Search

The objectives of this study were to determine nitrogen loss at different stages of growth and during the entire growing period and to investigate the associations between nitrogen excretion and production traits in growing pigs. Data from 315 pigs of an F-2 population which originated from crossing Pietrain sires with a commercial dam line were used. Nitrogen retention was derived from protein retention as measured using the deuterium dilution technique during different stages of growth (60 to 90 kg, 90 to 120 kg, and 120 to 140 kg). Pigs were fed ad libitum with 2 pelleted diets containing 17% (60 to 90 kg) and 16.5% (90 to 120 and 120 to 140 kg) CP. Average daily nitrogen excretion (ADNE) within each stage of growth was calculated on the basis of the accumulated difference between average daily nitrogen intake (ADNI) and average daily nitrogen retention (ADNR). Least ADNE, nitrogen excretion per BW gain (NEWG) and total nitrogen excretion (TNE) were observed during growth from 60 to 90 kg. In contrast, the greatest ADNE, NEWG, and TNE were found during growth from 120 to 140 kg. Statistical analyses indicated that gender, housing type, the ryanodine receptor 1 (RYR1) gene, and batch influenced nitrogen excretion (P

International audience; Meta-analyses have been carried out to quantify the effect of dietary vitamin E on α-tocopherol accumulation and on lipid oxidation in porcine M. longissimus. Published results of 13 (vitamin E accumulation) and 10 (lipid oxidation) experiments respectively were used for the analyses. After a number of standardization procedures, a nonlinear relationship was found between the supplementary vitamin E and the accumulation of α-tocopherol in pork which approached a maximum value of 6.4 μg/g tissue. Pork lipid oxidation levels were described in terms of Thiobarbituric Acid Reacting Substances (TBARS) values. The statistical analysis revealed significant effect of vitamin E dose, muscle α-tocopherol concentration and supplementation time on TBARS, resulting in two prediction models for lipid oxidation. Meta-analysis has proven to be a valuable tool for combining results from previous studies to quantify the effects of dietary vitamin E. Further studies, carried out with standardized experimental protocols would be beneficial for model validation and to increase the predictive power of the derived models.

The aim of the research was to gain a better understanding of the genomic regulation of meat quality by investigating individual and epistatic QTL in a three-generation full-sib population (Pietrain x crossbred dam line). In total, 386 animals were genotyped for 96 markers. Analysed traits included pH, reflectance value, conductivity, and meat colour. Thirteen significant individual QTL were identified. The most significant QTL were detected on SSC1 and SSC9 for pH, on SSC4 for meat colour, and on SSC8 for conductivity, accounting for 3.4 to 4.7 of the phenotypic variance. Nine significant epistatic QTL pairs were detected accounting for between 5.7 and 10.9 of the phenotypic variance. Epistatic QTL pairs showing the largest effects were for reflectance value between two locations of SSC4, and for pH between SSC10 and SSC13, explaining 9.5 and 10.9 of the phenotypic variance, respectively. This study indicates that meat quality traits are influenced by numerous QTL as well as a complex network of interactions. (C) 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.

Technological meat quality is a significant economic factor in pork production, and numerous publications have shown that it is strongly influenced both by genetic status and by rearing and slaughter conditions. The quality of meat is often described by meat pH at different times postmortem, as well as by color and drip loss, whereas carcass quality is often characterized by lean percentage. A meta-analysis of findings relating to 3,530 pigs reported in 23 publications was carried out to assess the effects of the halothane gene, sex, breed, and slaughter weight of animals on 7 selected variables: pH at 45 min postmortem, ultimate pH, reflectance (L*-value), redness (a*-value), yellowness (b*-value), drip loss, and lean percentage. Two statistical methods were used in the meta-analysis: the method of effect size and the better known random effects model. The method of effect size was associated with Markov chain Monte Carlo techniques for implementing Bayesian hierarchical models to avoid the problems of limited data and publication bias. The results of our meta-analysis showed that the halothane genotype had a significant effect on all analyzed pork quality variables. Between-study variance was evaluated with the Cochran (1954) Q-test of heterogeneity. Meta-regression was used to explain this variance, with covariates such as breed, sex, slaughter weight, and fasting duration being integrated into different regression models. The halothane gene effect was associated with the breed effect only for the following variables: L*-value, b*-value, and drip loss. Slaughter weight contributed significantly only to the explanation of differences in ultimate pH between homozygous genotypes. In response to inconsistencies reported in the literature regarding the difference between the genotypes NN and Nn, results of the meta-analysis showed that the difference between these 2 genotypes was significant for all the analyzed variables except the a*-value.

The present study focused on the identification of epistatic QTL pairs for body composition traits (carcass cut, lean tissue, and fat tissue weights) measured at slaughter weight (140 kg of BW) in a 3-generation full-sib population developed by crossing Pietrain sires with a crossbred dam line. Depending on the trait, phenotypic observations were available for 306 to 315 F(2) animals. For the QTL analysis, 386 animals were genotyped for 88 molecular markers covering chromosomes SSC1, SSC2, SSC4, SSC6, SSC7, SSC8, SSC9, SSC10, SSC13, and SSC14. In total, 23 significant epistatic QTL pairs were identified, with the additive x additive genetic interaction being the most prevalent. Epistatic QTL were identified across all chromosomes except for SSC13, and epistatic QTL pairs accounted for between 5.8 and 10.2% of the phenotypic variance. Seven epistatic QTL pairs were between QTL that resided on the same chromosome, and 16 were between QTL that resided on different chromosomes. Sus scrofa chromosome 1, SSC2, SSC4, SSC6, SSC8, and SSC9 harbored the greatest number of epistatic QTL. The epistatic QTL pair with the greatest effect was for the entire loin weight between 2 locations on SSC7, explaining 10.2% of the phenotypic variance. Epistatic associations were identified between regions of the genome that contain the IGF-2 or melanocortin-4 receptor genes, with QTL residing in other genomic locations. Quantitative trait loci in the region of the melanocortin-4 receptor gene and on SSC7 showed significant positive dominance effects for entire belly weight, which were offset by negative dominance x dominance interactions between these QTL. In contrast, the QTL in the region of the IGF-2 gene showed significant negative dominance effects for entire ham weight, which were largely overcompensated for by positive additive x dominance genetic effects with a QTL on SSC9. The study shows that epistasis is of great importance for the genomic regulation of body composition in pigs and contributes substantially to the variation in complex traits.

