Table_5_Accurate Prediction of a Quantitative Trait Using the Genes Controlling the Trait for Gene-Based Breeding in Cotton.XLSX

Accurate phenotype prediction of quantitative traits is paramount to enhanced plant research and breeding. Here, we report the accurate prediction of cotton fiber length, a typical quantitative trait, using 474 cotton (Gossypium ssp.) fiber length (GFL) genes and nine prediction models. When the SNPs/InDels contained in 226 of the GFL genes or the expressions of all 474 GFL genes was used for fiber length prediction, a prediction accuracy of r = 0.83 was obtained, approaching the maximally possible prediction accuracy of a quantitative trait. This has improved by 116%, the prediction accuracies of the fiber length thus far achieved for genomic selection using genome-wide random DNA markers. Moreover, analysis of the GFL genes identified 125 of the GFL genes that are key to accurate prediction of fiber length, with which a prediction accuracy similar to that of all 474 GFL genes was obtained. The fiber lengths of the plants predicted with expressions of the 125 key GFL genes were significantly correlated with those predicted with the SNPs/InDels of the above 226 SNP/InDel-containing GFL genes (r = 0.892, P = 0.000). The prediction accuracies of fiber length using both genic datasets were highly consistent across environments or generations. Finally, we found that a training population consisting of 100–120 plants was sufficient to train a model for accurate prediction of a quantitative trait using the genes controlling the trait. Therefore, the genes controlling a quantitative trait are capable of accurately predicting its phenotype, thereby dramatically improving the ability, accuracy, and efficiency of phenotype prediction and promoting gene-based breeding in cotton and other species.

精准预测数量性状（quantitative traits）的表型，对于推进植物研究与育种工作至关重要。本研究以典型数量性状——棉花纤维长度为对象，借助474个棉花（棉属（Gossypium ssp.））纤维长度（GFL）基因与9种预测模型，实现了精准预测。当采用226个GFL基因所携带的单核苷酸多态性/插入缺失多态性（SNPs/InDels），或是全部474个GFL基因的表达量开展纤维长度预测时，获得了r=0.83的预测精度，该结果接近数量性状的理论最大预测精度。相较于此前利用全基因组随机DNA标记开展基因组选择（genomic selection）时所达成的纤维长度预测精度，本研究将其提升了116%。此外，对GFL基因的分析筛选出125个对纤维长度精准预测具有关键作用的基因，利用该部分基因进行预测时，可获得与全部474个GFL基因相当的预测精度。利用这125个关键GFL基因的表达量所预测得到的植株纤维长度，与利用上述226个携带SNPs/InDels的GFL基因的变异位点所预测得到的纤维长度呈显著相关（r=0.892，P=0.000）。基于两类基因数据集的纤维长度预测精度，在不同环境或世代下均保持高度一致。最后，本研究发现，若利用控制性状的基因开展数量性状预测，仅需包含100~120株植株的训练群体即可完成模型训练。综上，控制数量性状的基因能够精准预测其表型，由此可大幅提升表型预测的能力、精度与效率，推动棉花乃至其他物种的基因导向育种工作。