References of "Gengler, Nicolas"
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See detailA review of inversion techniques related to the use of relationship matrices in animal breeding
Faux, Pierre ULg; Gengler, Nicolas ULg

in Biotechnologie, Agronomie, Société et Environnement = Biotechnology, Agronomy, Society and Environment [=BASE] (in press)

In animal breeding, prediction of genetic effects is usually obtained through the use of mixed models. For any of these genetic effects, mixed models require the inversion of the covariance matrix ... [more ▼]

In animal breeding, prediction of genetic effects is usually obtained through the use of mixed models. For any of these genetic effects, mixed models require the inversion of the covariance matrix associated to that effect, which is equal to the associated relationship matrix times the associated component of the genetic variance. Given the size of many genetic evaluation systems, computing the inverses of these relationship matrices is not trivial. In this review, we aim to cover computational techniques that ease inversion of relationship matrices used in animal breeding for prediction of the following different types of genetic effects: additive effect, gametic effect, effect due to presence of marked quantitative trait loci, dominance effect and different epistasis effects. Construction rules and inversion algorithms are detailed for each relationship matrix. In the final discussion, we draw up a common theoretical frame to most of the reviewed techniques. Two computational constraints come out of this theoretical frame: setting up the matrix of dependencies between levels of the effect and setting up some parts (diagonal or block-diagonal elements) of the relationship matrix to be inverted. [less ▲]

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See detailGenetic parameters for mid-infrared methane indicators based on milk fatty acids in dairy cows
Kandel, Purna Bhadra ULg; Gengler, Nicolas ULg; Soyeurt, Hélène ULg

in Journal of Applied Animal Research (in press)

Dairy production is identified as a major source of methane (CH4) emissions. Selective breeding canbe one method used to mitigate CH4 emissions but practical and cheap measurement of this trait is not ... [more ▼]

Dairy production is identified as a major source of methane (CH4) emissions. Selective breeding canbe one method used to mitigate CH4 emissions but practical and cheap measurement of this trait is not currently available. The mid-infrared (MIR) prediction of milk fatty acids is relevant in this context. Five MIR methane indicators were derived from the literature and were calibrated from 600 samples analyzed by gas chromatography. Genetic parameters for these traits were estimated using single trait random regression test-day models from 619,265 records collected from 2007 to 2011 on 71,188 Holstein cows in their first three lactations. For the published indicator showing the highest relationship with the methane data (R2 = 0.88), the average daily heritability was 0.34±0.01, 0.37±0.01 and 0.34±0.01 for the first three lactations, respectively. The methane emission (g/day) was increased from beginning of lactation, reached at the highest in peak of lactation and decreased towards end of lactation. The largest differences between estimated breeding values (EBV) of sires having daughters in production eructing the highest and the lowest methane content was 21.80, 22.75 and 24.89 kg per lactation for the first three parities. Positive genetic correlations were estimated between indicator traits and milk fat and protein content. Low negative correlation was observed with milk yield. In conclusion, this study shows the feasibility to predict methane indicator traits by MIR. Moreover, the estimated genetic parameters suggest also a potential genetic variability of the quantity of methane eructed by dairy cows. [less ▲]

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See detailIndividual methane predictions from milk mid-infrared spectra of Holstein dairy cows.
Soyeurt, Hélène ULg; Vanlierde, Amélie; Dehareng, Frédéric et al

in Journal of Dairy Science (in press)

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See detailEfficient computation of genomically-enhanced inbreeding coefficients
Faux, Pierre ULg; Gengler, Nicolas ULg

Poster (2014, February 07)

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See detailConsequences of Selection for Environmental Impact Trait in Dairy Cows
Kandel, Purna Bhadra ULg; Vanderick, Sylvie ULg; Vanrobays, Marie-Laure ULg et al

Scientific conference (2014, February 07)

Environmental sustainability is gaining importance in dairy industry due to enteric methane (CH4) emission from dairy cows. We predicted CH4 indicator trait (CH4 intensity: CH4 g/kg of milk) from Mid ... [more ▼]

