Deriving Gene Networks Underlying Fatty Acid Composition of the Triacylglycerol Lipid Fraction in Angus Longissimus Muscle

The fatty acid profile of beef is a complex trait that can benefit from a gene-interaction network analysis to understand the functional relationship among important loci contributing to phenotypic variation. Phenotypic measures of fatty acid profile from the triacylglycerol fraction of longissimus muscle, pedigree information, and Illumina 54k bovine SNPchip genotypes were utilized to derive an annotated gene network underlying the fatty acid composition in 2,285 Angus beef cattle. Restricted maximum likelihood methods combined with pedigree data were used to estimate variance components, heritabilities, and genetic correlations for 22 classes of fatty acids. Pedigree-based genetic correlations among individual fatty acids ranged from -0.89 to 0.88 indicating a significant genetic interaction among multiple phenotypes. The BayesB statistical model was utilized to perform a genome wide association study to estimate effects between 54k SNP genotypes and 39 individual fatty acid phenotypes. A partial correlation algorithm was used to illustrate correlated regions of the genome with a set of 17 different 1 Mb windows explaining 90 percent of the genetic variation in the phenotype. Elements within those windows were annotated using the bovine UMD3.1 assembly. Functional annotation, KEGG pathway analysis, and gene cluster scoring was carried out using DAVID. Significant pathways implicated in fatty acid metabolism through network analysis included PPAR signaling, glycerophospholipid metabolism, and the adipocytokine signaling pathway. A network analysis using partial correlations and annotation of significant SNP’s can yield functional information about the genetic mechanisms underlying the fatty acid profile of beef.