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Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in different cross-validation scenarios. By combining classification and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains.

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The objective was to evaluate the production of Megathyrsus maximusgenotypes (Syn. Panicum maximum), under different levels of water in the soil. This was a 5x5 factorial completely randomized design conducted in a greenhouse, combining five genotypes of M. maximus(B55, C10 and PM30, cv. Massai and cv. BRS Tamani) and five levels of soil field capacities (20%, 40%, 60%, 100% and 140%), with three replications. Dry matter production was evaluated: leaf, stem, dead material, root, shoot and total dry matters, as well as the number of tillers and leaf:stem and aboveground:root ratios. The qualitative factor (genotypes) was subjected to Duncan test at 5% probability. The quantitative factor (% field capacity) was subjected to regression, adopting 5% as a critical level of probability. There was no interaction between the factors for any of the evaluated characteristics. Significant differences amongthe genotypes were detected for tiller number, dead material dry mass, root and total dry mass and leaf:stem ratio. There was no significant effect of the percentage of field capacity on most of the characteristics, except for leaf:stem and aboveground:root ratios. Cultivar Massai showed the best forage production compared to the other genotypes, regardless of the percentage of field capacity evaluated. In general, the evaluated genotypes were more tolerant to excess water stress than to water deficit.(AU)

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Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.

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UNLABELLED: Cruciferous plants are important edible vegetables widely consumed around the world, including cabbage, cauli-flower and broccoli. The main disease that affects crucifer plants is black rot, caused by Xanthomonas campestris pv. campestris (Xcc). In order to better understand this specific plant-pathogen interaction, proteins responsive to Xcc infection in resistant (União) and susceptible (Kenzan) Brassica oleracea cultivars were investigated by 2-DE followed by mass spectrometry. A total of 47 variable spots were identified and revealed that in the susceptible interaction there is a clear reduction in the abundance of proteins involved in energetic metabolism and defense. It was interesting to observe that in the resistant interaction, these proteins showed an opposite behavior. Based on our results, we conclude that resistance is correlated with the ability of the plant to keep sufficient photosynthesis metabolism activity to provide energy supplies necessary for an active defense. As a follow-up study, qRT-PCR analysis of selected genes was performed and revealed that most genes showed an up-regulation trend from 5 to 15days after inoculation (DAI), showing highest transcript levels at 15DAI. These results revealed the gradual accumulation of transcripts providing a more detailed view of the changes occurring during different stages of the plant-pathogen interaction. BIOLOGICAL SIGNIFICANCE: In this study we have compared cultivars of Brassica oleracea (cabbage), susceptible and resistant to black rot, by using the classical 2-DE approach. We have found that resistance is correlated with the ability of the plant to keep sufficient photosynthesis metabolism activity to provide energy supplies necessary for an active defense.

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This paper reports the effects of three cycles of reciprocal recurrent selection (RRS) on the means, genetic variances, and on the genetic correlations for several traits in the IG-1 and IG-2 maize (Zea mays L.) populations. Interpopulation full-sib progenies from cycle zero (C0) and from cycle 3 (C3) of RRS were evaluated in two locations. RRS was highly effective to improve the traits according the objectives of the program: grain yield and prolificacy increased significantly, while plant height, ear height, and ear placement decreased significantly. Genetic variances for all traits decreased significantly from C0 to C3, but the genetic correlations did not change consistently across the cycles of selection. The expected responses to the fourth cycle of RRS and the probability of selecting double-crosses from C3 that outperform those from C0 showed that the decreases in the genetic variances were not great enough to limit the continued improvement of the populations as well as the use of the improved populations as sources of inbred lines to develop commercial hybrids. However, if the magnitudes of the genetic variances continue to decrease, new sources of improved germplasm should be incorporated into both populations to allow the continued improvement of the interpopulation by RRS

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