Environmental sustainability is gaining importance in dairy industry due to enteric methane (CH4) emission from dairy cows. We predicted CH4 indicator trait (CH4 intensity: CH4 g/kg of milk) from Mid-infrared spectra of milk samples and recorded milk yield. Genetic correlations between CH4 intensity and milk production traits were estimated on Holstein cows from correlations of estimated breeding values. Genetic correlations between CH4 intensity and milk yield (MY) was -0.67, fat yield (FY) -0.13, protein yield (PY) -0.46, somatic cell score (SCS) 0.02, longevity -0.07, fertility 0.31, body condition score (BCS) 0.27 and average of confirmation traits -0.23. Currently, there is no CH4 emission trait in genetic evaluation selection index. Putting an hypothetical 25% weight on CH4 intensity on current Walloon genetic evaluation selection index and proportional reduction on other selection traits, the response to selection will be reduction of CH4 emission intensity by 24%, increase in MY by 30%, FY by 17%, PY by 29%, SCS by -15%, longevity by 24%, fertility by -11%, BCS by -13% and conformation traits by 24%. In conclusion, introduction of environmental traits in current selection index will affect selection responses. As there is no economic value of these traits presently alternative methods like putting correlated traits with clear economic value (e.g. feed efficiency) in the selection objective could generate appropriate index weights. [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 ULg; Faux, Pierre ULg; 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 ULg; Gengler, Nicolas ULg

in Genetics, Selection, Evolution [=GSE] (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 ULg; Troch, Thibault ULg; 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 ULg; Troch, Thibault ULg; 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 detailGenetic variance in micro-environmental sensitivity for milk and milk quality in Walloon Holstein cattle
Vandenplas, Jérémie ULg; Bastin, Catherine ULg; Gengler, Nicolas ULg et al

in Journal of Dairy Science (2013), 96

Animals that are robust to environmental changes are desirable in the current dairy industry. Genetic differences in micro-environmental sensitivity can be studied through heterogeneity of residual ... [more ▼]

Animals that are robust to environmental changes are desirable in the current dairy industry. Genetic differences in micro-environmental sensitivity can be studied through heterogeneity of residual variance between animals. However, residual variance between animals is usually assumed homogeneous in traditional genetic evaluations. The aim of this study was to investigate genetic heterogeneity of residual variance by estimating variance components in residual variance for milk yield, somatic cell score, contents in milk (g/dL) of two groups of milk fatty acids (i.e. saturated fatty acids and unsaturated fatty acids) and the content in milk of one individual fatty acid (i.e. the oleic acid, C18:1 cis-9), for first-parity Holstein cows in the Walloon Region of Belgium. A total of 146,027 test-day records from 26,887 cows in 747 herds were available. All cows had at least three records and had a known sire. These sires had at least 10 cows with records and each herd x test-day had at least five cows. The five traits were analyzed separately based on fixed lactation curve and random regression test-day models for the mean. Estimation of variance components was performed by running iteratively Expectation Maximization-Restricted Maximum Likelihood algorithm by the implementation of double hierarchical generalized linear models. Based on fixed lactation curve test-day mean models, heritability for residual variances ranged between 1.01*10-3 and 4.17*10-3 for all traits. The genetic standard deviation in residual variance (i.e. approximately the genetic coefficient of variation of residual variance) ranged between 0.12 and 0.17. Therefore, some genetic variance in micro-environmental sensitivity existed in the Walloon Holstein dairy cattle for the five studied traits. The standard deviations due to herd x test-day and permanent environment in residual variance ranged between 0.36 and 0.45 for herd x test-day effect and between 0.55 and 0.97 for permanent environmental effect. Therefore, non-genetic effects also contributed substantially to the micro-environmental sensitivity. Results also showed that the addition of random regressions to the mean model did not reduce heterogeneity in residual variance and that genetic heterogeneity of residual variance was not simply an effect of an incomplete mean model. [less ▲]

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See detailMid-infrared prediction of cheese yield from milk and its genetic variability in first-parity cows
Colinet, Frédéric ULg; Troch, Thibault ULg; Vanden, Bossche et al

Conference (2013, August 29)

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See detailGenetic effects of heat stress on milk yield and MIR predicted methane emissions of Holstein cows
Vanrobays, Marie-Laure ULg; Gengler, Nicolas ULg; Kandel, Purna Bhadra ULg et al

Conference (2013, August 28)

