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

IF 6 1区 农林科学 Q1 AGRONOMY
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 , Xinhai Li
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引用次数: 0

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.

利用部分两系杂交设计从基因组学角度预测单交玉米杂交种的产量表现
植物育种中的基因组预测(GP)可以根据亲本品系的基因型预测和确定表现最佳的杂交种。在一项 GP 试验中,通过部分两两杂交设计选育出了 34 个近交系精英,组成了 285 个单交杂交种。这些品系代表了中国玉米种质的一个小型核心集合,包括 18 个硬秆杂交组近交系和 16 个非硬秆杂交组近交系。在中国玉米主产区夏播区(SUS)的两个地点和春播区(SPS)的三个地点对亲本进行了基因分型,并对 285 个杂交种的 9 个产量和产量相关性状进行了表型。采用多个 GP 模型评估杂交种性状预测的准确性。通过十倍交叉验证,用基因组最佳线性无偏预测(GBLUP)模型估计的杂交种在SUS和SPS的产量预测准确率分别为0.51和0.46。在 SUS 和 SPS 中,用 GBLUP 估算的其余产量相关性状的预测准确率分别为 0.49 至 0.86 和 0.53 至 0.89。当将加性效应、显性效应、外显效应、基因型与环境的交互作用以及多性状效应纳入预测模型时,杂交种产量性能的预测准确性有所提高。确定了产量预测的最佳训练群体与测试群体的比例和训练群体的大小。多重预测模型可提高杂交育种的预测精度。
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来源期刊
Crop Journal
Crop Journal Agricultural and Biological Sciences-Agronomy and Crop Science
CiteScore
9.90
自引率
3.00%
发文量
638
审稿时长
41 days
期刊介绍: The major aims of The Crop Journal are to report recent progresses in crop sciences including crop genetics, breeding, agronomy, crop physiology, germplasm resources, grain chemistry, grain storage and processing, crop management practices, crop biotechnology, and biomathematics. The regular columns of the journal are Original Research Articles, Reviews, and Research Notes. The strict peer-review procedure will guarantee the academic level and raise the reputation of the journal. The readership of the journal is for crop science researchers, students of agricultural colleges and universities, and persons with similar academic levels.
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