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

• Autor(a) principal: Mokry, Fabiana Barichello
• Data de Publicação: 2013
• Outros Autores: Higa, Roberto Hiroshi, de Alvarenga Mudadu, Maurício, Oliveira de Lima, Andressa, Meirelles, Sarah Laguna Conceição, Barbosa da Silva, Marcos Vinicius Gualberto, Cardoso, Fernando Flores, Morgado de Oliveira, Maurício, Urbinati, Ismael [UNESP], Méo Niciura, Simone Cristina, Tullio, Rymer Ramiz, Mello de Alencar, Maurício, Correia de Almeida Regitano, Luciana
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• DOI: 10.1186/1471-2156-14-47
• Texto Completo: http://dx.doi.org/10.1186/1471-2156-14-47; http://hdl.handle.net/11449/75614
• topic: Bovine; Lipid metabolism; Machine learning; Single nucleotide polymorphism (SNP); Subcutaneous fat; Tropical composite cattle; lipid; animal experiment; backfat thickness; beef cattle; breeding; carcass; controlled study; deregressed estimated breeding value; fat mass; female; food quality; genetic analysis; genetic association; lipid metabolism; male; meat; nonhuman; quantitative trait locus; random forest; single nucleotide polymorphism; subcutaneous fat; Animalia; Bos; Bovinae

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. © 2013 Mokry et al.; licensee BioMed Central Ltd.