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.

A critical step toward the success of the doubled haploid (DH) technique is the haploid identification within induction crosses. The R1-nj marker is the principal mechanism employed in this task enabling the selection of haploids at the seed stage. Although it seems easy to identify haploid seeds, this task is performed manually by visual classification, which becomes an inefficient process in terms of time and labor. Also, differential phenotypic expression of the R1-nj marker results in high rates of false positives among haploid seeds. For the first time, an image-based convolutional neural network (CNN) was trained to identify true positives among putative haploid seeds. The experiment was conducted using 3,000 maize (Zea mays L.) seeds from induction crosses classified as haploid (1,000), diploid (1,000), and inhibited (1,000) class. Images were taken from each seed, and then seeds were planted in the field to confirm their ploidy. For putative haploids (R1-nj phenotype), the classification accuracy on average was 94.39%, 97.07% for the haploid class, and 91.71% for the diploid class. However, the CNN model was unable to distinguish true haploid seeds among the putative haploid class, which indicates that CNN did not recognize different patterns between them. Finally, we provided a highly accurate and trained CNN model to the scientific community to classify haploid maize seeds via R1-nj, which can support maize breeders to optimize DH pipelines, mainly for small breeding programs with limited resources.

Background Success in any genomic prediction platform is directly dependent on establishing a representative training set. This is a complex task, even in single-trait single-environment conditions and tends to be even more intricated wherein additional information from envirotyping and correlated traits are considered. Here, we aimed to design optimized training sets focused on genomic prediction, considering multi-trait multi-environment trials, and how those methods may increase accuracy reducing phenotyping costs. For that, we considered single-trait multi-environment trials and multi-trait multi-environment trials for three traits: grain yield, plant height, and ear height, two datasets, and two cross-validation schemes. Next, two strategies for designing optimized training sets were conceived, first considering only the genomic by environment by trait interaction (GET), while a second including large-scale environmental data (W, enviromics) as genomic by enviromic by trait interaction (GWT). The effective number of individuals (genotypes × environments × traits) was assumed as those that represent at least 98% of each kernel (GET or GWT) variation, in which those individuals were then selected by a genetic algorithm based on prediction error variance criteria to compose an optimized training set for genomic prediction purposes. Results The combined use of genomic and enviromic data efficiently designs optimized training sets for genomic prediction, improving the response to selection per dollar invested by up to 145% when compared to the model without enviromic data, and even more when compared to cross validation scheme with 70% of training set or pure phenotypic selection. Prediction models that include G × E or enviromic data + G × E yielded better prediction ability. Conclusions Our findings indicate that a genomic by enviromic by trait interaction kernel associated with genetic algorithms is efficient and can be proposed as a promising approach to designing optimized training sets for genomic prediction when the variance-covariance matrix of traits is available. Additionally, great improvements in the genetic gains per dollar invested were observed, suggesting that a good allocation of resources can be deployed by using the proposed approach.

Genomic prediction (GP) success is directly dependent on establishing a training population, where incorporating envirotyping data and correlated traits may increase the GP accuracy. Therefore, we aimed to design optimized training sets for multi-trait for multi-environment trials (MTMET). For that, we evaluated the predictive ability of five GP models using the genomic best linear unbiased predictor model (GBLUP) with additive + dominance effects (M1) as the baseline and then adding genotype by environment interaction (G × E) (M2), enviromic data (W) (M3), W+G × E (M4), and finally W+G × W (M5), where G × W denotes the genotype by enviromic interaction. Moreover, we considered single-trait multi-environment trials (STMET) and MTMET for three traits: grain yield (GY), plant height (PH), and ear height (EH), with two datasets and two cross-validation schemes. Afterward, we built two kernels for genotype by environment by trait interaction (GET) and genotype by enviromic by trait interaction (GWT) to apply genetic algorithms to select genotype:environment:trait combinations that represent 98% of the variation of the whole dataset and composed the optimized training set (OTS). Using OTS based on enviromic data, it was possible to increase the response to selection per amount invested by 142%. Consequently, our results suggested that genetic algorithms of optimization associated with genomic and enviromic data efficiently design optimized training sets for genomic prediction and improve the genetic gains per dollar invested.

Enviromic-based kernels optimize resource allocation with multi-trait multi-environment genomic prediction for tropical maize / Raysa Gevartosky. versão revisada de acordo com a resolução CoPGr 6018 de 2011. --Piracicaba, 2021. 36 p. Dissertação (Mestrado) --USP / Escola Superior de Agricultura "Luiz de Queiroz". 1. Seleção genômica 2. População de treinamento otimizada 3. Caracterização ambiental 4. Resposta à seleção 5. Capacidade preditiva I. Título AGRADECIMENTOS À minha família pelo apoio e amor incondicional. À Escola Superior de Agricultura "Luiz de Queiroz" pela formação e estrutura, durante todos os anos de graduação e mestrado. Ao Professor Dr. Roberto Fritsche Neto pela orientação, oportunidade e valiosos ensinamentos. Aos colegas do Laboratório de Melhoramento de Plantas Alógamas pelo apoio, auxílio, convivência e amizade. À empresa Helix Sementes por ceder os dados. À CAPES -Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -pela concessão da bolsa durante o período de formação.Àqueles aqui não mencionados que de alguma maneira contribuíram para a realização deste trabalho.