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Fritsche Neto, R.

Publications and source records attributed to Fritsche Neto, R..

2 recordsLinked to original sources

Complementary approaches to dissect late leaf rust resistance in an interspecific raspberry population

Over the last ten years, global raspberry production has increased by 47.89%, based on the red species (Rubus idaeus). However, the black raspberry species (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, to which the red ones are susceptible. 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 the crop in Brazil. Therefore, the aim was to understand the genetic architecture that controls resistance to late rust in raspberries. For that, we used an interspecific diversity panel between the cited above species, two different statistical approaches to associate the phenotypes to the markers (GWAS and copula graphical models), and two phenotyping methodologies from the first to the seventeenth 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 possibly controls the resistance to late leaf rust, as both GWAS and the network suggested the same marker. Furthermore, a candidate defense-related gene cluster is close to this marker. Finally, the best stage to evaluate for disease severity is thirteen days after inoculation, confirmed by both traditional and high-throughput phenotyping. Although the network and GWAS indicated the same higher effect genomic region, the network identified other different regions complementing the genetic control comprehension.

genetics↗

Deciphering Rubber Tree Growth Using Network-Based Multi Omics Approaches

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.

plant biology↗