Reaction norm for yearling weight in beef cattle using single-step genomic evaluation

• Autor(a) principal: Oliveira, D. P. [UNESP]
• Data de Publicação: 2018
• Outros Autores: Lourenco, D. A. L., Tsuruta, S., Misztal, I., Santos, D. J. A. [UNESP], Araujo Neto, F. R. de, Aspilcueta-Borquis, R. R., Baldi, F. [UNESP], Carvalheiro, R. [UNESP], Camargo, G. M. F. de [UNESP], Albuquerque, L. G. [UNESP], Tonhati, H. [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1093/jas/skx006; http://hdl.handle.net/11449/166021
• topic: Bos taurus indicus; genomic prediction; genotype by environment; performance traits

When the environment on which the animals are raised is very diverse, selecting the best sires for different environments may require the use of models that account for genotype by environment interaction (G x E). The main objective of this study was to evaluate the existence of G x E for yearling weight (YW) in Nellore cattle using reaction norm models with only pedigree and pedigree combined with genomic relationships. Additionally, genomic regions associated with each environment gradient were identified. A total of 67,996 YW records were used in reaction norm models to calculate EBV and genomic EBV. The method of choice for genomic evaluations was single-step genomic BLUP (ssGBLUP). Traditional and genomic models were tested on the ability to predict future animal performance. Genetic parameters for YW were obtained with the average information restricted maximum likelihood method, with and without adding genomic information for 5,091 animals. Additive genetic variances explained by windows of 200 adjacent SNP were used to identify genomic regions associated with the environmental gradient. Estimated variance components for the intercept and the slope in traditional and genomic models were similar. In both models, the observed changes in heritabilities and genetic correlations for YW across environments indicate the occurrence of genotype by environment interactions. Both traditional and genomic models were capable of identifying the genotype by environment interaction; however, the inclusion of genomic information in reaction norm models improved the ability to predict animals' future performance by 7.9% on average. The proportion of genetic variance explained by the top SNP window was 0.77% for the regression intercept (BTA5) and 0.82% for the slope (BTA14). Single-step GBLUP seems to be a suitable model to predict genetic values for YW in different production environments.