Uso do melhor preditor linear não viesado (BLUP) em análises dialélicas e predição de híbridos

Iemma, Mariana

doi:10.11606/d.11.2003.tde-13052003-165635

Cited by 3 publications

(5 citation statements)

References 35 publications

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“…Consequently, it can be inferred that for grain yield, classification of the hybrids undergoes little change when mixed model and fixed model methodologies were considered. Similar results were obtained by Iemma (2003).…”

Section: Resultssupporting

confidence: 90%

“…A similar result was found by Iemma (2003), comparing methodology of mixed models with fixed model in diallel analysis, obtaining correlation of 0.99 between BLUP and BLUE, attributing this to fact that BLUP considers matrix identity in structure of variances and covariances, and thus there are not big differences in methodology. Consequently, it can be inferred that for grain yield, classification of the hybrids undergoes little change when mixed model and fixed model methodologies were considered.…”

Section: Resultssupporting

confidence: 80%

“…The use of adequate methodologies for identification of pairs of lines that can provide better hybrid combinations improves the efficiency of breeding programs (Iemma, 2003). In this regard, one strategy is evaluation of the combining ability between parents.…”

Section: Introductionmentioning

confidence: 99%

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Selection of S0:2 maize progenies using a mixed-model approach

Cancellier¹,

Pires²,

Júnior³

et al. 2016

Afr. J. Agric. Res.

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In breeding programs, obtaining breeding lines was important, and first selfing selection generations is common. In addition, an analytic approach through mixed models can lead to more success in genotype selection because it lends flexibility in analysis of unbalanced data and provides more precise genotypic values in regard to progenies evaluated. The goal of this study was to make early selection (in the S 2 generation) of progenies evaluated in top crosses, using a mixed-model approach. Five hundred S 2 progenies were plant derived from three populations using selection intensity (40%), which were crossbreeding with three testers. The hybrids obtained, together with control treatments, were set up in five experiments in Brazil: three in Minas Gerais, one in Santa Catarina, and one in Paraná, which evaluated grain yield. The REML method was used for calculation of variance components, and means were predicted through BLUP. The BLUPs of general combining ability (GCA) and specific combining ability (SCA) were also predicted, and the Spearman correlation coefficients among BLUPs were estimated. The dominance effects had a bigger influence on yield expression, as seen from wider amplitude in SCA values. There was an 86% coincidence considering strategy in which selection index was carried out within three populations, in relation to selection by the overall value of GCA. Considering superior hybrids, the progenies of population C exceeded the quantity of hybrids expected by 24.6%, whereas there was a reduction of 30.8% for A and 20% for B. The hybrids that exhibited the highest BLUP means were derived from crosses between progenies of population C together with the tester LE84. The low correlation among testers, both for SCA and for BLUPs, indicates that there is progeny per tester interaction.

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Section: Resultssupporting

confidence: 90%

Section: Resultssupporting

confidence: 80%

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Selection of S0:2 maize progenies using a mixed-model approach

Cancellier¹,

Pires²,

Júnior³

et al. 2016

Afr. J. Agric. Res.

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“…The SCA correlation values showed a moderate magnitude (0.617 to 0.704), yet were higher than those reported by Bernardo (1994Bernardo ( , 1995, André (1999) and Iemma (2003). Bernardo (1994Bernardo ( , 1995 attributed the low correlation between untested SCA predictions with observed SCA (r ≤ 0.15) to bias in the SCA variance.…”

Section: Discussionmentioning

confidence: 51%

Prediction of maize single-cross performance by mixed linear models with microsatellite marker information

Balestre

Pinho²,

Souza³

2010

Genet. Mol. Res.

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ABSTRACT. We evaluated the potential of the best linear unbiased predictor (BLUP) along with the relationship coefficient for predicting the performance of untested maize single-cross hybrids. Ninety S 0:2 progenies arising from three single-cross hybrids were used. The 90 progenies were genotyped with 25 microsatellite markers, with nine markers linked to quantitative trait loci for grain yield. Based on genetic similarities, 17 partial inbred lines were selected and crossed in a partial diallel design. Similarity and relationship coefficients were used to construct the additive and dominance genetic matrices; along with BLUP, they provided predictions for untested single-crosses. Five degrees of imbalance were simulated (5, 10, 20, 30, and 40 hybrids).The correlation values between the predicted genotypic values and the observed phenotypic means varied from 0.55 to 0.70, depending on the degree of imbalance. A similar result was observed for the specific combining ability predictions; they varied from 0.61 to 0.70. It was also found that the relationship coefficient based on BLUP provided more accurate predictions than similarity-in-state predictions. We conclude Prediction of maize single-cross hybrids that BLUP methodology is a viable alternative for the prediction of untested crosses in early progenies.

