Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice.

准确识别具有目标农艺性状的品种被认为有助于遗传选择并加速水稻育种进程。基因组选择 (GS) 是一种很有前途的技术，它使用覆盖整个基因组的标记来预测基因组估计育种值 (GEBV)，并能够在测量表型之前进行选择。为了选择适合育种工作的 GS 模型，我们分析了从 459 个不同水稻品种群体中测得的九个农艺性状的可预测性。通过对八个代表性GS模型的比较，我们发现预测精度在0.407到0.896之间，其中再生核希尔伯特空间（RKHS）对大多数性状具有最高的预测能力。进一步的结果表明，GS 的预测能力会受到多种因素的影响。此外，我们评估了将全基因组关联研究（GWAS）整合到各种 GS 模型中的方法。六种不同 GWAS 模型生成的 GS 组合峰相关标记的预测能力存在显着差异；针对 GWAS-GS 集成预测，推荐使用混合线性模型 (MLM)-RKHS。最后，基于上述结果，我们尝试将最佳GWAS模型获得的P值应用于岭回归最佳线性无偏预测（rrBLUP），这有利于水稻的低预测性状。