Genomic insights into population structure and predictive breeding for climate-resilient coffee.

Climate change poses a growing threat to global coffee production, particularly for Coffea arabica, the most widely cultivated species. Coffea canephora (Robusta), with greater tolerance to heat and environmental stress, represents a critical genetic resource for sustaining future supply. Despite its increasing importance, the species is still relatively understudied with respect to population structure and trait architecture-factors that are important for guiding breeding efforts. Here, we combine population genetic analyses with genomic prediction to inform the improvement of C. canephora using a representative breeding collection from West Africa. First, we characterized the genetic structure of the cultivated germplasm and confirmed the presence of three main genetic pools: Robusta, Conilon, and Guinean. Second, we quantified phenotypic variation and genetic parameters for 11 agronomic traits, demonstrating a significant contribution of non-additive effects-particularly for yield. Third, we evaluated the performance of genomic prediction models incorporating additive and dominance effects, and proposed their integration into a reciprocal recurrent selection scheme to exploit heterosis. Altogether, our findings highlight the utility of incorporating structured genetic diversity and non-additive effects into breeding strategies. The framework presented here provides a foundation for improving the predictive accuracy and long-term adaptability of C. canephora, with broader implications for genomic-assisted breeding under climate stress.