Genome Association Study for Visual Scores in Nellore Cattle Measured at Weaning

• Published in: BMC genomics Vol. 20; no. 1; pp. 150 - 9
• Main Authors: Carreño, Luis Orlando Duitama, da Conceição Pessoa, Matilde, Espigolan, Rafael, Takada, Luciana, Bresolin, Tiago, Cavani, Ligia, Baldi, Fernando, Carvalheiro, Roberto, de Albuquerque, Lucia Galvão, da Fonseca, Ricardo
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20-02-2019; BioMed Central; BMC
• Subjects: Alleles; Animals; BayesC; Bayesian LASSO; Breeding; Cattle; Cattle industry; Chromosomes; Female; Genes; Genetic aspects; Genetic research; Genetic Variation; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genomics; Genomics - methods; Genotype; Growth genes; Male; Phenotype; Phenotypes; Polymorphism, Single Nucleotide; Quantitative Trait Loci; BayesC; Bayesian LASSO; Growth genes
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-019-5520-9

Genome-wide association studies (GWAS) are utilized in cattle to identify regions or genetic variants associated with phenotypes of interest, and thus, to identify design strategies that allow for the increase of the frequency of favorable alleles. Visual scores are important traits of cattle production in Brazil because they are utilized as selection criteria, helping to choose more harmonious animals. Despite its importance, there are still no studies on the genome association for these traits. This study aimed to identify genome regions associated with the traits of conformation, precocity and muscling, based on a visual score measured at weaning.
Bayesian approaches with BayesC and Bayesian LASSO were utilized with 2873 phenotypes of Nellore cattle for a GWAS. The animals were genotyped with Illumina BovineHD BeadChip, and a total of 309,865 SNPs were utilized after quality control. In the analyses, phenotype and deregressed breeding values were utilized as dependent variables; a threshold model was utilized for the former and a linear model for the latter. The association criterion was the percentage of genetic variance explained by SNPs found in 1 Mb-long windows. The Bayesian approach BayesC was better adjusted to the data because it could explain a larger phenotypic variance for both dependent variables.
There were no large effects for the visual scores, indicating that they have a polygenic nature; however, regions in chromosomes 1, 3, 5, 7, 14, 15, 16, 19, 20 and 23 were identified and explained a large part of the genetic variance.