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See detailImpact of Heat Stress on Production in Holstein Cattle in four EU Regions. Selection Tools
Carabaño, Maria-Jesus; Hammami, Hedi ULiege; Logar, Betka et al

Scientific conference (2014)

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See detailImpact of Heat Stress on Production in Holstein Cattle in four EU Regions. Selection Tools
Carabaño, Maria-Jesus; Hammami, Hedi ULiege; Logar, Betka et al

Conference (2014)

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See detailGenotype x Climate interactions for protein yield using four European Holstein Populations
Hammami, Hedi ULiege; Carabaño, Maria-Jesus; Logar, Betka et al

Conference (2014)

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See detailGenetic analysis of pig survival up to commercial weight in a crossbred population
Dufrasne, Marie ULiege; Misztal, Ignacy; Tsuruta, Shogo et al

in Livestock Science (2014), 167

Records from 99,384 crossbred pigs from Duroc sires and Large White x Landrace dams were used to estimate genetic parameters for survival traits at different stages of the fattening period, and their ... [more ▼]

Records from 99,384 crossbred pigs from Duroc sires and Large White x Landrace dams were used to estimate genetic parameters for survival traits at different stages of the fattening period, and their relations with final weight. Traits analyzed were preweaning mortality (PWM), culling between weaning and harvesting (Call), culling during the farrowing period (Cfar), in the nursery site (Cnur), during the finishing phase (Cfin), and hot carcass weight (HCW). Because of the binary nature of PWM and culling traits, threshold-linear models were used: Model 1, including PWM, Call, and HCW; Model 2, including PWM, Cfar, Cnur, Cfin, and HCW. Both models included sex and parity number as fixed effects for all traits. Contemporary groups were considered as fixed effect for HCW and as random effects for the binary traits. Random effects were sire additive genetic, common litter, and residual effects for all traits and models. Heritability estimates were 0.03 for PWM, and 0.15 for HCW with both models, 0.06 for Call with Model 1, and 0.06 for Cfar, 0.14 for Cnur, and 0.10 for Cfin with Model 2. Litter variance explained a large part of the total variance and its influence declined slightly with age. For Model 1, genetic correlations were -0.36 between PWM and Call, -0.02 between PWM and HCW, and -0.25 between Call and HCW; correlations for litter effect were -0.15 between PWM and Call, -0.19 between PWM and HCW, and -0.21 between Call and HCW. For Model 2, genetic correlations were all positive between PWM and culling traits, except between PWM and Cnur (-0.61). Genetic correlations between HCW and the other traits were moderate and negative to null. Correlations for common litter effect were all negative between traits, except between Cfar and Cfin, and between Cnur and Cfin. Heritability of PWM and culling traits increased with age period. Therefore, selection for survival after weaning may be more efficient. The low genetic correlations between PWM and culling traits suggest that different genes influence pre- and postweaning mortality. The HCW was not correlated with the other traits. However, relationships are not strongly unfavorable, therefore selection for survival and high final weight is possible. [less ▲]

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See detailEstimation of dominance variance for live body weight in a crossbred population of pigs
Dufrasne, Marie ULiege; Faux, Pierre ULiege; Piedboeuf, Maureen et al

in Journal of Animal Science (2014), 92

The objective of this study was to estimate the dominance variance for repeated live BW records in a crossbred population of pigs. Data were provided by the Walloon Pig Breeding Association and included ... [more ▼]

The objective of this study was to estimate the dominance variance for repeated live BW records in a crossbred population of pigs. Data were provided by the Walloon Pig Breeding Association and included 22,197 BW records of 2,999 crossbred Piétrain × Landrace K+ pigs from 50 to 210 d of age. The BW records were standardized and adjusted to 210 d of age for analysis. Three single-trait random regression animal models were used: Model 1 without parental subclass effect, Model 2 with parental subclasses considered unrelated, and Model 3 with the complete parental dominance relationship matrix. Each model included sex, contemporary group, and heterosis as fixed effects as well as additive genetic, permanent environment, and residual as random effects. Variance components and their SE were estimated using a Gibbs sampling algorithm. Heritability tended to increase with age: from 0.50 to 0.64 for Model 1, from 0.19 to 0.42 for Model 2, and from 0.31 to 0.53 for Model 3. Permanent environmental variance tended to decrease with age and accounted for 29 to 44% of total variance with Model 1, 29 to 37% of total variance with Model 2, and 34 to 51% of total variance with Model 3. Residual variance explained <10% of total variance for the 3 models. Dominance variance was computed as 4 times the estimated parental subclass variance. Dominance variance accounted for 22 to 40% of total variance for Model 2 and 5 to 11% of total variance for Model 3, with a decrease with age for both models. Results showed that dominance effects exist for growth traits in pigs and may be reasonably large. The use of the complete dominance relationship matrix may improve the estimation of additive genetic variances and breeding values. Moreover, a dominance effect could be especially useful in selection programs for individual matings through the use of specific combining ability to maximize growth potential of crossbred progeny. [less ▲]