Dairy cows both contribute to and are affected by climate change. Breeding for heat tolerance and reduced methane (CH4) emissions is a key requirement to mitigate interactions between dairy cows and ... [more ▼]

Dairy cows both contribute to and are affected by climate change. Breeding for heat tolerance and reduced methane (CH4) emissions is a key requirement to mitigate interactions between dairy cows and climate change. This study was aimed to estimate genetic variation of milk yield and CH4 emissions over the whole trajectory of temperature humidity index (THI) using a reaction norm approach. A total of 257,635 milk test-day (TD) records and milk mid-infrared (MIR) spectra from 51,782 Holstein cows were used. Data were collected between January 2007 and December 2010 in 983 herds by the Walloon Breeding Association (Ciney, Belgium). The calibration equation developed by Vanlierde et al. (Abstract submitted to EAAP 2013; R² of cross-validation=0.70) was applied on the spectral data in order to predict CH4 emissions values (g CH4/d). These values were divided by fat and protein corrected milk yield (FPCM) defining a new CH4 trait (g CH4/kg of FPCM). Daily THI values were calculated using the mean of daily values of dry bulb temperature and relative humidity from meteorological data. Mean daily THI of the previous 3 days before each TD record was used as the THI of reference for that TD. Bivariate (milk yield and a CH4 trait) random regression TD mixed models with random linear regressions on THI values were used. Estimated average daily heritability for milk yield was 0.17 and decreased slightly at extreme THI values. However, heritabilities of MIR CH4 traits increased as THI values increase: from 0.10 (THI=28) to 0.14 (THI=75) for MIR CH4 (g/d) and from 0.14 (THI=28) to 0.21 (THI=75) for MIR CH4 (g/kg of FCPM). Genetic correlations between milk yield and MIR CH4 (g/d) ranged from -0.09 (THI=28) to -0.12 (THI=75) and those between milk yield and MIR CH4 (g/kg of FPCM) from -0.75 (THI=28) to -0.71 (THI=75). These results showed that milk production and CH4 emissions of dairy cows seemed to be influenced by THI. [less ▲]

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See detailThe potential of MIR spectra to certify milk geographic origin
Dale, Laura-Monica ULg; Laine, Aurélie ULg; Goubau, Amaury et al

in 64rd Annual Meeting of the European Federation of Animal Science (2013, August 26)

Protecting and supporting local production systems, regional authorities, as well as producers, give a very important role to milk quality. Therefore, this study was aimed to investigate the potential of ... [more ▼]

Protecting and supporting local production systems, regional authorities, as well as producers, give a very important role to milk quality. Therefore, this study was aimed to investigate the potential of mid-infrared spectroscopy (MIR) for certifying the geographic origin of milk. Because milk MIR spectral databases and extra phenotypes (breed, testday, livestock herd and origin appellation of traditional products) were available in the Belgium Walloon Region via European project OptiMIR (INTERREG IVB North West Europe Program), discrimination studies were conducted to distinguish Ardennes region (which is linked to PDO “Beurre d’Ardennes”) from the rest of Wallonia. A total of 542,733 spectral records linked to their geographic origin coming from Wallonia milk recording were used (97,369 of MIR spectra -Ardennes region and 450,326 -rest of Wallonia). A mixed model (fixed: breed, year and month of record, random: herd x year) was applied to obtain predicted MIR spectral values for all testdays and prediction errors (residuals) representing the factors not present in the model. In order to test the MIR ability to milk authentication, chemometric tools, such as partial least squat regression and linear discriminant analysis were applied to residuals for three MIR spectral regions (e.g. 930-1600 cm-1, 1710-1810 cm-1 and 2560-2990 cm-1). The classifications on not-corrected MIR spectral data were 95% and the cross-validation were 95% for Ardennes region. Results showed after correction of MIR spectra, the discriminant function constructed on the residuals spectra allowed a good discrimination. The results show that MIR spectroscopy techniques may provide useful fingerprints to detect geographic origin and could be potentially used in routine management decision and quality assurance tools. [less ▲]

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See detailHerd-test-day variability of methane emissions predicted from milk MIR spectra in Holstein cows
Vanrobays, Marie-Laure ULg; Kandel, Purna Bhadra ULg; Soyeurt, Hélène ULg et al

Poster (2013, August 26)

