Deriving Gene Networks from SNP Associated with Triacylglycerol and Phospholipid Fatty Acid Fractions from Ribeyes of Angus Cattle

Abstract

The fatty acid profile of beef is a complex trait that can benefit from gene-interaction network analysis to understand relationships among loci that contribute to phenotypic variation. Phenotypic measures of fatty acid profile from triacylglycerol and phospholipid fractions of longissimus muscle, pedigree information, and Illumina 54 k bovine SNP genotypes were utilized to derive an annotated gene network associated with fatty acid composition in 1,833 Angus beef cattle. The Bayes-B statistical model was utilized to perform a genome wide association study to estimate associations between 54 k SNP genotypes and 39 individual fatty acid phenotypes within each fraction. Posterior means of the effects were estimated for each of the 54 k SNP and for the collective effects of all the SNP in every 1-Mb genomic window in terms of the proportion of genetic variance explained by the window. Windows that explained the largest proportions of genetic variance for individual lipids were found in the triacylglycerol fraction. There was almost no overlap in the genomic regions explaining variance between the triacylglycerol and phospholipid fractions. Partial correlations were used to identify correlated regions of the genome for the set of largest 1 Mb windows that explained up to 35% genetic variation in either fatty acid fraction. SNP were allocated to windows based on the bovine UMD3.1 assembly. Gene network clusters were generated utilizing a partial correlation and information theory algorithm. Results were used in conjunction with network scoring and visualization software to analyze correlated SNP across 39 fatty acid phenotypes to identify SNP of significance. Significant pathways implicated in fatty acid metabolism through GO term enrichment analysis included homeostasis of number of cells, homeostatic process, coenzyme/cofactor activity, and immunoglobulin. These results suggest different metabolic pathways regulate the development of different types of lipids found in bovine muscle tissues. Network analysis using partial correlations and annotation of significant SNPs can yield information about the genetic architecture of complex traits.

Integrative analysis of microRNAs and mRNAs revealed regulation of composition and metabolism in Nelore cattle

“…The PCIT method identifies differences in pairs of correlated gene expression levels to measure a gene’s differential connectivity across levels of a phenotype [ 23 ]. Utilization of both PCIT and WGCNA have enabled a better understanding of the co-regulation of mRNAs and miRNAs for different phenotypes [ 21 , 24 – 27 ] to better comprehend the biological mechanisms and regulatory processes in lipid metabolism.…”

Genome-Wide Association Study for Fatty Acid Composition in American Angus Cattle

“…A genomic region on chromosome 19 (at 51 Mb) was associated with 15 fatty acid traits (LCFA, MCFA, MUFA, MUFA/SFA, SFA, UFA/SFA, C14:0, C14:1, C16:0, C16:1, C18:1c9, C16:0/C14:0, C16:1/C16:0, C16:1-C18:1/C16:0-C18:0, and C18:0/C16:0). This region was also previously reported to be associated with important fatty acid traits [ 18 , 26 ]. This SNP window contains a good candidate gene—fatty acid synthase ( FASN ).…”

“…It has also been reported to be associated with adipose composition, milk fatty acid composition, and milk fat content in many different breeds of cattle. These reports indicated that this gene has a pivotal role and is an important candidate gene for fatty acid composition [ 26 , 28 , 29 , 30 , 31 ].…”

Integrative analysis of microRNAs and mRNAs involved in regulation of intramuscular fat deposition in Nelore cattle

“…in the oxidation of fatty acids, mainly described in skeletal muscle where their expression is higher [19,20]. On the other hand, PPARγ is mainly important for adipogenesis, promoting the proliferation and differentiation of adipocytes and also improve insulin sensitivity through increased liver and muscle glucose uptake and reduced circulation in the bloodstream [21].…”

“…The PCIT method identifies differences in pairs of correlated gene expression levels to measure a gene's differential connectivity across levels of a phenotype[17]. Both the PCIT and WGCNA approaches have enabled a better understanding of the co-regulation of mRNAs and miRNAs for different phenotypes[15,[18][19][20][21] to better comprehend the biological mechanisms and regulatory processes in lipid metabolism.The aim of this study was to identify candidate regulatory genes and pathways that are regulated by miRNAs and understand the importance of integrative co-expression networks of mRNAs-miRNAs for fat deposition in cattle from a systems biology perspective.…”