Genomic prediction allows breeders to make selections based on the genomic estimated breeding value (GEBV) of selection candidates. GEBVs are assigned using a prediction model trained with genotypes and phenotypes from a training population. For this reason, the effectiveness of genomic selection is strongly tied to the prediction accuracy of the model used to estimate breeding values and the training population used to inform the model. The aim of this study was to evaluate the accuracy of the ridge regression best linear unbiased prediction (rrBLUP) model across different traits, parent population sizes, and breeding strategies when estimating breeding values in Phaseolus vulgaris. The model was trained on a simulated population genotyped for 1010 SNP markers including 38 known QTLs identified in the literature (Lin, 2022). Simulation results revealed that realized accuracies fluctuate depending on the factors investigated: trait genetic architecture, breeding strategy, and the number of initial parents involved in the breeding program. Trait architecture and breeding strategy appeared to have a larger impact on accuracy than the initial number of parents. Generally, maximum accuracies were achieved under a mass selection strategy followed by pedigree and single-seed descent methods. This study also investigated model updating, which involves re-training the prediction model with a more relevant set of genotypes and phenotypes. While it has been repeatedly shown that model updating generally improves prediction accuracy, it benefitted some breeding strategies more than others. For low heritability traits (e.g., yield) conventional phenotype-based selection methods showed consistent rates of genetic gain, but genetic gain under genomic selection reached a plateau in after fewer cycles.

The common bean, Phaseolus vulgaris L., like many crop species is vulnerable to the destructive necrotrophic fungus Sclerotinia sclerotiorum (Lib.) de Bary (Ss), the causal agent of the white mold disease. To slow Ss spread, farmers rely on costly fungicides that problematically are most effective when applied early, during which the plant lacks visual signs of infection. Internally, an early indicator of Ss infection is the acidification or decrease in plant pH caused by the secretion of oxalic acid released by Ss. The objective of this study was to determine if this early drop in apoplastic pH post-Ss infection could be detected using an Arduino platform-based potentiometric pH sensor with a carbon reference electrode on the leaf surface of a common bean. Interestingly, plant pH did not decrease but was statistically unchanged in the cultivars resistant to Ss (WM-12, WM-1, and G122) or intermediate tolerant (Eldorado, ICA Bunsi, and Beryl), while increasing in the susceptible cultivar (Montrose). This hints at possible Ss resistance mechanisms not present in the susceptible cultivars. Importantly, in seven of the common bean cultivars tested, the direction and magnitude of pH change pre and post-Ss infection measured using the carbon sensor were indistinguishable from the labor-intensive manually extracted leaf apoplastic fluid. Therefore, in the future, these sensors could conceivably be used for high-throughput screening of large germplasm collections to identify novel sources of genetic resistance to Ss that could be introduced into elite common bean cultivars to counter the highly destructive white mold disease.

Henry Cordoba-Novoa1, Isabella Chiaravalotti1 , Laura Esquivel1, Robert McGee1, Jeff Smith1, Shangpeng Sun1 and Valerio Hoyos-Villegas1