References of "Colinet, Frédéric"
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See detailGenetic variability of MIR predicted milk technological properties in Walloon dairy cattle
Colinet, Frédéric ULiege; Troch, Thibault ULiege; Baeten, Vincent et al

in Book of Abstracts of the 66th Annual Meeting of the European Federation of Animal Science (2015, August)

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See detailPotential of visible-near infrared spectroscopy for the characterization of butter properties
Troch, Thibault ULiege; Baeten, Vincent; Dehareng, Frédéric et al

in Book of Abstracts of the 66th Annual Meeting of the European Federation of Animal Science (2015, August)

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See detailOn the use of novel milk phenotypes as predictors of difficult-to-record traits in breeding programs
Bastin, Catherine ULiege; Colinet, Frédéric ULiege; Dehareng, Frédéric et al

in Book of Abstracts of the 66th Annual Meeting of the European Federation of Animal Science (2015, August)

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See detailGenetic heritage of the Eastern Belgium Red and White breed, an endangered local breed
Colinet, Frédéric ULiege; Bouffioux, Aude; Mayeres, Patrick et al

Poster (2015, August)

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See detailOverview of possibilities and challenges of the use of infrared spectrometry in cattle breeding
Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege; Dehareng, Frédéric et al

in Book of Abstracts of the 66th Annual Meeting of the European Federation of Animal Science (2015, August)

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See detailGenetic analysis to support the re-establishment of the Kempen breed
François, Liesbeth; Janssens, Steven; Colinet, Frédéric ULiege et al

in Book of Abstracts of the 66th Annual Meeting of the European Federation of Animal Science (2015, August)

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See detailGenetic heritage of the Eastern Belgium Red and White breed, an endangered local breed
Colinet, Frédéric ULiege; Bouffioux, Aude; Mayeres, Patrick et al

in Book of Abstracts of the 66th Annual Meeting of the European Federation of Animal Science (2015, August)

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See detailCapitalizing on fine milk composition for breeding and management of dairy cows
Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege; Dehareng, Frédéric et al

in Journal of Animal Science (2015, July 12), 93/ 98(Suppl. s3/ Suppl. 2), 4

Management and breeding of dairy cows face the challenge of permanently adapting to changing production circumstances under socioeconomic constraints. If management and breeding addresses different ... [more ▼]

Management and breeding of dairy cows face the challenge of permanently adapting to changing production circumstances under socioeconomic constraints. If management and breeding addresses different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, their products (i.e., milk and subsequently dairy products), their behavior and their environmental impact. Milk composition has been identified as an important source of information since it could reflect, at least partially, all these elements. Major milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other components might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using classical prediction equation based techniques. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate), that can then be useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., ketosis) are predicted directly from MIR spectra. In a second approach, in an innovative manner, patterns detected by comparing observed from expected MIR spectra can be used directly. All these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality and limit environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlation with other traits of interest. [less ▲]

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See detailSystème d'évaluations génomiques des bovins laitiers en Wallonie (Belgique)
Colinet, Frédéric ULiege; Vandenplas, Jérémie; Vanderick, Sylvie ULiege et al

Computer development (2015)

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See detailIntegration of external estimated breeding values and associated reliabilities using correlation among traits and effects
Vandenplas, J.; Colinet, Frédéric ULiege; Glorieux, G. et al

in Journal of Dairy Science (2015), 98(12), 90449050

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See detailUnified method to integrate and blend several, potentially related, sources of information for genetic evaluation
Vandenplas, Jérémie ULiege; Colinet, Frédéric ULiege; Gengler, Nicolas ULiege

in Genetics, Selection, Evolution (2014), 46

Background A condition to predict unbiased estimated breeding values by best linear unbiased prediction is to use simultaneously all available data. However, this condition is not often fully met. For ... [more ▼]

Background A condition to predict unbiased estimated breeding values by best linear unbiased prediction is to use simultaneously all available data. However, this condition is not often fully met. For example, in dairy cattle, internal (i.e. local) populations lead to evaluations based only on internal records while widely used foreign sires have been selected using internally unavailable external records. In such cases, internal genetic evaluations may be less accurate and biased. Because external records are unavailable, methods were developed to combine external information that summarizes these records, i.e. external estimated breeding values and associated reliabilities, with internal records to improve accuracy of internal genetic evaluations. Two issues of these methods concern double-counting of contributions due to relationships and due to records. These issues could be worse if external information came from several evaluations, at least partially based on the same records, and combined into a single internal evaluation. Based on a Bayesian approach, the aim of this research was to develop a unified method to integrate and blend simultaneously several sources of information into an internal genetic evaluation by avoiding double-counting of contributions due to relationships and due to records. Results This research resulted in equations that integrate and blend simultaneously several sources of information and avoid double-counting of contributions due to relationships and due to records. The performance of the developed equations was evaluated using simulated and real datasets. The results showed that the developed equations integrated and blended several sources of information well into a genetic evaluation. The developed equations also avoided double-counting of contributions due to relationships and due to records. Furthermore, because all available external sources of information were correctly propagated, relatives of external animals benefited from the integrated information and, therefore, more reliable estimated breeding values were obtained. Conclusions The proposed unified method integrated and blended several sources of information well into a genetic evaluation by avoiding double-counting of contributions due to relationships and due to records. The unified method can also be extended to other types of situations such as single-step genomic or multi-trait evaluations, combining information across different 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

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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