73 resultados para Blup


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The present paper deals with estimation of variance components, prediction of breeding values and selection in a population of rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Müell.-Arg.] from Rio Branco, State of Acre, Brazil. The REML/BLUP (restricted maximum likelihood/best linear unbiased prediction) procedure was applied. For this purpose, 37 rubber tree families were obtained and assessed in a randomized complete block design, with three unbalanced replications. The field trial was carried out at the Experimental Station of UNESP, located in Selvíria, State of Mato Grosso do Sul, Brazil. The quantitative traits evaluated were: girth (G), bark thickness (BT), number of latex vessel rings (NR), and plant height (PH). Given the unbalanced condition of the progeny test, the REML/BLUP procedure was used for estimation. The narrow-sense individual heritability estimates were 0.43 for G, 0.18 for BT, 0.01 for NR, and 0.51 for PH. Two selection strategies were adopted: one short-term (ST - selection intensity of 8.85%) and the other long-term (LT - selection intensity of 26.56%). For G, the estimated genetic gains in relation to the population average were 26.80% and 17.94%, respectively, according to the ST and LT strategies. The effective population sizes were 22.35 and 46.03, respectively. The LT and ST strategies maintained 45.80% and 28.24%, respectively, of the original genetic diversity represented in the progeny test. So, it can be inferred that this population has potential for both breeding and ex situ genetic conservation as a supplier of genetic material for advanced rubber tree breeding programs. Copyright by the Brazilian Society of Genetics.

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The objective of this study was to assess families and highlight the superior progenies of sugarcane originating from 38 biparental crosses for the following attributes: tons of cane per hectare (TCH), tons of biomass per hectare (TBIOH), brix (% cane juice), fiber content, purity, pol and total recoverable sugar (TRS). The data were analyzed by mixed model REML / BLUP in the REML (Restricted Maximum Likelihood) allowed us to estimate genetic parameters and BLUP (best linear unbiased prediction) to predict the additive and genotypic values. The best family for the attributes TCH and TBIOH was 41, whose parents are cultivars IACSP022019 x CTC9. In individual selection for TCH, the plant number 3 of Block 2, the crossing 78, showed the best results. To TBIOH the plant number 33, Block 1, family 41, showed the best results. Families 40, 41, 43, 68, 69, 79, 91, 92 and 147, were higher for the variables brix, pol, purity, and ATR, where as 85 families, 147, 148, 149, 161, 163, 177, 178, 179, and 183 were higher for fiber. The family 147 whose parents are IACSP042286 x IACSP963055, showed three progenies ranked among the top ten for both brix, and for fiber, which identifies the combination as a potential source of progenies for bioenergy production.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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The aim of this study was to estimate genetic parameters to support the selection of bacuri progenies for a first cycle of recurrent selection, using the REML/BLUP (restricted maximum likelihood/best linear unbiased prediction) procedure to estimate the variance components and genotypic values. Were evaluated twelve variables in a total of 210 fruits from 39 different seed trees, from a field trial with an experimental design of incomplete blocks with clonal replies among subplots. The three variables related with the fruit development (weight, diameter, length) showed strong correlation, and where fruit length showed higher heritability and potential to be used for indirect selection. Among the 39 progenies evaluated in this study, five present potential to compose the next cycle of recurrent selection, due they hold good selection differential either to agrotechnological variables as to development of bacuri fruit.