The response to infection from porcine reproductive and respiratory syndrome virus (PRRSV) for 2 genetically diverse commercial pig lines was investigated. Seventy-two pigs from each line, aged 6 wk, were challenged with PRRSV VR-2385, and 66 litter-mates served as control. The clinical response to infection was monitored throughout the study and pigs were necropsied at 10 or 21 d postinfection. Previous analyses showed significant line differences in susceptibility to PRRSV infection. This study also revealed significant line differences in growth during infection. Line B, characterized by faster growth rate than line A in the absence of infection, suffered more severe clinical disease and greater reduction in BW growth after infection. Correlations between growth and disease-related traits were generally negative, albeit weak. Correlations were also weak among most clinical and pathological traits. Clinical disease traits such as respiratory scores and rectal temperatures were poor indicators of virus levels, pathological damage, or growth during PRRSV infection. Relationships between traits varied over time, indicating that different disease-related mechanisms may operate at different time scales and, therefore, that the time of assessing host responses may influence the conclusions drawn about biological significance. Three possible mechanisms underlying growth under PRRSV infection were proposed based on evidence from this and previous studies. It was concluded that a comprehensive framework describing the interaction between the biological mechanisms and the genetic influence on these would be desirable for achieving progress in the genetic control of this economically important disease.

An in silico mathematical model was used to explore the effect of, and the interaction between, (i) nutrition, (ii) genotype for growth and (iii) genotype for resistance, on the estimates of genetic parameters for resistance and performance in a population of lambs trickle-challenged daily with 3,000 L3s of Teladorsagia circumcincta. A previously published model for nematode infections in sheep was developed to include heritable variation in sheep growth traits, as well as in immunologically controlled traits such as establishment of incoming larvae, mortality of the adult worms and fecundity of the adult female worms. The simulated population comprised 10,000 lambs, these being the offspring of 250 sires mated to 5,000 dams. The model assumed the lambs to be parasitologically naïve at weaning (2 months of age), at which point the trickle challenge commenced and the model was updated daily until slaughter (at 6 months of age). Dietary treatments included a good and a poor quality feed, offered ad libitum. Two genotypes for growth were assumed: (i) fast and (ii) slow growing. Three genotypes for resistance were used: (i) benchmark, (ii) susceptible and (iii) resistant, differing in their ability to cope with nematode infections. Genetic parameters for output traits, including growth rate, food intake, worm burden and faecal egg count were estimated using a linear mixed model, fitting sire as a random effect to capture genetic effects. Heritabilities and correlations were found to change over time. In general, the heritabilities of immunity traits increased over time, whereas genetic correlations between production and immunity traits became weaker. Diet had a significant effect on the means and the estimated correlations of output traits, while genotypes for growth and resistance had smaller effects. These results suggest that discrepancies between published genetic parameters for nematode resistance may be a function of environmental factors rather than differences in host genotype.

Mechanistic animal growth models can incorporate a description of the genotype as represented by underlying biological traits that aim to specify the animal’s genetic potential for performance, independent from the environmental factors captured by the models. It can be argued that these traits may therefore be more closely associated to genetic potential, or components of genetic merit that are more robust across environments, than the environmentally dependent phenotypic traits currently used for genetic evaluation. The prediction of merit for underlying biological traits can be valuable for breeding and development of selection strategies across environments. Model inversion has been identified as a valid method for obtaining estimates of phenotypic and genetic components of the biological traits representing the genotype in the mechanistic model. The present study shows how these estimates were obtained for two existing pig breeds based on genetic and phenotypic components of existing performance trait records. Some of the resulting parameter estimates associated with each breed differ substantially, implying that the genetic differences between the breeds are represented in the underlying biological traits. The estimated heritabilities for the genetic potentials for growth, carcass composition and feed efficiency as represented by biological traits exceed the heritability estimates of related phenotypic traits that are currently used in evaluation processes for both breeds. The estimated heritabilities for maintenance energy requirements are however relatively small, suggesting that traits associated with basic survival processes have low heritability, provided that maintenance processes are appropriately represented by the model. The results of this study suggest that mechanistic animal growth models can be useful to animal breeding through the introduction of new biological traits that are less influenced by environmental factors than phenotypic traits currently used. Potential value comes from the estimation of underlying biological trait components and the explicit description of their expression across a range of environments as predicted by the model equations.

There is increasing recognition that genetic diversity can affect the spread of diseases, potentially affecting plant and livestock disease control as well as the emergence of human disease outbreaks. Nevertheless, even though computational tools can guide the control of infectious diseases, few epidemiological models can simultaneously accommodate the inherent individual heterogeneity in multiple infectious disease traits influencing disease transmission, such as the frequently modeled propensity to become infected and infectivity, which describes the host ability to transmit the infection to susceptible individuals. Furthermore, current quantitative genetic models fail to fully capture the heritable variation in host infectivity, mainly because they cannot accommodate the nonlinear infection dynamics underlying epidemiological data. We present in this article a novel statistical model and an inference method to estimate genetic parameters associated with both host susceptibility and infectivity. Our methodology combines quantitative genetic models of social interactions with stochastic processes to model the random, nonlinear, and dynamic nature of infections and uses adaptive Bayesian computational techniques to estimate the model parameters. Results using simulated epidemic data show that our model can accurately estimate heritabilities and genetic risks not only of susceptibility but also of infectivity, therefore exploring a trait whose heritable variation is currently ignored in disease genetics and can greatly influence the spread of infectious diseases. Our proposed methodology offers potential impacts in areas such as livestock disease control through selective breeding and also in predicting and controlling the emergence of disease outbreaks in human populations.

