Phenotyping replication strongly influences genomic predictive ability in sweet sorghum (Sorghum bicolor [L.] Moench)

ABSTRACT Genomic selection can increase the rate of genetic gain in crop breeding programs, but its effectiveness depends on the reliability of phenotypic data, the size and composition of the training population (TP), and the statistical model used to estimate genomic breeding values. These design choices are especially important in resource-limited breeding programs, where additional replication, larger TPs, and more extensive genotyping compete for the same resources. Using empirical data from a sweet sorghum [Sorghum bicolor (L.) Moench] breeding population, developed by CHIBAS, we evaluated the effects of phenotyping replication, TP size, training-validation genomic relatedness, and genomic prediction (GP) model on predictive ability (PA). Grain yield, plant height, stem weight, and total soluble solids were evaluated across three field environments. Few studies in sorghum have examined these factors together with comparable empirical rigor. Increasing replication improved genomic heritability and PA for all traits and environments, with the largest gains observed for grain yield. Larger TPs and increased training-validation genomic relatedness also improved PA, but their effects were most significant when phenotype estimates were based on multiple replicates. Different GP models had comparable PA across all evaluated traits, with a few exceptions. These findings provide practical guidance for optimizing genomic selection in resource-limited sorghum breeding programs. ARTICLE SUMMARY Genomic selection can accelerate breeding only when the phenotypes used to train predictive models are reliable. Using a sweet sorghum breeding population evaluated in three Haitian field environments, we quantified how replication number, training population size, training-validation genomic relatedness, and prediction model affected genomic predictive ability for grain yield, plant height, stem weight, and total soluble solids. Replication increased genomic heritability and predictive ability for all traits, with the strongest effects for grain yield. Larger and more connected training populations improved prediction, particularly when evaluated with greater replication. These results provide practical guidance for resource-limited breeding programs. Core ideas In this empirical sweet sorghum breeding population, phenotyping replication was the dominant factor explaining variation in genomic predictive ability across traits and environments. The benefit of larger training populations and greater training-validation genomic relatedness increased when phenotype estimates were based on more replicates. Grain yield, the most environmentally sensitive trait evaluated, showed the largest response to improved replication and training-population design. Bayesian models, rrBLUP, and GBLUP showed similar predictive abilities across traits and environments, suggesting that phenotyping and experimental design may be more important than model complexity.