Genome-wide association study and predictive ability for growth traits in Nellore cattle

• Autor(a) principal: Carvalho, F. E.
• Data de Publicação: 2020
• Outros Autores: Espigolan, R., Berton, M. P. [UNESP], Neto, J. B.S. [UNESP], Silva, R. P., Grigoletto, L., Silva, R. M.O., Ferraz, J. B.S., Eler, J. P., Aguilar, I., Lôbo, R. B., Baldi, F. [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.livsci.2019.103861; http://hdl.handle.net/11449/198143
• topic: Beef cattle; Genomic; Predictive ability; Single nucleotide polymosphism

This study aimed to identify genomic regions influencing growth traits in Nellore cattle and evaluate the predictive ability of each trait based on results obtained from single-step genome-wide association analyzes (ssGWAS) considering different single nucleotide polymorphims (SNP) densities of markers. The National Association of Breeders and Researchers provided the dataset, from eighteen Nellore herds participating of the Nellore Brazilian breeding program. The traits birth weight (BW), adjusted weight at 210 (W210) and at 450 (W450) days of age and adult cow weight (ACW) were considered. A total of 963 animals, genotyped using the Illumina BovineHD BeadChip, were used as a reference population to impute genotypes of 7,689 animals, genotyped in low-density panel. Genotype imputation was performed using the FImpute 2.2 software. The ssGWAS was used to identify genomic regions associated to growth traits. Several genes in enrichment analysis were related to muscle and adipose tissue development and metabolism, feed efficiency, milk composition and maternal behavior. The predictive ability varied from low (0.10) to moderate (0.68). The predictive ability and bias for both panels were similar for all traits. The results found in this study should improve the understanding of genetic and physiologic mechanism associated with growth traits. However, the association of these results with other approaches, like system biologic and other omics information should improve the identification of causative genetic variants in growth traits in indicine cattle.