Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle

Animal

F.B. Lopes¹  F. Baldi²  L.C. Brunes³  M.G. Narciso⁴  R.B. Lobo⁵  C.U. Magnabosco⁶  R. Espigolan⁷  M.F.O. Costa⁸ 

[1] Corresponding author at: Animal Science Department, Goiás Federal University, 74690-900 Goiânia, GO, Brazil.;Embrapa Rice and Beans, GO-462, km 12, 75375-000 Santo Antônio de Goiás, GO, Brazil;Animal Science Department, Goiás Federal University, 74690-900 Goiânia, GO, Brazil;Animal Science Department, São Paulo State University – Júlio de Mesquita Filho (UNESP), Prof. Paulo Donato Castelane, 14884-900 Jaboticabal, SP, Brazil;Cobb-Vantress, Inc., 72761 Siloam Springs, AR, USA;Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of Sao Paulo, 13635-900 Pirassununga, SP, Brazil;Embrapa Rice and Beans, GO-462, km 12, 75375-000 Santo Antônio de Goiás, GO, Brazil;National Association of Breeders and Researchers, 14020-230 Ribeirão Preto, Brazil;

关键词: Genomic breeding value;    Residual body weight gain;    Residual feed intake;    Single nucleotide polymorphisms;    Zebu;   

DOI  :  

来源: DOAJ

【 摘 要 】

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 Cπ, 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.

【 授权许可】

Unknown