Open-pollinated cultivars provide a number of benefits for organic and smallholder farmers, allowing them to save seed, conduct on-farm selection, and maintain on-farm crop genetic diversity. Synthetic open-pollinated cultivars provide advantages over traditional open-pollinated cultivars for such farmers. The objective of this research was to determine the usefulness of marker-based prediction models relative to structured mating design-based predictions for selecting untested sweet corn synthetic cultivars for organic production systems. This study used marker data and phenotypic data collected in 2015 and 2016 in 11 organic trials across six locations on 40 sweet corn (Zea mays L.) inbreds and 100 hybrid progeny formed from four disconnected North Carolina Design II (NC DII) mating blocks to predict performance of untested synthetic open-pollinated sweet corn populations. In 2017, validation trials of 26 previously untested synthetic populations were grown in five organic environments to assess correlations between actual performance and performance predicted by genomic best linear unbiased prediction (GBLUP) or NC DII general combining abilities (GCAs). Correlations between values measured in 2017 validation trials and values predicted from the complete dataset ranged from 0.28 to 0.68 for GCA-based predictions, from 0.25 to 0.67 for the additive GBLUP model, and from 0.28 to 0.68 for the additive plus dominance GBLUP model. In general, neither the genomic prediction model with solely additive effects nor genomic prediction with both additive and dominance effects demonstrated consistent increases in accuracy of predictions of synthetic population performance above predictions based solely on general combining ability. However, genomic prediction could be used to predict synthetic populations that shared alleles with the training set, even if the parents were never included in the training set, which is not possible with traditional general combining ability models.

中文翻译：

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有机环境中人工授粉甜玉米性能的经典和基因组预测

开放授粉品种为有机农户和小农户提供了许多好处，使他们能够保存种子、进行农场选择并保持农场作物遗传多样性。合成的开放授粉栽培品种为这些农民提供了优于传统开放授粉栽培品种的优势。本研究的目的是确定基于标记的预测模型相对于基于结构化交配设计的预测的有效性，用于为有机生产系统选择未经测试的甜玉米合成品种。本研究使用了 2015 年和 2016 年收集的标记数据和表型数据，在 6 个地点的 40 种甜玉米（Zea maysL.) 近交和 100 个杂交后代由四个不相连的北卡罗来纳设计 II (NC DII) 交配块形成，以预测未经测试的合成开放授粉甜玉米种群的性能。2017 年，在五种有机环境中对 26 个以前未经测试的合成种群进行了验证试验，以评估实际性能与基因组最佳线性无偏预测 (GBLUP) 或 NC DII 通用组合能力 (GCA) 预测的性能之间的相关性。2017 年验证试验中测量的值与从完整数据集预测的值之间的相关性范围为基于 GCA 的预测的 0.28 到 0.68，加性 GBLUP 模型的范围从 0.25 到 0.67，加性加优势 GBLUP 模型的范围从 0.28 到 0.68。一般来说，仅具有加性效应的基因组预测模型和具有加性和显性效应的基因组预测均未证明合成种群性能预测的准确性高于仅基于一般组合能力的预测。然而，基因组预测可用于预测与训练集共享等位基因的合成种群，即使父母从未包含在训练集中，这在传统的一般组合能力模型中是不可能的。