Estrutura populacional e otimização de esquemas de acasalamento em ovinos com uso de algoritmos evolucionários

• Ano de defesa: 2014
• Autor(a) principal: Barreto Neto, Arnaldo Dantas
• Orientador(a): Barbosa, Leandro Teixeira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Sergipe
• Programa de Pós-Graduação: Pós-Graduação em Zootecnia
• País: Brasil
• Palavras-chave em Português: Zootecnia; Ovino; Acasalamento; Endogamia; Otimização matemática; Algoritmos genéticos; Reprodução animal; Acasalamento seletivo; EVA
• Palavras-chave em Inglês: Inbreeding; Mate selection; Mathematical optimization; Genetics algorithms
• Área do conhecimento CNPq: CIENCIAS AGRARIAS::ZOOTECNIA
• Link de acesso: https://ri.ufs.br/handle/riufs/6383

It has been sought to evaluate the population structure and the genetic progress observed in Santa Ines breed sheep, distributed in 51 selection nucleus herds which are part of the ASCCO/USP Genetic Breeding Program. Also the usage of genetic algorithms to find the optimal genetic contribution to the next generation of animals that are part of this nucleus herds with structured pedigree and genetic value for traits of economic relevance estimated by Best Linear Unbiased Predictors (DEP-BLUP). Information about and genetic ancestry and genetic values at 60 days weight from ASCCO/USP Santa Ines Breed Genetic Breeding Program database were used, the analysis were been made by the EVA program, developed by NORDGEN, of open usage. The population data described were the number of animals born, the number of inbred animals, the average inbreeding coefficient, the average rate of coancestry, the effective population size, the expected difference in average progeny breeding value for the trait weight at 60 days (DEP P60) and the level of pedigree completeness. Results suggest a decrease in effective population size, increment in coancestry and high level of pedigree completeness. Also, the results indicate low use of reproduction techniques such as artificial insemination and a suboptimal rate of genetic gain. The mathematical optimization use genetic algorithms contained in the EVA program, where its usefulness was demonstrated to optimize the genetic gain with inbreeding control, in nucleus herds with mid-sized databases. Minimum computer requirements grow exponentially with the number of candidates for selection which can become a serious restriction to their use in large databases. The results were additionally compared to the breeding results from DEP-BLUP selection with a truncation point and to random breeding. The number and distribution of selected males varies according to a penalty attributed to inbreeding in the objective function. The results proved the effectiveness of the method for better genetic gains than random mating and better control over inbreeding compared to selection by DEP-BLUP.