Publications of Nicolas Gengler
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See detailBeyond studying genetic diversity: how can pedigree and genomic data help us assigning individuals to breeds?
Wilmot, Hélène ULiege; Bormann, Jeanne; Gengler, Nicolas ULiege

Poster (2020, June)

Determining to which breed an individual belongs is not always an unbiased choice. Definition of breeds is not always clear and takes some subjective elements into account (e.g. phenotypes or ... [more ▼]

Determining to which breed an individual belongs is not always an unbiased choice. Definition of breeds is not always clear and takes some subjective elements into account (e.g. phenotypes or administrative rules). Moreover, insufficient pedigree deepness worsens this issue. This explains the need for development of breed assignment tools and their routine use. This kind of tools supposes a known "Reference population" containing maximum genetic diversity of the breed considered. Moreover, "Candidate individuals" have to be close enough to this "Reference population" to allow correct individual assignment. Tools based on classification methods allow breed assignment and subsequently subsidy payment schemes in Wallonia (Southern Belgium) and Luxembourg. Currently, a principal component analysis (PCA) based on genotypes is used as a routine to determine if individuals belong to two local dual-purpose cattle breeds (i.e. East Belgian Red and White, Belgium, and Ösling Red Pied, Luxembourg). This analysis relies on the position of individuals on the PCA compared to those of reference individuals from different breeds (East Belgian Red and White and Ösling Red Pied but also “sister breeds” and (Red-)Holstein). However, the continuum of Red-Pied breeds in Western Europe makes it difficult to choose to which breed the animal belongs. One example is the overlapping observed on the PCA between East Belgian Red and White and Ösling Red Pied. One solution is maybe to include these animals in both herdbooks. This would allow exchange program between so closely related breeds. Furthermore, the question arises to what extent peripherical individuals, but potentially phenotypically interesting, should be included, as they could provide more diversity to the current gene pool. In these cases, the study of different parameters that inform us about inbreeding (e.g. runs of homozygosity or effective population size) and admixture within breeds or differentiation levels (e.g. fixation index) between breeds, can help to assign individuals to breeds. [less ▲]

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See detailAssessing animal welfare: Deriving individual welfare phenotypes from existing milk recording data
Franceschini, Sébastien ULiege; Leblois, Julie ULiege; Lepot, F. et al

Poster (2020, June)

Animal welfare is an increasing concern in dairy production. Consumers want an ethical production while farmers want to ensure the health of the animals. Animal welfare measurements at the herd level such ... [more ▼]

Animal welfare is an increasing concern in dairy production. Consumers want an ethical production while farmers want to ensure the health of the animals. Animal welfare measurements at the herd level such as the Welfare Quality® (WQ®) Protocol already exist but are time-consuming and costly. Moreover, assessing the overall well-being at the animal level becomes a challenge as herd measures for welfare can not be directly translated to the animal level. Two projects, active in the Walloon Region of Belgium, HappyMoo (Interreg NWE) and ScorWelCow, are trying to define individual welfare scores (IWS) and their prediction from routinely measured milk recording data, including mid-infrared spectral data representing fine milk composition. Data from WQ® Protocol and routine milk recording was collected during the same timeframe in 18 dairy farms with 1386 cows, the majority being genotyped. Two approaches to assess and to predict individual animal welfare were developed. The first approach consisted of two steps: translating the WQ® principles into IWS and predicting these from milk recording data. The variation observed in the first step while regressing WQ® animal measures on WQ® principles was considered representative of the biological variation between cows. IWS prediction Partial Least Square regression for the 4 principles of the welfare quality scores have R2 between 0.65 and 0.77. Moreover, results from this first approach showed a significant welfare assessor effect suggesting that welfare measurements were strongly human interpretation-dependent. This suggested the need for an alternative approach. The second approach directly used milk recording data such as spectral data to cluster cows in different groups, bypassing a priori definition of welfare by WQ®. Those groups were compared to results from the first approach and showed possible discrimination for herds with enhanced WQ® score ( Specificity = 1.00 but Sensitivity = 0.10) thus suggesting further unsupervised analysis. Based on this research, novel individual welfare traits could be developed allowing future genomic selection for improved welfare. [less ▲]

