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      Polymorphism in the ELOVL6 Gene Is Associated with a Major QTL Effect on Fatty Acid Composition in Pigs

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          Abstract

          Background

          The ELOVL fatty acid elongase 6 ( ELOVL6), the only elongase related to de novo lipogenesis, catalyzes the rate-limiting step in the elongation cycle by controlling the fatty acid balance in mammals. It is located on pig chromosome 8 (SSC8) in a region where a QTL affecting palmitic, and palmitoleic acid composition was previously detected, using an Iberian x Landrace intercross. The main goal of this work was to fine-map the QTL and to evaluate the ELOVL6 gene as a positional candidate gene affecting the percentages of palmitic and palmitoleic fatty acids in pigs.

          Methodology and Principal Findings

          The combination of a haplotype-based approach and single-marker analysis allowed us to identify the main, associated interval for the QTL, in which the ELOVL6 gene was identified and selected as a positional candidate gene. A polymorphism in the promoter region of ELOVL6, ELOVL6:c.-533C>T, was highly associated with the percentage of palmitic and palmitoleic acids in muscle and backfat. Significant differences in ELOVL6 gene expression were observed in backfat when animals were classified by the ELOVL6:c.-533C>T genotype. Accordingly, animals carrying the allele associated with a decrease in ELOVL6 gene expression presented an increase in C16:0 and C16:1(n-7) fatty acid content and a decrease of elongation activity ratios in muscle and backfat. Furthermore, a SNP genome-wide association study with ELOVL6 relative expression levels in backfat showed the strongest effect on the SSC8 region in which the ELOVL6 gene is located. Finally, different potential genomic regions associated with ELOVL6 gene expression were also identified by GWAS in liver and muscle, suggesting a differential tissue regulation of the ELOVL6 gene.

          Conclusions and Significance

          Our results suggest ELOVL6 as a potential causal gene for the QTL analyzed and, subsequently, for controlling the overall balance of fatty acid composition in pigs.

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          Most cited references35

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          Best linear unbiased estimation and prediction under a selection model.

          Mixed linear models are assumed in most animal breeding applications. Convenient methods for computing BLUE of the estimable linear functions of the fixed elements of the model and for computing best linear unbiased predictions of the random elements of the model have been available. Most data available to animal breeders, however, do not meet the usual requirements of random sampling, the problem being that the data arise either from selection experiments or from breeders' herds which are undergoing selection. Consequently, the usual methods are likely to yield biased estimates and predictions. Methods for dealing with such data are presented in this paper.
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            Identification of a lipokine, a lipid hormone linking adipose tissue to systemic metabolism.

            Dysregulation of lipid metabolism in individual tissues leads to systemic disruption of insulin action and glucose metabolism. Utilizing quantitative lipidomic analyses and mice deficient in adipose tissue lipid chaperones aP2 and mal1, we explored how metabolic alterations in adipose tissue are linked to whole-body metabolism through lipid signals. A robust increase in de novo lipogenesis rendered the adipose tissue of these mice resistant to the deleterious effects of dietary lipid exposure. Systemic lipid profiling also led to identification of C16:1n7-palmitoleate as an adipose tissue-derived lipid hormone that strongly stimulates muscle insulin action and suppresses hepatosteatosis. Our data reveal a lipid-mediated endocrine network and demonstrate that adipose tissue uses lipokines such as C16:1n7-palmitoleate to communicate with distant organs and regulate systemic metabolic homeostasis.
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              Design of a High Density SNP Genotyping Assay in the Pig Using SNPs Identified and Characterized by Next Generation Sequencing Technology

              Background The dissection of complex traits of economic importance to the pig industry requires the availability of a significant number of genetic markers, such as single nucleotide polymorphisms (SNPs). This study was conducted to discover several hundreds of thousands of porcine SNPs using next generation sequencing technologies and use these SNPs, as well as others from different public sources, to design a high-density SNP genotyping assay. Methodology/Principal Findings A total of 19 reduced representation libraries derived from four swine breeds (Duroc, Landrace, Large White, Pietrain) and a Wild Boar population and three restriction enzymes (AluI, HaeIII and MspI) were sequenced using Illumina's Genome Analyzer (GA). The SNP discovery effort resulted in the de novo identification of over 372K SNPs. More than 549K SNPs were used to design the Illumina Porcine 60K+SNP iSelect Beadchip, now commercially available as the PorcineSNP60. A total of 64,232 SNPs were included on the Beadchip. Results from genotyping the 158 individuals used for sequencing showed a high overall SNP call rate (97.5%). Of the 62,621 loci that could be reliably scored, 58,994 were polymorphic yielding a SNP conversion success rate of 94%. The average minor allele frequency (MAF) for all scorable SNPs was 0.274. Conclusions/Significance Overall, the results of this study indicate the utility of using next generation sequencing technologies to identify large numbers of reliable SNPs. In addition, the validation of the PorcineSNP60 Beadchip demonstrated that the assay is an excellent tool that will likely be used in a variety of future studies in pigs.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                14 January 2013
                : 8
                : 1
                : e53687
                Affiliations
                [1 ]Departament de genètica animal, Centre de Recerca en Agrigenòmica (CRAG), Bellaterra, Spain
                [2 ]Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
                [3 ]Genètica i Millora Animal, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Lleida, Spain
                [4 ]Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain
                University of Queensland, Australia
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Principal investigator of the project: JMF. Conceived and designed the experiments: JC JMF MB. Performed the experiments: JC YR-C AP-O MB. Analyzed the data: JC YR-C AP-O MB. Contributed reagents/materials/analysis tools: JC YR-C AP-O DP-M JLN JMF MB. Wrote the paper: JC YR-C AP-O JMF MB.

                Article
                PONE-D-12-23580
                10.1371/journal.pone.0053687
                3544903
                23341976
                2bcebf6f-de97-418c-9e7f-643cf1ec5c32
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 3 August 2012
                : 3 December 2012
                Page count
                Pages: 12
                Funding
                This study has been funded by MICINN projects (AGL2008-04818-C03 and AGL2011-29821-CO2) and the Innovation Consolider-Ingenio 2012 Program (CSD2007-00036, Center for Research in Agrigenomics). J. Corominas was funded by a Formación de Personal Investigador (FPI) PhD grant from Spanish Ministerio de Educación (BES-2009-018223), Y. Ramayo by a Formación del Profesorado Universitario (FPU) PhD grant (AP2008-01450) and A. Puig was funded by a Personal Investigador en Formación (PIF) PhD grant from the Universitat Autónoma de Barcelona (458-01-1/2011). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Agriculture
                Agricultural Biotechnology
                Marker-Assisted Selection
                Animal Management
                Animal Breeding
                Animal Genetics
                Animal Production
                Biology
                Genetics
                Gene Expression
                DNA modification
                DNA transcription
                Animal Genetics
                Gene Function
                Genome-Wide Association Studies
                Genomics
                Genome Complexity
                Genome Expression Analysis

                Uncategorized
                Uncategorized

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