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In this paper we present livestock breeding developments that could be taken into consideration in the genetic improvement of farmed aquaculture species, especially in freshwater fish. Firstly, the current breeding objective in aquatic species has focused almost exclusively on the improvement of body weight at harvest or on growth related traits. This is unlikely to be sufficient to meet the future needs of the aquaculture industry. To meet future demands breeding programs will most likely have to include additional traits, such as fitness related ones (survival, disease resistance), feed efficiency, or flesh quality, rather than only growth performance. In order to select for a multi-trait breeding objective, genetic variation in traits of interest and the genetic relationships among them need to be estimated. In addition, economic values for these traits will be required. Generally, there is a paucity of data on variable and fixed production costs in aquaculture, and this could be a major constraint in the further expansion of the breeding objectives. Secondly, genetic evaluation systems using the restricted maximum likelihood method (REML) and best linear unbiased prediction (BLUP) in a framework of mixed model methodology could be widely adopted to replace the more commonly used method of mass selection based on phenotypic performance. The BLUP method increases the accuracy of selection and also allows the management of inbreeding and estimation of genetic trends. BLUP is an improvement over the classic selection index approach, which was used in the success story of the genetically improved farmed tilapia (GIFT) in the Philippines, with genetic gains from 10 to 20 per cent per generation of selection. In parallel with BLUP, optimal genetic contribution theory can be applied to maximize genetic gain while constraining inbreeding in the long run in selection programs. Thirdly, by using advanced statistical methods, genetic selection can be carried out not only at the nucleus level but also in lower tiers of the pyramid breeding structure. Large scale across population genetic evaluation through genetic connectedness using cryopreserved sperm enables the comparison and ranking of genetic merit of all animals across populations, countries or years, and thus the genetically superior brood stock can be identified and widely used and exchanged to increase the rate of genetic progress in the population as a whole. It is concluded that sound genetic programs need to be established for aquaculture species. In addition to being very effective, fully pedigreed breeding programs would also enable the exploration of possibilities of integrating molecular markers (e.g., genetic tagging using DNA fingerprinting, marker (gene) assisted selection) and reproductive technologies such as in-vitro fertilization using cryopreserved spermatozoa.

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Métodos para GWS; Teoria dos métodos de regressão; Computação do método Random (Ridge) Regression BLUP (RR-BLUP/GWS); Fenótipos corrigidos; Frequências alélicas, variância dos marcadores e herdabilidade; Marcadores codominantes (SNP) ? Modelo genotípico; Marcadores dominantes (DArT) - Modelo genotípico; Marcadores codominantes (SNP) ? Modelo gamético ou alélico; Número de marcadores com efeitos significativos; Populações de estimação, validação e seleção; População de validação e Jacknife; Correlação e regressão entre valores genéticos preditos e fenótipos na população de validação; Análise de associação na GWAS; Software Selegen Genômica: Random (Ridge) Regression BLUP: RR-BLUP/GWS; Exemplo aplicado ao melhoramento do eucalipto.

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O Ensaio Elite Sul vem sendo conduzido desde a década de 90 em vários locais representativos da região subtropical. O objetivo desse trabalho foi avaliar o desempenho, a adaptabilidade e a estabilidade de híbridos de milho avaliados no ensaio Elite Sul pela metodologia de modelos mistos. Os ensaios foram em 15 ambientes com número variável de tratamentos. Todos em delineamento látice, com duas linhas de cinco metros, espaçamento entre linhas de 0,80m e duas repetições. Para as análises de adaptabilidade e estabilidade, foram utilizados os dados de rendimento de grão, corrigidos para 13% de umidade, e aplicada a metodologia de Modelos Lineares Mistos seguindo o modelo 52 do software SELEGEN-REML/BLUP. Considerando-se os valores genotípicos e capitalizando-se a interação média entre genótipos e ambiente (u+g+ge) os híbridos Maximus, 1I1002, 1I998, 2F633 5 e 1G748 5 foram os melhores. Houve completa concordância na seleção dos cinco híbridos de melhor desempenho, entre os valores genotípicos e MHPRVG (medida concomitante de produtividade, adaptabilidade e estabilidade).

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Mixed models may be defined with or without reference to sampling, and can be used to predict realized random effects, as when estimating the latent values of study subjects measured with response error. When the model is specified without reference to sampling, a simple mixed model includes two random variables, one stemming from an exchangeable distribution of latent values of study subjects and the other, from the study subjects` response error distributions. Positive probabilities are assigned to both potentially realizable responses and artificial responses that are not potentially realizable, resulting in artificial latent values. In contrast, finite population mixed models represent the two-stage process of sampling subjects and measuring their responses, where positive probabilities are only assigned to potentially realizable responses. A comparison of the estimators over the same potentially realizable responses indicates that the optimal linear mixed model estimator (the usual best linear unbiased predictor, BLUP) is often (but not always) more accurate than the comparable finite population mixed model estimator (the FPMM BLUP). We examine a simple example and provide the basis for a broader discussion of the role of conditioning, sampling, and model assumptions in developing inference.

