Biology subjects

Moraes, A. d. C. L.

Publications and source records attributed to Moraes, A. d. C. L..

6 recordsLinked to original sources

bioRxiv2026

Multi-omics integration reveals candidate genes and molecular mechanisms associated with resistance to sugarcane smut

Sugarcane smut, caused by the fungus Sporisorium scitamineum (Syd.), is one of the main diseases affecting sugarcane and is capable of causing severe yield losses in susceptible varieties. Given the limitations of classical breeding, mainly due to the genetic complexity of sugarcane cultivars, molecular and omics approaches have gained increasing prominence for studying the genetic basis of traits such as disease resistance. In this context, we investigated sugarcane resistance to smut through the integration of association mapping methods and transcriptomics. A panel of 155 genotypes was genotyped with 33,335 SNP markers and inoculated with S. scitamineum generating three phenotypic variables. These data were jointly analyzed through genome-wide association analyses to identify marker-trait associations. In addition, transcriptomic analyses were performed using publicly available datasets from four studies evaluating sugarcane responses to smut. The integration of these multi-omics data enabled the identification of candidate genes involved in structural reinforcement, immune signaling, redox homeostasis, genome maintenance, and cellular adaptation. These findings provide valuable molecular resources for functional validation and breeding strategies aimed at developing sugarcane cultivars with enhanced smut resistance.

genomics↗

bioRxiv2023

Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits in Urochloa ruziziensis

Tropical forage grasses, especially species of the genus Urochloa, play an important role in cattle production and are the main food source for animals in tropical/subtropical regions. Most of the species are apomictic and tetraploid, which gives special importance to U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to assist in breeding programs, this study investigates the applicability of genome-wide family prediction (GWFP) in U. ruziziensis half-sibling families to predict growth and biomass production. Machine learning and feature selection algorithms were used to reduce the necessary number of markers for prediction and to enhance the predictive ability across the phenotypes. Beyond that, to investigate the regulation of agronomic traits, the positions of the markers with more importance for the prediction were considered putatively associated to quantitative trait loci (QTLs), and in a multiomic approach, genes obtained in the species transcriptome were mapped and linked to those markers. Furthermore, a gene coexpression network was modeled, enabling the investigation of not only the mapped genes but also their coexpressed genes. The functional annotation showed that the mapped genes are mainly associated with auxin transport and biosynthesis of lignin, flavonol and folic acid, while the coexpressed genes are associated with DNA metabolism, stress response and circadian rhythm. The results provide a viable marker-assisted breeding approach for tropical forages and identify target regions for future molecular studies on these agronomic traits.

genetics↗

bioRxiv2023

Advances in genomic characterization of Urochloa humidicola: exploring polyploid inheritance and apomixis

Tropical forage grasses are an important food source for animal feeding, with Urochloa humidicola, also known as Koronivia grass, being one of the main pasture grasses for poorly drained soils in the tropics. However, genetic and genomic resources for this species are lacking due to its genomic complexity, including high heterozygosity, evidence of segmental allopolyploidy, and reproduction by apomixis. These complexities hinder the application of marker-assisted selection (MAS) in breeding programs. Here, we developed the highest-density linkage map currently available for the hexaploid tropical forage grass U. humidicola. This map was constructed using a biparental F1 population generated from a cross between the female parent H031 (CIAT 26146), the only known sexual genotype for the species, and the apomictic male parent H016 (BRS cv. Tupi). The linkage analysis included 4,873 single nucleotide polymorphism (SNP) markers with allele dosage information. It allowed mapping of the apospory locus and phenotype to linkage group 3, in a region syntenic with chromosome 3 of Urochloa ruziziensis and chromosome 1 of Setaria italica. We also identified hexaploid haplotypes for all individuals, assessed the meiotic configuration, and estimated the level of preferential pairing in parents during the meiotic process, which revealed the autopolyploid origin of sexual H031 in contrast to H016, which presented allopolyploid behavior in preferential pairing analysis. These results provide new information regarding the genetic organization, mode of reproduction, and allopolyploid origin of U. humidicola, potential SNPs markers associated to apomixes for MAS and resources for research on polyploids and tropical forage grasses. Key messageWe present the highest-density genetic map for the hexaploid Urochloa humidicola. SNP markers expose genetic organization, reproduction, and species origin, aiding polyploid and tropical forage research.

genomics↗

bioRxiv2022

A joint learning approach for genomic prediction in polyploid grasses

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.

bioinformatics↗

bioRxiv2021

An Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses

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, self-fertilized individuals, half-siblings and full contaminants, 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 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.

genomics↗

bioRxiv2019

High-resolution Linkage Map with Allele Dosage Allows the Identification of an Apomixis Region and Complex Traits in Guinea Grass (Megathyrsus maximus)

Forage grasses are mainly used in animal feed to fatten cattle and dairy herds. Among tropical forage crops that reproduce by seeds, guinea grass (Megathyrsus maximus) is considered one of the most productive. This species has several genomic complexities, such as autotetraploidy and apomixis, due to the process of domestication. Consequently, approaches that relate phenotypic and genotypic data are incipient. In this context, we built a linkage map with allele dosage and generated novel information about the genetic architecture of traits that are important for the breeding of M. maximus. From a full-sib progeny, a linkage map containing 858 single nucleotide polymorphism (SNP) markers with allele dosage information expected for an autotetraploid was obtained. The high genetic variability of the progeny allowed us to map ten quantitative trait loci (QTLs) related to agronomic traits, such as regrowth capacity and total dry matter, and 36 QTLs related to nutritional quality, which were distributed among all homology groups (HGs). Various overlapping regions associated with the quantitative traits suggested QTL hotspots. In addition, we were able to map one locus that controls apospory (apo-locus) in HG II. A total of 55 different gene families involved in cellular metabolism and plant growth were identified from markers adjacent to the QTLs and apomixis locus by using the Panicum virgatum genome as a reference in comparisons with the genomes of Arabidopsis thaliana and Oryza sativa. Our results provide a better understanding of the genetic basis of reproduction by apomixis and traits important for breeding programs that considerably influence animal productivity as well as the quality of meat and milk.

plant biology↗