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See detailShort communication: Alteration of priors for random effects in Gaussian linear mixed models
Vandenplas, Jérémie ULiege; Christensen, Ole F.; Gengler, Nicolas ULiege

in Journal of Dairy Science (2014), 97(9), 5880-5884

Linear mixed models, for which the prior multivariate normal distributions of random effects are assumed to have a mean equal to 0, are commonly used in animal breeding. However, some statistical analyses ... [more ▼]

Linear mixed models, for which the prior multivariate normal distributions of random effects are assumed to have a mean equal to 0, are commonly used in animal breeding. However, some statistical analyses (e.g., the consideration of a population under selection into a genomic scheme breeding, multiple-trait predictions of lactation yields, Bayesian approaches integrating external information into genetic evaluations) need to alter both the mean and (co)variance of the prior distributions and, to our knowledge, most software packages available in the animal breeding community do not permit such alterations. Therefore, the aim of this study was to propose a method to alter both the mean and (co)variance of the prior multivariate normal distributions of random effects of linear mixed models while using currently available software packages. The proposed method was tested on simulated examples with three different software packages available in animal breeding. The examples showed the possibility of the proposed method to alter both the mean and (co)variance of the prior distributions with currently available software packages through the use of an extended data file and a user supplied (co)variance matrix. [less ▲]

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See detailEstimation of dominance variance for growth traits with sire-dam subclass effects in a crossbred population of pigs
Dufrasne, Marie ULiege; Faux, Pierre ULiege; Piedboeuf, Maureen et al

Poster (2014)

Nonadditive genetic effects may be not negligible but are often ignored in genetic evaluations. The most important nonadditive effect is probably dominance. Prediction of dominance effects should allow a ... [more ▼]

Nonadditive genetic effects may be not negligible but are often ignored in genetic evaluations. The most important nonadditive effect is probably dominance. Prediction of dominance effects should allow a more precise estimation of the total genetic merit, particularly in populations that use specialized sire and dam lines, and with large number of full-sibs, like pigs. Computation of the inverted dominance relationship matrix, D-1, is difficult with large datasets. But, D-1 can be replaced by the inverted sire-dam subclass relationship matrix F-1, which represents the average dominance effect of full-sibs. The aim of this study was to estimate dominance variance for longitudinal measurements of body weight (BW) in a crossbred population of pigs The dataset consisted of 20,120 BW measurements recorded between 50 and 210 d of age on 2,341 crossbred pigs (Piétrain X Landrace). A random regression model was used to estimate variance components. Fixed effects were sex and date of recording. Random effects were additive genetic, permanent environment, parental dominance and residual. Dominance variance represented 7 to 9% of the total variance and 11 to 30% of additive variance. Those results showed that dominance variance exists for growth traits in pigs and may be relatively large. The estimation of dominance effects may be useful for mate selection program to maximize genetic merit of progeny. [less ▲]

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See detailInversion of a part of the numerator relationship matrix using pedigree information
Faux, Pierre ULiege; Gengler, Nicolas ULiege

in Genetics, Selection, Evolution (2013), 45

Background. In recent theoretical developments, the information available (e.g. genotypes) divides the original population into two groups: animals with this information (selected animals) and animals ... [more ▼]

