02091naa a2200325 a 450000100080000000500110000800800410001902200140006002400370007410000180011124500900012926000090021952011710022865000100139965000200140965000300142965000120145965000230147165000230149465300210151765300240153870000190156270000190158170000260160070000230162670000200164970000270166970000190169677300500171520958352018-09-17       2018    bl uuuu     u00u1 u    #d  a1678-45967 a10.1590/0103-8478cr201704972DOI1 aBARILI, L. D.  aGenome prediction accuracy of common bean via Bayesian models.h[electronic resource]  c2018  aWe aimed to apply genomic information based on SNP (single nucleotide polymorphism) markers for the genetic evaluation of the traits ?stay-green? (SG), plant architecture (PA), grain aspect (GA) and grain yield (GY) in common bean through Bayesian models. These models were compared in terms of prediction accuracy and ability for heritability estimation for each one of the mentioned traits. A total of 80 cultivars were genotyped for 377 SNP markers, whose effects were estimated by five different Bayesian models: Bayes A (BA), B (BB), C (BC), LASSO (BL) e Ridge regression (BRR). Although, prediction accuracies calculated by means of cross-validation have been similar within each trait, the BB model stood out for the trait SG, whereas the BRR was indicated for the remaining traits. The heritability estimates for the traits SG, PA, GA and GY were 0.61, 0.28, 0.32 and 0.29, respectively. In summary, the Bayesian methods applied here were effective and ease to be implemented. The used SNP markers can help in the early selection of promising genotypes, since incorporating genomic information increase the prediction accuracy of the estimated genetic merit.  aBeans  aGenetic markers  aMarker-assisted selection  aFeijão  aMarcador Molecular  aPhaseolus Vulgaris  aCross-validation  aValidação cruzada1 aVALE, N. M. do1 aSILVA, F. R. e1 aCARNEIRO, J. E. de S.1 aOLIVEIRA, H. R. de1 aVIANELLO, R. P.1 aVALDISSER, P. A. M. R.1 aNASCIMENTO, M.  tCiência Ruralgv. 48, n. 8, e20170497, 2018.