A whole genome association (WGA) study was performed to detect significant polymorphisms for meat quality traits in an F2 cross population (N = 478) that were generated with Korean native pig sires and Landrace dams in National Livestock Research Institute, Songwhan, Korea. The animals were genotyped using Illumina porcine 60k SNP beadchips, in which a set of 46,865 SNPs were available for the WGA analyses on ten carcass quality traits; live weight, crude protein, crude lipids, crude ash, water holding capacity, drip loss, shear force, CIE L, CIE a and CIE b. Phenotypes were regressed on additive and dominance effects for each SNP using a simple linear regression model, after adjusting for sex, sire and slaughter stage as fixed effects. With the significant SNPs for each trait (p<0.001), a stepwise regression procedure was applied to determine the best set of SNPs with the additive and/or dominance effects. A total of 106 SNPs, or quantitative trait loci (QTL) were detected, and about 32 to 66% of the total phenotypic variation was explained by the significant SNPs for each trait. The QTL were identified in most porcine chromosomes (SSCs), in which majority of the QTL were detected in SSCs 1, 2, 12, 13, 14 and 16. Several QTL clusters were identified on SSCs 12, 16 and 17, and a cluster of QTL influencing crude protein, crude lipid, drip loss, shear force, CIE a and CIE b were located between 20 and 29 Mb of SSC12. A pleiotropic QTL for drip loss, CIE L and CIE b was also detected on SSC16. These QTL need to be validated in commercial pig populations for genetic improvement in meat quality via marker-assisted selection.

The purpose of this study was to detect significant SNPs for blood components that were related to immunity using high single nucleotide polymorphism (SNP) density panels in a Korean native pig (KNP)×Yorkshire (YK) cross population. A reciprocal design of KNP×YK produced 249 F2 individuals that were genotyped for a total of 46,865 available SNPs in the Illumina porcine 60K beadchip. To perform whole genome association analysis (WGA), phenotypes were regressed on each SNP under a simple linear regression model after adjustment for sex and slaughter age. To set up a significance threshold, 0.1% point-wise p value from F distribution was used for each SNP test. Among the significant SNPs for a trait, the best set of SNP markers were determined using a stepwise regression procedure with the rates of inclusion and exclusion of each SNP out of the model at 0.001 level. A total of 54 SNPs were detected; 10, 6, 4, 4, 5, 4, 5, 10, and 6 SNPs for neutrophil, lymphocyte, monocyte, eosinophil, basophil, atypical lymph, immunoglobulin, insulin, and insulin-like growth factor-I, respectively. Each set of significant SNPs per trait explained 24 to 42% of phenotypic variance. Several pleiotropic SNPs were detected on SSCs 4, 13, 14 and 15.