Estimation of Myostatin gene effects on production traits and fatty acid contents in bovine milk

peer reviewedThe aim of this study was to estimate the genetic parameters of milk, fat, and protein yields, saturated (SFA) and monounsaturated fatty acid (MUFA) contents in bovine milk and to estimate the Myostatin (mh) gene effect on these traits. For this purpose, 51,614 test-day records (24,124, 16,145, and 11,345 for first, second and third lactation, respectively) of 3,098 dual purpose Belgian Blue cows in 38 herds from the Walloon Region of Belgium were used. Because only 2,301 animals, including 1,082 cows with test-day records, were genotyped for mh, the gene content of non-genotyped animals was predicted from animals with a known genotype using the relationships with these animals. Variance components were estimated using Restricted Maximum Likelihood. A 3-lactations, 5-traits random regression test-day mixed model, based on the official Walloon genetic evaluation model for production traits, was used with an additional fixed regression on mh gene content to estimate allele substitution effects. Daily heritability estimates (average of 3 lactations) were 0.34 for SFA and 0.16 for MUFA and were higher than those for production traits (0.11, 0.10, and 0.09 for milk, fat, and protein yields, respectively). Allele substitution effects approximate standard-errors) for mh through the three lactations were-0.628 (+0.343),-0.024 (0.014) and -0.021 (+0.009) kg per day for milk, fat, and protein yields, respectively. Concerning SFA and MUFA contents in milk, the average allele substitution effects were -0.001 (+0.027) and 0.029 (+0.023) g/dl of milk. To conclude, results from this study showed that milk performance traits and milk fatty acid profile are influenced by mh genotypes

Genetic parameters for direct and maternal calving ease in Walloon dairy cattle based on linear and threshold models

Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a dataset including 33,155 calving records. Included in the models were season, herd and sex of calf age of dam classes group of calvings interaction as fixed effects, herd year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was about 8% with linear models and about 12% with threshold models. Maternal heritabilities were about 2% and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85,118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17% and 23 % greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice

Potential use of mid-infrared spectrometry to predict cheese yield from milk and to study its genetic variability

peer reviewedFournir 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.ProFARMilk, BlueSe

Use of visible-near infrared spectroscopy to determine cheese properties

ProFARMil

Estimation of myostatin gene effects on production traits and fatty acid contents in bovine milk

peer reviewedThe aim of this study was to estimate the genetic parameters of milk, fat, and protein yields, saturated (SFA) and monounsaturated fatty acid (MUFA) contents in bovine milk and to estimate the Myostatin (mh) gene effect on these traits. For this purpose, 51,614 test-day records (24,124, 16,145, and 11,345 for first, second and third lactation, respectively) of 3,098 dual purpose Belgian Blue cows in 38 herds from the Walloon Region of Belgium were used. Because only 2,301 animals, including 1,082 cows with test-day records, were genotyped for mh, the gene content of non-genotyped animals was predicted from animals with a known genotype using the relationships with these animals. Variance components were estimated using Restricted Maximum Likelihood. A 3-lactations, 5-traits random regression test-day mixed model, based on the official Walloon genetic evaluation model for production traits, was used with an additional fixed regression on mh gene content to estimate allele substitution effects. Daily heritability estimates (average of 3 lactations) were 0.34 for SFA and 0.16 for MUFA and were higher than those for production traits (0.11, 0.10, and 0.09 for milk, fat, and protein yields, respectively). Allele substitution effects approximate standard-errors) for mh through the three lactations were-0.628 (+0.343),-0.024 (0.014) and -0.021 (+0.009) kg per day for milk, fat, and protein yields, respectively. Concerning SFA and MUFA contents in milk, the average allele substitution effects were -0.001 (+0.027) and 0.029 (+0.023) g/dl of milk. To conclude, results from this study showed that milk performance traits and milk fatty acid profile are influenced by mh genotypes

Utiliser les résultats du contrôle laitier pour améliorer son rendement fromager

ProFARMil

Implementing a National Routine Genetic Evaluation for Milk Fat Compositions as First Step Towards Genomic Predictions

Currently the Walloon Region of Belgium is one of the first regions in the World where mid-infra red (MIR) spectral data is recorded in routine for nearly all cows under milk recording. Based on this data, in some herds collected since 2007, saturated (SFA) and monounsaturated fatty acid (MUFA) contents in milk are predicted for each test-day. Together with correlated traits as milk, fat and protein yields, estimated breeding values (EBV) are now computed in routine for SFA and MUFA starting in June 2012. A total of 499 821, 392 255, 277 465 fatty acid records were available in first, second and third lactation for this run. A restricted selection index, called NQI (nutritional quality index) was developed that puts a negative weight on SFA, a positive weight on MUFA and restricts changes in milk and fat yields to zero. By using this index for a constant fat content, milk fat will be selected to be less saturated with a high contribution from MUFA. Based on this system a single-step genomic evaluation is under development including the introduction of MACE breeding values for correlated traits. The final step is to offer for owners of genotyped animals, a service to provide them with genomically enhanced NQI. Similar systems are under development in Wallonia for other novel traits (e.g., methane emissions) based on the ability to predict them from MIR spectral data

Genetic evaluation of calving ease for Walloon Holstein dairy cattle.

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

Cow milk coagulation: process description, variation factors and evaluation methodologies. A review.

Introduction. For dairy producers who want to transform their milk, the ability of milk to coagulate is an important parameter. It makes it possible to transform milk into cheese. Therefore, it is necessary to understand the coagulation process and the techniques to measure it in order to achieve the best transformation performance. The objective of this review is to describe the milk coagulation process, the factors influencing it and the methods for measuring the coagulation of milk at lab level. Literature. The processing of milk into cheese involves three steps: coagulation, dewatering and refining. Coagulation is a key step which involves the use of rennet and depends on several parameters (pH, calcium content, temperature, etc.). Some milks never coagulate. To measure the coagulation ability of milk and identify different parameters in milk coagulation properties, the Formagraph, the computerized renneting meter and the Optigraph have been developed (reference methods). Equations have been developed using infrared spectrometry to predict the parameters obtained by the reference methods. Conclusions. The milk coagulation mechanism is known. However, the issue of non-coagulating milk persists and represents a real challenge in terms of yield. The use of infrared is a faster alternative to reference methods that measure the coagulation properties of milk, but still requires an improvement in prediction equations.Profarmil