Sun Qi, Wang Ping, Li Wenlan, Li Wencai, Lu Shouping, Yu Yanli, Zhao Meng, Meng Zhaodong
Maize Institute, Shandong Academy of Agricultural Sciences/National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture, P.R. China, No. 202 North of Industry Road, Licheng District, Jinan, Shandong Province 250100, China.
Tai'an Academy of Agricultural Science, No. 16 Tailai Road, Tai'an, Shandong Province, 271000, China.
Breed Sci. 2019 Jun;69(2):266-271. doi: 10.1270/jsbbs.18141. Epub 2019 Apr 11.
Genomic selection (GS) is the one of the new method for molecular marker-assisted selection (MAS) that can improve selection efficiency and thereby accelerate selective breeding progress. In the present study, we used the exotic germplasm LK1 to improve the shelling percentage of Qi319 by GS. Genome-wide marker effects for each trait were estimated based on the performance of the testcross and SNP data for F progenies in the training population. The accuracy of genomic predictions was estimated as the correlation between marker-predicted genotypic values and phenotypic values of the testcrosses for each trait in the validation population. Our study result indicated that selection response for shell percentage was 33.7%, which is greater than those for grain yield, kernel number per ear, or grain moisture at harvest. Selection response for tassel branch number and weight per 100 kernels was greater than 60%. The Higher trait heritability resulted in better prediction efficiency; Prediction accuracy increased with the training population size; Prediction efficiency did not differ significantly between SNP densities of 1000 bp and 55,000 bp. The results of the present research project will provide a basis for genome-wide selection technology in maize breeding, and lay the groundwork for the application of GS to germplasms that are useful in China.
基因组选择（GS）是分子标记辅助选择（MAS）的新方法之一，能够提高选择效率，从而加快育种进程。在本研究中，我们利用外来种质LK1通过基因组选择提高齐319的出籽率。基于测验种的表现和训练群体中F子代的SNP数据，估计每个性状的全基因组标记效应。基因组预测的准确性通过标记预测的基因型值与验证群体中每个性状测验种表型值之间的相关性来估计。我们的研究结果表明，出籽率的选择响应为33.7%，高于籽粒产量、穗粒数或收获时籽粒含水量的选择响应。雄穗分支数和百粒重的选择响应大于60%。较高的性状遗传力导致更好的预测效率；预测准确性随着训练群体规模的增加而提高；1000 bp和55000 bp的SNP密度之间的预测效率没有显著差异。本研究项目的结果将为玉米育种中的全基因组选择技术提供依据，并为基因组选择在中国有用种质中的应用奠定基础。