The objective of this study was to investigate the genetic relationships between skin lesion traits in group housed growing pigs as a measure of short- (in a newly mixed group) and long- (in a socially stable group) term aggression and commonly used commercial performance measures: growth, feed intake, feed efficiency, and carcass traits. Data on 2,413 growing pigs (138 groups) were available. Pigs were mixed into new social groups of 18 animals, and skin lesions were counted 24 h (SL24h) and 5 wk (SL5wk) postmixing. The animal model was used to estimate genetic parameters for skin lesion traits, test daily gain, lifetime daily gain, daily feed intake, feed efficiency (calculated as test daily gain divided by daily feed intake), loin depth, back fat, and HCW. Skin lesions had a heritable component, ranging from 0.08 for anterior SL24h to 0.22 for central SL5wk and would, therefore, be suitable as a method of phenotyping aggression for selection purposes. Significant positive genetic correlations were found between SL24h and SL5wk (0.46 to 0.81). Positive genetic correlations were also found between SL24h (central and posterior body regions) or SL5wk (all body regions) and the production traits lifetime daily gain, test daily gain, and HCW (0.29 to 0.54). Central SL24h, anterior SL5wk, and posterior SL5wk were found to correlate positively with feed efficiency (0.39 to 0.50), suggesting that pigs with more lesions convert feed more efficiently. Where significant, the magnitude of phenotypic correlations was low but positive (0.07 to 0.10). These results suggest that, genetically, animals that receive many lesions show improved performance compared to those with few lesions, except for anterior SL24h, which had previously been shown to be genetically positively correlated with the initiation of nonreciprocal attacks. It may, therefore, be possible, via selection against anterior skin lesions at mixing, to reduce this form of 1-sided aggression without adversely affecting production traits.

The objective of this study was to investigate the genetic relationships between skin lesion traits in group housed growing pigs as a measure of short- (in a newly mixed group) and long- (in a socially stable group) term aggression and commonly used commercial performance measures: growth, feed intake, feed efficiency, and carcass traits. Data on 2,413 growing pigs (138 groups) were available. Pigs were mixed into new social groups of 18 animals, and skin lesions were counted 24 h (SL24h) and 5 wk (SL5wk) postmixing. The animal model was used to estimate genetic parameters for skin lesion traits, test daily gain, lifetime daily gain, daily feed intake, feed efficiency (calculated as test daily gain divided by daily feed intake), loin depth, back fat, and HCW. Skin lesions had a heritable component, ranging from 0.08 for anterior SL24h to 0.22 for central SL5wk and would, therefore, be suitable as a method of phenotyping aggression for selection purposes. Significant positive genetic correlations were found between SL24h and SL5wk (0.46 to 0.81). Positive genetic correlations were also found between SL24h (central and posterior body regions) or SL5wk (all body regions) and the production traits lifetime daily gain, test daily gain, and HCW (0.29 to 0.54). Central SL24h, anterior SL5wk, and posterior SL5wk were found to correlate positively with feed efficiency (0.39 to 0.50), suggesting that pigs with more lesions convert feed more efficiently. Where significant, the magnitude of phenotypic correlations was low but positive (0.07 to 0.10). These results suggest that, genetically, animals that receive many lesions show improved performance compared to those with few lesions, except for anterior SL24h, which had previously been shown to be genetically positively correlated with the initiation of nonreciprocal attacks. It may, therefore, be possible, via selection against anterior skin lesions at mixing, to reduce this form of 1-sided aggression without adversely affecting production traits.

International audience; Pig breeding programs worldwide continue to focus on both productivity and robustness. This selection emphasis has to be accompanied by provision of better-quality environments to pigs to improve performance and to enhance health and welfare of pigs. Definition of broader breeding objectives that include robustness traits in addition to production traits is the first step in the development of selection strategies for productivity and robustness. An approach has been presented which facilitates extension of breeding objectives. Post-weaning survival, maternal genetic effects for growth as an indicator of health status and sow mature weight are examples of robustness traits. Further, breeding objectives should be defined for commercial environments and selection indexes should account for genotype by environment interactions (GxE). Average performances of groups of pigs have been used to quantify the additive effects of multiple environmental factors on performance of pigs. For growth, GxE existed when environments differed by 60 g/day between groups of pigs. This environmental variation was observed even on well managed farms. Selection for improved health of pigs should focus on disease resistance to indirectly reduce pathogen loads on farms and on disease resilience to improve the ability of pigs to cope with infection challenges. Traits defining disease resiliencemaybe based on performance andimmune measures, disease incidence or survival rates of pigs. Residual feed intake is a trait that quantifies feed efficiency. The responses of divergent selection lines for residual feed intake to various environmental challenges were often similar or even favourable for the more efficient, low residual feed intake line. These somewhat unexpected results highlight the need to gain a better understanding of the metabolic differences between more or less productive pigs. These physiological differences lead to interactions between the genetic potential of pigs for productivity and robustness and the prevalence of specific environmental conditions.

Random regression models were used to analyse the daily growth data for a total of 25 pigs of two commercial crossbred types between 75 and 140 days of age. A visual imaging system placed above a feeding station provided daily the plan area and length measurements of different body parts. Daily live-weight measurements of the pigs were obtained from a platform balance integrated into an electronic feeding station. Growth curves associated with different measures, pigs and types were compared. Significant differences in the age growth curves between the pig types could only be found in the ham width measurements (P < 0.05). The linear measure of ham width showed the greatest difference between the two types, and the lowest coefficient of variation among individual animals. Size measures were shown to be a more consistent indicator of pig performance during growth than live weight: pigs with a relatively large surface area or ham width at the early growth stage also have relatively large surface area or ham width at later stages and the between-animal variation in these measurements remains constant with age. Gain in live weight relative to increase in size differed significantly between the two pig types (P < 0.05). Pigs of the two types had significantly different shapes, but the change of shape during growth did not differ significantly between them. The allometric relationships between surface area and ham width1.85and between body length and ham width0.85indicate that the ham widths of pigs increase faster in proportion to full body measures. Variations between individual animals in size increase and shape change are significant (P < 0.05). The analysis suggests that VIA size and shape measurements provide valid descriptors of pig growth.

