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

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 repr...

Descripción completa

Detalles Bibliográficos
Autores principales: Ping Luo, Houwen Wang, Zhiyong Ni, Ruisi Yang, Fei Wang, Hongjun Yong, Lin Zhang, Zhiqiang Zhou, Wei Song, Mingshun Li, Jie Yang, Jianfeng Weng, Zhaodong Meng, Degui Zhang, Jienan Han, Yong Chen, Runze Zhang, Liwei Wang, Meng Zhao, Wenwei Gao, Xiaoyu Chen, Wenjie Li, Zhuanfang Hao, Junjie Fu, Xuecai Zhang, Xinhai Li
Formato: Journal Article
Lenguaje:Inglés
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://hdl.handle.net/10568/137827
_version_ 1855521127832485888
author Ping Luo
Houwen Wang
Zhiyong Ni
Ruisi Yang
Fei Wang
Hongjun Yong
Lin Zhang
Zhiqiang Zhou
Wei Song
Mingshun Li
Jie Yang
Jianfeng Weng
Zhaodong Meng
Degui Zhang
Jienan Han
Yong Chen
Runze Zhang
Liwei Wang
Meng Zhao
Wenwei Gao
Xiaoyu Chen
Wenjie Li
Zhuanfang Hao
Junjie Fu
Xuecai Zhang
Xinhai Li
author_browse Degui Zhang
Fei Wang
Hongjun Yong
Houwen Wang
Jianfeng Weng
Jie Yang
Jienan Han
Junjie Fu
Lin Zhang
Liwei Wang
Meng Zhao
Mingshun Li
Ping Luo
Ruisi Yang
Runze Zhang
Wei Song
Wenjie Li
Wenwei Gao
Xiaoyu Chen
Xinhai Li
Xuecai Zhang
Yong Chen
Zhaodong Meng
Zhiqiang Zhou
Zhiyong Ni
Zhuanfang Hao
author_facet Ping Luo
Houwen Wang
Zhiyong Ni
Ruisi Yang
Fei Wang
Hongjun Yong
Lin Zhang
Zhiqiang Zhou
Wei Song
Mingshun Li
Jie Yang
Jianfeng Weng
Zhaodong Meng
Degui Zhang
Jienan Han
Yong Chen
Runze Zhang
Liwei Wang
Meng Zhao
Wenwei Gao
Xiaoyu Chen
Wenjie Li
Zhuanfang Hao
Junjie Fu
Xuecai Zhang
Xinhai Li
author_sort Ping Luo
collection Repository of Agricultural Research Outputs (CGSpace)
description 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.
format Journal Article
id CGSpace137827
institution CGIAR Consortium
language Inglés
publishDate 2023
publishDateRange 2023
publishDateSort 2023
publisher Elsevier
publisherStr Elsevier
record_format dspace
spelling CGSpace1378272025-12-08T10:11:39Z Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design Ping Luo Houwen Wang Zhiyong Ni Ruisi Yang Fei Wang Hongjun Yong Lin Zhang Zhiqiang Zhou Wei Song Mingshun Li Jie Yang Jianfeng Weng Zhaodong Meng Degui Zhang Jienan Han Yong Chen Runze Zhang Liwei Wang Meng Zhao Wenwei Gao Xiaoyu Chen Wenjie Li Zhuanfang Hao Junjie Fu Xuecai Zhang Xinhai Li maize genetics hybrids performance assessment 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. 2023-12 2024-01-16T23:02:36Z 2024-01-16T23:02:36Z Journal Article https://hdl.handle.net/10568/137827 en Open Access application/pdf Elsevier Luo, P., Wang, H., Ni, Z., Yang, R., Wang, F., Yong, H., Zhang, L., Zhou, Z., Song, W., Li, M., Yang, J., Weng, J., Meng, Z., Zhang, D., Han, J., Chen, Y., Zhang, R., Wang, L., Zhao, M., … Li, X. (2023). Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design. The Crop Journal, 11(6), 1884–1892. https://doi.org/10.1016/j.cj.2023.09.009
spellingShingle maize
genetics
hybrids
performance assessment
Ping Luo
Houwen Wang
Zhiyong Ni
Ruisi Yang
Fei Wang
Hongjun Yong
Lin Zhang
Zhiqiang Zhou
Wei Song
Mingshun Li
Jie Yang
Jianfeng Weng
Zhaodong Meng
Degui Zhang
Jienan Han
Yong Chen
Runze Zhang
Liwei Wang
Meng Zhao
Wenwei Gao
Xiaoyu Chen
Wenjie Li
Zhuanfang Hao
Junjie Fu
Xuecai Zhang
Xinhai Li
Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design
title Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design
title_full Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design
title_fullStr Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design
title_full_unstemmed Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design
title_short Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design
title_sort genomic prediction of yield performance among single cross maize hybrids using a partial diallel cross design
topic maize
genetics
hybrids
performance assessment
url https://hdl.handle.net/10568/137827
work_keys_str_mv AT pingluo genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT houwenwang genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT zhiyongni genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT ruisiyang genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT feiwang genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT hongjunyong genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT linzhang genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT zhiqiangzhou genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT weisong genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT mingshunli genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT jieyang genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT jianfengweng genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT zhaodongmeng genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT deguizhang genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT jienanhan genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT yongchen genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT runzezhang genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT liweiwang genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT mengzhao genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT wenweigao genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT xiaoyuchen genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT wenjieli genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT zhuanfanghao genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT junjiefu genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT xuecaizhang genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign
AT xinhaili genomicpredictionofyieldperformanceamongsinglecrossmaizehybridsusingapartialdiallelcrossdesign