Open Access Highly Accessed Research article

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

Fabiana Barichello Mokry1*, Roberto Hiroshi Higa2, Maurício de Alvarenga Mudadu3, Andressa Oliveira de Lima1, Sarah Laguna Conceição Meirelles4, Marcos Vinicius Gualberto Barbosa da Silva5, Fernando Flores Cardoso6, Maurício Morgado de Oliveira6, Ismael Urbinati7, Simone Cristina Méo Niciura3, Rymer Ramiz Tullio3, Maurício Mello de Alencar3 and Luciana Correia de Almeida Regitano3

Author Affiliations

1 Department of Genetics and Evolution, Federal University of São Carlos, Rodovia Washington Luiz, km 235, PO BOX 676, 13565-905, São Carlos, Brazil

2 Embrapa Agricultural Informatics, Avenida André Tosello, 209, PO BOX 6041, 13083-886, Campinas, Brazil

3 Embrapa Southeast Livestock, Rodovia Washington Luiz, km 234, PO BOX 339, 13560-970, São Carlos, Brazil

4 Department of Animal Science, Federal University of Lavras, PO BOX 3037, 37200-00, Lavras, Brazil

5 Embrapa Dairy Cattle, Rua Eugênio do Nascimento, 610, 36038-330, Juiz de Fora, Brazil

6 Embrapa Southern Region Animal Husbandry, BR 153, km 603, PO BOX 242, 96401-970, Bagé, Brazil

7 Department of Exact Science, São Paulo State University, PO BOX 53453, 14884-900, Jaboticabal, Brazil

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BMC Genetics 2013, 14:47  doi:10.1186/1471-2156-14-47

Published: 5 June 2013

Abstract

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.

Keywords:
Bovine; Lipid metabolism; Machine learning; Single nucleotide polymorphism (SNP); Subcutaneous fat; Tropical composite cattle