Genomic-assisted breeding for climate-smart coffee

Maria Amélia G Ferrão, Aymbire F A da Fonseca, Paulo S Volpi, Lucimara C de Souza, Marcone Comério, Abraão C Verdin Filho, Elaine M Riva-Souza, Patricio R Munoz

Plant Genome; 2024 Mar; 17(1):e20321. doi: 10.1002/tpg2.20321.

Abstract

Coffee is a universal beverage that drives a multi-industry market on a global basis. Today, the sustainability of coffee production is threatened by accelerated climate changes. In this work, we propose the implementation of genomic-assisted breeding for climate-smart coffee in Coffea canephora. This species is adapted to higher temperatures and is more resilient to biotic and abiotic stresses. After evaluating two populations, over multiple harvests, and under severe drought weather condition, we dissected the genetic architecture of yield, disease resistance, and quality-related traits. By integrating genome-wide association studies and diallel analyses, our contribution is four-fold: (i) we identified a set of molecular markers with major effects associated with disease resistance and post-harvest traits, while yield and plant architecture presented a polygenic background; (ii) we demonstrated the relevance of nonadditive gene actions and projected hybrid vigor when genotypes from different geographically botanical groups are crossed; (iii) we computed medium-to-large heritability values for most of the traits, representing potential for fast genetic progress; and (iv) we provided a first step toward implementing molecular breeding to accelerate improvements in C. canephora. Altogether, this work is a blueprint for how quantitative genetics and genomics can assist coffee breeding and support the supply chain in the face of the current global changes.

See https://pubmed.ncbi.nlm.nih.gov/36946358/

Figure 5: Values of posterior inclusion probabilities (PIPs) per 1 kb window for all selected traits in a population of Coffeea canephora.Windows with PIP ≥ 0.8 are marked with a vertical line where this threshold is exceeded are indicated. We used a more liberal threshold andindicated traits with PIP > 0.4