Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, Florida, USA.
Statistics Department, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil.
Plant Genome. 2024 Sep;17(3):e20488. doi: 10.1002/tpg2.20488. Epub 2024 Aug 1.
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.
基因组预测是一种现代方法，它利用全基因组标记来预测未表型个体的遗传优势。由于有可能减少育种周期并提高选择准确性，因此该工具旨在对基因型进行排名并最大化遗传增益。尽管具有重要意义，但在育种计划中实际实施该技术需要对其在预测框架中的应用进行资源的合理分配。在这项研究中，我们整合了遗传和数据驱动的方法，针对基因组预测来分配表型和基因型资源。为此，我们使用了一个历史悠久的蓝莓（Vaccinium corymbosum L.）育种数据集，其中包含 3000 多个个体，这些个体使用基于探针的靶向测序进行了基因分型，并在多年内对三种果实品质性状进行了表型分析。我们在这项研究中的贡献有三点：（i）对于基因分型资源分配，使用遗传数据驱动的方法选择最佳的标记集，略微提高了所有性状的预测结果；（ii）对于长期影响，我们进行了模拟研究，并强调数据驱动的方法比随机标记采样在 30 个周期内导致遗传增益略有提高；（iii）对于表型资源分配，我们比较了不同的优化算法来选择训练群体，表明可以利用该方法来提高预测性能。总的来说，我们通过展示与基因组预测相关的资源分配的关键育种决策可以通过统计和遗传方法的结合来解决，为育种者提供了一种面向数据的决策方法。
