Genetic evaluation of growth traits and resistance to verminoses in Santa Inês sheep using random regression models and cluster analysis

• Grant number: 17/13320-4
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Start date: July 31, 2017
• End date: October 30, 2017
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Danísio Prado Munari
• Grantee: Luara Afonso de Freitas Januário
• Supervisor: Maria Jesus Carabano Luengo
• Host Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
• Institution abroad: Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Spain
• Associated to the scholarship: 16/10583-1 - Genetic evaluation of growth characteristics and resistance to worms in sheep breed Santa Inês using random regression models and cluster analysis, BP.MS

Abstract

The random regression models have as main advantage a better adjustment of the longitudinal data. Multivariate analyzes are used to explore and understand the information contained in a dataset and allow a better understanding of the genetic structure of the population by exploiting the breeding values of the animals. The aim of this study is the use of cluster analyzes to explore the genetic profile of the animals for meat production, resistance and resilience, and to identify superior animals and to use them as future breeders in Santa Inês sheep selection. To perform the analysis, a dataset with 2,531 records of 771 Santa Inês sheep will be used. The traits that will be analyze are: body weight (BW), egg count per gram of feces (EPG) and globular volume (GV). The components of variance will be calculate using unitrait regression models. Based on the predicted additive breeding values of the studied traits, group analyzes will be performe by the hierarchical and non hierarchical method, in order to characterize the groups that will be submitted to selection.