RESUMO
Over the last 10 years, global raspberry production has increased by 47.89%, based mainly on the red raspberry species (Rubus idaeus). However, the black raspberry (Rubus occidentalis), although less consumed, is resistant to one of the most important diseases for the crop, the late leaf rust caused by Acculeastrum americanum fungus. In this context, genetic resistance is the most sustainable way to control the disease, mainly because there are no registered fungicides for late leaf rust in Brazil. Therefore, the aim was to understand the genetic architecture that controls resistance to late leaf rust in raspberries. For that, we used an interspecific multiparental population using the species mentioned above as parents, 2 different statistical approaches to associate the phenotypes with markers [GWAS (genome-wide association studies) and copula graphical models], and 2 phenotyping methodologies from the first to the 17th day after inoculation (high-throughput phenotyping with a multispectral camera and traditional phenotyping by disease severity scores). Our findings indicate that a locus of higher effect, at position 13.3 Mb on chromosome 5, possibly controls late leaf rust resistance, as both GWAS and the network suggested the same marker. Of the 12 genes flanking its region, 4 were possible receptors, 3 were likely defense executors, 1 gene was likely part of signaling cascades, and 4 were classified as nondefense related. Although the network and GWAS indicated the same higher effect genomic region, the network identified other different candidate regions, potentially complementing the genetic control comprehension.
Assuntos
Basidiomycota , Resistência à Doença , Estudo de Associação Genômica Ampla , Fenótipo , Doenças das Plantas , Rubus , Resistência à Doença/genética , Rubus/microbiologia , Rubus/genética , Doenças das Plantas/microbiologia , Doenças das Plantas/genética , Locos de Características Quantitativas , Folhas de Planta/microbiologia , Folhas de Planta/genética , Polimorfismo de Nucleotídeo Único , Mapeamento CromossômicoRESUMO
Raspberries (Rubus spp) are temperate climate fruits with profitable high returns and have the potential for diversification of fruit growing in mid to low-latitude regions. However, there are still no cultivars adapted to climatic conditions and high pressure of diseases that occurs in tropical areas. In this context, our objective was to evaluate the genetic diversity from a 116 raspberry genotypes panel obtained from interspecific crosses in a testcross scheme with four cultivars already introduced in Brazil. The panel was genotyped via genotyping-by-sequencing. 28,373 and 27,281 SNPs were obtained, using the species R. occidentalis and R. idaeus genomes as references, respectively. A third marker dataset was constructed consisting of 41,292 non-coincident markers. Overall, there were no differences in the results when using the different marker sets for the subsequent analyses. The mean heterozygosity was 0.54. The average effective population size was 174, indicating great genetic variability. The other analyses revealed that the half-sibling families were structured in three groups. It is concluded that the studied panel has great potential for breeding and further genetic studies. Moreover, only one of the three marker matrices is sufficient for diversity studies.
Assuntos
Basidiomycota , Doenças do Tecido Conjuntivo , Eczema , Doenças do Sistema Imunitário , Rubus , Dermatopatias Bacterianas , Humanos , Melhoramento Vegetal , Brasil , Doenças das Plantas/genéticaRESUMO
Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as "genomic images." In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP. Also, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.