Mapeamento de QTLs para qualidade de grãos em aveia

Abstract

To meet the growing demand for food in the world, agriculture needs to be increasingly efficient, sustainable and technological. The production of food with nutrition quality is among the sector's priorities. In this sense, white oat, as a functional food, has an even greater importance in this context. To maximize the yield potential and to improve even more the nutritional quality of white oat cultivars, current research and genetic improvement strongly focus on the study and identification of locus associated with nutritional traits, genetic resistance against diseases and abiotic stresses. QTL mapping (Quantitative Trait Loci) and GWAS (Genome Wide Association Studies) are important tools to identify genetic regions associated with these characters. Thus, the first part of this thesis composes a bibliographic review entitled: “Advances in the identification of locus of agronomic interest in white oats”. Some traits, because they are difficult to measure and their multigenic origin, represent a big challenge for genetic improvement. Therefore, the use of tools such as QTL mapping and GWAS are important to the identification of gene regions of interest. Several works with RIL, Double Haploid and backcross populations were reviewed. Disease resistance showed a large influence of genetic variations and genetic control. Constant attention to the genetic regions associated with resistance is required due to the constant breakdown of resistance that occurs in the field. The studies aiming to identify regions associated with nutritional quality show that there is a large influence of the environment on these traits, but advances in increasing nutritional quality in the crop are still possible and necessary. The phenotypic associations allow the research to act in the indirect selection of the traits. So also, the correlation between phenotypic and genotypic data. “Phenotypic and genotypic associations in a RIL (Recombinant Inbreed Line) population of white oats” is the subject of the second chapter of this thesis. To measure the morphological characteristics, the population was cultivated in the experimental area of the Genomics and Phyto-improvement Center, located in the Centro Agropecuário da Palma, belonging to the Federal University of Pelotas and located in the municipality of Capão do Leão/RS. Sowing in the field was done on 06/20/2019. Each family was cultivated in rows of 1.5 meters, with three plants randomly harvested and evaluated. Yield related traits presented a strong correlation with each other, in most cases. Strong correlations between yield and nutritional traits were not observed. Path analysis showed a significant correlation of the Beta Glucans with other nutritional traits. On the other hand, starch content presented negative correlations with the other nutritional characters. The principal components analysis allowed a better understanding of the behavior of the characters in isolation and also in two large groups. When performing the hierarchical grouping of phenotypic traits, it was observed that a large part of the population is concentrated in one of the seven groups formed, with a large group that concentrated 56% of the individuals. Both parents were found in the same group. The grouping through SNPs markers had the formation of eight groups, with the two parents again being in the same group. This time there was no formation of a large group, and the largest group concentrated 19% of individuals, including both parents. The third chapter of this thesis presents a “Mapping of QTLs associated with nutritional quality and yield in white oats”. Two methods were used toidentify the QTLs. The first approach was through single marker analysis (SMA). By this methodology, five QTLs (CPP, Proteins, Beta Glucans, Fat and Fibers) were identified. Another method used to identify QTLs was through interval mapping (IM). Using this methodology, three QTLs (Protein, Fat and Fiber) were identified, confirming some of the QTLs before identified. The three QTLs confirmed by the two methodologies were found in very close regions in both situations. The SNPs associated with QTLs in that work were blasted into a database (Avenagenome). The results show a great similarity with the reference genome. It was not possible, however, to associate the SNPs with genes reported in other studies, since there is no annotation of genes in the database. However, once the annotation is carried out, the present work can provide valuable information for the identification of genes in white oats.