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We consider a generalized leverage matrix useful for the identification of influential units and observations in linear mixed models and show how a decomposition of this matrix may be employed to identify high leverage points for both the marginal fitted values and the random effect component of the conditional fitted values. We illustrate the different uses of the two components of the decomposition with a simulated example as well as with a real data set.

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Prediction of random effects is an important problem with expanding applications. In the simplest context, the problem corresponds to prediction of the latent value (the mean) of a realized cluster selected via two-stage sampling. Recently, Stanek and Singer [Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 119-130] developed best linear unbiased predictors (BLUP) under a finite population mixed model that outperform BLUPs from mixed models and superpopulation models. Their setup, however, does not allow for unequally sized clusters. To overcome this drawback, we consider an expanded finite population mixed model based on a larger set of random variables that span a higher dimensional space than those typically applied to such problems. We show that BLUPs for linear combinations of the realized cluster means derived under such a model have considerably smaller mean squared error (MSE) than those obtained from mixed models, superpopulation models, and finite population mixed models. We motivate our general approach by an example developed for two-stage cluster sampling and show that it faithfully captures the stochastic aspects of sampling in the problem. We also consider simulation studies to illustrate the increased accuracy of the BLUP obtained under the expanded finite population mixed model. (C) 2007 Elsevier B.V. All rights reserved.

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O objetivo deste trabalho foi estimar os ganhos genéticos de um teste de progênies de seringueira para a produção de borracha seca e, com base no maior tamanho efetivo populacional e maior ganho genético, obter os melhores indivíduos. Foram utilizadas 30 progênies de meios-irmãos, provenientes de sementes de polinização mista - alogamia e autogamia - de testes clonais no Estado de São Paulo. Utilizou-se o delineamento experimental de blocos ao acaso, com 30 tratamentos (progênies), 3 repetições e parcelas lineares de 10 plantas, em um espaçamento de 3x3 m, o que totalizou 900 plantas úteis. Aos três anos, o perímetro, a 50 cm do solo (PA50), e a produção de borracha seca (PBS) foram avaliadas por meio do teste precoce de produção Hamaker Morris-Mann (HMM). As variáveis foram analisadas pelo método de modelo linear misto, via procedimento REML/BLUP, em progênies com sistema reprodutivo misto e taxa de autofecundação de 22%. A identificação dos 20 melhores indivíduos quanto à PBS e ao PA50 proporcionou ganho genético de 67,96 e 16,48%, respectivamente, e um coeficiente de endogamia de aproximadamente 2,82%. O teste de progênies proporciona produção de sementes com melhor valor genético, grande variabilidade e baixa endogamia

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A caprinocultura leiteira no Brasil, apesar de ser uma atividade rural consolidada há algumas décadas, tem se mostrado totalmente dependente de outros países no que se refere ao melhoramento genético. A maioria dos plantéis existentes atualmente tem como base animais importados, e a renovação do material genético é feita por meio da importação de sêmen. Inexistem informações sobre o valor genético dos animais e sua evolução no decorrer dos anos. No presente trabalho, foram estimadas a herdabilidade e a repetibilidade da produção de leite utilizando o REML. Os valores obtidos foram 0,21557 e 0,21564, respectivamente. Para a predição do valor gênico dos animais, foi usado o procedimento BLUP com modelo animal. A mudança na tendência genética anual estimada por um modelo quadrático foi -0,8109 kg/ano², indicando desaceleração no ganho genético. A correlação de Pearson entre os valores gênicos dos bodes estimados com base na média da capacidade provável de produção das filhas obtida pelo método de mínimos quadrados com as estimadas pelas equações do modelo misto foi de 0,5751. A correlação de SPEARMAN entre as classificações dos bodes obtidos pelos dois métodos foi de 0,5813.