Genomic prediction for drought tolerance using multienvironment data in a common bean ( Phaseolus vulgaris ) breeding program

This 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) over capg)) 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) over capg) were detected under NS (r((g) over capg) = 0.49) in 2016 (r((g) over capg) = 0.49) and in the joint analysis for the WS condition (r((g) over capg) = 0.33), both for models using local landraces. For SW under NS, the r((g) over capg) was higher for the elite lines (r((g) over capg) = 0.72), whereas for WS, the r((g) over capg) 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 r((g) over capg) 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 x Environment, the multienvironment model performed better especially for more complex traits (GY/NS: r((g) over capg) = 0.32). The GP approach has great potential to help commercial bean breeding programs improving the performance of target quantitative traits.