Seleção genômica ampla em suínos usando o modelo de sobrevivência de Cox

• Ano de defesa: 2013
• Autor(a) principal: Santos, Vinicius Silva dos
• Orientador(a): Martins Filho, Sebastião
• Banca de defesa: Nascimento, Carlos Souza do
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Mestrado em Estatística Aplicada e Biometria
• Departamento: Estatística Aplicada e Biometria
• País: BR
• Palavras-chave em Português: Modelos mistos; GBLUP; Herdabilidade; SNPs
• Palavras-chave em Inglês: Mixed models; GBLUP; Heritability; SNPs
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA
• Link de acesso: http://locus.ufv.br/handle/123456789/4071

The genomic wide selection (GWS) emerged in 2001 with the goal of increasing efficiency and accelerating the selection gain in genetic improvement based exclusively on markers after their genetic effects estimated from phenotypic data. In the context of survival analysis, Cox s proportional risk model with random effects was compared to the mixed linear model, both using parenthood matrices based on markers in substitution to basing on pedigree, this method being named GBLUP. The application was made on real data from an F2 population of pigs in which the dependent variable was the time in days, from birth to slaughter of the animal and the covariables: SNP markers (238), sex and handled lot. The data was previously corrected for fixed effects and the accuracy of the method was calculated based on the correlation of the ranks of genomic genetic values predicted in both models with the phenotypic values corrected. The analysis was repeated considering the least number of SNP markers that presented the greatest effect in module. The results showed agreement in the prediction of genomic genetic values and estimation of the effects of markers for both models in the situation of uncensored data and normality. However, when considering censored data, the Cox model with normal random effect was more appropriate, since there was no agreement in the prediction of genomic genetic values and estimation of the effects of markers with the mixed linear model with imputed data. The selection of markers allowed an increase in correlations between the positions of genomic genetic values predicted by the linear model and the Cox frailty model with phenotypic values corrected, being that for the characteristic being analyzed, 120 markers were sufficient to increase the predictive power.