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EXCELMEAT Workshop
Biosensing Pork Quality
Lleida, 25 October 2012
Constructing gene networks underlying fat metabolism in pigs
Aznárez N1, Hernández J1, Cánovas A1, Pena RN1,3, Manunza A2, Mercadé A2, Amills M2, Quintanilla R1
1
Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Genètica i Millora Animal, Rovira Roure
191, 25198 Lleida, Spain. 2Universitat Autònoma de Barcelona (UAB), Dept. Ciència Animal i dels
Aliments, 08193 Bellaterra, Spain. 3Animal Production Department, Universitat de Lleida, 25198
Lleida, Spain.
Lipid metabolism in pigs represents a complex system gathering traits related to animal health,
carcass performance, and meat quality. In this study, phenotype and gene networks underlying fat
metabolism were inferred from global liver expression (GeneChip Porcine Genome arrays,
Affymetrix) and high-density SNP (Illumina PorcineSNP60 BeadChip) data of 100 and 350 Duroc pigs,
respectively. Ten fat-related traits were measured: serum lipid levels (cholesterol, LDL, HDL and
triglycerides), fatness (backfat thickness and lean percentage), intramuscular fat content, and fatty
acid composition (SFA, MUFA and PUFA). Subsequently, phenotype networks on the basis of their
associations with transcriptomic and genomic data were constructed by using the PCIT algorithm to
filter out indirect pair-wise correlations. Transcriptomic phenotype network was notably denser and
showed much higher correlation values between traits. Besides, a weighted gene co-expression
network (WGCN) was constructed on the basis of soft thresholding, using a power function and scale
free topology. Topological overlap information and hierarquical clustering of this WGCN allowed
identifying 17 modules of genes with a co-expression pattern in liver. Two of these modules showed
highly significant associations with most of the phenotypes, and were selected for further analyses.
Gene Ontology and KEGG pathway analyses unveil that one of these modules (containing 89 genes)
enriched amino acid metabolism processes, while the other one (containing 113 genes) enriched for
steroid biosynthesis and metabolism, terpenoid biosynthesis and PPAR signalling pathway, among
other lipid-related categories. The gene network among genes of these two modules allowed us
detecting 12 hubs or highly connected genes (ESR1, HMGCS1, LIPIN2, LIPIN1, GPAM, PPARD, EBP,
LDLR, HMGCR, IDI1, SCD y GOT1) and 3 genes connecting several of these hubs (FST, ETS2 y GLUL).