Data from: Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models

Genomic selection have been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies of how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee — production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee.基因组选择（Genomic selection）已被提议作为动植物育种中预测育种值的标准方法。尽管部分作物已从该方法中获益，但咖啡属（Coffea）相关研究仍处于起步阶段。截至目前，尚未有研究探讨基因组预测模型在咖啡不同复杂性状下，跨群体与环境的预测表现。鉴于预测模型基于生物学与统计学假设，其预测性能会随这些假设与表型真实遗传架构的契合程度而变化。为探究这一问题，本研究使用了两个刚果咖啡（Coffea canephora）轮回选择群体的数据，这些群体在两个试验点开展表型鉴定，并通过测序分型（Genotyping-by-Sequencing, GBS）获得了单核苷酸多态性（Single Nucleotide Polymorphism, SNP）标记。具体而言，本研究评估了13种统计学方法的预测性能，用于预测咖啡的3个重要性状：咖啡豆产量、叶锈病发病率与青豆产量。本研究分别开展了环境内预测、跨地点预测以及跨群体预测分析，以评估基因组选择的可靠性。总体而言，各竞争模型的预测精度差异较小，尽管贝叶斯方法相较其他方法略有提升，但代价是计算耗时更长。正如预期，环境内分析的平均预测精度高于跨地点与跨群体预测。本研究结果证实了基因组选择可用于重塑传统植物育种体系的潜力。实际应用中，本研究有望通过缩短咖啡轮回选择的周期，提升单位时间内的遗传增益。