Unlocking the potential of Capsicum Germplasm Collections for Climate Resilience and Fruit Quality
Halpin-McCormick, A.; Nalla, M. K.; Radlicz, Z.; Zhang, A.; Fumia, N.; Lin, T.-h.; Lin, S.-w.; Wang, Y.-w.; Zohoungbogbo, H. P. F.; Wang, D. R.; Runck, B.; Gore, M. A.; Kantar, M. B.; Barchenger, D. W.
Show abstract
Climate change increasingly threatens global Capsicum (pepper) production. Accelerating the deployment of climate-resilient cultivars requires effective use of genetic diversity conserved in genebanks. We implement a "turbocharging" strategy in Capsicum by integrating genome-wide association studies and genomic prediction in a core collection (n = 423), followed by genomic prediction across the global collection (n = 10,250) using the core as a training population. We generated genomic estimated breeding values (GEBVs) for 31 high-accuracy traits (r > 0.5) encompassing hyperspectral phenotypes (heat/control), agronomic performance (heat/control) and fruit quality. To enhance accessibility and decision-making, we developed a large language model (LLM) integrated application that enables flexible, preference-based selection of candidates. By narrowing the parental decision space, this framework streamlines screening of large germplasm collections while balancing climate resilience, quality attributes and market demands. Our approach provides a scalable decision-support system to accelerate climate-resilient Capsicum breeding and maximize global genetic resources.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.