Pork quality and carcass characteristics are now being integrated into swine breeding objectives because of their economic value. Understanding the genetic basis for these traits is necessary for this to be accomplished. The objective of this study was to estimate phenotypic and genetic parameters for carcass and meat quality traits in 2 Canadian swine populations. Data from a genomic selection study aimed at improving meat quality with a mating system involving hybrid Landrace × Large White and Duroc pigs were used to estimate heritabilities and phenotypic and genetic correlations among them. Data on 2,100 commercial crossbred pigs for meat quality and carcass traits were recorded with pedigrees compromising 9,439 animals over 15 generations. Significant fixed effects (company, sex, and slaughter batch), covariates (cold carcass weight and slaughter age), and random additive and common litter effects were fitted in the models. A series of pairwise bivariate analyses were implemented in ASReml to estimate phenotypic and genetic parameters. Heritability estimates (±SE) for carcass traits were moderate to high and ranged from 0.22 ± 0.08 for longissimus dorsi muscle area to 0.63 ± 0.04 for trimmed ham weight, except for firmness, which was low. Heritability estimates (±SE) for meat quality traits varied from 0.10 ± 0.04 to 0.39 ± 0.06 for the Minolta b* of ham quadriceps femoris muscle and shear force, respectively. Generally, most of the genetic correlations were significant (P < 0.05) and ranged from low (0.18 ± 0.07) to high (-0.97 ± 0.35). There were high negative genetic correlations between drip loss with pH and shear force and a positive correlation with cooking loss. Genetic correlations between carcass weight (both hot and cold) with carcass marbling were highly positive. It was concluded that selection for increasing primal and subprimal cut weights with better pork quality may be possible. Furthermore, the use of pH is confirmed as an indicator for pork water-holding capacity and cooking loss. The heritabilities of carcass and pork quality traits indicated that they can be improved using traditional breeding methods and genomic selection, respectively. The estimated genetic parameters for carcass and meat quality traits can be incorporated into the breeding programs that emphasize product quality in these Canadian swine populations.

Genetic correlations between performance traits with meat quality and carcass traits were estimated on 6,408 commercial crossbred pigs with performance traits recorded in production systems with 2,100 of them having meat quality and carcass measurements. Significant fixed effects (company, sex and batch), covariates (birth weight, cold carcass weight, and age), random effects (additive, litter and maternal) were fitted in the statistical models. A series of pairwise bivariate analyses were implemented in ASREML to estimate heritability, phenotypic, and genetic correlations between performance traits (n = 9) with meat quality (n = 25) and carcass (n = 19) traits. The animals had a pedigree compromised of 9,439 animals over 15 generations. Performance traits had low-to-moderate heritabilities (±SE), ranged from 0.07±0.13 to 0.45±0.07 for weaning weight, and ultrasound backfat depth, respectively. Genetic correlations between performance and carcass traits were moderate to high. The results indicate that: (a) selection for birth weight may increase drip loss, lightness of longissimus dorsi, and gluteus medius muscles but may reduce fat depth; (b) selection for nursery weight can be valuable for increasing both quantity and quality traits; (c) selection for increased daily gain may increase the carcass weight and most of the primal cuts. These findings suggest that deterioration of pork quality may have occurred over many generations through the selection for less backfat thickness, and feed efficiency, but selection for growth had no adverse effects on pork quality. Low-to-moderate heritabilities for performance traits indicate that they could be improved using traditional selection or genomic selection. The estimated genetic parameters for performance, carcass and meat quality traits may be incorporated into the breeding programs that emphasize product quality in these Canadian swine populations.

