Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids.

For many plant and animal species, commercial products are hybrids between individuals from different genetic groups. For allogamous plant species such as maize, the breeding objective is to produce single-cross hybrid varieties from two inbred lines each selected in complementary groups. Efficient hybrid breeding requires methods that (1) quickly generate homozygous and homogeneous parental lines with high combining abilities, (2) efficiently choose among the large number of available parental lines the most promising ones, and (3) predict the performances of sets of non-phenotyped single-cross hybrids, or hybrids phenotyped in a limited number of environments, based on their relationship with another set of hybrids with known performances. The maize breeding community has been developing model-based prediction of hybrid performances well before the genomic era. This chapter (1) provides a reminder of the maize breeding scheme before the genomic era; (2) describes how genomic data were incorporated in the prediction models involved in different steps of genomic-based single-cross maize hybrid breeding; and (3) reviews factors affecting the accuracy of genomic prediction, approaches for optimizing GP-based single-cross maize hybrid breeding schemes, and ensuring the long-term sustainability of genomic selection.

Martins Oliveira IC ，Bernardeli A ，Soler Guilhen JH ，Pastina MM ... -  《-》

Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data.

Dias KOG ，Piepho HP ，Guimarães LJM ，Guimarães PEO ，Parentoni SN ，Pinto MO ，Noda RW ，Magalhães JV ，Guimarães CT ，Garcia AAF ，Pastina MM ... -  《-》

Genomic prediction applied to multiple traits and environments in second season maize hybrids.

de Oliveira AA ，Resende MFR Jr ，Ferrão LFV ，Amadeu RR ，Guimarães LJM ，Guimarães CT ，Pastina MM ，Margarido GRA ... -  《-》

Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids.

Acosta-Pech R ，Crossa J ，de Los Campos G ，Teyssèdre S ，Claustres B ，Pérez-Elizalde S ，Pérez-Rodríguez P ... -  《-》

Accurate prediction of complex traits for individuals and offspring from parents using a simple, rapid, and efficient method for gene-based breeding in cotton and maize.

Accurate, simple, rapid, and inexpensive prediction of complex traits controlled by numerous genes is paramount to enhanced plant breeding, animal breeding, and human medicine. Here we report a novel method that enables accurate, simple, and rapid prediction of complex traits of individuals or offspring from parents based on the number of favorable alleles (NFAs) of the genes controlling the objective traits. The NFAs of 226 cotton fiber length (GFL) genes and nine maize hybrid grain yield related (ZmF1GY) genes were directly used to predict cotton fiber lengths of individual plants and maize grain yields of F1 hybrids from parents, respectively, using prediction model-based methods as controls. The NFAs of the 226 GFL genes predicted cotton fiber lengths at an accuracy of 0.85, as the model methods and outperforming genomic prediction by 82 % - 170 %. The NFAs of the nine ZmF1GY genes predicted grain yields of maize hybrids from parents at an accuracy of 0.80, outperforming genomic prediction by 67 %. Moreover, the prediction accuracies of these traits were consistent across years, environments, and eco-agricultural systems. Importantly, the accurate prediction of these traits directly using the NFAs of the genes allows breeding to be performed in greenhouse, phytotron, or off-season, without the need of the model training and validation steps essential and costly for model-based genomic or genic prediction. Therefore, this new method dramatically outperforms the current model-based genomic methods used for phenotype prediction and streamlines the process of breeding, thus promising to substantially enhance current plant and animal breeding.

Liu YH ，Zhang M ，Scheuring CF ，Cilkiz M ，Sze SH ，Smith CW ，Murray SC ，Xu W ，Zhang HB ... -  《-》