Merging Phenotypic Stability Analysis and Genomic Prediction for Multi-Environment Breeding in Capsicum spp.

Capsicum spp. support diverse fresh and processing value chains, yet integrated assessments of phenotypic stability and genome-enabled prediction remain limited. In this study, 32 representative accessions, selected from a panel of 235 genotyped entries from the Colombian Capsicum germplasm collection, were evaluated across three contrasting environments to characterize physicochemical traits (texture, pH, soluble solids, color) and biochemical attributes (total carotenoids, capsaicin, dihydrocapsaicin, phenolics, antioxidant capacity). Variance partitioning and AMMI models quantified the contributions of genotype (G), environment (E), and G × E interactions (GEIs). Significant effects were detected for most traits. The AMMI analysis identified stable genotypes across locations for pH, moisture, firmness, and cohesiveness. In contrast, color attributes, total carotenoids, and phenolic compounds showed greater environmental responsiveness. Texture-related and solid content traits showed broad adaptability and high phenotypic stability, making them reliable targets for selection under variable production conditions. For the genomic component, we analyzed 235 accessions genotyped with 68,481 high-quality SNPs obtained through GBS. These data were used to estimate genomic heritability and prediction accuracy with Bayesian and semi-parametric models. Among them, BayesC showed the best performance. Prediction accuracy reached r = 0.94 within the training environment and ranged from r = 0.64 to 0.73 when tested across contrasting environments. Genomic heritability was highest for pH (h2 = 0.48) and pungency-related traits, including capsaicin (h2 = 0.39) and dihydrocapsaicin (h2 = 0.48), indicating strong additive genetic control. Finally, by integrating AMMI-based stability analysis and BayesC genomic prediction, we identified genotypes exhibiting both high performance and environmental robustness. This combined selection approach provides a comprehensive framework for genomic-assisted breeding to enhance fruit quality, carotenoid content, and pungency stability in Capsicum spp. under heterogeneous environments.