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ABSTRACT. The identification of genes related to heat tolerance is fundamental for the development of high-quality seeds that are tolerant to heat stress condition. The objective of this study was to evaluate maize lineages and the gene expression involved in high temperature tolerance during germination using physiological tests, proteomics, and transcriptome analysis. Seeds from six maize lineages (30, 44, 54, 63, 64, and 91) with different levels of tolerance to high temperatures were used. Lineages 54 and 91 were observed to be more tolerant to high temperature conditions. The highest expression of α-amylase was observed in maize seeds from lineages 30 and 91 that were subjected to controlled deterioration. The highest expression of α-amylase was observed in maize seeds from lineages 30 and 91 that were subjected to controlled deterioration; with the controlled deterioration, the highest level of gene expression did not occur in the most tolerant materials; the association of lower expression of genes involved in heat-resistant protein systems was observed in seeds from lineage 44, which were more susceptible to high temperatures, and the highest gene expression of LEA D-34, ZmAN13, and AOX-1 was observed in seeds from lineage 64 when submitted to controlled deterioration.

Selections via the mixed model and the multivariate analysis approach can be powerful tools for selecting cultivars in plant breeding programs. Therefore, this study aimed to compare the use of mixed models, multivariate analysis and traditional phenotypic selection to identify superior maize (Zea mays L.) genotypes. Seventy-one (71) maize Topcrosses and three commercial cultivars were evaluated using these three methods. Plant height, ear height, ear placement, stalk lodging and breakage, and grain yield were evaluated. There was a difference between selection methods, as the selection with mixed models and the selection based on the average phenotypic afforded the inclusion of genotypes with high productivity, which did not occur for the multivariate analysis. The selection by multivariate analysis allowed the inclusion of genotypes with better agronomic and other desirable traits, not only those with highest productivity, in a maize breeding program.

Constant advances in studies on the behavior of maize genotypes and their interactions with the environment are of great importance for the best performance of the plant. This study verifies effects and causes of agronomic variables of maize hybrids on grain yields and performs the indirect selection of superior genotypes by principal component analysis (PCA). Two hundred and thirty maize genotypes were used, with two hundred and twenty--nine topcross hybrids (consisting of crossings of two hundred and twenty-nine partially inbred genotypes with a tester) and one check in a randomized block design with two repetitions. The genotypes were evaluated during the 2016 and 2016/2017 crops considering the agronomic variables plant height, ear insertion height, ear position, lodging, breakage, and grain yield. Data were submitted to analysis of variance and means were compared by the Scott-Knott test (p<0.05) with subsequent multivariate exploratory analysis by PCA. In the principal component analysis, components explained 52.07% and 55.69% of the variance contained in the original variables for the 2016 and 2016/2017 crops, respectively. The variable that was most significant in both crops was ear insertion height, allowing the indirect selection of more productive genotypes. Indirect selection of the most productive genotypes was also conducted through variables that contributed significantly in the principal component analysis. Thus, the use of multivariate exploratory analysis is efficient in the characterization and selection of maize genotypes evaluated in different crop seasons.