| Grant number: |
12/21969-7
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| Support type: | Scholarships in Brazil - Doctorate |
| Effective date (Start): |
April 01, 2013
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| Effective date (End): |
February 28, 2015
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| Field of knowledge: | Agronomical Sciences
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Animal Husbandry
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Genetics and Improvement of Domestic Animals |
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| Principal Investigator: | Lucia Galvão de Albuquerque |
| Grantee: | Ana Fabrícia Braga Magalhães |
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| Home Institution: |
Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
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| Associated research grant: | 09/16118-5 - Genomic tools to genetic improvement of direct economic important traits in Nelore cattle,
AP.TEM
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| Associated scholarship(s): | 13/08006-8 - Genomic selection for genetic breeding of meat traits in cattle Nelore,
BE.EP.DR
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Abstract
The traits of meat quality are essential factors in the purchase decision of the consumer, however, these have not been considered in selection programs in beef cattle because they are difficult to measure and high cost. Thus, in such traits, the application of genomic selection through the use of single nucleotide polymorphisms (SNPs) may decrease the cost of selection, besides reducing the generation interval. However, still being evaluated methods that allow the application of genomic selection to improve the performance of beef cattle. Therefore, the aim of this project is to compare different methodologies for predicting the genomic value associated to the quality of the meat, for application of these results in terms of meat production in Brazil. Will be used between 1700-2000 males Nellore, with age next two years, about 100 children of bulls from six herds, all with performance information and genealogy. Meat traits evaluated are: tenderness, color and percentage of lipids by chemical extraction. In genotyping of the animals will be used a panel of approximately 777,000 SNPs (Illumina BovineHD Beadchip). The genomic information will be analyzed considering four different methodologies: BLUP (assumes normal distribution with only variance for all effects), BayesA (assuming normal conditional distribution, with a chi-square scaled inverted for variances); BayesB (assuming point mass at zero) and Bayesian Lasso (assuming double exponential distribution for genetic effects). For validation of the methodology will be used cross-validation (cross-validation). We will also compare the results of genetic evaluations with conventional genomic data for evaluations considering the quality of the meat.
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Scientific publications(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
BRAGA MAGALHAES, ANA FABRICIA;
SCHENKEL, FLAVIO SCHRAMM;
GARCIA, DIOGO ANASTACIO;
MANSAN GORDO, DANIEL GUSTAVO;
TONUSSI, RAFAEL LARA;
ESPIGOLAN, RAFAEL;
DE OLIVEIRA SILVA, RAFAEL MEDEIROS;
BRAZ, CAMILA URBANO;
FERNANDES JUNIOR, GERARDO ALVES;
BALDI, FERNANDO;
CARVALHEIRO, ROBERTO;
BOLIGON, ARIONE AUGUSTI;
DE OLIVEIRA, HENRIQUE NUNES;
LOYOLA CHARDULO, LUIS ARTHUR;
DE ALBUQUERQUE, LUCIA GALVAO.
Genomic selection for meat quality traits in Nelore cattle.
MEAT SCIENCE,
v. 148,
p. 32-37,
FEB 2019.
Web of Science Citations: 0.
MAGALHAES, ANA F. B.;
DE CAMARGO, GREGORIO M. F.;
FERNANDES JUNIOR, GERARDO A.;
GORDO, DANIEL G. M.;
TONUSSI, RAFAEL L.;
COSTA, RAPHAEL B.;
ESPIGOLAN, RAFAEL;
SILVA, RAFAEL M. DE O.;
BRESOLIN, TIAGO;
DE ANDRADE, WILLIAN B. F.;
TAKADA, LUCIANA;
FEITOSA, FABIELI L. B.;
BALDI, FERNANDO;
CARVALHEIRO, ROBERTO;
CHARDULO, LUIS A. L.;
DE ALBUQUERQUE, LUCIA G.
Genome-Wide Association Study of Meat Quality Traits in Nellore Cattle.
PLoS One,
v. 11,
n. 6
JUN 30 2016.
Web of Science Citations: 22.
TONUSSI, R. L.;
ESPIGOLAN, R.;
GORDO, D. G. M.;
MAGALHAES, A. F. B.;
VENTURINI, G. C.;
BALDI, F.;
DE OLIVEIRA, H. N.;
CHARDULO, L. A. L.;
TONHATI, H.;
DE ALBUQUERQUE, L. G.
Genetic association of growth traits with carcass and meat traits in Nellore cattle.
Genetics and Molecular Research,
v. 14,
n. 4,
p. 18713-18719,
2015.
Web of Science Citations: 6.