2 resultados para Linear discriminant analysis
Resumo:
Blast is a major disease of rice in Brazil, the largest rice-producing country outside Asia. This study aimed to assess the genetic structure and mating-type frequency in a contemporary Pyricularia oryzae population, which caused widespread epidemics during the 2012/13 season in the Brazilian lowland subtropical region. Symptomatic leaves and panicles were sampled at flooded rice fields in the states of Rio Grande do Sul (RS, 34 fields) and Santa Catarina (SC, 21 fields). The polymorphism at ten simple sequence repeats (SSR or microsatellite) loci and the presence of MAT1-1 or MAT1-2 idiomorphs were assessed in a population comprised of 187 isolates. Only the MAT1-2 idiomorph was found and 162 genotypes were identified by the SSR analysis. A discriminant analysis of principal components (DAPC) of SSR data resolved four genetic groups, which were strongly associated with the cultivar of origin of the isolates. There was high level of genotypic diversity and moderate level of gene diversity regardless whether isolates were grouped in subpopulations based on geographic region, cultivar host or cultivar within region. While regional subpopulations were weakly differentiated, high genetic differentiation was found among subpopulations comprised of isolates from different cultivars. The data suggest that the rice blast pathogen population in southern Brazil is comprised of clonal lineages that are adapting to specific cultivar hosts. Farmers should avoid the use of susceptible cultivars over large areas and breeders should focus at enlarging the genetic basis of new cultivars.
Resumo:
The GxE interaction only became widely discussed from evolutionary studies and evaluations of the causes of behavioral changes of species cultivated in environments. In the last 60 years, several methodologies for the study of adaptability and stability of genotypes in multiple environments trials were developed in order to assist the breeder's choice regarding which genotypes are more stable and which are the most suitable for the crops in the most diverse environments. The methods that use linear regression analysis were the first to be used in a general way by breeders, followed by multivariate analysis methods and mixed models. The need to identify the genetic and environmental causes that are behind the GxE interaction led to the development of new models that include the use of covariates and which can also include both multivariate methods and mixed modeling. However, further studies are needed to identify the causes of GxE interaction as well as for the more accurate measurement of its effects on phenotypic expression of varieties in competition trials carried out in genetic breeding programs.