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Date
2022
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Zhang, A., Pérez‐Rodríguez, P., San Vicente, F. M., Palacios‐Rojas, N., Dhliwayo, T., Liu, Y., Cui, Z., Guan, Y., Wang, H., Zheng, H., Olsen, M., Prasanna, B. M., Ruan, Y., Crossa, J., & Zhang, X. (2022). Genomic prediction of the performance of hybrids and the combining abilities for line by tester trials in maize. Crop Journal, 10(1), 109–116. https://doi.org/10.1016/j.cj.2021.04.007

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Abstract
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The two most important activities in maize breeding are the development of inbred lines with high values of general combining ability (GCA) and specific combining ability (SCA), and the identification of hybrids with high yield potentials. Genomic selection (GS) is a promising genomic tool to perform selection on the untested breeding material based on the genomic estimated breeding values estimated from the genomic prediction (GP). In this study, GP analyses were carried out to estimate the performance of hybrids, GCA, and SCA for grain yield (GY) in three maize line-by-tester trials, where all the material was phenotyped in 10 to 11 multiple-location trials and genotyped with a mid-density molecular marker platform. Results showed that the prediction abilities for the performance of hybrids ranged from 0.59 to 0.81 across all trials in the model including the additive effect of lines and testers. In the model including both additive and non-additive effects, the prediction abilities for the performance of hybrids were improved and ranged from 0.64 to 0.86 across all trials. The prediction abilities of the GCA for GY were low, ranging between − 0.14 and 0.13 across all trials in the model including only inbred lines; the prediction abilities of the GCA for GY were improved and ranged from 0.49 to 0.55 across all trials in the model including both inbred lines and testers, while the prediction abilities of the SCA for GY were negative across all trials. The prediction abilities for GY between testers varied from − 0.66 to 0.82; the performance of hybrids between testers is difficult to predict. GS offers the opportunity to predict the performance of new hybrids and the GCA of new inbred lines based on the molecular marker information, the total breeding cost could be reduced dramatically by phenotyping fewer multiple-location trials.
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https://hdl.handle.net/10883/21975
Copyright
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Journal
Crop Journal
Journal volume
10
Journal issue
1
Article number
Place of Publication
Netherlands
Publisher
Elsevier
DOI
https://doi.org/10.1016/j.cj.2021.04.007
AGROVOC
MAIZE; MARKER-ASSISTED SELECTION; COMBINING ABILITY
Related Datasets
Link to the dataset

CGIAR Initiatives

Initiative
Accelerated Breeding
Impact Area
Nutrition, health & food security
Action Area
Genetic Innovation
Donor or Funder
Shanghai Agriculture Applied Technology Development Program
Bill & Melinda Gates Foundation (BMGF)
CGIAR Research Program on Maize
National Key Research and Development Program of China
National Natural Science Foundation of China
Shenyang City Key Laboratory of Maize Genomic Selection
Shanghai Municipal Finance Bureau
Liaoning Province Key Scientific and Technological Research and Development Project
CGSpace URL
https://hdl.handle.net/10568/126428
Collections
Maize
Genetic Resources