Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design

文献类型： 外文期刊

第一作者： Luo, Ping

作者： Luo, Ping;Wang, Houwen;Yang, Ruisi;Wang, Fei;Yong, Hongjun;Zhou, Zhiqiang;Li, Mingshun;Zhang, Degui;Han, Jienan;Chen, Yong;Zhang, Runze;Gao, Wenwei;Chen, Xiaoyu;Li, Wenjie;Hao, Zhuanfang;Fu, Junjie;Li, Xinhai;Luo, Ping;Zhang, Xuecai;Ni, Zhiyong;Zhang, Lin;Song, Wei;Wang, Liwei;Yang, Jie;Weng, Jianfeng;Meng, Zhaodong;Zhao, Meng

作者机构：

关键词： Maize; Genomic prediction; Prediction model; Genetic effects; Hybrid performance

期刊名称：CROP JOURNAL （ 影响因子：6.6； 五年影响因子：6.5 ）

ISSN： 2095-5421

年卷期： 2023 年 11 卷 6 期

页码：

收录情况： SCI

摘要： Genomic prediction (GP) in plant breeding has the potential to predict and identify the best-performing hybrids based on the genotypes of their parental lines. In a GP experiment, 34 elite inbred lines were selected to make 285 single-cross hybrids in a partial-diallel cross design. These lines represented a mini-core collection of Chinese maize germplasm and comprised 18 inbred lines from the Stiff Stalk het-erotic group and 16 inbred lines from the Non-Stiff Stalk heterotic group. The parents were genotyped by sequencing and the 285 hybrids were phenotyped for nine yield and yield-related traits at two locations in the summer sowing area (SUS) and three locations in the spring sowing area (SPS) in the main maize-producing regions of China. Multiple GP models were employed to assess the accuracy of trait prediction in the hybrids. By ten-fold cross-validation, the prediction accuracies of yield performance of the hybrids estimated by the genomic best linear unbiased prediction (GBLUP) model in SUS and SPS were 0.51 and 0.46, respectively. The prediction accuracies of the remaining yield-related traits estimated with GBLUP ranged from 0.49 to 0.86 and from 0.53 to 0.89 in SUS and SPS, respectively. When additive, dominance, epistasis effects, genotype-by-environment interaction, and multi-trait effects were incorporated into the prediction model, the prediction accuracy of hybrid yield performance was improved. The ratio of training to testing population and size of training population optimal for yield prediction were determined. Multiple prediction models can improve prediction accuracy in hybrid breeding. (c) 2023 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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