Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review

Abstract

The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype–environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.

Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data

“…Whole‐genome molecular markers have been widely used for complex trait prediction (Crossa et al., 2013; Fritsche‐Neto et al., 2021; Meuwissen et al., 2001). Genomic prediction performance mainly relies on the genetic relationship between individuals in the reference and target populations and linkage disequilibrium between genetic markers and quantitative trait loci.…”

Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

“…This situation should lead to the highest predictive abilities, and while the combination of populations proposed in this study is a suitable approach to initiate the prediction of hybrids, a training set composed of hybrids only should be envisioned as soon as enough phenotypic and genotypic data for the hybrids are available. Such a training set can be built gradually, by replacing a part of the genotypes of the two populations by newly phenotyped and genotyped hybrids ( Fritsche-Neto et al 2021 ).…”

Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

“…This situation should lead to the highest predictive abilities, and while the combination of populations proposed in this study is a suitable approach to initiate the prediction of hybrids, a training set composed of hybrids only should be envisioned as soon as enough phenotypic and genotypic data for the hybrids is available. Such a training set can be built gradually, by replacing a part of the genotypes of the two populations by newly phenotyped and genotyped hybrids (Fritsche-Neto et al 2021).…”