02143naa a2200253 a 450000100080000000500110000800800410001902400350006010000200009524501420011526000090025752013710026665000100163765000250164765300230167265300270169565300110172265300130173370000180174670000170176470000200178170000250180177300630182620111312017-07-24       2014    bl uuuu     u00u1 u    #d7 a10.1017/S00218596130002702DOI1 aVIANA, J. M. S.  aBest linear unbiased prediction for genetic evaluation in reciprocal recurrent selection with popcorn populations.h[electronic resource]  c2014  aThe 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%.  aMilho  aSeleção Recorrente  aEspécie agrícola  aMelhoramento genético  aPipoca  aZea maya1 aMUNDIM, G. B.1 aDELIMA, R. O1 aSILVA, F. F. E.1 aRESENDE, M. D. V. de  tJournal of Agricultural Sciencegv. 152, p. 428-438, 2014.