Pigs from the F(2) generation of a Duroc x Pietrain resource population were evaluated to discover QTL affecting carcass composition and meat quality traits. Carcass composition phenotypes included primal cut weights, skeletal characteristics, backfat thickness, and LM area. Meat quality data included LM pH, temperature, objective and subjective color information, marbling and firmness scores, and drip loss. Additionally, chops were analyzed for moisture, protein, and fat composition as well as cook yield and Warner-Bratzler shear force measurements. Palatability of chops was determined by a trained sensory panel. A total of 510 F(2) animals were genotyped for 124 microsatellite markers evenly spaced across the genome. Data were analyzed with line cross, least squares regression interval, mapping methods using sex and litter as fixed effects and carcass weight or slaughter age as covariates. Significance thresholds of the F-statistic for single QTL with additive, dominance, or imprinted effects were determined on chromosome- and genome-wise levels by permutation tests. A total of 94 QTL for 35 of the 38 traits analyzed were found to be significant at the 5% chromosome-wise level. Of these 94 QTL, 44 were significant at the 1% chromosome-wise, 28 of these 44 were also significant at the 5% genome-wise, and 14 of these 28 were also significant at the 1% genome-wise significance thresholds. Putative QTL were discovered for 45-min pH and pH decline from 45 min to 24 h on SSC 3, marbling score and carcass backfat on SSC 6, carcass length and number of ribs on SSC 7, marbling score on SSC 12, and color measurements and tenderness score on SSC 15. These results will facilitate fine mapping efforts to identify genes controlling carcass composition and meat quality traits that can be incorporated into marker-assisted selection programs to accelerate genetic improvement in pig populations.

BackgroundCurrently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects.ResultsThe standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements.The following steps are used to locate genome segments displaying strong association. The marker with the highest − log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance.ConclusionsThe standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-246) contains supplementary material, which is available to authorized users.

Pigs from the F(2) generation of a Duroc x Pietrain resource population were evaluated to discover QTL affecting growth and composition traits. Body weight and ultrasound estimates of 10th-rib backfat, last-rib backfat, and LM area were serially measured throughout development. Estimates of fat-free total lean, total body fat, empty body protein, empty body lipid, and ADG from 10 to 22 wk of age were calculated, and random regression analyses were performed to estimate individual animal phenotypes representing intercept and linear rates of increase in these serial traits. A total of 510 F(2) animals were genotyped for 124 micro-satellite markers evenly spaced across the genome. Data were analyzed with line cross, least squares regression, interval mapping methods using sex and litter as fixed effects. Significance thresholds of the F-statistic for single QTL with additive, dominance, or imprinted effects were determined at the chromosome- and genome-wise levels by permutation tests. A total of 43 QTL for 22 of the 29 measured traits were found to be significant at the 5% chromosome-wise level. Of these 43 QTL, 20 were significant at the 1% chromosome-wise significance threshold, 14 of these 20 were also significant at the 5% genome-wise significance threshold, and 10 of these 14 were also significant at the 1% genome-wise significance threshold. A total of 22 QTL for the animal random regression terms were found to be significant at the 5% chromosome-wise level. Of these 22 QTL, 6 were significant at the 1% chromosome-wise significance threshold, 4 of these 6 were also significant at the 5% genome-wise significance threshold, and 3 of these 4 were also significant at the 1% genome-wise significance threshold. Putative QTL were discovered for 10th-rib and last-rib backfat on SSC 6, body composition traits on SSC 9, backfat and lipid composition traits on SSC 11, 10th-rib backfat and total body fat tissue on SSC 12, and linear regression of last-rib backfat and total body fat tissue on SSC 8. These results will facilitate fine-mapping efforts to identify genes controlling growth and body composition of pigs that can be incorporated into marker-assisted selection programs to accelerate genetic improvement in pig populations.