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See detailInvestigation of the temporal relationships between milk mid-infrared predicted biomarkers and lameness events in later lactation
Mineur, Axelle ULiege; Hammami, Hedi ULiege; Grelet, Clément et al

in Journal of Dairy Science (2020), 103(5), 4475-4482

This study reports on the exploration of temporal relationships between milk mid-infrared predicted biomarkers and lameness events. Lameness in dairy cows is an issue that can vary greatly in severity and ... [more ▼]

This study reports on the exploration of temporal relationships between milk mid-infrared predicted biomarkers and lameness events. Lameness in dairy cows is an issue that can vary greatly in severity and is of concern for both producers and consumers. Metabolic disorders are often associated with lameness. However, lameness can arise weeks or even months after the metabolic disorder, making the detection of causality difficult. We already use mid-infrared technology to predict major milk components, such as fat or protein, during routine milk recording and for milk payment. It was recently shown that this technology can also be used to predict novel biomarkers linked to metabolic disorders in cows, such as oleic acid (18:1 cis-9), β-hydroxybutyrate, acetone, and citrate in milk. We used these novel biomarkers as proxies for metabolic issues. Other studies have explored the possibility of using mid-infrared spectra to predict metabolic diseases and found it (potentially) usable for indicating classes of metabolic problems. We wanted to explore the possible relationship between mid-infrared-based metabolites and lameness over the course of lactation. In total, data were recorded from 6,292 cows on 161 farms in Austria. Lameness data were recorded between March 2014 and March 2015 and consisted of 37,555 records. Mid-infrared data were recorded between July and December 2014 and consisted of 9,152 records. Our approach consisted of fitting preadjustments to the data using fixed effects, computing pair-wise correlations, and finally applying polynomial smoothing of the correlations for a given biomarker at a certain month in lactation and the lameness events scored on severity scale from sound or non-lame (lameness score of 1) to severely lame (lameness score of 5) throughout the lactation. The final correlations between biomarkers and lameness scores were significant, but not high. However, for the results of the present study, we should not look at the correlations in terms of absolute values, but rather as indicators of a relationship through time. When doing so, we can see that metabolic problems occurring in mo 1 and 3 seem more linked to longterm effects on hoof and leg health than those in mo 2. However, the quantity (only 1 pair-wise correlation exceeded 1,000 observations) and the quality (due to limited data, no separation according to more metabolic-related diseases could be done) of the data should be improved. [less ▲]

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See detailPotential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation
Grelet, Clément; Froidmont, Éric; Foldager, Leslie et al

in Journal of Dairy Science (2020), 103(5), 4435-4445

Improving nitrogen use efficiency (NUE) at both the individual cow and the herd level has become a key target in dairy production systems, for both environmental and economic reasons. Cost-effective and ... [more ▼]

Improving nitrogen use efficiency (NUE) at both the individual cow and the herd level has become a key target in dairy production systems, for both environmental and economic reasons. Cost-effective and large-scale phenotyping methods are required to improve NUE through genetic selection and by feeding and management strategies. The aim of this study was to evaluate the possibility of using mid-infrared (MIR) spectra of milk to predict individual dairy cow NUE during early lactation. Data were collected from 129 Holstein cows, from calving until 50 d in milk, in 3 research herds (Denmark, Ireland, and the UK). In 2 of the herds, diets were designed to challenge cows metabolically, whereas a diet reflecting local management practices was offered in the third herd. Nitrogen intake (kg/d) and nitrogen excreted in milk (kg/d) were calculated daily. Nitrogen use efficiency was calculated as the ratio between nitrogen in milk and nitrogen intake, and expressed as a percentage. Individual daily values for NUE ranged from 9.7 to 81.7%, with an average of 36.9% and standard deviation of 10.4%. Milk MIR spectra were recorded twice weekly and were standardized into a common format to avoid bias between apparatus or sampling periods. Regression models predicting NUE using milk MIR spectra were developed on 1,034 observations using partial least squares or support vector machines regression methods. The models were then evaluated through (1) a cross-validation using 10 subsets, (2) a cow validation excluding 25% of the cows to be used as a validation set, and (3) a diet validation excluding each of the diets one by one to be used as validation sets. The best statistical performances were obtained when using the support vector machines method. Inclusion of milk yield and lactation number as predictors, in combination with the spectra, also improved the calibration. In cross-validation, the best model predicted NUE with a coefficient of determination of cross-validation of 0.74 and a relative error of 14%, which is suitable to discriminate between low- and high-NUE cows. When performing the cow validation, the relative error remained at 14%, and during the diet validation the relative error ranged from 12 to 34%. In the diet validation, the models showed a lack of robustness, demonstrating difficulties in predicting NUE for diets and for samples that were not represented in the calibration data set. Hence, a need exists to integrate more data in the models to cover a maximum of variability regarding breeds, diets, lactation stages, management practices, seasons, MIR instruments, and geographic regions. Although the model needs to be validated and improved for use in routine conditions, these preliminary results showed that it was possible to obtain information on NUE through milk MIR spectra. This could potentially allow large-scale predictions to aid both further genetic and genomic studies, and the development of farm management tools. © 2020 American Dairy Science Association [less ▲]