BackgroundImproving meat quality is a high priority for the pork industry to satisfy consumers’ preferences. GWAS have become a state-of-the-art approach to genetically improve economically important traits. However, GWAS focused on pork quality are still relatively rare.ResultsSix genomic regions were shown to affect loin pH and Minolta colour a* and b* on both loin and ham through GWAS in 1943 crossbred commercial pigs. Five of them, located on Sus scrofa chromosome (SSC) 1, SSC5, SSC9, SSC16 and SSCX, were associated with meat colour. However, the most promising region was detected on SSC15 spanning 133–134 Mb which explained 3.51% - 17.06% of genetic variance for five measurements of pH and colour. Three SNPs (ASGA0070625, MARC0083357 and MARC0039273) in very strong LD were considered most likely to account for the effects in this region. ASGA0070625 is located in intron 2 of ZNF142, and the other two markers are close to PRKAG3, STK36, TTLL7 and CDK5R2. After fitting MARC0083357 (the closest SNP to PRKAG3) as a fixed factor, six SNPs still remained significant for at least one trait. Four of them are intragenic with ARPC2, TMBIM1, NRAMP1 and VIL1, while the remaining two are close to RUFY4 and CDK5R2. The gene network constructed demonstrated strong connections of these genes with two major hubs of PRKAG3 and UBC in the super-pathways of cell-to-cell signaling and interaction, cellular function and maintenance. All these pathways play important roles in maintaining the integral architecture and functionality of muscle cells facing the dramatic changes that occur after exsanguination, which is in agreement with the GWAS results found in this study.ConclusionsThere may be other markers and/or genes in this region besides PRKAG3 that have an important effect on pH and colour. The potential markers and their interactions with PRKAG3 require further investigationElectronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0192-1) contains supplementary material, which is available to authorized users.

Simple Summary: Understanding the animal growth is important for optimized management and feeding practices as well as genetic improvement of animals; however, little is known about the growth of mink raised in Canada. This study evaluated the performances of ten models to find the best models describing the growth curves in mink. The results showed that Logistic and Richards were the best model for males and females, respectively. Growth curves were different between males and females. These results suggested that Richards model can be used for modelling the mink growth and modelling might be performed separately for male and female individuals.Abstract: Modelling the growth curves of animals is important for optimizing the management and efficiency of animal production; however, little is known about the growth curves in American mink (Neovison vison). The study evaluated the performances of four three-parameter (Logistic, Gompertz, von Bertalanffy, and Brody), four four-parameter (Richards, Weibull, Bridges, and Janoscheck) and two polynomial models for describing the growth curves in mink. Body weights were collected from the third week of life to the week 31 in 738 black mink (373 males and 365 females). Models were fitted using the nls and nlsLM functions in stats and minpack.lm packages in R software, respectively. The Akaike's information criterion (AIC) and Bayesian information criterion (BIC) were used for model comparison. Based on these criteria, Logistic and Richards were the best models for males and females, respectively. Four-parameter models had better performance compared to the other models except for Logistic model. The estimated maximum weight and mature growth rate varied among the models and differed between males and females. The results indicated that males and females had different growth curves as males grew faster and reached to the maximum body weight later compared to females. Further studies on genetic parameters and selection response for growth curve parameters are required for development of selection programs based on the shape of growth curves in mink.

Phasing genotypes to haplotypes is becoming increasingly important due to its applications in the study of diseases, population and evolutionary genetics, imputation, and so on. Several studies have focused on the development of computational methods that infer haplotype phase from population genotype data. The aim of this study was to compare phasing algorithms implemented in Beagle, Findhap, FImpute, Impute2, and ShapeIt2 software using 50k and 777k (HD) genotyping data. Six scenarios were considered: no-parents, sire-progeny pairs, sire-dam-progeny trios, each with and without pedigree information in Holstein cattle. Algorithms were compared with respect to their phasing accuracy and computational efficiency. In the studied population, Beagle and FImpute were more accurate than other phasing algorithms. Across scenarios, phasing accuracies for Beagle and FImpute were 99.49-99.90% and 99.44-99.99% for 50k, respectively, and 99.90-99.99% and 99.87-99.99% for HD, respectively. Generally, FImpute resulted in higher accuracy when genotypic information of at least one parent was available. In the absence of parental genotypes and pedigree information, Beagle and Impute2 (with double the default number of states) were slightly more accurate than FImpute. Findhap gave high phasing accuracy when parents' genotypes and pedigree information were available. In terms of computing time, Findhap was the fastest algorithm followed by FImpute. FImpute was 30 to 131, 87 to 786, and 353 to 1,400 times faster across scenarios than Beagle, ShapeIt2, and Impute2, respectively. In summary, FImpute and Beagle were the most accurate phasing algorithms. Moreover, the low computational requirement of FImpute makes it an attractive algorithm for phasing genotypes of large livestock populations.