Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/38016
metadata.artigo.dc.title: Prediction of maize single-cross performance by mixed linear models with microsatellite marker information
metadata.artigo.dc.creator: Balestre, M.
Von Pinho, R. G.
Souza, J. C.
metadata.artigo.dc.subject: Molecular markers
Best linear unbiased predictor
Maize - Genetic improvement
Marcadores moleculares
Melhor preditor imparcial linear
Milho - Melhoramento genético
metadata.artigo.dc.publisher: Fundação de Pesquisas Científicas de Ribeirão Preto
metadata.artigo.dc.date.issued: 2010
metadata.artigo.dc.identifier.citation: BALESTRE, M.; VON PINHO, R. G.; SOUZA, J. C. Prediction of maize single-cross performance by mixed linear models with microsatellite marker information. Genetics and Molecular Research, [S. l.], v. 9, n. 2, p. 1054-1068, 2010.
metadata.artigo.dc.description.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 S0:2 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 that BLUP methodology is a viable alternative for the prediction of untested crosses in early progenies.
metadata.artigo.dc.identifier.uri: https://www.geneticsmr.com/articles/889
http://repositorio.ufla.br/jspui/handle/1/38016
metadata.artigo.dc.language: en_US
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