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. Copyright © 2014. American Society of Animal Science .

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.

Disease is a global problem for animal farming industries causing tremendous economic losses (>USD 220 billion over the last decade) and serious animal welfare issues. The limitations and deficiencies of current non-selection disease control methods (e.g., vaccination, treatment, eradication strategy, genome editing, and probiotics) make it difficult to effectively, economically, and permanently eliminate the adverse influences of disease in the farm animals. These limitations and deficiencies drive animal breeders to be more concerned and committed to dealing with health problems in farm animals by selecting animals with favorable health traits. Both genetic selection and genomic selection contribute to improving the health of farm animals by selecting certain health traits (e.g., disease tolerance, disease resistance, and immune response), although both of them face some challenges. The objective of this review was to comprehensively review the potential of selecting health traits in coping with issues caused by diseases in farm animals. Within this review, we highlighted that selecting health traits can be applied as a method of disease control to help animal agriculture industries to cope with the adverse influences caused by diseases in farm animals. Certainly, the genetic/genomic selection solution cannot solve all the disease problems in farm animals. Therefore, management, vaccination, culling, medical treatment, and other measures must accompany selection solution to reduce the adverse impact of farm animal diseases on profitability and animal welfare.

Improving 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. Six 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. There 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 investigation

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.

Miar, Y., Plastow, G. S., Bruce, H. L., Moore, S. S., Durunna, O. N., Nkrumah, J. D. and Wang, Z. 2014. Estimation of genetic and phenotypic parameters for ultrasound and carcass merit traits in crossbred beef cattle. Can. J. Anim. Sci. 94: 273–280. Ultrasound measurements of 852 crossbred steers along with carcass merit measurements on 756 of them were used to examine their genetic and phenotypic parameters. Traits including ultrasound backfat thickness (UBF), ultrasound ribeye area (UREA), ultrasound marbling (UMAR), carcass weight (CWT), carcass grade fat (CGF), carcass average backfat thickness (CABF), carcass ribeye area (CREA), carcass marbling score (CMAR), and carcass lean meat yield (CLMY) were measured on 6 yr of residual feed intake trials from 2003 to 2008. Pairwise bivariate animal models were performed for each combination of traits using ASReml software to estimate heritability, phenotypic and genetic correlations among the traits. Significant fixed effects (contemporary group, and sire breed), covariates (age of dam, slaughter weight, and start test age of animal), and random additive effect were fitted in the models. The heritability estimates for UBF, UREA, UMAR, CWT, CGF, CABF, CREA, CMAR, and CLMY were 0.31, 0.17, 0.37, 0.40, 0.22, 0.25, 0.24, 0.38, and 0.28, respectively. Most of the phenotypic correlations were significant (P<0.05). CWT had low to moderate phenotypic correlations with most of the traits. Results show that heavier CWT tends to have more UREA, CGF, CABF, and CREA. Genetic correlations among these traits varied from weak to strong, but most of them were not significantly different from zero. Greater CREA may lead to decreased UMAR, and UBF due to negative genetic correlations (−0.56±0.32, and −0.45±0.23, respectively). The results support the potential value of ultrasound technology in crossbreed beef cattle breeding programs to generate indicator traits for carcass quality. In addition, carcass lean meat yield correlated favourably with backfat thickness and rib eye area but correlated unfavourably with UMAR. The estimated genetic parameters for ultrasound and carcass merit traits can be incorporated into breeding programs that emphasize carcass quality in Canadian crossbred beef cattle populations.