No abstract

Exploring the symbiosis between plants and plant-growth-promoting bacteria (PGPB) is a new challenge for sustainable agriculture. Even though many works have reported the beneficial effects of PGPB in increasing plant resilience for several stresses, its potential is not yet widely explored. One of the many reasons is the differential symbiosis performance depending on the host genotype. This opens doors to plant breeding programs to explore the genetic variability and develop new cultivars with higher responses to PGPB interaction and, therefore, have higher resilience to stress. Hence, we aimed to study the genetic architecture of the symbiosis between PGPB and tropical maize germplasm, using a public association panel and its impact on plant resilience. Our findings reveal that the synthetic PGPB population can modulate and impact root architecture traits, improve resilience to nitrogen stress, and 37 regions were significant for controlling the symbiosis between PGPB and tropical maize. In addition, we found two overlapping SNPs in the GWAS analysis indicating strong candidates for further investigations. Furthermore, genomic prediction analysis with genomic relationship matrix computed using only significant SNPs obtained from GWAS analysis substantially increased the predictive ability for several traits endorsing the importance of these genomic regions for the response of PGPB. Finally, the public tropical panel reveals a significant genetic variability to the symbiosis with the PGPB and can be a source of alleles to improve plant resilience.

Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.

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.

The selection of informative markers has been studied massively as an alternative to reduce genotyping costs for the genomic selection (GS) application. Low-density marker panels are attractive for GS because they decrease computational time-consuming and multicollinearity beyond more individuals can be genotyped with the same cost. Nevertheless, these inferences are usually made empirically using "static" training sets and populations, which are adequate only to predict a breeding program's initial cycles but might not for long-term cycles. Moreover, to the best of our knowledge, none of these inferences considered the inclusion of dominance into the GS models, which is particularly important to predict cross-pollinated crops. Therefore, that reveals an important and unexplored topic for allogamous long-term breeding. To achieve this goal, we employed two approaches: the former used empirical maize datasets, and the latter simulations of long-term breeding cycles of phenotypic and genomic recurrent selection (intrapopulation and reciprocal). Then, we observed the reducing marker density effect on populations (mean, the best genotypes performance, accuracy, additive variance) over cycles and models (additive, additive-dominance, specific combining ability (SCA)). Our results indicate that the markers reduction based on different linkage disequilibrium (LD) levels is viable only within a cycle and brings a significant decrease in predictive ability over generations. Furthermore, in the long-term, regardless of the selection scheme adopted, the more makers, the better because they buffer LD losses caused by recombination over breeding cycles. Finally, regarding the accuracy, the additive-dominant models tend to outperform the additive ones and perform similar to the SCA.