Comparison between Direct and Competitive Models to Investigate Variation of Carcass and Ham Quality Traits in Heavy Pigs (original) (raw)
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Animals
Neglecting dominance effects in genetic evaluations may overestimate the predicted genetic response achievable by a breeding program. Additive and dominance genetic effects were estimated by pedigree-based models for growth, carcass, fresh ham and dry-cured ham seasoning traits in 13,295 crossbred heavy pigs. Variance components estimated by models including litter effects, dominance effects, or both, were compared. Across traits, dominance variance contributed up to 26% of the phenotypic variance and was, on average, 22% of the additive genetic variance. The inclusion of litter, dominance, or both these effects in models reduced the estimated heritability by 9% on average. Confounding was observed among litter, additive genetic and dominance effects. Model fitting improved for models including either the litter or dominance effects, but it did not benefit from the inclusion of both. For 15 traits, model fitting slightly improved when dominance effects were included in place of litt...
Genetic and phenotypic parameters for carcass and meat quality traits in commercial crossbred pigs1
Journal of Animal Science, 2014
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 and Phenotypic Parameters for Carcass and Meat Quality Traits in Commercial Crossbred Pigs
Journal of Animal Science, 2014
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.
PloS one, 2014
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 re...
Estimation of social genetic effects on feeding behaviour and production traits in pigs
Animal
Pigs are housed in groups during the test period. Social effects between pen mates may affect average daily gain (ADG), backfat thickness (BF), feed conversion rate (FCR), and the feeding behaviour traits of pigs sharing the same pen. The aim of our study was to estimate the genetic parameters of feeding behaviour and production traits with statistical models that include social genetic effects (SGEs). The data contained 3075 Finnish Yorkshire, 3351 Finnish Landrace, and 968 F1-crossbred pigs. Feeding behaviour traits were measured as the number of visits per day (NVD), time spent in feeding per day (TPD), daily feed intake (DFI), time spent in feeding per visit (TPV), feed intake per visit (FPV), and feed intake rate (FR). The test period was divided into five periods of 20 days. The number of pigs per pen varied from 8 to 12. Two model approaches were tested, i.e. a fixed group size model and a variable group size model. For the fixed group size model, eight random pigs per pen were included in the analysis, while all pigs in a pen were included for the variable group size model. The linear mixed-effects model included sex, breed, and herd*year*season as fixed effects and group (batch*pen), litter, the animal itself (direct genetic effect (DGE)), and pen mates (SGEs) as random effects. For feeding behaviour traits, estimates of the total heritable variation (T 2 ± SE) and classical heritability (h 2 ± SE, values given in brackets) from the variable group size model (e.g. period 1) were 0.34 ± 0.13 (0.30 ± 0.04) for NVD, 0.41 ± 0.10 (0.37 ± 0.04) for TPD, 0.40 ± 0.15 (0.14 ± 0.03) for DFI, 0.53 ± 0.15 (0.28 ± 0.04) for TPV, 0.66 ± 0.17 (0.28 ± 0.04) for FPV, and 0.29 ± 0.13 (0.22 ± 0.03) for FR. The effect of social interaction was minimal for ADG (T 2 = 0.29 ± 0.11 and h 2 = 0.29 ± 0.04), BF (T 2 = 0.48 ± 0.12 and h 2 = 0.38 ± 0.07), and FCR (T 2 = 0.37 ± 0.12 and h 2 = 0.29 ± 0.04) using the variable group size model. In conclusion, the results indicate that social interactions have a considerable indirect genetic effect on the feeding behaviour and FCR of pigs but not on ADG and BF.
