Utilização de informações genômicas para o melhoramento genético de características da carne em bovinos da raça Nelore

• Ano de defesa: 2015
• Autor(a) principal: Magalhães, Ana Fabrícia Braga [UNESP]
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual Paulista (Unesp)
• Palavras-chave em Português: Bovino de corte; Nelore (Zebu); Carne bovina; Genômica; Bovino - Melhoramento genetico; Marcadores genéticos; Genomics
• Link de acesso: http://hdl.handle.net/11449/123659; http://www.athena.biblioteca.unesp.br/exlibris/bd/cathedra/08-06-2015/000834573.pdf

Consumers have become stricter for higher quality meat and are seeking for traits such as tenderness, color and flavor. Few has been done for improving meat quality traits, due to the difficultness and high cost to measure, as well as to the necessity of culling animals. An alternative to inclusion of these measures in breeding programs may be the use of SNPs in association studies and genomic selection. Animals Nellore (1,875) were feedlot finished and slaughtered at commercial slaughterhouses were used, with an average age of 731 ± 81 days. The samples were analyzed for tenderness, lipid content, marbling, lightness (L*), redness (a*) and yellowness (b*). The animals were genotyped with a panel of 777,962 SNPs (IlluminaBovineHDBeadchip) and after quality control remained 1,634 animals with genotypes and 369,722 SNPs. In chapter 2, three genomic selection methods were evaluated: GBLUP (assumes normal distribution with variance only for all SNPs effects), BayesCπ (mixed distribution to the SNPs effects) and Bayesian Lasso (assumes double exponential distribution for SNPs effects). The adjusted phenotype for the fixed effects (Yc) and the estimated breeding value (EBV) were used as pseudo-phenotypes. For cross-validation, the population was randomly divided into five groups of similar sizes. To compare the predictive ability of the methods, the following procedures were used: correlation between the genomic values (GEBV) and the adjusted phenotypes and the correlation value divided by the square root of the heritability; correlation between EBV and GEBV; regression coefficient of the GEBV on adjusted phenotype and GEBV on EBV. The EBV as pseudo-phenotype was more accurate than the adjusted phenotype. The GBLUP showed better predictive ability for meat color. For the other traits, the Bayesian methodologies had higher predictive ability. Among the Bayesian methodologies, BayesCπ was more accurate than Lasso for all traits. In Chapter ...