo misto animal, incluindo-se os efeitos de grupos de contemporâneas, ordem de lactação, dias em lactação e os efeitos aditivo genético, ambiente permanente e residual. Duas abordagens foram desenvolvidas: uma tradicional, na qual a matriz de relacionamentos é baseada no pedigree; e uma genômica, na qual esta matriz é construída combinando-se a informação de pedigree e dos SNP. As herdabilidades variaram de 0,07 a 0,39. As correlações genéticas entre PROD e os componentes do leite variaram entre -0,45 e -0,13 enquanto correlações altas e positivas foram estimadas entre GOR e os ácidos graxos. O uso da abordagem genômica não alterou as estimativas de parâmetros genéticos; contudo, houve aumento entre 1,5% e 6,8% na acurácia dos VG, à exceção de IPP, para a qual houve uma redução de 1,9%. No segundo estudo, o objetivo foi incorporar a informação genômica no desenvolvimento de índices econômicos de seleção. Neste, os VG para PROD, GOR, PROT, teor de ácidos graxos insaturados (INSAT), ECS e vida produtiva foram combinados em índices de seleção ponderados por valores econômicos estimados sob três cenários de pagamento: exclusivamente por volume de leite (PAG1); por volume e por componentes do leite (PAG2); por volume e componentes do leite incluindo INSAT (PAG3). Esses VG foram preditos a partir de fenótipos de 4.293 vacas e genótipos de 755 animais em um modelo multi-característica sob as abordagens tradicional e genômica. O uso da informação genômica influenciou os componentes de variância, VG e a resposta à seleção. Entretanto, as correlações de ranking entre as abordagens foram altas nos três cenários, com valores entre 0,91 e 0,99. Diferenças foram principalmente observadas entre PAG1 e os demais cenários, com correlações entre 0,67 e 0,88. A importância relativa das características e o perfil dos melhores animais foram sensíveis ao cenário de remuneração considerado. Assim, verificou-se como essencial a consideração dos valores econômicos das características na avaliação genética e decisões de seleção.

Genetic algorithms for continuous optimization problems - a concept of parameter-space size adjustment

The concept of parameter-space size adjustment is pn,posed in order to enable successful application of genetic algorithms to continuous optimization problems. Performance of genetic algorithms with six different combinations of selection and reproduction mechanisms, with and without parameter-space size adjustment, were severely tested on eleven multiminima test functions. An algorithm with the best performance was employed for the determination of the model parameters of the optical constants of Pt, Ni and Cr.

Parameter Identification of a Fed-Batch Cultivation of S. Cerevisiae using Genetic Algorithms

Fermentation processes as objects of modelling and high-quality control are characterized with interdependence and time-varying of process variables that lead to non-linear models with a very complex structure. This is why the conventional optimization methods cannot lead to a satisfied solution. As an alternative, genetic algorithms, like the stochastic global optimization method, can be applied to overcome these limitations. The application of genetic algorithms is a precondition for robustness and reaching of a global minimum that makes them eligible and more workable for parameter identification of fermentation models. Different types of genetic algorithms, namely simple, modified and multi-population ones, have been applied and compared for estimation of nonlinear dynamic model parameters of fed-batch cultivation of S. cerevisiae.

On The Use of Genetic Algorithms for Parameter Identification in Anaerobic Digestion Modelling

Anaerobic digestion (AD) of wastewater is a very interesting option for waste valorization, energy production and environment protection. It is a complex, naturally occurring process that can take place inside bioreactors. The capability of predicting the operation of such bioreactors is important to optimize the design and the operation conditions of the reactors, which, in part, justifies the numerous AD models presently available. The existing AD models are not universal, have to be inferred from prior knowledge and rely on existing experimental data. Among the tasks involved in the process of developing a dynamical model for AD, the estimation of parameters is one of the most challenging. This paper presents the identifiability analysis of a nonlinear dynamical model for a batch reactor. Particular attention is given to the structural identifiability of the model, which considers the uniqueness of the estimated parameters. To perform this analysis, the GenSSI toolbox was used. The estimation of the model parameters is achieved with genetic algorithms (GA) which have already been used in the context of AD modelling, although not commonly. The paper discusses its advantages and disadvantages.

Genetic Algorithms for Parameter Estimation in Circadian Model

This paper proposes an algorithm to estimate two parameter values vs, transcription of frq gene, and vd, maximum rate of FRQ protein degradation for an existing 3rd order Neurospora model in literature. Details of the algorithm with simulation results are shown in this paper.

Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population ( linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging. Results: We present a more practical method to build GM that describe LD. The method is based on learning weighted Bayesian network structures from haplotype data, extracting equivalence structure classes and using them to model LD. The results obtained in public data from the HapMap database showed that the method is a promising tool for modeling LD. The associations represented by the learned models are correlated with the traditional measure of LD D`. The method was able to represent LD blocks found by standard tools. The granularity of the association blocks and the readability of the models can be controlled in the method. The results suggest that the causality information gained by our method can be useful to tell about the conservability of the genetic markers and to guide the selection of subset of representative markers.

