Gene expression and genetic control to cold tolerance during maize seed germination

BACKGROUND

The study of cold tolerance in maize seeds and seedlings through physiological quality assessments, as well as the genetic control associated with this trait, allows an early characterization of genotypes. Here we studied the genetic control for cold tolerance during the germination process in maize seeds and genes influenced by this stress.

RESULTS

Six maize lines were used, three classified as tolerant and three as susceptible to low germination temperature. A field was developed to produce the hybrid seeds, in a partial diallel scheme including the reciprocal crosses. For the expression analysis, seeds from two contrasting lines were used, as well as their hybrid combination and their reciprocal crosses, on dried and moistened seeds at 10 °C for 4 and 7 days. It was evaluated the catalase (CAT) and esterase (EST) enzymes, heat-resistant proteins and the genes Putative stearoyl-ACP desaturase (SAD), Ascorbate Peroxidase (APX), Superoxide Dismutase (SOD) and Mitogen Activated Protein Kinase (ZmMPK5). The estimated values ​​for heterosis, general and specific combining abilities and reciprocal maternal and non-maternal effects were carried out and it showed that there is heterosis for germination at low temperatures, also the non-additive genes were more important and there was a reciprocal effect.

CONCLUSIONS

There is a greater expression of the CAT and EST enzymes in moistened seeds at seven days and there is less expression of heat-resistant proteins and the SAD gene at seven days of moistening. Also, there are variations in the expression of the APX, SOD and ZmMPK5 genes in dried and moistened seeds, as well as among the genotypes studied.

Multigenerational prediction of genetic values using genome-enabled prediction

The identification of elite individuals is a critical component of most breeding programs. However, the achievement of this goal is limited by the high cost of phenotyping and experimental research. A significant benefit of genomic selection (GS) to plant breeding is the identification of elite individuals without the need for phenotyping. This study aimed to propose different calibration strategies using combinations between generations from different genetic backgrounds to improve the reliability of GS and to investigate the effects of LD in different types of mating systems: outcrossing (A_n) self-pollination (S_n) and hybridization (H_n). For this purpose, we simulated a genome with 10 linkage groups. In each group, two QTL were simulated. Subsequently, an F₂ population was created, followed by four generations of inbreeding (S₁ to S_4, H₁ to H _4, A₁, to A_4,). Quantitative traits were simulated in three scenarios considering three degrees of dominance (d/a = 0, 0.5 and 1) and two broad sense heritabilities (h² = 0.30 and 0.70), totaling six genetic architectures. To evaluate prediction reliability, a model (RR-BLUP) was trained in one generation and used to predict the following generations of mating systems. For example, the marker effects estimated in the F₂ population were used to estimate the expected genomic breeding value (GEBV) in populations S₁ through A_4. The squared correlation between the GEBV and the true genetic value were used to measure the reliability of the predictions. Independently of the population used to estimate the marker effect, reliability showed the lowest values in the scenario where d = 1. For any scenario, the use of the multigenerational prediction methodology improved the reliability of GS.

Selection index using the graphical area applied to sugarcane breeding

This study aimed to develop a multivariate selection index based on the graphical area of a polygon formed by standardized values, also known as radar chart. This methodology may be used to assist selection of superior genotypes in sugarcane breeding programs. Seven technological traits in 37 sugarcane genotypes were evaluated. An area index (AI) was constructed and the resulting polygon areas were used to rank genotypes under selection. In this study, we propose the use of restricted maximum likelihood to estimate genetic parameters and mixed model equations to predict genotypic and breeding values. The area of each polygon was calculated for phenotypic, genotypic, and estimated breeding values. Thereby, the genotypes with larger area can be selected based on a detailed a posteriori evaluation of the radar charts. The proposed AI can be adjusted based on the breeders' specific interests, it is perfectly useful in other crops, and may also be applied to studies on genotype-environment interactions. Moreover, AI is a powerful tool that can evaluate trait stability of genotypes based on slight differences in the area formed by each genotype. Hence, this method is easy to apply and shows great potential for use in sugarcane breeding programs as well as in other breeding programs.