BackgroundEconomically important growth and meat quality traits in pigs are controlled by cascading molecular events occurring during development and continuing throughout the conversion of muscle to meat. However, little is known about the genes and molecular mechanisms involved in this process. Evaluating transcriptomic profiles of skeletal muscle during the initial steps leading to the conversion of muscle to meat can identify key regulators of polygenic phenotypes. In addition, mapping transcript abundance through genome-wide association analysis using high-density marker genotypes allows identification of genomic regions that control gene expression, referred to as expression quantitative trait loci (eQTL). In this study, we perform eQTL analyses to identify potential candidate genes and molecular markers regulating growth and meat quality traits in pigs.ResultsMessenger RNA transcripts obtained with RNA-seq of longissimus dorsi muscle from 168 F2 animals from a Duroc x Pietrain pig resource population were used to estimate gene expression variation subject to genetic control by mapping eQTL. A total of 339 eQTL were mapped (FDR ≤ 0.01) with 191 exhibiting local-acting regulation. Joint analysis of eQTL with phenotypic QTL (pQTL) segregating in our population revealed 16 genes significantly associated with 21 pQTL for meat quality, carcass composition and growth traits. Ten of these pQTL were for meat quality phenotypes that co-localized with one eQTL on SSC2 (8.8-Mb region) and 11 eQTL on SSC15 (121-Mb region). Biological processes identified for co-localized eQTL genes include calcium signaling (FERM, MRLN, PKP2 and CHRNA9), energy metabolism (SUCLG2 and PFKFB3) and redox hemostasis (NQO1 and CEP128), and results support an important role for activation of the PI3K-Akt-mTOR signaling pathway during the initial conversion of muscle to meat.ConclusionCo-localization of eQTL with pQTL identified molecular markers significantly associated with both economically important phenotypes and gene transcript abundance. This study reveals candidate genes contributing to variation in pig production traits, and provides new knowledge regarding the genetic architecture of meat quality phenotypes.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5386-2) contains supplementary material, which is available to authorized users.

To study differential gene expression in porcine skeletal muscle, a porcine complementary DNA (cDNA) macroarray was produced that contained 327 expressed sequence tags (EST) derived from whole embryo and adult skeletal muscle, and differential display PCR products from fetal and postnatal muscle. Total RNA from four muscle samples, 75- and 105-d fetal hind limb muscles, and 1- and 7-wk postnatal semitendinosus muscle was used to make radiolabeled targets for duplicate hybridization to the macroarray membranes in an initial screen for expression. All EST that gave clear signals (n = 238) were then re-arrayed, and hybridization was conducted with additional biological replication of samples in the 75-d and 1-wk ages. Signal intensity for each gene was normalized to signal intensity measured at control spots on each membrane, which consisted of total cDNA from liver, lung, spleen, and skeletal muscle. Both normalized ratio levels and a mixed linear model analyses were used to identify genes differentially expressed among the muscle samples. Results showed 28 genes had differences in expression level greater than twofold between the 75-d fetal and 1-wk muscle RNA samples. All 28 genes were also identified as genes with significantly different (P < 0.01) expression using a mixed linear model analysis. Nineteen of these 28 genes had significant matches (basic local alignment search tool [BLAST] score > 100; P < 0.01) to known genes, two matched genes encoding human hypothetical proteins, and seven had no significant matches to Genbank nonredundant and dbEST (database of expressed sequence tags) entries. These results were confirmed for representative genes with RNA blot analysis of seven developmental time points, including RNA from the same muscle samples tested previously in the macroarray. The RNA blot results confirmed the macroarray results for all selected genes, demonstrating that the macroarray technique used in this study is accurate and reproducible. An unknown muscle clone (M218) with a slightly less than twofold increase in expression from the 75-d to the 1-wk age (1 wk/75 d = 1.94; P = 0.0114) was also shown to differ between these two ages using RNA blot analysis, demonstrating the methods used to identify differentially expressed genes may be conservative. The association between expression patterns of vimentin and desmin was also investigated. Results indicate the switch in intermediate filament protein from vimentin to desmin occurs primarily at the level of transcription and/or RNA processing.