Background. In recent theoretical developments, the information available (e.g. genotypes) divides the original population into two groups: animals with this information (selected animals) and animals without this information (excluded animals). These developments require inversion of the part of the pedigree-based numerator relationship matrix that describes the genetic covariance between selected animals (A22). Our main objective was to propose and evaluate methodology that takes advantage of any potential sparsity in the inverse of A22 in order to reduce the computing time required for its inversion. This potential sparsity is brought out by searching the pedigree for dependencies between the selected animals. Jointly, we expected distant ancestors to provide relationship ties that increase the density of matrix A22 but that their effect on A22i might be minor. This hypothesis was also tested. Methods. The inverse of A22 can be computed from the inverse of the triangular factor (T-1 ) obtained by Cholesky root-free decomposition of A22 . We propose an algorithm that sets up the sparsity pattern of T-1 using pedigree information. This algorithm provides positions of the elements of T-1 worth to be computed (i.e. different from zero). A recursive computation of A22i is then achieved with or without information on the sparsity pattern and time required for each computation was recorded. For three numbers of selected animals (4000; 8000 and 12 000), A22 was computed using different pedigree extractions and the closeness of the resulting A22i to the inverse computed using the fully extracted pedigree was measured by an appropriate norm. Results. The use of prior information on the sparsity of T-1 decreased the computing time for inversion by a factor of 1.73 on average. Computational issues and practical uses of the different algorithms were discussed. Cases involving more than 12 000 selected animals were considered. Inclusion of 10 generations was determined to be sufficient when computing A22. Conclusions. Depending on the size and structure of the selected sub-population, gains in time to compute A22 are possible and these gains may increase as the number of selected animals increases. Given the sequential nature of most computational steps, the proposed algorithm can benefit from optimization and may be convenient for genomic evaluations. [less ▲]

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See detailPotentiel d'utilisation de la spectrometrie moyen infrarouge pour prédire le rendement fromager du lait et étudier sa variabilité génétique
Colinet, Frédéric ULiege; Troch, Thibault ULiege; Abbas, O. et al

Conference (2013, December 04)

Providing a quick, reliable and cheap indication of the expected cheese yield for a milk sample by avoiding (empirical or theoretical) formulas based on previously determined milk constituents would be an ... [more ▼]

Providing a quick, reliable and cheap indication of the expected cheese yield for a milk sample by avoiding (empirical or theoretical) formulas based on previously determined milk constituents would be an economically valuable tool useful for farmers and the dairy industry. In order to study the genetic variability of cheese yield on a large scale, mid-infrared (MIR) chemometric methods were used to predict fresh or dry Individual Laboratory Cheese Yield (RdFF and RdFS, respectively). RdFF and RdFS were determined on a total of 258 milks samples also analyzed by a MIR spectrometer. Equations to predict RdFF and RdFS from milk MIR spectra were developed using partial least square regression (PLS) after first derivative pre-traitment applied to the spectra. The cross-validation coefficients of determination (R²cv) of the two equations were equal to 0.81 for the prediction of RdFF and 0.82 for the prediction RdFS. The ratios of performance to deviation (RPD) of the two equations were both equal to 2.3. Therefore, these results suggest a practical utility of these two equations, i.e. for genetic research. Both equations were applied on the spectral database generated during the Walloon routine milk recording. The variances components were estimated using univariate random regressions animal test-day model. The dataset included 51 537 predicted records from 7 870 Holstein first-parity cows. Estimated daily heritabilities ranged from 0.31 (at 5th day in milk (DIM)) to 0.59 (at 279th DIM) for RdFF and from 0.31 (at 5th DIM) to 0.57 (at 299th DIM) for RdFS. Those moderate to high daily heritabilities indicated potential of selection for both traits. [less ▲]

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See detailPotentiel d'utilisation de la spectrometrie moyen infrarouge pour prédire le rendement fromager du lait et étudier sa variabilité génétique
Colinet, Frédéric ULiege; Troch, Thibault ULiege; Abbas, O. et al

in 20èmes Rencontres Recherches Ruminants, Paris, les 4 et 5 Décembre 2013 (2013, December)

Fournir une indication rapide, fiable et bon marché du rendement fromager pour un lait donné, sans devoir appliquer des formules (empiriques ou théoriques) à partir des concentrations préalablement ... [more ▼]

