Literature DB >> 23567132

Association of bovine meat quality traits with genes included in the PPARG and PPARGC1A networks.

N Sevane1, E Armstrong, O Cortés, P Wiener, R Pong Wong, S Dunner.   

Abstract

Understanding which are the genetic variants underlying the nutritional and sensory properties of beef, enables improvement in meat quality. The aim of this study is to identify new molecular markers for meat quality through an association study using candidate genes included in the PPARG and PPARGC1A networks given their master role in coordinating metabolic adaptation in fat tissue, muscle and liver. Amongst the novel associations found in this study, selection of the positive marker variants of genes such as BCL3, LPL, PPARG, SCAP, and SCD will improve meat organoleptic characteristics and health by balancing the n-6 to n-3 fatty acid ratio in meat. Also previous results on GDF8 and DGAT1 were validated, and the novel ATF4, HNF4A and PPARGC1A associations, although slightly under the significance threshold, are consistent with their physiological roles. These data contribute insights into the complex gene-networks underlying economically important traits.
Copyright © 2013 Elsevier Ltd. All rights reserved.

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Year:  2013        PMID: 23567132     DOI: 10.1016/j.meatsci.2013.02.014

Source DB:  PubMed          Journal:  Meat Sci        ISSN: 0309-1740            Impact factor:   5.209


  11 in total

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2.  Transcriptome analysis and identification of significantly differentially expressed genes in Holstein calves subjected to severe thermal stress.

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Authors:  Xiang Z Li; C G Yan; Qi S Gao; Y Yan; Seong H Choi; Stephen B Smith
Journal:  J Anim Sci       Date:  2018-06-29       Impact factor: 3.159

Review 4.  An interpretive review of selective sweep studies in Bos taurus cattle populations: identification of unique and shared selection signals across breeds.

Authors:  Beatriz Gutiérrez-Gil; Juan J Arranz; Pamela Wiener
Journal:  Front Genet       Date:  2015-05-13       Impact factor: 4.599

5.  Association of ADIPOQ, OLR1 and PPARGC1A gene polymorphisms with growth and carcass traits in Nelore cattle.

Authors:  Patrícia D da S Fonseca; Fábio R P de Souza; Gregório M F de Camargo; Fernanda M M Gil; Diercles F Cardoso; Larissa Zetouni; Camila U Braz; Arione A Boligon; Renata H Branco; Lucia G de Albuquerque; Maria E Z Mercadante; Humberto Tonhati
Journal:  Meta Gene       Date:  2015-03-06

6.  Phenotypic and genotypic background underlying variations in fatty acid composition and sensory parameters in European bovine breeds.

Authors:  Natalia Sevane; Hubert Levéziel; Geoffrey R Nute; Carlos Sañudo; Alessio Valentini; John Williams; Susana Dunner
Journal:  J Anim Sci Biotechnol       Date:  2014-04-15

7.  Genetic characterisation of PPARG, CEBPA and RXRA, and their influence on meat quality traits in cattle.

Authors:  Daniel Estanislao Goszczynski; Juliana Papaleo Mazzucco; María Verónica Ripoli; Edgardo Leopoldo Villarreal; Andrés Rogberg-Muñoz; Carlos Alberto Mezzadra; Lilia Magdalena Melucci; Guillermo Giovambattista
Journal:  J Anim Sci Technol       Date:  2016-04-01

8.  Genome-wide association and genomic prediction of breeding values for fatty acid composition in subcutaneous adipose and longissimus lumborum muscle of beef cattle.

Authors:  Liuhong Chen; Chinyere Ekine-Dzivenu; Michael Vinsky; John Basarab; Jennifer Aalhus; Mike E R Dugan; Carolyn Fitzsimmons; Paul Stothard; Changxi Li
Journal:  BMC Genet       Date:  2015-11-21       Impact factor: 2.797

9.  The application of transcriptomic data in the authentication of beef derived from contrasting production systems.

Authors:  Torres Sweeney; Alex Lejeune; Aidan P Moloney; Frank J Monahan; Paul Mc Gettigan; Gerard Downey; Stephen D E Park; Marion T Ryan
Journal:  BMC Genomics       Date:  2016-09-21       Impact factor: 3.969

10.  Dynamic transcriptome and DNA methylome analyses on longissimus dorsi to identify genes underlying intramuscular fat content in pigs.

Authors:  Yuding Wang; Cai Ma; Yi Sun; Yi Li; Li Kang; Yunliang Jiang
Journal:  BMC Genomics       Date:  2017-10-12       Impact factor: 3.969

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