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<p>Climate change poses a growing threat to global coffee production, particularly for <i>Coffea arabica</i>, the most widely cultivated species. <i>Coffea canephora</i> (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 <i>C. canephora</i> 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 <i>C. canephora</i>, with broader implications for genomic-assisted breeding under climate stress.</p>

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

  • 期刊论文

  • 发表日期：

    2025年11月19日

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  • N’Da. Desire Pokou,
  • Kossia Manzan Karine Gba,
  • Hyacinthe Legnate,
  • Matheus M. Suela,
  • Christophe Montagnon,
  • Luis Felipe V. Ferrão

摘要

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.