Genome-wide association study for backfat thickness in Canchim beef cattle using Random Forest approach.

• Autor(a) principal: MOKRY, F. B.
• Data de Publicação: 2013
• Outros Autores: HIGA, R. H., MUDADU, M. de A., LIMA, A. O. de, MEIRELLES, S. L. C., SILVA, M. V. G. B. da, CARDOSO, F. F., OLIVEIRA, M. M. de, URBINATI, I., NICIURA, S. C. M., TULLIO, R. R., ALENCAR, M. M. de, REGITANO, L. C. de A.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
• Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/977539
• topic: Polimorfismo de nucleotídeo único; Metabolismo lipídico; Aprendizado de máquina; Inteligência artificial; Machine learning; Tecido adiposo subcutâneo; Gado de Corte; Single nucleotide polymorphism; Beef cattle; Lipid metabolism; Artificial intelligence; Subcutaneous fat

Background: Meat quality involves many traits, such as marbling, tenderness, juiciness, and backfat thickness, all of which require attention from livestock producers. Backfat thickness improvement by means of traditional selection techniques in Canchim beef cattle has been challenging due to its low heritability, and it is measured late in an animal?s life. Therefore, the implementation of new methodologies for identification of single nucleotide polymorphisms (SNPs) linked to backfat thickness are an important strategy for genetic improvement of carcass and meat quality. Results: The set of SNPs identified by the random forest approach explained as much as 50% of the deregressed estimated breeding value (dEBV) variance associated with backfat thickness, and a small set of 5 SNPs were able to explain 34% of the dEBV for backfat thickness. Several quantitative trait loci (QTL) for fat-related traits were found in the surrounding areas of the SNPs, as well as many genes with roles in lipid metabolism. Conclusions: These results provided a better understanding of the backfat deposition and regulation pathways, and can be considered a starting point for future implementation of a genomic selection program for backfat thickness in Canchim beef cattle.