Abstract

BackgroundPorcine fatty acid composition is a key factor for quality and nutritive value of pork. Several QTLs for fatty acid composition have been reported in diverse fat tissues. The results obtained so far seem to point out different genetic control of fatty acid composition conditional on the fat deposits. Those studies have been conducted using simple approaches and most of them focused on one single tissue. The first objective of the present study was to identify tissue-specific and tissue-consistent QTLs for fatty acid composition in backfat and intramuscular fat, combining linkage mapping and GWAS approaches and conducted under single and multitrait models. A second aim was to identify powerful candidate genes for these tissue-consistent QTLs, using microarray gene expression data and following a targeted genetical genomics approach.ResultsThe single model analyses, linkage and GWAS, revealed over 30 and 20 chromosomal regions, 24 of them identified here for the first time, specifically associated to the content of diverse fatty acids in BF and IMF, respectively. The analyses with multitrait models allowed identifying for the first time with a formal statistical approach seven different regions with pleiotropic effects on particular fatty acids in both fat deposits. The most relevant were found on SSC8 for C16:0 and C16:1(n-7) fatty acids, detected by both linkage and GWAS approaches. Other detected pleiotropic regions included one on SSC1 for C16:0, two on SSC4 for C16:0 and C18:2, one on SSC11 for C20:3 and the last one on SSC17 for C16:0. Finally, a targeted eQTL scan focused on regions showing tissue-consistent effects was conducted with Longissimus and fat gene expression data. Some powerful candidate genes and regions were identified such as the PBX1, RGS4, TRIB3 and a transcription regulatory element close to ELOVL6 gene to be further studied.ConclusionsComplementary genome scans have confirmed several chromosome regions previously associated to fatty acid composition in backfat and intramuscular fat, but even more, to identify new ones. Although most of the detected regions were tissue-specific, supporting the hypothesis that the major part of genes affecting fatty acid composition differs among tissues, seven chromosomal regions showed tissue-consistent effects. Additional gene expression analyses have revealed powerful target regions to carry the mutation responsible for the pleiotropic effects.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-14-845) contains supplementary material, which is available to authorized users.

Highlights

  • Porcine fatty acid composition is a key factor for quality and nutritive value of pork

  • In the current study, Quantitative trait locus (QTL) scans by linkage and Genome Wide Association approach (GWAS) have been conducted in order to identify chromosomal regions with significant tissue-specific and tissueconsistent effects on the fatty acids (FA) composition of two fat deposits, Intramuscular fat (IMF) and BF

  • At least 14 significant QTLs (q-value < 0.05) with effects on the percentages in IMF of C14:0, C16:0, C16:1(n-7), C18:0, C18:2, and C20:3 were identified in nine porcine autosomes (SSC3, SSC4, SSC7, SSC8, SSC10, SSC11, SSC14, SSC16 and SSC17) (Table 2)

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Summary

Introduction

Porcine fatty acid composition is a key factor for quality and nutritive value of pork. The results obtained so far seem to point out different genetic control of fatty acid composition conditional on the fat deposits. Those studies have been conducted using simple approaches and most of them focused on one single tissue. Classical studies of QTL detection, based on microsatellites genotyping data from experimental crosses, have reported several QTL affecting FA composition in different porcine tissues [9,10,11,12,13,14], and a few significant QTLs in SSC4 [10], SSC7 [14] and SSC14 [13] affecting the content in diverse fat locations of a particular FA. More complex multitrait models are required for a rigorous checking of pleiotropic QTL effects and for increasing the precision of its estimated location in the genome [15]

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