Data/hora: 23/09/2024 02:14:14

Biblioteca(s): 	Embrapa Arroz e Feijão; Embrapa Cerrados.	
Data corrente: 	07/05/2021	
Data da última atualização: 	07/05/2021	
Autoria: 	BRUNES, L. C.; BALDI, F.; NARCISO, M. G.; LOBO, R. B.; ESPIGOLAN, R.; COSTA, M. F. O.; MAGNABOSCO, C. U.	
Afiliação: 	LUDMILLA C. BRUNES, UFG; FERNANDO BALDI, UNESP, Jaboticabal-SP; MARCELO GONCALVES NARCISO, CNPAF; RAYSILDO B. LOBO, ASSOCIAÇÃO NACIONAL DE CRIADORES, Ribeirão Preto-SP; R. ESPIGOLAN, USP; MARCOS FERNANDO OLIVEIRA E COSTA, CNPAF; CLAUDIO DE ULHOA MAGNABOSCO, CPAC.	
Título: 	Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle.	
Ano de publicação: 	2021	
Fonte/Imprenta: 	Animal. The International Journal of Animal Biosciences, v. 15, 100085, 2021.	
ISSN: 	1751-7311	
DOI: 	https://doi.org/10.1016/j.animal.2020.100085	
Idioma: 	Inglês	
Conteúdo: 	There is a growing interest to improve feed efficiency (FE) traits in cattle. The genomic selection was proposed to improve these traits since they are difficult and expensive to measure. Up to date, there are scarce studies about the implementation of genomic selection for FE traits in indicine cattle under different scenarios of pseudo-phenotypes, models, and validation strategies on a commercial large scale. Thus, the aim was to evaluate the feasibility of genomic selection implementation for FE traits in Nelore cattle applying different models and pseudo-phenotypes under validation strategies. Phenotypic and genotypic information from 4 329 and 3 467 animals were used, respectively, which were tested for residual feed intake, DM intake, feed efficiency, feed conversion ratio, residual BW gain, and residual intake and BW gain. Six prediction methods were used: single-step genomic best linear unbiased prediction, Bayes A, Bayes B, Bayes C&#960;, Bayesian least absolute shrinkage and selection operator (BLASSO), and Bayes R. Phenotypes adjusted for fixed effects (Y*), estimated breeding value (EBV), and EBV deregressed (DEBV) were used as pseudo-phenotypes. The validation approaches used were: (1) random: the data was randomly divided into ten subsets and the validation was done in each subset at a time; (2) age: the partition into training and testing sets was based on year of birth and testing animals were born after 2016; and (3) EBV accuracy: the data was split into two groups, being animals with accuracy above 0.45 the training set; and below 0.45 the validation set. In the analyses that used the Y* as pseudo-phenotype, prediction ability (PA) was obtained by dividing the correlation between pseudo-phenotype and genomic EBV (GEBV) by the square root of the heritability of the trait. When EBV and DEBV were used as the pseudo-phenotype, the simple correlation of this quantity with the GEBV was considered as PA. The prediction methods show similar results for PA and bias. The random cross-validation presented higher PA (0.17) than EBV accuracy (0.14) and age (0.13). The PA was higher for Y* than for EBV and DEBV (30.0 and 34.3%, respectively). Random validation presented the highest PA, being indicated for use in populations composed mainly of young animals and traits with few generations of data recording. For high heritability traits, the validation can be done by age, enabling the prediction of the next-generation genetic merit. These results would support breeders to identify genomic approaches that are more viable for genomic prediction for FE-related traits.	
Palavras-Chave: 	Residual body weight gain; Residual feed intake; Single nucleotide polymorphisms.	
Thesagro: 	Gado Nelore; Gado Zebu; Genética Animal.	
Thesaurus Nal: 	Animal breeding; Feed intake; Genomics; Zebu.	
Categoria do assunto: 	L Ciência Animal e Produtos de Origem Animal	
Registro original: 	Embrapa Arroz e Feijão (CNPAF)	
Exemplares: 	CNPAF - (36021-1) - UPC - AP - DD - 2021 -  - 2021 - DIS
CPAC - (37052-1) - UPC - AP - DD - DIGITAL -  - DIGITAL - DIS