The aims of this study were to (i) compare models estimating residual energy intake (REI) using either lean and fat tissue growth or their proxy traits (average daily gain (ADG) and backfat thickness (BF)); (ii) determine genetic characteristics of REI at different growth stages and the entire test period; and (iii) examine 9 genetic and phenotypic relationships of REI with other production traits. Data from 315 pigs of an F 2 generation were used which originated from crossing Pietrain sires with a commercial crossbred dam population. Average daily protein (APD) and lipid deposition (ALD), as measurements of lean and fat tissue growth, were obtained using the deuterium dilution technique on live animals. During growth from 60 to 140 kg, REI was estimated using 4 different models for energy intake that included, besides other systematic effects, (1) ADG and BF; (2) APD and ALD; (3) and (4) incorporated the same covariables as the first two models, respectively, but pre-adjusted for systematic effects. Genetic parameters and estimated breeding values were obtained based on univariate animal models using REML analysis. Over the entire growing period, heritabilities of different REI using different models were all estimated at 0.44 and their genetic correlations were at unity. At different growth stages heritabilities for REI were greater ranging from 0.47 to 0.50. Genetic correlations between REI estimates at different stages of growth, obtained using genetic model 4, indicated that REI at 60 to 90 kg was non-significantly ( P >0.05) associated with REI at 90–120 kg (0.32±0.29) and 120–140 kg (0.28±0.28), but REI of the latter growth stages showed a significant ( P

Background Improvement of feed efficiency in pigs is of great economical and environmental interest and contributes to use limited resources efficiently to feed the world population. Genome scans for feed efficiency traits are of importance to reveal the underlying biological causes and increase the rate of genetic gain. The aim of this study was to determine the genomic architecture of feed efficiency measured by residual energy intake (REI), in association with production, feed conversion ratio (FCR) and nitrogen excretion traits through the identification of quantitative trait loci (QTL) at different stages of growth using a three generation full-sib design population which originated from a cross between Pietrain and a commercial dam line. Results Six novel QTL for REI were detected explaining 2.7-6.1% of the phenotypic variance in REI. At growth from 60–90 kg body weight (BW), a QTL with a significant dominance effect was identified for REI on SSC14, at a similar location to the QTL for feed intake and nitrogen excretion traits. At growth from 90–120 kg BW, three QTL for REI were detected on SSC2, SSC4 and SSC7 with significant additive, imprinting and additive effects, respectively. These QTL (except for the imprinted QTL) were positionally overlapping with QTL for FCR and nitrogen excretion traits. During final growth (120–140 kg BW), a further QTL for REI was identified on SSC8 with significant additive effect, which overlapped with QTL for nitrogen excretion. During entire analysed growth (60–140 kg BW), a novel additive QTL for REI on SSC4 was observed, with no overlapping with QTL for any other traits considered. Conclusions The occurrence of only one overlapping QTL of REI with feed intake suggests that only a small proportion of the variance in REI was explained by change in feed intake, whereas four overlapping QTL of REI with those of nitrogen excretion traits suggests that mostly underlying factors of feed utilisation such as metabolism and protein turnover were the reason for change in REI. Different QTL for REI were identified at different growth stages, indicating that different genes are responsible for efficiency in feed utilisation at different stages of growth.

International audience; Meta-analysis was performed to quantify the effects of gender in combination with carcass weight and breed on pork quality. Altogether published results from 43 references were used. The traits analyzed were pH at 45 min (pH45min) and pH at 24 h (pH24hr) postmortem, objective color attributes lightness (L*), redness (a*), and yellowness (b*; CIE color system), color and marbling scores, drip loss, intramuscular fat content (IMF), and backfat thickness (P2), as well as sensory scores of juiciness and tenderness. Data for 2 muscle types, LM and Musculus semimembranosus (SMM), were used for the analysis. Swine genders were defined as intact/entire male (EM), surgically castrated male (SM), immunocastrated male (IM), and entire female (EF). After standardization of scaled traits (color, marbling scores, juiciness, tenderness) and accounting for cold carcass weight (CW), statistical analysis was performed using mixed models where breed was included as random effect. The analysis found a general effect of gender on each trait and multiple comparisons identified significant differences among the individual genders for L* (lightness), marbling scores, IMF, P2 in LM, and pH24hr in SMM. For these traits, when genders were grouped into gender categories as "castrates" (IM, SM) and "natural genders" (EM, EF), significant differences were found among estimates related to these categories. Furthermore, significant differences were found between castrates and individual gender types, indicating that castrated animals statistically segregated regarding their pork quality and regardless of type of castration. Pork of SM/EM animals has been found to be the fattest/leanest and there is indication that IM pork has the lightest meat color. Carcass weight dependence was found to be nonlinear (quadratic) for a*, P2, and marbling scores, and linear for b* and color scores in LM and pH24hr in SMM. The analysis identified significant breed effects for all traits, with large variation in the actual magnitudes (∼10 to 100%) of breed effects among individual traits. The established CW dependencies of pork quality traits in combination with the other influencing factors investigated here provides pork producers with the opportunity to achieve desired pork quality targets for a wide range of CW (∼30 to 150 kg) under standard indoor-rearing conditions.

International audience; Background Genetic selection for host resistance offers a desirable complement to chemical treatment to control infectious disease in livestock. Quantitative genetics disease data frequently originate from field studies and are often binary. However, current methods to analyse binary disease data fail to take infection dynamics into account. Moreover, genetic analyses tend to focus on host susceptibility, ignoring potential variation in infectiousness, i.e. the ability of a host to transmit the infection. This stands in contrast to epidemiological studies, which reveal that variation in infectiousness plays an important role in the progression and severity of epidemics. In this study, we aim at filling this gap by deriving an expression for the probability of becoming infected that incorporates infection dynamics and is an explicit function of both host susceptibility and infectiousness. We then validate this expression according to epidemiological theory and by simulating epidemiological scenarios, and explore implications of integrating this expression into genetic analyses.ResultsOur simulations show that the derived expression is valid for a range of stochastic genetic-epidemiological scenarios. In the particular case of variation in susceptibility only, the expression can be incorporated into conventional quantitative genetic analyses using a complementary log-log link function (rather than probit or logit). Similarly, if there is moderate variation in both susceptibility and infectiousness, it is possible to use a logarithmic link function, combined with an indirect genetic effects model. However, in the presence of highly infectious individuals, i.e. super-spreaders, the use of any model that is linear in susceptibility and infectiousness causes biased estimates. Thus, in order to identify super-spreaders, novel analytical methods using our derived expression are required.ConclusionsWe have derived a genetic-epidemiological function for quantitative genetic analyses of binary infectious disease data, which, unlike current approaches, takes infection dynamics into account and allows for variation in host susceptibility and infectiousness.

Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment. Selection for host resistance has proven difficult, however, due to low heritability estimates. These low estimates may be caused by a failure to capture all the relevant genetic variance in disease resistance, as genetic analysis currently is not taylored to estimate genetic variation in infectivity. Host infectivity is the propensity of transmitting infection upon contact with a susceptible individual, and can be regarded as an indirect effect to disease status. It may be caused by a combination of physiological and behavioural traits. Though genetic variation in infectivity is difficult to measure directly, Indirect Genetic Effect (IGE) models, also referred to as associative effects or social interaction models, allow the estimation of this variance from more readily available binary disease data (infected/non-infected). We therefore generated binary disease data from simulated populations with known amounts of variation in susceptibility and infectivity to test the adequacy of traditional and IGE models. Our results show that a conventional model fails to capture the genetic variation in infectivity inherent in populations with simulated infectivity. An IGE model, on the other hand, does capture some of the variation in infectivity. Comparison with expected genetic variance suggests that there is scope for further methodological improvement, and that potential responses to selection may be greater than values presented here. Nonetheless, selection using an index of estimated direct and indirect breeding values was shown to have a greater genetic selection differential and reduced future disease risk than traditional selection for resistance only. These findings suggest that if genetic variation in infectivity substantially contributes to disease transmission, then breeding designs which explicitly incorporate IGEs might help reduce disease prevalence.

This paper identifies issues associated with field disease data and their implications on the interpretation of estimated genetic parameters and experimental designs. The main focus is on concepts relating to the impacts of diagnostic test properties and exposure to infection, and how exposure to infection is intricately related to within-herd epidemic dynamics. The following are raised challenges: (i) to more fully understand and describe the dynamic impacts of disease epidemics on genetic interpretations; (ii) to develop statistical methods to jointly estimate epidemiological and genetic parameters from complex epidemiological data; (iii) to develop and explore optimal experimental designs for case-control studies, exploiting field disease data. Solving these problems would add insight to both disease genetic and epidemiological studies, as well as enabling us to better select animals for increased disease resistance.

The objectives of this study were to determine nitrogen loss at different stages of growth and during the entire growing period and to investigate the associations between nitrogen excretion and production traits in growing pigs. Data from 315 pigs of an F-2 population which originated from crossing Pietrain sires with a commercial dam line were used. Nitrogen retention was derived from protein retention as measured using the deuterium dilution technique during different stages of growth (60 to 90 kg, 90 to 120 kg, and 120 to 140 kg). Pigs were fed ad libitum with 2 pelleted diets containing 17% (60 to 90 kg) and 16.5% (90 to 120 and 120 to 140 kg) CP. Average daily nitrogen excretion (ADNE) within each stage of growth was calculated on the basis of the accumulated difference between average daily nitrogen intake (ADNI) and average daily nitrogen retention (ADNR). Least ADNE, nitrogen excretion per BW gain (NEWG) and total nitrogen excretion (TNE) were observed during growth from 60 to 90 kg. In contrast, the greatest ADNE, NEWG, and TNE were found during growth from 120 to 140 kg. Statistical analyses indicated that gender, housing type, the ryanodine receptor 1 (RYR1) gene, and batch influenced nitrogen excretion (P

Selection for improved host response to infectious disease offers a desirable alternative to chemical treatment but has proven difficult in practice, due to low heritability estimates of disease traits. Disease data from field studies is often binary, indicating whether an individual has become infected or not following exposure to an infectious disease. Numerous studies have shown that from this data one can infer genetic variation in individuals’ underlying susceptibility. In a previous study, we showed that with an indirect genetic effect (IGE) model it is possible to capture some genetic variation in infectivity, if present, as well as in susceptibility. Infectivity is the propensity of transmitting infection upon contact with a susceptible individual. It is an important factor determining the severity of an epidemic. However, there are severe shortcomings with the Standard IGE models as they do not accommodate the dynamic nature of disease data. Here we adjust the Standard IGE model to (1) make expression of infectivity dependent on the individuals’ disease status (Case Model) and (2) to include timing of infection (Case-ordered Model). The models are evaluated by comparing impact of selection, bias, and accuracy of each model using simulated binary disease data. These were generated for populations with known variation in susceptibility and infectivity thus allowing comparisons between estimated and true breeding values. Overall the Case Model provided better estimates for host genetic susceptibility and infectivity compared to the Standard Model in terms of bias, impact, and accuracy. Furthermore, these estimates were strongly influenced by epidemiological characteristics. However, surprisingly, the Case-Ordered model performed considerably worse than the Standard and the Case Models, pointing toward limitations in incorporating disease dynamics into conventional variance component estimation methodology and software used in animal breeding. - See more at: http://journal.frontiersin.org/Journal/10.3389/fgene.2012.00215/full#h1

We propose two novel approaches for describing and quantifying the response of individual hosts to pathogen challenge in terms of infection severity and impact on host performance. The first approach is a direct extension of the methodology for estimating group tolerance (the change in performance with respect to changes in pathogen burden in a host population) to the level of individuals. The second approach aims to capture the dynamic aspects of individual resistance and tolerance over the entire time course of infections. In contrast to the first approach, which provides a means to disentangle host resistance from tolerance, the second approach focuses on the combined effects of both characteristics. Both approaches provide new individual phenotypes for subsequent genetic analyses and come with specific data requirements. In particular, both approaches rely on the availability of repeated performance and pathogen burden measurements of individuals over the time course of one or several episodes of infection. Consideration of individual tolerance also highlights some of the assumptions hidden within the concept of group tolerance, indicating where care needs to be taken in trait definition and measurement. © 2012 Doeschl-Wilson, Bishop, Kyriazakis and Villanueva.

The objectives of this study were to determine nitrogen loss at different stages of growth and during the entire growing period and to investigate the associations between nitrogen excretion and production traits in growing pigs. Data from 315 pigs of an F(2) population which originated from crossing Pietrain sires with a commercial dam line were used. Nitrogen retention was derived from protein retention as measured using the deuterium dilution technique during different stages of growth (60 to 90 kg, 90 to 120 kg, and 120 to 140 kg). Pigs were fed ad libitum with 2 pelleted diets containing 17% (60 to 90 kg) and 16.5% (90 to 120 and 120 to 140 kg) CP. Average daily nitrogen excretion (ADNE) within each stage of growth was calculated on the basis of the accumulated difference between average daily nitrogen intake (ADNI) and average daily nitrogen retention (ADNR). Least ADNE, nitrogen excretion per BW gain (NEWG) and total nitrogen excretion (TNE) were observed during growth from 60 to 90 kg. In contrast, the greatest ADNE, NEWG, and TNE were found during growth from 120 to 140 kg. Statistical analyses indicated that gender, housing type, the ryanodine receptor 1 (RYR1) gene, and batch influenced nitrogen excretion (P0.05), but the degree and direction of influences differed between growth stages. Gender differences showed that gilts excreted less nitrogen than barrows (P0.05), which was associated with decreased feed conversion ratio (FCR; feed:gain) and lipid:protein gain ratio. Single-housed pigs showed reduced nitrogen excretion compared with group-housed pigs (P0.05). In comparison to other genotypes, pigs carrying genotype NN (homozygous normal) at the RYR1 locus had the least nitrogen excretion (P0.05) at all stages of growth except from 60 to 90 kg. The residual correlations indicated that NEWG and TNE have large positive correlations with FCR (r = 0.99 and 0.91, respectively) and moderate negative correlations with ADG (r = -0.53 and -0.48, respectively), for the entire growing period. Improvement in FCR, increase in ADG and reduction in lipid:protein gain ratio by 1 phenotypic SD reduced TNE per pig by 709 g, 307 g, and 211 g, respectively, over the entire growing period. The results indicate that nitrogen excretion changes substantially during growth, and it can be reduced most effectively by improvement of feed efficiency and to a lesser extent through the improvement of BW gain or body composition or both.

Compared with the application of mathematical models to study human diseases, models that describe animal responses to pathogen challenges are relatively rare. The aim of this review is to explain and show the role of mathematical host–pathogen interaction models in providing underpinning knowledge for improving animal health and sustaining livestock production. Existing host–pathogen interaction models can be assigned to one of three categories: (i) models of the infection and immune system dynamics, (ii) models that describe the impact of pathogen challenge on health, survival and production and (iii) models that consider the co-evolution of host and pathogen. State-of-the-art approaches are presented and discussed for models belonging to the first two categories only, as they concentrate on the host–pathogen dynamics within individuals. Models of the third category fall more into the class of epidemiological models, which deserve a review by themselves. An extensive review of published models reveals a rich spectrum of methodologies and approaches adopted in different modelling studies, and a strong discrepancy between models concerning diseases in animals and models aimed at tackling diseases in humans (most of which belong to the first category), with the latter being generally more sophisticated. The importance of accounting for the impact of infection not only on health but also on production poses a considerable challenge to the study of host–pathogen interactions in livestock. This has led to relatively simplistic representations of host–pathogen interaction in existing models for livestock diseases. Although these have proven appropriate for investigating hypotheses concerning the relationships between health and production traits, they do not provide predictions of an animal's response to pathogen challenge of sufficient accuracy that would be required for the design of appropriate disease control strategies. A synthesis between the modelling methodologies adopted in categories 1 and 2 would therefore be desirable. The progress achieved in mathematical modelling to study immunological processes relevant to human diseases, together with the current advances in the generation and analysis of biological data related to animal diseases, offers a great opportunity to develop a new generation of host–pathogen interaction models that take on a fundamental role in the study and control of disease in livestock.

Technological meat quality is a significant economic factor in pork production, and numerous publications have shown that it is strongly influenced both by genetic status and by rearing and slaughter conditions. The quality of meat is often described by meat pH at different times postmortem, as well as by color and drip loss, whereas carcass quality is often characterized by lean percentage. A meta-analysis of findings relating to 3,530 pigs reported in 23 publications was carried out to assess the effects of the halothane gene, sex, breed, and slaughter weight of animals on 7 selected variables: pH at 45 min postmortem, ultimate pH, reflectance (L*-value), redness (a*-value), yellowness (b*-value), drip loss, and lean percentage. Two statistical methods were used in the meta-analysis: the method of effect size and the better known random effects model. The method of effect size was associated with Markov chain Monte Carlo techniques for implementing Bayesian hierarchical models to avoid the problems of limited data and publication bias. The results of our meta-analysis showed that the halothane genotype had a significant effect on all analyzed pork quality variables. Between-study variance was evaluated with the Cochran (1954) Q-test of heterogeneity. Meta-regression was used to explain this variance, with covariates such as breed, sex, slaughter weight, and fasting duration being integrated into different regression models. The halothane gene effect was associated with the breed effect only for the following variables: L*-value, b*-value, and drip loss. Slaughter weight contributed significantly only to the explanation of differences in ultimate pH between homozygous genotypes. In response to inconsistencies reported in the literature regarding the difference between the genotypes NN and Nn, results of the meta-analysis showed that the difference between these 2 genotypes was significant for all the analyzed variables except the a*-value.

