02189naa a2200325 a 450000100080000000500110000800800410001902400350006010000170009524501050011226000090021752012350022665000150146165000240147665000180150065000140151865000350153265000240156765300090159165300120160065300280161270000290164070000190166970000250168870000210171370000170173470000230175170000190177477300700179321000942018-11-27       2018    bl uuuu     u00u1 u    #d7 a10.1007/s11295-018-1292-72DOI1 aALVES, R. S.  aMultiple-trait BLUPba suitable strategy for genetic selection of Eucalyptus.h[electronic resource]  c2018  aUsually, genetic selection is carried out based on several traits, which can be genetically correlated. In this case, selection bias may occur if these traits are analyzed individually. Thus, the present work aimed to evaluate the applicability and efficiency of multiple-trait best linear unbiased prediction (BLUP) in the genetic selection of Eucalyptus. The data used in this work refer to the evaluation of a partial diallel of Eucalyptus spp. in relation to height, diameter at breast height (DBH), and volume. Variance components and genetic and non-genetic parameters were estimated via residual maximum likelihood (REML). Multiple-trait BLUP led to estimates of mean additive genetic variance higher than the estimates obtained via single-trait BLUP and, consequently, led to higher estimates of narrow-sense individual interpopulational heritabilities and mean accuracies. Partial genetic correlations obtained via multiple-trait BLUP allowed a real understanding of the association between traits, differently from those obtained via single-trait BLUP. Multiple-trait BLUP led to higher gains predicted with the selection for height, DBH, and volume and can be efficiently applied in the genetic selection of Eucalyptus.  aEucalyptus  aGenetic correlation  aTree breeding  aEucalipto  aMelhoramento Genético Vegetal  aSeleção Genética  aBLUP  aDiallel  aMixed model methodology1 aROCHA, J. R. do A. de C.1 aTEODORO, P. E.1 aRESENDE, M. D. V. de1 aHENRIQUES, E. P.1 aSILVA, L. A.1 aCARNEIRO, P. C. S.1 aBHERING, L. L.  tTree Genetics & Genomesgv. 14, n. 5, article 77, Oct. 2018. 8 p.