Livestock Science, 2009
Genetic parameters were estimated for crossbred progeny of Bavarian Piétrain sires housed in two test environments on the two Bavarian test stations. The data contained 13,980 pigs housed in traditional pens for 2 pigs and 3,454 pigs housed in big pens for 10-14 pigs with automatic feeding system recorded between 2000 and 2004. In total, 584 sires having progeny in both housing systems were available to estimate genetic correlations between the two test environments. The analysis showed that the housing of pigs in big pens is more demanding with respect to the test design than in 2-pig pens. Further, the results show differences in both phenotypic performance and genetic parameters between the two environments. Daily gain is lower and lean meat content is higher in big pens with automatic feeding system. Therefore, it is suspected that pigs develop slower in the new housing due to a different feed intake behavior in comparison to 2-pig pens. This might be the main reason for the moderate genetic correlations among fattening performance traits (0.5-0.7 ± 0.13), which result in re-rankings of selection candidates depending which kind of information is utilized. Genetic correlations of slaughter and meat quality traits, however, are close to 1. Differences between the variance components in the two test stations have been found and simple pooling of data is problematic with respect to the breeding value estimation.
2014
Growth rate, carcass and meat quality properties are vital factors influencing the cost of fattener production and viability in pig production enterprises. These factors are related to genetic potential and various environmental factors, where the overall efficiency of production depends on the successful interaction of these two factors. There has been a distinctive association of genetics and individual levels of various non genetic factors such as nutrition, management, litter size, parity etc with different production parameters, carcass and meat quality properties in pig production. Whilst genetics is a major influence on these traits, there are a large number of non genetic factors that impinge on maximizing production, hence the need to manipulate them to improve the final product, which is pork. The preceding review gives insight on the role of genetics and non genetic factors on production traits, carcass and meat quality properties in pig production. An examination of the impacts of pre-slaughter stressors on pig carcass and meat quality should be considered in corrective strategies for remediating and preventing pre-slaughter stress which result in poor carcass quality. Some suggestions to guarantee appropriate pre slaughter conditions and obtain the best meat quality are reviewed. The discussion concludes that a holistic approach which give emphasis on understanding both genetic and non genetic factors Contents lists available at Sjournals
Genetic Variation Degree for Meat Production Traits in Pure-Bred Pigs
Biotechnology & Biotechnological Equipment, 2010
The genetic variation degree for the meat production traits (traits of rearing, carcass and meat quality) were considered and evaluated in 4 pure-breeds (Durok, Hampshire, Yorkshire and Landras) totaling some 120 heads, coming from various farms which were bred in trial-out farms. The pigs slaughtered weighed some 105 kg each, which later were subject to further evaluation for various parts of the carcass. A mixed model was used for each farm the pigs of mixed breeds were coming from and the following factors were looked at and closely considered: herd origin, litter, error as random effects, breed, season and the fixed effects. For some of the afore-mentioned traits it proved that the variation among breeds of the same herd (pigs pertaining to a certain farm and of various breeds) were much bigger when compared with pigs of the same breed but which belonged to various herds. While with regard to meat quality traits (marbling appearance, color, structure) were relatively bigger among breeds of various herds. The differences for these traits when compared among herds are far smaller. For all of the traits, the variation concerning the genetic value of livestock within the herds and breeds of the same herd is much broader compared with that among various breeds and breeds belonging to the same herds. For most traits, it is more important to choose the best source of breeding stock than the best breed.
Social genetic effects for growth in Landrace pigs with varying group sizes
2018
The aim of this study was to investigate if social genetic effects for average daily gain (ADG) in pigs depend on group size. Records included 119,919 pigs from 13 nucleus Landrace herds in Denmark. Pigs entered the performance test at ~30 kg and were assigned to pens containing between 8 to 15 pigs. Each pen had approximately the same stocking density. A total of 10,803 groups of pigs were included. The ADG from 30 kg to the end of the test (~94 kg) was 1012 g per day. ADG was analysed both separately for each of the eight different group sizes (8 to 15), and on the whole data, including all group sizes. Variance components were first estimated using a classical animal model including the fixed effect of sex, contemporary compartment, along with age and age squared at end of the test as covariates in addition to random effects of animal, group and litter. Thereafter variance components were estimated using a social genetic model, which in addition to the effects included in the cla...