Título:	Best linear unbiased prediction for genetic evaluation in reciprocal recurrent selection with popcorn populations.
Autoria:	VIANA, J. M. S. MUNDIM, G. B. DELIMA, R. O SILVA, F. F. E. RESENDE, M. D. V. de
Afiliação:	J M S VIANA, UFV; G. B. MUNDIM, UFV; R. O. DELIMA, UFV; F. F. E. SILVA, UFV; MARCOS DEON VILELA DE RESENDE, CNPF.
Ano de publicação:	2014
Referência:	Journal of Agricultural Science, v. 152, p. 428-438, 2014.
Conteúdo:	The objective of the present study was to present the theory and application of best linear unbiased prediction (BLUP) in reciprocal recurrent selection (RRS). Seven progeny tests from two RRS programmes with popcorn (Zea mays L. ssp. mays [syn. Zea mays L. ssp. everta (Sturtev.) Zhuk.]) populations were conducted and analysed for expansion volume and grain yield. The interpopulation half- and full-sib family models were fitted using ASReml software. Half-sib selection is equivalent to selection for the general combining ability (GCA) of the common parents. With inbred full-sib progeny and BLUP analysis, it is possible to predict the general and specific combining ability effects. The standard error of prediction of the progeny effect was lower than the standard deviation of the best linear unbiased estimation (BLUE) estimate. For half- and full-sib RRS, the BLUE and BLUP provided highly correlated estimates of progeny genotypic values. The coincidence between selected parents ranged from 64 to 95%. With inbred full-sib progeny, the correlations between the BLUE of progeny genotypic values and the BLUP of GCA effects were lower. Consequently, the coincidence between selected parents was lower, ranging from 0 to 57%. The percentage of common selected inbred progeny based on the BLUE and BLUP of the progeny genotypic value ranged from 57 to 100%.
Thesagro:	Milho Seleção Recorrente
Palavras-chave:	Pipoca Zea maya Espécie agrícola Melhoramento genético
Digital Object Identifier:	10.1017/S0021859613000270
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPF)