Gene Networks Involved in Tenderness and Sensory Attributes of Steaks from Angus Beef Cattle

Beef tenderness and sensory eating quality are complex traits that can benefit from a gene-interaction network analysis to understand the functional relationship among important loci contributing to phenotypic variation. Phenotypic measures of Warner Bratzler Shear Force, marbling score, and trained sensory panel attributes including tenderness, connective tissue, juiciness, and beef flavor intensity were combined with pedigree information and Illumina 54k bovine SNPchip genotypes to derive annotated gene networks underlying tenderness and sensory eating quality in steaks from Angus beef cattle. The Bayes-B statistical model was utilized to perform a genome wide association study (GWAS) to estimate effects between 54k SNP genotypes and the 7 tenderness and eating quality phenotypes. The top 100 significant SNP were first selected from the Warner Bratzler Shear Force GWAS by filtering out SNP with log transformed effect sizes less than 3.5 (–log(P) < 3.5). Effects for those initial 100 SNP were then populated across the remaining 6 phenotypes, and a partial correlation algorithm (PCIT) was used to illustrate correlated SNP across all 7 phenotypes. SNP were annotated using the bovine UMD3.1 assembly, and gene network clusters were generated in conjunction with network scoring and visualization software to analyze correlated SNP associated with tenderness and eating quality phenotypes. Significant genes and pathways implicated in tenderness and eating quality through network analysis included calpain and calpastatin muscular metabolism, regulation of myoblast differentiation, cell-cell and cell-matrix adhesion, and energy homeostasis. A network analysis using partial correlations and annotation of significant SNP’s can yield functional information about the genetic mechanisms underlying the tenderness and sensory attributes of beef.