@article{Luo2023, 
author = {Ping Luo and Houwen Wang and Zhiyong Ni and Ruisi Yang and Fei Wang and Hongjun Yong and Lin Zhang and Zhiqiang Zhou and Wei Song and Mingshun Li and Jie Yang and Jianfeng Weng and Zhaodong Meng and Degui Zhang and Jienan Han and Yong Chen and Runze Zhang and Liwei Wang and Meng Zhao and Wenwei Gao and Xiaoyu Chen and Wenjie Li and Zhuanfang Hao and Junjie Fu and Xuecai Zhang and Xinhai Li},
title = {Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design},
year = {2023},
journal = {The Crop Journal},
volume = {11},
number = {6},
pages = {1884-1892},
keywords = {Maize, Genomic prediction, Prediction model, Genetic effects, Hybrid performance},
url = {https://www.sciopen.com/article/10.1016/j.cj.2023.09.009},
doi = {10.1016/j.cj.2023.09.009},
abstract = {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 heterotic 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.}
}