02428naa a2200289 a 450000100080000000500110000800800410001902200140006002400440007410000250011824501520014326000090029552015110030465000100181565000370182565000220186265000130188465000120189765000350190965000230194465000240196770000220199170000270201370000170204070000200205777300610207721553032023-08-02       2023    bl uuuu     u00u1 u    #d  a0011-183X7 ahttps://doi.org/10.1002/csc2.210002DOI1 aMORAIS JUNIOR, O. P.  aGenomic prediction for drought tolerance using multienvironment data in a common bean (Phaseolus vulgaris) breeding program.h[electronic resource]  c2023  aThis work evaluated the efficiency of different genomic prediction (GP) methods in a diverse Mesoamerican panel of 339 common bean accessions, genotyped with 3398 SNP markers. Field experiments were carried out for three consecutive years, with adequate water supply (non-stress?NS) and water restriction imposition (water-stress?WS), analyzing seed weight (SW) and grain yield (GY). Two methods to predict the accuracies (r?g) were adopted (GBLUP and Bayes) and also considered the environmental variation (GBLUP-based reaction norm model). Similar accuracies were observed for both methods. For GY, the highest r?g were detected under NS (rgg = 0.49) in 2016 (r?g = 0.49) and in the joint analysis for the WS condition (rgg = 0.33), both for models using local landraces. For SW under NS, the rgg was higher for the elite lines (rgg = 0.72), whereas for WS, the rgg dropped considerably, ranging from 0.45 to 0.61 for the joint analysis, considering the landraces and all samples, respectively. For GY and SW, under NS, the rgg using both models increased with increasing number of SNPs, until reaching a plateau of 800 and 300 SNPs, respectively. Increasing the training population (TP) size resulted in greater accuracy. Taking in account the Genotype × Environment, the multienvironment model performed better especially for more complex traits (GY/NS: rgg = 0.32). The GP approach has great potential to help commercial bean breeding programs improving the performance of target quantitative traits.  aBeans  aBreeding and Genetic Improvement  aDrought tolerance  aGenomics  aFeijão  aMelhoramento Genético Vegetal  aPhaseolus Vulgaris  aResistência a Seca1 aMÜLLER, B. S. F.1 aVALDISSER, P. A. M. R.1 aBRONDANI, C.1 aVIANELLO, R. P.  tCrop Sciencegv. 63, n. 4, p. 2145-2161, July/Aug. 2023.