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“…Por esse motivo, alguns autores preferem denominar os BLUPs obtidos dessa forma como EBLUPs, em que E significa "empirical", indicando que os valores paramétricos das variáveis são substituídos por suas estimativas (IEMMA, 2003). Para tanto foram usadas estimativas dessas variâncias pelo método da máxima verossimilhança restrita (REML), usando um processo iterativo baseado no algoritmo EM (Expectation-Maximization) (DEMPSTER; N.M.; D.B., 1977), ou de maximização da esperança (HENDERSON, 1984).…”

Section: Predição Do Efeitos Aleatóriosunclassified

Análise de experimentos em látice quadrado no melhoramento vegetal utilizando modelos mistos

Paulenas¹

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A Deus pelo dom da vida, por me iluminar e pela oportunidade de concluir mais esta nova etapa.Aos meus pais, Bruno e Eliana, às minhas irmãs Mariana e Paula, à minha querida avó Carmen Aparecida (Zita), aos meus padrinhos Angela e Sidney e a toda a minha família, pelo incentivo, pelos abraços calorosos, pelo amor e pela compreensão nos momentos de ausência. Ao meu avô Paulo (in memorian), que infelizmente não pode acompanhar esta conquista até o fim, mas que ficaria muito feliz em vê-la concluída. À Escola Superior de Agricultura "Luiz de Queiroz", seus professores e funcionários, pela estrutura e oportunidade oferecidas e por tantos anos de acolhimento.À querida Professora Sônia Maria De Stefano Piedade pela orientação, confiança, pela amizade e por sempre irradiar seu amor pela nossa Gloriosa ESALQ por onde quer que passe.À querida Professora Renata Alcarde Sermarini pelas sugestões, pelos esclarecimentos prestados e principalmente pela atenção.Aos Professores do Programa de Pós-Graduação em Agronomia -Estatística e Experimentação Agronômica, por compartilharem seus conhecimentos.Ao Prof. Cláudio Lopes de Souza Júnior do Departamento de Genética pelas sugestões e pela concessão dos dados experimentais sem os quais eu não teria a oportunidade da aplicação. À doutoranda Melina Andrade pela intermediação.Às secretárias Solange Sabadin e Luciane Brajão por todas as informações e esclarecimentos oferecidos, além de todo apoio e atenção.Aos funcionários do Departamento de Ciências Exatas, Mayara, Eduardo, Jorge e Rosni pelo suporte e à Tina, por manter a ordem e a limpeza dos ambientes de estudo.Aos colegas de turma César Jotta pelo papo descontraído, ao Christopher Silva de Pádua por me ajudar a desvendar os segredos do L A T E X, R em geral, pela amizade e companhia nas gordices, à Gislaine Pereira pelas conversas e incentivo, à Luiza Barros pelo incentivo na fase final da dissertação, à Maíra Fatoretto pela amizade e por me ajudar a entender vários conceitos estatísticos e por último, mas não menos importante, meu amigo Vinícius Menarin (Polacão) pelas valiosas ajudas em R, L A T E X, Excel e pela amizade.À querida Alessandra dos Santos pela sugestão do tema do trabalho, pela amizade e competência. Mesmo estando na Austrália, não deixou de me auxiliar na realização deste trabalho. À Natália Martins que me deu todo o apoio para que eu regressasse aos estudos, à Natalie Rondinel pelo material e valiosa ajuda. 6Ao amigo de longa data Victor Augusto Forti por me mostrar os encantos da Estatística na Agronomia.Aos funcionários da Divisão de Biblioteca e Documentação da ESALQ pela estrutura e por todo o apoio nas buscas bibliográficas em especial à Thais Cristiane Campos de Moraes pelos sorrisos amistosos e pela constante disposição em me ajudar.Aos amigos do G9, André Colaço, Camila Ortiz, Diego Chiou, Erik Barreto Jr., Heitor Arakawa, Larissa Pagliuca, Leonardo Bordin e Mônica Georgino pelos churrascos, momentos de descontração e incentivo constante.Às meninas do F5, Alessandra e Érica da Paz, Marisol Bandeira, Mônic...

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Uso do melhor preditor linear não viesado (BLUP) em análises dialélicas e predição de híbridos

Cited by 3 publications

References 35 publications

Selection of S0:2 maize progenies using a mixed-model approach

Selection of S0:2 maize progenies using a mixed-model approach

Prediction of maize single-cross performance by mixed linear models with microsatellite marker information

Análise de experimentos em látice quadrado no melhoramento vegetal utilizando modelos mistos

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