The present study focused on the identification of epistatic QTL pairs for body composition traits (carcass cut, lean tissue, and fat tissue weights) measured at slaughter weight (140 kg of BW) in a 3-generation full-sib population developed by crossing Pietrain sires with a crossbred dam line. Depending on the trait, phenotypic observations were available for 306 to 315 F(2) animals. For the QTL analysis, 386 animals were genotyped for 88 molecular markers covering chromosomes SSC1, SSC2, SSC4, SSC6, SSC7, SSC8, SSC9, SSC10, SSC13, and SSC14. In total, 23 significant epistatic QTL pairs were identified, with the additive x additive genetic interaction being the most prevalent. Epistatic QTL were identified across all chromosomes except for SSC13, and epistatic QTL pairs accounted for between 5.8 and 10.2% of the phenotypic variance. Seven epistatic QTL pairs were between QTL that resided on the same chromosome, and 16 were between QTL that resided on different chromosomes. Sus scrofa chromosome 1, SSC2, SSC4, SSC6, SSC8, and SSC9 harbored the greatest number of epistatic QTL. The epistatic QTL pair with the greatest effect was for the entire loin weight between 2 locations on SSC7, explaining 10.2% of the phenotypic variance. Epistatic associations were identified between regions of the genome that contain the IGF-2 or melanocortin-4 receptor genes, with QTL residing in other genomic locations. Quantitative trait loci in the region of the melanocortin-4 receptor gene and on SSC7 showed significant positive dominance effects for entire belly weight, which were offset by negative dominance x dominance interactions between these QTL. In contrast, the QTL in the region of the IGF-2 gene showed significant negative dominance effects for entire ham weight, which were largely overcompensated for by positive additive x dominance genetic effects with a QTL on SSC9. The study shows that epistasis is of great importance for the genomic regulation of body composition in pigs and contributes substantially to the variation in complex traits.

A meta-analysis was carried out to quantify the effects of dietary vitamin E and storage conditions on colour changes of pork from M. longissimus dorsi. After standardisation procedures, redness of pork (CIE colour specification a*), one of the most important objective colour attributes, was used as an indicator for colour changes in this analysis. The analysis was based on results from five experiments, which met selection criteria. Analysis of changes of other objective colour attributes, lightness (L*) and yellowness (b*) was not possible due to lack of published data. The statistical analysis (using mixed models) found significant effects of tissue α-tocopherol concentration in M. longissimus dorsi, simplified supplemented vitamin E levels as well as storage time and storage light on redness of pork and its changes over time. The relationship between redness and α-tocopherol concentration was found to be linear, and between redness and storage time was non-linear (third degree polynomial) in one model. This model suggested that an increase of 1 μg of α-tocopherol in the muscle led to an expected increase a* value of 0.11. Another model identified significant interactions about 0.28 between α-tocopherol concentration and storage time in late storage periods. A third model found a significant difference of −0.48 between predicted a* values at lower (≤50 IU/kg feed) and higher supplemented vitamin E levels (≥100 IU/kg feed). The models predicted an initial increase for 3 days, a stable period for 5 days and then a decrease for a* values over storage time. The a* values were significantly lower by about 1.4 when samples were exposed to light in the models, the effect of light found to be constant over time. Further studies, carried out with standardized methods, are needed to increase the predictive power of the derived models and to validate the models for other muscles.

Technological meat quality is a significant economic factor in pork production, and numerous publications have shown that it is strongly influenced both by genetic status and by rearing and slaughter conditions. The quality of meat is often described by meat pH at different times postmortem, as well as by color and drip loss, whereas carcass quality is often characterized by lean percentage. A meta-analysis of findings relating to 3,530 pigs reported in 23 publications was carried out to assess the effects of the halothane gene, sex, breed, and slaughter weight of animals on 7 selected variables: pH at 45 min postmortem, ultimate pH, reflectance (L*-value), redness (a*-value), yellowness (b*-value), drip loss, and lean percentage. Two statistical methods were used in the meta-analysis: the method of effect size and the better known random effects model. The method of effect size was associated with Markov chain Monte Carlo techniques for implementing Bayesian hierarchical models to avoid the problems of limited data and publication bias. The results of our meta-analysis showed that the halothane genotype had a significant effect on all analyzed pork quality variables. Between-study variance was evaluated with the Cochran (1954) Q-test of heterogeneity. Meta-regression was used to explain this variance, with covariates such as breed, sex, slaughter weight, and fasting duration being integrated into different regression models. The halothane gene effect was associated with the breed effect only for the following variables: L*-value, b*-value, and drip loss. Slaughter weight contributed significantly only to the explanation of differences in ultimate pH between homozygous genotypes. In response to inconsistencies reported in the literature regarding the difference between the genotypes NN and Nn, results of the meta-analysis showed that the difference between these 2 genotypes was significant for all the analyzed variables except the a*-value.

Technological meat quality is a significant economic factor in pork production, and numerous publications have shown that it is strongly influenced both by genetic status and by rearing and slaughter conditions. The quality of meat is often described by meat pH at different times postmortem, as well as by color and drip loss, whereas carcass quality is often characterized by lean percentage. A meta-analysis of findings relating to 3,530 pigs reported in 23 publications was carried out to assess the effects of the halothane gene, sex, breed, and slaughter weight of animals on 7 selected variables: pH at 45 min postmortem, ultimate pH, reflectance (L*-value), redness (a*-value), yellowness (b*-value), drip loss, and lean percentage. Two statistical methods were used in the meta-analysis: the method of effect size and the better known random effects model. The method of effect size was associated with Markov chain Monte Carlo techniques for implementing Bayesian hierarchical models to avoid the problems of limited data and publication bias. The results of our meta-analysis showed that the halothane genotype had a significant effect on all analyzed pork quality variables. Between-study variance was evaluated with the Cochran (1954) Q-test of heterogeneity. Meta-regression was used to explain this variance, with covariates such as breed, sex, slaughter weight, and fasting duration being integrated into different regression models. The halothane gene effect was associated with the breed effect only for the following variables: L*-value, b*-value, and drip loss. Slaughter weight contributed significantly only to the explanation of differences in ultimate pH between homozygous genotypes. In response to inconsistencies reported in the literature regarding the difference between the genotypes NN and Nn, results of the meta-analysis showed that the difference between these 2 genotypes was significant for all the analyzed variables except the a*-value.

