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Coffee is an important crop with economic and social importance in several countries. With a daily consumption estimated at 2.2 billion cups, its sustainability is facing critical challenges given the projected climate changes. Coffea arabica, which represents ∼60% of the global market coffee is a delicate crop, quite susceptible to diseases and biotic stresses. Developing climate‐resilience cultivars is necessary, and it includes coffee plants adapted to new farming conditions that can meet the demand for biotic and abiotic tolerance and quality. In this context, Coffea canephora emerges as a potential candidate if the crop combines plasticity and cupping quality. Plant plasticity refers to adjusted phenotypic performance when grown in different environments, a fact that may help mitigate the detrimental effect of climate changes. In this study, using a multiple environment trial, we combined genomic and genotype‐by‐environment analyses to answer the following main question: How the climate effects may affect the phenotypic plasticity in C. canephora? Our contributions in this paper are fourfold: (i) we draw attention to the cupping quality and yield performance of C. canephora cultivars when evaluated in high‐altitude and cold weather, (ii) we compared C. arabica and C. canephora phenotypic plasticity and highlight genotypes with broad and specific adaptation to certain environmental conditions, and finally, (iii) using stochastic simulation, we emphasize the potential of molecular breeding in the long term in coffee. Altogether, we present an emerging view on how C. canephora could be a valid alternative for climate‐smart cultivars in a projected scenario of altered climatic conditions. [ABSTRACT FROM AUTHOR]

Genomic prediction is a modern approach that uses genome-wide markers to predict the genetic merit of unphenotyped individuals. With the potential to reduce the breeding cycles and increase the selection accuracy, this tool has been designed to rank genotypes and maximize genetic gains. Despite this importance, its practical implementation in breeding programs requires critical allocation of resources for its application in a predictive framework. In this study, we integrated genetic and data-driven methods to allocate resources for phenotyping and genotyping tailored to genomic prediction. To this end, we used a historical blueberry (Vaccinium corymbosun L.) breeding dataset containing more than 3000 individuals, genotyped using probe-based target sequencing and phenotyped for three fruit quality traits over several years. Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data-driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long-term implication, we carried out a simulation study and emphasized that data-driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. Altogether, we provided a data-oriented decision-making approach for breeders by demonstrating that critical breeding decisions associated with resource allocation for genomic prediction can be tackled through a combination of statistics and genetic methods., (© 2024 The Author(s). The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)

Estimation of allele dosage, using genomic data, in autopolyploids is challenging and current methods often result in the misclassification of genotypes. Some progress has been made when using SNP arrays, but the major challenge is when using next generation sequencing data. Here we compare the use of read depth as continuous parameterization with ploidy parameterizations in the context of genomic selection (GS). Additionally, different sources of information to build relationship matrices were compared. A real breeding population of the autotetraploid species blueberry ( Vaccinium corybosum ), composed of 1,847 individuals was phenotyped for eight yield and fruit quality traits over two years. Continuous genotypic based models performed as well as the best models. This approach also reduces the computational time and avoids problems associated with misclassification of genotypic classes when assigning dosage in polyploid species. This approach could be very valuable for species with higher ploidy levels or for emerging crops where ploidy is not well understood. To our knowledge, this work constitutes the first study of genomic selection in blueberry. Accuracies are encouraging for application of GS for blueberry breeding. GS could reduce the time for cultivar release by three years, increasing the genetic gain per cycle by 86% on average when compared to phenotypic selection, and 32% when compared with pedigree-based selection. Finally, the genotypic and phenotypic data used in this study are made available for comparative analysis of dosage calling and genomic selection prediction models in the context of autopolyploids., (Copyright © 2019 de Bem Oliveira et al.)