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

Area of Science:

• Plant Genetics
• Climate Change Adaptation
• Agricultural Science

Background:

• Global coffee production, especially Coffea arabica, faces significant climate change threats.
• Coffea canephora (Robusta) is vital for future coffee supply due to its stress tolerance.
• Limited understanding of Robusta's population structure and trait architecture hinders breeding efforts.

Purpose of the Study:

• To analyze the genetic structure of cultivated Coffea canephora germplasm.
• To quantify phenotypic variation and genetic parameters for key agronomic traits.
• To evaluate and propose genomic prediction models for improving Robusta coffee breeding.

Main Methods:

• Population genetic analyses were used to characterize genetic structure.
• Genomic prediction models incorporating additive and dominance effects were evaluated.
• Phenotypic data for 11 agronomic traits were quantified.

Main Results:

• Three main genetic pools (Robusta, Conilon, Guinean) were identified in the germplasm.
• Non-additive genetic effects significantly influenced phenotypic variation, particularly for yield.
• Genomic prediction models showed promise for integration into selection schemes.

Conclusions:

• Understanding genetic diversity and non-additive effects is crucial for effective Robusta breeding.
• The proposed genomic-assisted framework enhances predictive accuracy and adaptability for climate-stressed coffee.
• This research provides a foundation for developing climate-resilient coffee varieties.