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Non-hierarchical cluster analysis with the breeding values for reproductive and productive traits in beef cattle

Monday, August 18, 2014
Posters (The Westin Bayshore)
Claudia C. P. Paz , Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto - Departamento de Genética (USP/FMRP), Ribeirão Preto-SP, Brazil
Heverton L. Moreira , Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto - Departamento de Genética (USP/FMRP), Ribeirão Preto-SP, Brazil
Rodrigo P. Savegnago , Universidade Estadual Paulista Júlio de Mesquita, Faculdade de Ciências Agrárias e Veterinárias, Departamento de Ciências Exatas, Jaboticabal-SP, Brazil
Marcos E. Buzanskas , Universidade Estadual Paulista Júlio de Mesquita, Faculdade de Ciências Agrárias e Veterinárias, Departamento de Ciências Exatas, Jaboticabal-SP, Brazil
Raysildo B. Lôbo , Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto - Departamento de Genética (USP/FMRP), Ribeirão Preto-SP, Brazil
Danísio P. Munari , Universidade Estadual Paulista "Júlio de Mesquita Filho", Jaboticabal, Brazil
Abstract Text: The objective of this work was to explore the genetic patterns of the individuals using the non-hierarchical cluster analysis with the breeding values for productive and reproductive traits of Brazilian Nellore cattle to help in the selection process of the animals. The population was divided into three groups, each one with animals of different genetic patterns. The cluster 1 has grouped animals with EBV-BW210 and EBV-BW365 below the population mean (zero), EBV-AFC e EBV-GL around the population mean and EBV-SC365 and EBV-SC450 above the population mean. The cluster 2 has grouped animals with genetic patterns of EBV-SC365, EBV-SC450, EBV-BW210 and EBV-BW365 below the population mean and EBV-AFC and EBV-GL around the population mean. The cluster 3 has grouped animals with genetic patterns of EBV-AFC below the population mean, EBV-GL around the population mean, EBV-SC365, EBV-SC450, EBV-BW210 and EBV-BW365 above the population mean.

Keywords:

breeding value

cluster analysis

gain genetic