Genome-wide Association Study to Identify Quantitative Trait Loci for Meat and Carcass Quality Traits in Berkshire

  • Iqbal, Asif (School of Biotechnology, Yeungnam University) ;
  • Kim, You-Sam (School of Biotechnology, Yeungnam University) ;
  • Kang, Jun-Mo (School of Biotechnology, Yeungnam University) ;
  • Lee, Yun-Mi (School of Biotechnology, Yeungnam University) ;
  • Rai, Rajani (School of Biotechnology, Yeungnam University) ;
  • Jung, Jong-Hyun (Jung P&C Institute) ;
  • Oh, Dong-Yup (Livestock Research Institute) ;
  • Nam, Ki-Chang (Department of Animal Science and Technology, Sunchon National University) ;
  • Lee, Hak-Kyo (Department of Animal Biotechnology, Chonbuk National University) ;
  • Kim, Jong-Joo (School of Biotechnology, Yeungnam University)
  • Received : 2015.09.08
  • Accepted : 2015.10.03
  • Published : 2015.11.01


Meat and carcass quality attributes are of crucial importance influencing consumer preference and profitability in the pork industry. A set of 400 Berkshire pigs were collected from Dasan breeding farm, Namwon, Chonbuk province, Korea that were born between 2012 and 2013. To perform genome wide association studies (GWAS), eleven meat and carcass quality traits were considered, including carcass weight, backfat thickness, pH value after 24 hours (pH24), Commission Internationale de l'Eclairage lightness in meat color (CIE L), redness in meat color (CIE a), yellowness in meat color (CIE b), filtering, drip loss, heat loss, shear force and marbling score. All of the 400 animals were genotyped with the Porcine 62K SNP BeadChips (Illumina Inc., USA). A SAS general linear model procedure (SAS version 9.2) was used to pre-adjust the animal phenotypes before GWAS with sire and sex effects as fixed effects and slaughter age as a covariate. After fitting the fixed and covariate factors in the model, the residuals of the phenotype regressed on additive effects of each single nucleotide polymorphism (SNP) under a linear regression model (PLINK version 1.07). The significant SNPs after permutation testing at a chromosome-wise level were subjected to stepwise regression analysis to determine the best set of SNP markers. A total of 55 significant (p<0.05) SNPs or quantitative trait loci (QTL) were detected on various chromosomes. The QTLs explained from 5.06% to 8.28% of the total phenotypic variation of the traits. Some QTLs with pleiotropic effect were also identified. A pair of significant QTL for pH24 was also found to affect both CIE L and drip loss percentage. The significant QTL after characterization of the functional candidate genes on the QTL or around the QTL region may be effectively and efficiently used in marker assisted selection to achieve enhanced genetic improvement of the trait considered.


Supported by : Rural Development Administration


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