Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review - Fritsche-Neto, Roberto; Galli, Giovanni; Borges, Karina Lima Reis; Costa-Neto, Germano; Alves, Filipe Couto; Sabadin, Felipe; Lyra, Danilo Hottis; Morais, Pedro Patric Pinho; Braatz de Andrade, Luciano Rogério; Granato, Italo; Crossa, Jose (Frontiers in Plant Science, 2021)
Hybrid breeding programs are based on the pure line methods, including the development of inbred lines produced by self-pollination or double-haploids, followed by evaluating those selected by single-cross performances when crossed to other lines. A significant challenge in this approach is achieving adequate testing of the inbreds to assess their performance in all possible pairwise combinations. Therefore, we have carried out several studies that have indicated the usefulness of genomic prediction (GP). However, many factors may affect the accuracy of predictions, such as the mating design and the genotypes used to compose the training population, the presence of population structure (PS), the importance of non-additive effects controlling the desired trait, the source of the molecular markers, and genotype × environment interaction (G×E). Hence, the aim is describe the most important results we obtained and how they can improve the accuracy of prediction in tropical maize hybrids. Another concern is creating tools that can help implement genomic selection in breeding pipelines. Finally, along with the great advances that have been made, we find that what is yet to come is exciting. New tools and models, such as high-throughput phenotyping and crop-modeling, may bring more resolution and realism to predicting genotype performances.