The aim of this study was to estimate the herd-test-day (HTD) effect on milk yield, fat and protein content, and methane (CH4) emissions of Walloon Holstein first-parity cows. A total of 412,520 test-day ... [more ▼]

The aim of this study was to estimate the herd-test-day (HTD) effect on milk yield, fat and protein content, and methane (CH4) emissions of Walloon Holstein first-parity cows. A total of 412,520 test-day records and milk mid-infrared (MIR) spectra of 69,223 cows in 1,104 herds were included in the data set. The prediction equation developed by Vanlierde et al. (Abstract submitted to EAAP 2013; R² of cross-validation=0.70) was applied on the recorded spectral data to predict CH4 emissions (g/d). Daily CH4 emissions expressed in g/kg of milk were computed by dividing CH4 emissions (g/d) by daily milk yield of cows. Several bivariate (a CH4 trait with a production trait) random regression test-day models including HTD and classes of days in milk and age at calving as fixed effects and permanent environment and genetic as random effects were used. HTD solutions of studied traits obtained from these models were studied and presented large deviations (CV=17.54%, 8.93%, 4.68%, 15.51%, and 23.18% for milk yield, fat and protein content, MIR CH4 (g/d), and MIR CH4 (g/kg of milk), respectively) indicating differences among herds, especially for milk yield and CH4 traits. HTD means per month of milk yield and fat and protein contents presented similar patterns within year. The maximum of monthly HTD means corresponded to the spring (pastern release) for milk yield and to the winter for fat and protein contents. The minimum corresponded to the month of November for milk yield and to the summer for the other traits. For MIR CH4 (g/d), monthly HTD means showed similar patterns as fat and protein content within year. MIR CH4 (g/kg of milk) presented maximum values of monthly HTD means in November and minimum values in May. Finally, results of this study showed that HTD effects on milk production traits and on MIR CH4 emissions varied through herds and seasons. [less ▲]

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

Poster (2013, August 26)

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, assuming unrelated sire-dam subclass effects. The edited dataset consisted of 20,120 BW measurements recorded between 50 and 210 d of age on 2,341 crossbred pigs from 89 Piétrain sires and 169 Landrace dams. 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, sire-dam subclass and residual. Random effects, except residual, were modeled with linear splines. Only full-sib contributions were considered by using uncorrelated sire-dam classes. Estimated heritability of BW increased with age from 0.40 to 0.60. Inversely, estimated dominance decreased with age, from 0.28 to 0.01. Ratio of dominance relative to additive variance was high at early age (58.3% at 50 d) and decreased with age (2.6% at 200 d). Those results showed that dominance effects might be important for early growth traits in pigs. However, this need to be confirmed and dominance relationships will be included in the next steps. [less ▲]

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See detailGenetic evaluation of calving ease for Walloon Holstein dairy cattle
Vanderick, Sylvie ULg; Troch, Thibault ULg; Gillon, Alain et al

Conference (2013, August 25)

Calving complications have an incidence on the economic profitability of dairy herds. In the Walloon Region of Belgium, calving ease data recording is being done on voluntary basis since 2000. This allows ... [more ▼]

Calving complications have an incidence on the economic profitability of dairy herds. In the Walloon Region of Belgium, calving ease data recording is being done on voluntary basis since 2000. This allows now the implementation of a genetic evaluation of Holstein dairy cattle addressing the need of dairy breeders to select bulls in order to reduce frequency of calving problems. Calving ease scores were analyzed using univariate animal linear models, which were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Variance components and related genetic parameters were estimated from a dataset including 33,155 calving records. Included in the models were fixed season effects, fixed herd effects and fixed sex of calf*age of dam classes*group of calvings interaction effects, random herd*year of calving effects, random maternal permanent environment effects, and random animal direct and maternal additive genetic effects. For both models, direct and maternal heritabilities for calving ease were about 8% and about 2%, respectively. Genetic correlation between direct and maternal additive effects was found to be non-significantly different from zero. So, an animal linear model with genetic correlation between direct and maternal effects constrained to zero was adopted for the routine genetic evaluation of calving ease for Walloon Holstein dairy cattle. This model was validated by Interbull in January 2013 and, since April 2013, the Walloon Region of Belgium has officially participated to the international MACE evaluation for calving traits. [less ▲]

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