Breeding soybeans for higher latitudes requires cultivars with an increased chilling stress tolerance, especially when flowering occurs. Phenotyping in climate chambers to select for this trait is labour‐intensive and requires an optimal allocation of resources due to limited space. We screened a diversity panel of 35 early maturity cultivars and a biparental population of 103 RILs for their cold stress tolerance at flowering stage. Pod number under control and stress conditions is highly heritable and showed only a weak correlation between the two treatments. Based on different testing scenarios, we could show that testing more genotypes with less replicates yields much higher responses to selection and hence should be pursued in such climate‐controlled experiments. We identified quantitative trait loci (QTL) for pod number under both conditions (chromosomes 7 and 13) and a cold tolerance‐specific QTL (chromosome 11). Furthermore, we performed genomic predictions using different test set scenarios and prediction models, showing that genomic prediction is a promising tool to select for cold stress tolerance, particularly if known QTL can be used as fixed effects in the model.

中文翻译：

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开花期大豆的耐冷胁迫：可控条件下的QTL定位和有效选择策略

要在高纬度地区选育大豆，需要提高耐寒性的品种，特别是在开花时。在气候箱中进行表型分选以选择该性状是劳动密集型的，并且由于空间有限而需要对资源进行最佳分配。我们筛选了35个早熟品种和103个RIL的双亲群体的多样性小组，以了解开花期的耐冷胁迫能力。在控制和胁迫条件下的荚数是高度可遗传的，并且在两种处理之间仅显示出弱的相关性。根据不同的测试场景，我们可以证明测试更多基因型且重复次数较少会产生更高的选择响应，因此应在此类气候控制实验中进行。我们在两种条件下（染色体7和13）和耐寒性特异性QTL（染色体11）鉴定了荚果数量的数量性状基因座（QTL）。此外，我们使用不同的测试集场景和预测模型进行了基因组预测，表明基因组预测是选择耐寒性的有前途的工具，特别是如果已知的QTL可以在模型中用作固定效应的话。