Blueberry (Vaccinium spp.) is an important autopolyploid crop with significant benefits for human health. Apart from its genetic complexity, the feasibility of genomic prediction has been proven for blueberry, enabling a reduction in the breeding cycle time and increasing genetic gain. However, as for other polyploid crops, sequencing costs still hinder the implementation of genome-based breeding methods for blueberry. This motivated us to evaluate the effect of training population sizes and composition, as well as the impact of marker density and sequencing depth on phenotype prediction for the species. For this, data from a large real breeding population of 1804 individuals were used. Genotypic data from 86,930 markers and three traits with different genetic architecture (fruit firmness, fruit weight, and total yield) were evaluated. Herein, we suggested that marker density, sequencing depth, and training population size can be substantially reduced with no significant impact on model accuracy. Our results can help guide decisions toward resource allocation (e.g., genotyping and phenotyping) in order to maximize prediction accuracy. These findings have the potential to allow for a faster and more accurate release of varieties with a substantial reduction of resources for the application of genomic prediction in blueberry. We anticipate that the benefits and pipeline described in our study can be applied to optimize genomic prediction for other diploid and polyploid species.

中文翻译：

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优化同源四倍体蓝莓育种的全基因组预测

蓝莓 (Vaccinium spp.) 是一种重要的同源多倍体作物，对人类健康具有显着益处。除了其遗传复杂性外，基因组预测的可行性已被证明适用于蓝莓，从而缩短育种周期时间并增加遗传增益。然而，对于其他多倍体作物，测序成本仍然阻碍了基于基因组的蓝莓育种方法的实施。这促使我们评估训练种群大小和组成的影响，以及标记密度和测序深度对物种表型预测的影响。为此，使用了来自 1804 个人的大型真实繁殖种群的数据。评估了来自 86,930 个标记和具有不同遗传结构（果实硬度、果实重量和总产量）的三个性状的基因型数据。在此处，我们建议可以大幅降低标记密度、测序深度和训练种群规模，而不会显着影响模型准确性。我们的结果可以帮助指导资源分配决策（例如，基因分型和表型），以最大限度地提高预测准确性。这些发现有可能允许更快、更准确地发布品种，同时大幅减少用于蓝莓基因组预测应用的资源。我们预计我们研究中描述的好处和管道可用于优化其他二倍体和多倍体物种的基因组预测。我们的结果可以帮助指导资源分配决策（例如，基因分型和表型），以最大限度地提高预测准确性。这些发现有可能允许更快、更准确地发布品种，同时大幅减少用于蓝莓基因组预测应用的资源。我们预计我们研究中描述的好处和管道可用于优化其他二倍体和多倍体物种的基因组预测。我们的结果可以帮助指导资源分配决策（例如，基因分型和表型），以最大限度地提高预测准确性。这些发现有可能允许更快、更准确地发布品种，同时大幅减少用于蓝莓基因组预测应用的资源。我们预计我们研究中描述的好处和管道可用于优化其他二倍体和多倍体物种的基因组预测。