Integrative Analysis of Metabolomic,Proteomic and Genomic Data to Reveal Functional Pathways and Candidate Genes for Drip Loss in Pigs |
| |
Authors: | Julia Welzenbach Christiane Neuhoff Hanna Heidt Mehmet Ulas Cinar Christian Looft Karl Schellander Ernst Tholen Christine Gro?e-Brinkhaus |
| |
Affiliation: | 1Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115 Bonn, Germany; (J.W.); (C.N.); (H.H.); (M.U.C.); (C.L.); (K.S.); (E.T.);2Institute for Organic Agriculture Luxembourg, Association sans but lucratif (A.S.B.L.), 13 Rue Gabriel Lippmann, L-5365 Munsbach, Luxembourg;3Department of Animal Science, Faculty of Agriculture, Erciyes University, Talas Bulvari No. 99, 38039 Kayseri, Turkey |
| |
Abstract: | The aim of this study was to integrate multi omics data to characterize underlying functional pathways and candidate genes for drip loss in pigs. The consideration of different omics levels allows elucidating the black box of phenotype expression. Metabolite and protein profiling was applied in Musculus longissimus dorsi samples of 97 Duroc × Pietrain pigs. In total, 126 and 35 annotated metabolites and proteins were quantified, respectively. In addition, all animals were genotyped with the porcine 60 k Illumina beadchip. An enrichment analysis resulted in 10 pathways, amongst others, sphingolipid metabolism and glycolysis/gluconeogenesis, with significant influence on drip loss. Drip loss and 22 metabolic components were analyzed as intermediate phenotypes within a genome-wide association study (GWAS). We detected significantly associated genetic markers and candidate genes for drip loss and for most of the metabolic components. On chromosome 18, a region with promising candidate genes was identified based on SNPs associated with drip loss, the protein “phosphoglycerate mutase 2” and the metabolite glycine. We hypothesize that association studies based on intermediate phenotypes are able to provide comprehensive insights in the genetic variation of genes directly involved in the metabolism of performance traits. In this way, the analyses contribute to identify reliable candidate genes. |
| |
Keywords: | drip loss pork quality metabolomics proteomics enrichment analysis genome-wide association study (GWAS) candidate genes |
|
|