Genomic prediction of the performance of hybrids and the combining abilities for line by tester trials in maize
Ao Zhang,
Paulino Pérez-Rodríguez,
Felix San Vicente,
Natalia Palacios-Rojas,
Thanda Dhliwayo,
Yubo Liu,
Zhenhai Cui,
Yuan Guan,
Hui Wang,
Hongjian Zheng,
Michael Olsen,
Boddupalli M. Prasanna,
Yanye Ruan,
Jose Crossa,
Xuecai Zhang
Affiliations
Ao Zhang
College of Biological Science and Technology, Shenyang Agricultural University, Shenyang 110866, Liaoning, China; International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico; CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 200063, China
Paulino Pérez-Rodríguez
Colegio de Postgraduados, Estado De México, Mexico
Felix San Vicente
International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico
Natalia Palacios-Rojas
International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico
Thanda Dhliwayo
International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico
Yubo Liu
CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 200063, China; Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 200063, China
Zhenhai Cui
College of Biological Science and Technology, Shenyang Agricultural University, Shenyang 110866, Liaoning, China
Yuan Guan
CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 200063, China; Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 200063, China
Hui Wang
CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 200063, China; Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 200063, China
Hongjian Zheng
CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 200063, China; Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 200063, China
Michael Olsen
International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market, Nairobi 00621, Kenya
Boddupalli M. Prasanna
International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market, Nairobi 00621, Kenya
Yanye Ruan
College of Biological Science and Technology, Shenyang Agricultural University, Shenyang 110866, Liaoning, China; Corresponding authors.
Jose Crossa
International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico; Corresponding authors.
Xuecai Zhang
International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico; Corresponding authors.
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.