Background: Both host genetic potentials for growth and disease resistance, as well as nutrition are known to affect responses of individuals challenged with micro-parasites, but their interactive effects are difficult to predict from experimental studies alone. Methodology/Principal Findings: Here, a mathematical model is proposed to explore the hypothesis that a host’s response to pathogen challenge largely depends on the interaction between a host’s genetic capacities for growth or disease resistance and the nutritional environment. As might be expected, the model predicts that if nutritional availability is high, hosts with higher growth capacities will also grow faster under micro-parasitic challenge, and more resistant animals will exhibit a more effective immune response. Growth capacity has little effect on immune response and resistance capacity has little effect on achieved growth. However, the influence of host genetics on phenotypic performance changes drastically if nutrient availability is scarce. In this case achieved growth and immune response depend simultaneously on both capacities for growth and disease resistance. A higher growth capacity (achieved e.g. through genetic selection) would be detrimental for the animal’s ability to cope with pathogens and greater resistance may reduce growth in the short-term. Significance: Our model can thus explain contradicting outcomes of genetic selection observed in experimental studies and provides the necessary biological background for understanding the influence of selection and/or changes in the nutritional environment on phenotypic growth and immune response.

The response to infection from porcine reproductive and respiratory syndrome virus (PRRSV) for 2 genetically diverse commercial pig lines was investigated. Seventy-two pigs from each line, aged 6 wk, were challenged with PRRSV VR-2385, and 66 litter-mates served as control. The clinical response to infection was monitored throughout the study and pigs were necropsied at 10 or 21 d postinfection. Previous analyses showed significant line differences in susceptibility to PRRSV infection. This study also revealed significant line differences in growth during infection. Line B, characterized by faster growth rate than line A in the absence of infection, suffered more severe clinical disease and greater reduction in BW growth after infection. Correlations between growth and disease-related traits were generally negative, albeit weak. Correlations were also weak among most clinical and pathological traits. Clinical disease traits such as respiratory scores and rectal temperatures were poor indicators of virus levels, pathological damage, or growth during PRRSV infection. Relationships between traits varied over time, indicating that different disease-related mechanisms may operate at different time scales and, therefore, that the time of assessing host responses may influence the conclusions drawn about biological significance. Three possible mechanisms underlying growth under PRRSV infection were proposed based on evidence from this and previous studies. It was concluded that a comprehensive framework describing the interaction between the biological mechanisms and the genetic influence on these would be desirable for achieving progress in the genetic control of this economically important disease.

Background Existing mathematical models for scrapie dynamics in sheep populations assume that the PrP gene is only associated with scrapie susceptibility and with no other fitness related traits. This assumption contrasts recent findings of PrP gene associations with post-natal lamb survival in scrapie free Scottish Blackface populations. Lambs with scrapie resistant genotypes were found to have significantly lower survival rates than those with susceptible genotypes. The present study aimed to investigate how these conflicting PrP gene associations may affect the dynamic patterns of PrP haplotype frequencies and disease prevalence. Methodology/Principal Findings A deterministic mathematical model was developed to explore how the associations between PrP genotype and both scrapie susceptibility and postnatal lamb mortality affect the prevalence of scrapie and the associated change in PrP gene frequencies in a closed flock of sheep. The model incorporates empirical evidence on epidemiological and biological characteristics of scrapie and on mortality rates induced by causes other than scrapie. The model results indicate that unfavorable associations of the scrapie resistant PrP haplotypes with post-natal lamb mortality, if sufficiently strong, can increase scrapie prevalence during an epidemic, and result in scrapie persisting in the population. The range of model parameters, for which such effects were observed, is realistic but relatively narrow. Conclusions/Significance The results of the present model suggest that for most parameter combinations an unfavourable association between PrP genotype and post-natal lamb mortality does not greatly alter the dynamics of scrapie and, hence, would not have an adverse impact on a breeding programme. There were, however, a range of scenarios, narrow, but realistic, in which such an unfavourable association resulted in an increased prevalence and in the persistence of infection. Consequently, associations between PrP genotypes and fitness traits should be taken into account when designing future models and breeding programmes. © 2009 Doeschl-Wilson et al.

The wide range of genetic parameter estimates for production traits and nematode resistance in sheep obtained from field studies gives rise to much speculation. Using a mathematical model describing host – parasite interactions in a genetically heterogeneous lamb population, we investigated the consequence of: (i) genetic relationships between underlying growth and immunological traits on estimated genetic parameters for performance and nematode resistance, and (ii) alterations in resource allocation on these parameter estimates. Altering genetic correlations between underlying growth and immunological traits had large impacts on estimated genetic parameters for production and resistance traits. Extreme parameter values observed from field studies could only be reproduced by assuming genetic relationships between the underlying input traits. Altering preferences in the resource allocation had less pronounced effects on the genetic parameters for the same traits. Effects were stronger when allocation shifted towards growth, in which case worm burden and faecal egg counts increased and genetic correlations between these resistance traits and body weight became stronger. Our study has implications for the biological interpretation of field data, and for the prediction of selection response from breeding for nematode resistance. It demonstrates the profound impact that moderate levels of pleiotropy and linkage may have on observed genetic parameters, and hence on outcomes of selection for nematode resistance.