Genomic selection for genetic breeding of meat traits in cattle Nelore

• Grant number: 13/08006-8
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: July 01, 2013
• End date: May 31, 2014
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Lucia Galvão de Albuquerque
• Grantee: Ana Fabrícia Braga Magalhães
• Supervisor: Flavio S. Schenkel
• Host Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
• Institution abroad: University of Guelph, Canada
• Associated to the scholarship: 12/21969-7 - Use of genomic information for genetic improvement of traits in beef cattle Nellore, BP.DR

Abstract

The meat quality traits have not been considered in selection programs in beef cattle in Brazil, being difficult to measure traits and high cost. Thus, in such traits, the application of genomic selection through the use of single nucleotide polymorphisms (SNPs) can decrease the costs of selection, in addition to reducing the generation interval. However, are still being evaluated methods that allow the application of genomic selection in order to improve the performance of beef cattle. Therefore, the aim of this project is to study different methodologies for predicting for genomic value associated to the quality of the meat, allowing the application of these results in the production of beef from Brazil. Will be used between 1700-2000 males Nellore with age next two years. Meat traits to be evaluated are: tenderness, color and percentage of lipids by chemical extraction. Genotyping of animals will be used a panel with approximately 777,000 SNPs (Illumina BeadChip BovineHD). The genomic information will be analyzed considering four different methodologies: BLUP (assumes normal distribution with variance one for all effects), BayesA (assuming conditional normal distribution with a inverted scaled chi-square distribution for variances); BayesB (assuming point mass at zero) and Bayesian Lasso (assuming double exponential distribution for genetic effects). (AU)