With the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees. Stochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ. Including the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10–20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required. This study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

中文翻译：

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优化蜜蜂基因组选择的模拟研究

随着蜜蜂单核苷酸多态性（SNP）芯片的完成，为基因组选择奠定了技术基础。然而，为了在实践中应用，估计基因组育种值的方法需要适应该物种的遗传学和育种基础设施的特殊性。产生雄蜂的蜂后 (DPQ) 用于交配控制，通常，它们会引导将放置在交配站上的非表型菌落。繁殖蜂王 (BQ) 头部群体，旨在进行表型分析并用于产生新蜂王。我们的目标是评估启动蜜蜂基因组选择的不同育种计划设计。进行随机模拟以评估估计育种值的质量。我们开发了基因组关系矩阵的变体，以包括 DPQ 的基因型，并测试了不同大小的参考群体。结果用于估计基因组育种计划的初始选择周期中的遗传增益。该计划运行了六年，每年考虑不同数量的基因分型蜂王。可以分配资源来增加参考种群，或执行 BQ 和/或 DPQ 的基因组预选。根据参考种群的大小和所考虑的性状，包括 5000 个表型 BQ 的基因型将育种值的预测准确性提高了 173%。要启动育种计划，每年至少需要对 1000 只蜂王进行基因分型。在这种情况下，当 DPQ 的基因组预选与 10-20% 表型 BQ 的基因分型相结合时，遗传增益最高。为了每个使用的基因型获得最大的遗传增益，每年需要超过 2500 只基因分型的蜂王和所有 BQ 和 DPQ 的预选。这项研究表明，育种计划的首要任务是对 BQ 表型进行基因分型，以获得足够大的参考种群，从而成功地对皇后进行基因组预选。为了最大限度地提高遗传增益，应预先选择 DPQ，并将它们的基因型包含在基因组关系矩阵中。我们建议，所开发的基因组预测方法适用于基因组蜜蜂育种计划。每年需要超过 2500 只基因分型的皇后，并且需要对所有 BQ 和 DPQ 进行预选。这项研究表明，育种计划的首要任务是对 BQ 表型进行基因分型，以获得足够大的参考种群，从而成功地对皇后进行基因组预选。为了最大限度地提高遗传增益，应预先选择 DPQ，并将它们的基因型包含在基因组关系矩阵中。我们建议，所开发的基因组预测方法适用于基因组蜜蜂育种计划。每年需要超过 2500 只基因分型的皇后，并且需要对所有 BQ 和 DPQ 进行预选。这项研究表明，育种计划的首要任务是对 BQ 表型进行基因分型，以获得足够大的参考种群，从而成功地对皇后进行基因组预选。为了最大限度地提高遗传增益，应预先选择 DPQ，并将它们的基因型包含在基因组关系矩阵中。我们建议，所开发的基因组预测方法适用于基因组蜜蜂育种计划。以及它们的基因型包含在基因组关系矩阵中。我们建议，所开发的基因组预测方法适用于基因组蜜蜂育种计划。以及它们的基因型包含在基因组关系矩阵中。我们建议，所开发的基因组预测方法适用于基因组蜜蜂育种计划。