Exploring the Potential of Heterosis to Improve Nitrogen Use Efficiency in Popcorn Plants.

Nitrogen is crucial for plant growth and development, and improving nitrogen use efficiency (NUE) is a viable strategy for reducing dependence on nitrogen inputs and promoting sustainability. While the benefits of heterosis in corn are well known, the physiological mechanisms underlying this phenomenon in popcorn are less understood. We aimed to investigate the effects of heterosis on growth and physiological traits in four popcorn lines and their hybrids under two contrasting nitrogen conditions.

Marker-Assisted Recurrent Selection for Pyramiding Leaf Rust and Coffee Berry Disease Resistance Alleles in L.

In this study, marker-assisted recurrent selection was evaluated for pyramiding resistance gene alleles against coffee leaf rust (CLR) and coffee berry diseases (CBD) in A total of 144 genotypes corresponding to 12 hybrid populations from crosses between eight parent plants with desired morphological and agronomic traits were evaluated. Molecular data were used for cross-certification, diversity study and resistance allele marker-assisted selection (MAS) against the causal agent of coffee leaf rust () and coffee berry disease (. In addition, nine morphological and agronomic traits were evaluated to determine the components of variance, select superior hybrids, and estimate genetic gain.

Genomic prediction through machine learning and neural networks for traits with epistasis.

Genomic wide selection (GWS) is one contributions of molecular genetics to breeding. Machine learning (ML) and artificial neural networks (ANN) methods are non-parameterized and can develop more accurate and parsimonious models for GWS analysis. Multivariate Adaptive Regression Splines (MARS) is considered one of the most flexible ML methods, automatically modeling nonlinearities and interactions of the predictor variables.

Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice.

The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated.

Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora.

Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions about the relationships between inputs and the output allowing great flexibility to handle different types of complex non-additive effects, such as dominance and epistasis. Despite this advantage, the biological interpretability of ANNs is still limited.

Prediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation study.

The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant breeding. Data of an F2 population represented by 500 individuals, obtained from a cross between contrasting homozygous parents, were simulated. Phenotypic traits were simulated based on previously established means and heritability estimates (30%, 50%, and 80%); traits were distributed in a genome with 10 linkage groups, considering two alleles per marker.

Multivariate diallel analysis by factor analysis for establish mega-traits.

In plant breeding, the dialelic models univariate have aided the selection of parents for hybridization. Multivariate analyses allow combining and associating the multiple pieces of information of the genetic relationships between traits. Therefore, multivariate analyses might refine the discrimination and selection of the parents with greater potential to meet the goals of a plant breeding program.

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.

Quantification of anti-nutritional factors and their correlations with protein and oil in soybeans.

Soybeans contain about 30% carbohydrate, mainly consisting of non-starch polysaccharides (NSP) and oligosaccharides. NSP are not hydrolyzed in the gastrointestinal tract of monogastric animals. These NSP negatively affect the development of these animals, especially the soluble fraction.

Quantile regression for genome-wide association study of flowering time-related traits in common bean.

Flowering is an important agronomic trait. Quantile regression (QR) can be used to fit models for all portions of a probability distribution. In Genome-wide association studies (GWAS), QR can estimate SNP (Single Nucleotide Polymorphism) effects on each quantile of interest.

Joint analysis of phenotypic and molecular diversity provides new insights on the genetic variability of the Brazilian physic nut germplasm bank.

The genetic variability of the Brazilian physic nut (Jatropha curcas) germplasm bank (117 accessions) was assessed using a combination of phenotypic and molecular data. The joint dissimilarity matrix showed moderate correlation with the original matrices of phenotypic and molecular data. However, the correlation between the phenotypic dissimilarity matrix and the genotypic dissimilarity matrix was low.

Genetic diversity of 'Ubá' mango tree using ISSR markers.

In this study, the genetic diversity of 'Ubá' mango trees cultivated at the Zona da Mata of Minas Gerais State, Brazil, was assessed, to identify whether there is variability in the plants grown in the region, justifying the mass selection as a breeding method. We used 102 accessions. Leaves were collected for extraction of genomic DNA, which was amplified with nine ISSR primers.

Linkage analysis between dominant and co-dominant makers in full-sib families of out-breeding species.

As high-throughput genomic tools, such as the DNA microarray platform, have lead to the development of novel genotyping procedures, such as Diversity Arrays Technology (DArT) and Single Nucleotide Polymorphisms (SNPs), it is likely that, in the future, high density linkage maps will be constructed from both dominant and co-dominant markers. Recently, a strictly genetic approach was described for estimating recombination frequency (r) between co-dominant markers in full-sib families. The complete set of maximum likelihood estimators for r in full-sib families was almost obtained, but unfortunately, one particular configuration involving dominant markers, segregating in a 3:1 ratio and co-dominant markers, was not considered.

Revisiting the DNA C-values of the genome size-standards used in plant flow cytometry to choose the "best primary standards".

Flow cytometry (FCM) techniques have enabled characterization of the genome size for various plant species. In order to measure the nuclear genome size of a species, reference standards with well-established DNA content are necessary. However, different 2C-values have been described for the same species used as reference standard.

Comparison between simulated annealing algorithms and rapid chain delineation in the construction of genetic maps.

The efficiency of simulated annealing algorithms and rapid chain delineation in establishing the best linkage order, when constructing genetic maps, was evaluated. Linkage refers to the phenomenon by which two or more genes, or even more molecular markers, can be present in the same chromosome or linkage group. In order to evaluate the capacity of algorithms, four F(2) co-dominant populations, 50, 100, 200 and 1000 in size, were simulated.