Table_7_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.

精确预测数量性状表型对于提升植物研究和育种至关重要。本研究报告了利用474个棉花（Gossypium ssp.）纤维长度（GFL）基因和九种预测模型，准确预测棉花纤维长度的成果。当使用包含于226个GFL基因中的SNPs/InDels或所有474个GFL基因的表达进行纤维长度预测时，获得了r = 0.83的预测精度，接近数量性状可能达到的最大预测精度。这一精度相较于以往使用全基因组随机DNA标记进行基因组选择所实现的纤维长度预测精度提高了116%。通过对GFL基因的分析，确定了125个对纤维长度准确预测至关重要的基因，其预测精度与所有474个GFL基因相当。利用这125个关键GFL基因的表达预测的植物纤维长度与上述226个含有SNPs/InDels的GFL基因的SNPs/InDels预测的纤维长度显著相关（r = 0.892，P = 0.000）。使用这两种基因数据集进行纤维长度预测的精度在环境或世代间高度一致。最终，我们发现由100至120个植物组成的训练群体足以训练一个模型，以基因控制的数量性状进行准确的表型预测。因此，控制数量性状的基因能够准确预测其表型，从而显著提升表型预测的能力、精度和效率，并促进棉花及其他物种的基于基因的育种。