Breeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multi-environment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids' genotypes were inferred based on their parents' genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids.

Water deficit is one of the most common causes of severe crop‐production losses worldwide in maize (Zea mays L.). The main goal of this study was to infer about genotype × environment interaction (G × E) and to estimate genetic correlations between drought tolerance traits in maize using factor analytic (FA) multiplicative mixed models in the context of multi‐environment trial (MET) and multi‐trait multi‐environment trial (MTMET) analyses. The traits measured were: grain yield (GY), ears per plot (EPP), anthesis‐silking interval (ASI), female flowering time (FFT), and male flowering time (MFT). Three‐hundred and eight hybrids were evaluated in a total of eight trials conducted under water‐stressed (WS) and well‐watered (WW) conditions across 2 yr and two locations in Brazil. For most of the traits (GY, ASI, and FFT), the magnitude of the genetic variances differed across WS and WW conditions. Genetic correlations between water conditions for FFT and MFT were 0.81 and 0.82, respectively, indicating that it might be unnecessary to measure these traits in both water conditions. Grain yield and EPP showed moderate to high G × E, with genetic correlations of 0.57 and 0.39 between WS and WW conditions, respectively, which suggested that gene expression was not consistent across different water regimes. Therefore, it is necessary to evaluate these traits under both water conditions. Genetic correlations between pairs of traits, in general, were higher under WS conditions compared with WW conditions. Grain yield exhibited moderate correlations with EPP (r = 0.62) and FFT (r = −0.42) under WS conditions. The FA models can be a useful tool for MET and MTMET analyses in maize breeding programs for drought tolerance.

Family selection is an important procedure to be considered in the early stage of sugarcane (Saccharum spp.) breeding. Different approaches are available, but few comparative studies are performed in practice. The aim of this study was to evaluate the potential genetic gain when different selection strategies at early sugarcane breeding stages are considered. Two experiments involving the first and second selection stages of the Sugarcane Breeding Program of RIDESA/UFSCar were performed. In the first stage, three selection methods based on the concept of selection between and within families were applied to predict the highest genetic gain, that is, BLUPi: simultaneously contemplates family and individual information for selection; BLUPis: promotes the dynamic allocation of individuals to be selected in each family; BLUP AUS : identifies high potential families and establishes differentiated selection intensities; additionally, mass and random selection methods were also performed. In the second stage, the selected clones were evaluated to compare the realized genetic gain. In the first stage, BLUP AUS had the highest predicted gain from selection (P GS; 12.7%) in tonnes of Pol per hectare (TPH). The BLUPis was highly correlated with BLUP AUS and was efficient. Moreover, BLUPi proved to be economically impracticable since phenotypic evaluations must be performed at the individual level. Family selection via BLUP AUS was equivalent to mass selection probably due to the low coefficient of genetic variation (CV g ≤ 15) among the families. However, the family selection strategy provides extra information for breeders that cannot be ignored; the possibility of studying the combining ability of genotypes for identifying promising parents for future cross combinations.

No abstract

, pela receptividade inicial, pelos ensinamentos, confiança e amizade, sempre com seu grande incentivo. Aos meus pais Evaneide e Luiz, por me apoiarem e incentivarem meus sonhos, por me aconchegarem nos momentos que precisei e me ensinarem que com amor e dedicação, alcançamos nossos objetivos. Se cheguei até aqui, foi por eles. Ao meu irmão Ricardo que mesmo tão distante esteve presente ao longo dessa caminhada, com suas alegrias. À minha avó Maria, meu exemplo de vida. Ao Renato Cruz Neves, que com seu amor, paciência, dedicação e imensurável compreensão, me apoiou fortemente ao longo desse trabalho. Ao Prof. Dr. Luiz Alexandre Peternelli que me apresentou o fantástico mundo científico, pela confiança, pelos ensinamentos e principalmente pela sua amizade. Serei eternamente grata pela sua dedicação que muito contribui em minha caminhada. Ao Departamento de Genética da ESALQ/USP, pela oportunidade concedida. Ao CNPq pela bolsa de estudos inicial e à FAPESP, pela bolsa concedida nos demais meses do curso de Mestrado. À Prof a. Dr a. Anete Pereira de Souza e à Dr a Luciana Rossini Pinto pela parceria, disponibilizando os dados moleculares e fenotípicos. Em especial à Melina C. Mancini, sempre disposta em solucionar minhas dúvidas relacionadas aos dados aqui utilizados.