| Full text | |
| Author(s): Show less - |
Braga Magalhaes, Ana Fabricia
[1]
;
Schenkel, Flavio Schramm
[2]
;
Garcia, Diogo Anastacio
[3]
;
Mansan Gordo, Daniel Gustavo
[1]
;
Tonussi, Rafael Lara
[1]
;
Espigolan, Rafael
[1]
;
de Oliveira Silva, Rafael Medeiros
[1]
;
Braz, Camila Urbano
[1]
;
Fernandes Junior, Gerardo Alves
[1]
;
Baldi, Fernando
[1]
;
Carvalheiro, Roberto
[1]
;
Boligon, Arione Augusti
[4]
;
de Oliveira, Henrique Nunes
[1]
;
Loyola Chardulo, Luis Arthur
[5]
;
de Albuquerque, Lucia Galvao
[1]
Total Authors: 15
|
| Affiliation: | [1] Sao Paulo State Univ Unesp, Sch Agr & Vet Sci, Jaboticabal, SP - Brazil
[2] Univ Guelph, Ctr Genet Improvement Livestock, Guelph, ON - Canada
[3] BRF Co, Curitiba, Parana - Brazil
[4] Fed Univ Pelotas UFPel, Pelotas, RS - Brazil
[5] Sao Paulo State Univ Unesp, Coll Vet & Anim Sci, Botucatu, SP - Brazil
Total Affiliations: 5
|
| Document type: | Journal article |
| Source: | MEAT SCIENCE; v. 148, p. 32-37, FEB 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
The objective of this study was to present heritability estimates and accuracy of genomic prediction using different methods for meat quality traits in Nelore cattle. Approximately 5000 animals with phenotypes and genotypes of 412,000 SNPs, were divided into two groups: (1) training population: animals born from 2008 to 2013 and (2) validation population: animals born in 2014. A single-trait animal model was used to estimate heritability and to adjust the phenotype. The methods of GBLUP, Improved Bayesian Lasso and Bayes C pi were performed to estimate the SNP effects. Accuracy of genomic prediction was calculated using Pearson's correlations between direct genomic values and adjusted phenotypes, divided by the square root of heritability of each trait (0.03-0.19). The accuracies varied from 0.23 to 0.73, with the lowest accuracies estimated for traits associated with fat content and the greatest accuracies observed for traits of meat color and tenderness. There were small differences in genomic prediction accuracy between methods. (AU) | |
| FAPESP's process: | 12/21969-7 - Use of genomic information for genetic improvement of traits in beef cattle Nellore |
| Grantee: | Ana Fabrícia Braga Magalhães |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 09/16118-5 - Genomic tools to genetic improvement of direct economic important traits in Nelore cattle |
| Grantee: | Lucia Galvão de Albuquerque |
| Support Opportunities: | Research Projects - Thematic Grants |