Fournir une indication rapide, fiable et bon marché du rendement fromager pour un lait donné, sans devoir appliquer des formules (empiriques ou théoriques) à partir des concentrations préalablement déterminées pour différents constituants du lait, serait un outil utile et économiquement intéressant tant pour les éleveurs que pour l’industrie laitière. En vue d’étudier la variabilité génétique du rendement fromager à l’échelle du cheptel bovin wallon, des méthodes chimiométriques ont été utilisées afin de développer des équations de prédictions basées sur des spectres moyen infrarouge (MIR) pour les rendements fromagers déterminés en laboratoire et exprimés en frais (RdFF) ou en sec (RdFS). Ceux-ci ont été déterminés sur 258 échantillons de lait analysés en spectrométrie MIR. Les équations de prédiction à partir du spectre MIR du lait ont été développées en utilisant la régression des moindres carrés partiels (PLS) avec une validation croisée interne appliquée sur la dérivée première des spectres MIR. Les coefficients de détermination de validation croisée (R²cv) des équations étaient de 0,81 pour les prédictions du RdFF et de 0,82 pour les celles du RdFS. Les rapports des performances sur les variabilités (RPD) étaient égaux à 2,3. Ces résultats peuvent permettre d’envisager une bonne utilité pratique pour leur prédiction respective, notamment dans le cadre de recherches génétiques. Ces équations ont été appliquées sur la base de données spectrales générée dans le cadre du contrôle laitier wallon. Les composantes de la variance ont été estimées séparément pour le RdFF et le RdFS basées sur un modèle animal « contrôles élémentaires » utilisant des régressions aléatoires. Le jeu de données utilisé comportait 51 537 prédictions pour 7 870 vaches primipares Holstein. Les héritabilités journalières moyennes variaient entre 0,31 (au 5ème jour de lactation (JDL)) et 0,59 (au 279ème JDL) pour le RdFF et entre 0,31 (au 5ème JDL) et 0,57 (au 299ème JDL) pour le RdFS. Ces héritabilités journalières modérées à élevées ont indiqué le potentiel de sélection génétique pour ces deux caractères. [less ▲]

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See detailCombinaison d'informations génomiques, phénotypiques et généalogiques
Vandenplas, Jérémie ULiege; Gengler, Nicolas ULiege

Conference given outside the academic context (2013)

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See detailStandardization of health data. ICAR guidelines including health key
Stock, K.F.; Cole, J.; Pryce, J. et al

in Egger-Danner, C.; Hansen, O.K.; Stock, k. (Eds.) et al ICAR Technical Series (2013, October 30)

Systematic improvement of animal health requires knowledge about the status quo and reliable measures to characterize it. In dairy herds, health monitoring has gained importance to ensure sustainable and ... [more ▼]

Systematic improvement of animal health requires knowledge about the status quo and reliable measures to characterize it. In dairy herds, health monitoring has gained importance to ensure sustainable and cost-efficient milk production in accordance with public expectations. In this context, standardized recording of health data is essential for comparability and interpretability of health-related analyses, implying the need for generally accepted and clear guidelines. To assist implementation of health monitoring and convey harmonization, the ICAR Functional Traits Working Group has compiled the ICAR guidelines for Recording, Evaluation and Genetic Improvement of Health Traits, which were approved in June 2012. Disease diagnoses and observations of impaired health can be classified as direct health data, providing the basis for targeted approaches to improve the animal health status. Data sources need to be taken into account because of their impact on information content and specificity. The key for health data recording is characterized by a hierarchical structure that makes it possible to record on different levels of detail and includes comprehensive recording options with coverage of all organ systems and types of diseases. Important features are compatibility with other recording systems and broad usability as a reference regardless of specific intentions and contexts of health data collection. Input can range from very specific diagnoses of veterinarians to very general diagnoses or observations by producers, and the unique coding of clearly defined health incidents minimizes the risk of misinterpretations and facilitates analyses of different types of health data. The overall quality and success of health monitoring is substantially influenced byappropriate use of standards and available recording tools, implying the need for tailored support particularly in the implementation phase. In integrated concepts, specific qualifications of professions can be used synergistically to further standardize recording of health data and thereby benefit efficiency of animal health improvement on farm and at the population level. [less ▲]

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See detailChallenges and benefits of health data recording in the context of food chain quality, management and breeding.
Egger-Danner, C.; Hansen, O.K.; Stock, K. et al

Book published by ICAR, Via G. Tomassetti - Proceedings of the ICAR Conference held in Aarhus, Denmark (2013)

Improved animal health is becoming increasingly important worldwide, because of its effect on farm economy, animal welfare and food safety. The precondition for monitoring, elaboration and implementation ... [more ▼]

Improved animal health is becoming increasingly important worldwide, because of its effect on farm economy, animal welfare and food safety. The precondition for monitoring, elaboration and implementation of measures to improve animal health and reduce the use of antimicrobials are reliable health data. The main difficulty in setting up such systems is that many different parties are involved and need to cooperate. Precondition of success is the benefit of the parties involved. Systems set up in cooperation with the legal bodies as well as different parties offer the possibility to use synergies and be of higher effectiveness. The aim of the International Committee for Animal Recording (ICAR) is to promote the development and improvement of the activities of performance recording and the evaluation of farm livestock. As an international, non-profit body ICAR can serve as a platform to share information and knowledge. The main emphasis within this conference was on aspects of logistics of data recording as well as motivation and benefit of different parties and the community. [less ▲]

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