Genomic selection in tea plant (Camellia sinensis) breeding has the potential to accelerate efficiency of choosing parents with desirable traits at the seedling stage. The study evaluated different genome‐enabled prediction models for black tea quality and drought tolerance traits in discovery and validation populations. The discovery population comprised of two segregating tea populations (TRFK St. 504 and TRFK St. 524) with 255 F₁ progeny and 56 individual tea cultivars in validation population genotyped using 1,421 DArTseq markers. Twofold cross‐validation was used for training the prediction models in the discovery population on eight different phenotypic traits. The best prediction models in the discovery population were consequently fitted to the validation population. Of all the four model‐based prediction approaches, putative QTLs (Quantitative Trait Loci) + annotated proteins + KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathway‐based prediction approach showed more robustness. The findings have for the first time opened up a new avenue for future application of genomic selection in tea breeding.

中文翻译：

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基因组预测的红茶质量和耐旱性状预测模型

茶树（茶花）育种中的基因组选择具有提高幼苗期选择具有理想性状的亲本的效率。这项研究评估了发现和验证人群中红茶质量和耐旱性状的不同基因组预测模型。发现种群包括两个单独的茶种群（TRFK St. 504和TRFK St. 524）和255 F ₁后代和56个个体茶树种使用1,421个DArTseq标记进行基因分型。双重交叉验证用于在发现种群中针对八个不同的表型性状训练预测模型。因此，将发现种群中的最佳预测模型拟合到验证种群。在所有四种基于模型的预测方法中，假定的QTL（定量性状位点）+带注释的蛋白质+ KEGG（基因和基因组京都百科全书）基于路径的预测方法显示出更高的鲁棒性。这些发现首次为基因组选择在茶育种中的未来应用开辟了一条新途径。