Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA.
Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural-Incaper, Vitoria, ES 29052-010, Brazil.
G3 (Bethesda). 2023 Jun 1;13(6). doi: 10.1093/g3journal/jkad062.
Coffee is one of the most important beverages and trade products in the world. Among the multiple research initiatives focused on coffee sustainability, plant breeding provides the best means to increase phenotypic performance and release cultivars that could meet market demands. Since coffee is well adapted to a diversity of tropical environments, an important question for those confronting the problem of evaluating phenotypic performance is the relevance of genotype-by-environment interaction. As a perennial crop with a long juvenile phase, coffee is subjected to significant temporal and spatial variations. Such facts not only hinder the selection of promising materials but also cause a majority of complaints among growers. In this study, we hypothesized that trait stability in coffee is genetically controlled and therefore is predictable using molecular information. To test it, we used genome-based methods to predict stability metrics computed with the primary goal of selecting coffee genotypes that combine high phenotypic performance and stability for target environments. Using 2 populations of Coffea canephora, evaluated across multiple years and locations, our contribution is 3-fold: (1) first, we demonstrated that the number of harvest evaluations may be reduced leading to accelerated implementation of molecular breeding; (2) we showed that stability metrics are predictable; and finally, (3) both stable and high-performance genotypes can be simultaneously predicted and selected. While this research was carried out on representative environments for coffee production with substantial crossover in genotypic ranking, we anticipate that genomic prediction can be an efficient tool to select coffee genotypes that combine high performance and stability across years and the target locations here evaluated.
咖啡是世界上最重要的饮料和贸易产品之一。在众多关注咖啡可持续性的研究计划中，植物育种是提高表型性能并释放能够满足市场需求的品种的最佳手段。由于咖啡很好地适应了各种热带环境，因此对于那些面临评估表型性能问题的人来说，一个重要的问题是基因型与环境互作的相关性。作为一种具有长幼期的多年生作物，咖啡受到很大的时间和空间变化的影响。这些事实不仅阻碍了有前途的材料的选择，也导致了种植者的大多数抱怨。在本研究中，我们假设咖啡的性状稳定性是由遗传控制的，因此可以使用分子信息进行预测。为了验证这一点，我们使用基于基因组的方法来预测稳定性指标，主要目的是选择结合高表型性能和目标环境稳定性的咖啡基因型。我们使用了两个经过多年和多个地点评估的小粒种咖啡群体，我们的贡献有三方面：（1）首先，我们证明了可以减少收获评估的次数，从而加速分子育种的实施；（2）我们表明稳定性指标是可预测的；最后，（3）可以同时预测和选择稳定和高性能的基因型。虽然这项研究是在咖啡生产的代表性环境中进行的，基因型排名有很大的交叉，但我们预计基因组预测可以成为选择结合多年和目标地点高性能和稳定性的咖啡基因型的有效工具。