Simulated-annealing-based genetic algorithm for modeling the optical constants of solids

We propose a simulated-annealing-based genetic algorithm for solving model parameter estimation problems. The algorithm incorporates advantages of both genetic algorithms and simulated annealing. Tests on computer-generated synthetic data that closely resemble optical constants of a metal were performed to compare the efficiency of plain genetic algorithms against the simulated-annealing-based genetic algorithms. These tests assess the ability of the algorithms to and the global minimum and the accuracy of values obtained for model parameters. Finally, the algorithm with the best performance is used to fit the model dielectric function to data for platinum and aluminum. (C) 1997 Optical Society of America.

Identification of genetic alterations by multiple ligation-dependent probe amplification (MLPA) analysis in oligodendrogliomas

Concurrent deletion at 1p/19q is a common signature of oligodendrogliomas, and it may, be identified in low-grade tumours (grade II) suggesting it represents an early event in the development of these brain neoplasms. Additional non-random changes primarily involve CDKN2A, PTEN and EGFR. Identification of all of these genetic changes has become an additional parameter in the evaluation of the clinical patients` prognosis, including good response to conventional chemotherapy. Multiple ligation-dependent probe amplification (MLPA) analysis is a new methodology that allows an easy identification of the oligodendrogliomas` abnormalities in a single step. No need of the respective constitutional DNA from each patient is another advantage of this method. We used MLPA kits P088 and P105 to determine the molecular characteristics of a series of 40 oligodendrogliomas. Deletions at I p and 19q were identified in 45% and 65% of cases, respectively. Alterations of EGFR, CDKN2A, ERBB2, PTEN and TP53 were also identified in variable frequencies among 7% to 35% of tumours. These findings demonstrate that MLPA is a reliable technique to the detection of molecular genetic changes in oligodendrogliomas.

Robust parameter settings of evolutionary algorithms for the optimisation of agricultural systems models

Numerical optimisation methods are being more commonly applied to agricultural systems models, to identify the most profitable management strategies. The available optimisation algorithms are reviewed and compared, with literature and our studies identifying evolutionary algorithms (including genetic algorithms) as superior in this regard to simulated annealing, tabu search, hill-climbing, and direct-search methods. Results of a complex beef property optimisation, using a real-value genetic algorithm, are presented. The relative contributions of the range of operational options and parameters of this method are discussed, and general recommendations listed to assist practitioners applying evolutionary algorithms to the solution of agricultural systems. (C) 2001 Elsevier Science Ltd. All rights reserved.

Phenotypic and genetic analyses of a short measure of psychosis-proneness in a large-scale Australian twin study

Previous genetic analyses of psychosis proneness have been limited by their small sample size. For the purposes of large-scale screening, a 12-item questionnaire was developed through a two-stage process of reduction from the full Chapman and Chapman scales. 3685 individuals (including 1438 complete twin pairs) aged 18–25 years and enrolled in the volunteer Australian Twin Registry returned a mail questionnaire which included this psychosis proneness scale and the Eysenck Personality Questionnaire. Despite the brevity of the questionnaire, item and factor analysis identified four unambiguous and essentially uncorrelated scales. There were (1) Perceptual Aberration – Magical Ideation; (2) Hypomania – Impulsivity/Nonconformity; (3) Social Anhedonia and (4) Physical Anhedonia. Model-fitting analyses showed additive genetic and specific environmental factors were sufficient for three of the four scales, with the Social Anhedonia scale requiring also a parameter for genetic dominance. There was no evidence for the previously hypothesised sex differences in the genetic determination of psychosis-proneness. The potential value of multivariate genetic analysis to examine the relationship between these four scales and dimensions of personality is discussed. The growing body of longitudinal evidence on psychosis-proneness suggests the value of incorporating this brief measure into developmental twin studies.

Estimating Two-Stage Models for Genetic Influences on Alcohol, Tobacco or Drug Use Initiation and Dependence Vulnerability in Twin and Family Data

Genetic research on risk of alcohol, tobacco or drug dependence must make allowance for the partial overlap of risk-factors for initiation of use, and risk-factors for dependence or other outcomes in users. Except in the extreme cases where genetic and environmental risk-factors for initiation and dependence overlap completely or are uncorrelated, there is no consensus about how best to estimate the magnitude of genetic or environmental correlations between Initiation and Dependence in twin and family data. We explore by computer simulation the biases to estimates of genetic and environmental parameters caused by model misspecification when Initiation can only be defined as a binary variable. For plausible simulated parameter values, the two-stage genetic models that we consider yield estimates of genetic and environmental variances for Dependence that, although biased, are not very discrepant from the true values. However, estimates of genetic (or environmental) correlations between Initiation and Dependence may be seriously biased, and may differ markedly under different two-stage models. Such estimates may have little credibility unless external data favor selection of one particular model. These problems can be avoided if Initiation can be assessed as a multiple-category variable (e.g. never versus early-onset versus later onset user), with at least two categories measurable in users at risk for dependence. Under these conditions, under certain distributional assumptions., recovery of simulated genetic and environmental correlations becomes possible, Illustrative application of the model to Australian twin data on smoking confirmed substantial heritability of smoking persistence (42%) with minimal overlap with genetic influences on initiation.