Do the unselected genotypes influence the identification of the best soybean lines with the inbreeding generations?

• Ano de defesa: 2021
• Autor(a) principal: Villela, Gabriel Mendes
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Lavras; Programa de Pós-Graduação em Genética e Melhoramento de Plantas; UFLA; brasil; Departamento de Biologia
• Palavras-chave em Português: Soja - Melhoramento genético; Soja - Parâmetros genéticos; Soja - Parâmetros fenotípicos; Simulação computacional; Genética quantitativa; Soybean; Computer simulation; Quantitative genetics; Melhoramento vegetal
• Link de acesso: http://repositorio.ufla.br/jspui/handle/1/48842

In the need to increase soybean yield, it is understood that several agribusiness sectors are involved, especially related to the agronomic aspects of the crop. In this case, research on plant genetic breeding has a relevant contribution to increase soybean yield, like several other crops produced in the country. The aim of the study was to verify the influence of these unselected genotypes in the ranking of superior soybean lines through phenotypic data sets of progenies evaluated in different generations and the feasibility of using computer simulation in breeding programs such as cross validation. The approach via mixed models in the selection of superior genotypes proved to be extremely advantageous in advancing research in genetic breeding. Another analysis strategy that complements advances in plant breeding programs was the use of computer simulation. In stages of evaluation of breeding programs, the selected genotypes have a lot of information associated with them, which enables the adoption of different research strategies to select the best lines. Thus, the literature allows to support different analyzes of analyzes that follow the classification of superior genotypes in the selection of soybean lines with accuracy and feasibility of using computer simulation in breeding programs. The use of selected and unselected genotypes in a soybean breeding program with the progress of generations allows drastically to change the ranking, generating a bias in the selection of the best progenies with the advancement of inbreeding generations. This behavior was observed both in the field data evaluations and in the data simulation aspect (cross validation). It is suggested that when performing statistical analysis of the data, previous information of the progenies should be included via approaches that consider the imbalance of data, given that this alternative does not entail additional costs for the breeding program.