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See detailDiagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy?
Delhez, Pauline ULiege; Ho, Phuong; Gengler, Nicolas ULiege et al

in Journal of Dairy Science (2020), 103(4), 3264-3274

Pregnancy diagnosis is an essential part of successful breeding programs on dairy farms. Milk composition alters with pregnancy, and this is well documented. Fourier-transform mid-infrared (MIR ... [more ▼]

Pregnancy diagnosis is an essential part of successful breeding programs on dairy farms. Milk composition alters with pregnancy, and this is well documented. Fourier-transform mid-infrared (MIR) spectroscopy is a rapid and cost-effective method for providing milk spectra that reflect the detailed composition of milk samples. Therefore, the aim of this study was to assess the ability of MIR spectroscopy to predict the pregnancy status of dairy cows. The MIR spectra and insemination records were available from 8,064 Holstein cows of 19 commercial dairy farms in Australia. Three strategies were studied to classify cows as open or pregnant using partial least squares discriminant analysis models with random cow-independent 10-fold cross-validation and external validation on a cow-independent test set. The first strategy considered 6,754 MIR spectra after insemination used as independent variables in the model. The results showed little ability to detect the pregnancy status as the area under the receiver operating characteristic curve was 0.63 and 0.65 for cross-validation and testing, respectively. The second strategy, involving 1,664 records, aimed to reduce noise in the MIR spectra used as predictors by subtracting a spectrum before insemination (i.e., open spectrum) from the spectrum after insemination. The accuracy was comparable with the first approach, showing no the superiority of the method. Given the limited results for these models when using combined data from all stages after insemination, the third strategy explored separate models at 7 stages after insemination comprising 348 to 1,566 records each (i.e., progressively greater gestation) with single MIR spectra after insemination as predictors. The models developed using data recorded after 150 d of pregnancy showed promising prediction accuracy with the average value of area under the receiver operating characteristic curve of 0.78 and 0.76 obtained through cross-validation and testing, respectively. If this can be confirmed on a larger data set and extended to somewhat earlier stages after insemination, the model could be used as a complementary tool to detect fetal abortion. [less ▲]

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See detailPedigree relatedness and pseudo-phenotypes as a first approach to assess and maintain genetic diversity of the Walloon Piétrain pig population
Wilmot, Hélène ULiege; REIS MOTA, Rodrigo ULiege; Vanderick, Sylvie ULiege et al

in Livestock Science (2020), 233

The breeding of pure Piétrain animals is currently performed in two different contexts: industrial lines and individual breeders. As one of the four main pig breeds worldwide, the Piétrain breed might not ... [more ▼]

The breeding of pure Piétrain animals is currently performed in two different contexts: industrial lines and individual breeders. As one of the four main pig breeds worldwide, the Piétrain breed might not be considered to be endangered. However, in Wallonia (southern Belgium), even though the Belgian Piétrain programme aims to preserve the Walloon Piétrain population through cryopreservation of semen of relevant boars, only 10 pure Piétrain breeders remain and produce traditional breeding stock. Current breeders are retiring and no new breeders are replacing them. Moreover, the genetic diversity of the pigs from these individual breeders may highly contribute to the global gene pool of the breed, therefore it is important to assess this diversity. This was done on a local level by using pedigree relatedness but also differences in phenotypes. Pedigree parameters such as effective population size, genetic diversity and inbreeding coefficients were estimated for 219 boars from which offspring performances were recorded at the Walloon test station. A multi-dimensional scaling (MDS) was performed based on genetic distances. Considering the current owners of the boars, a principal component analysis (PCA) was made on deregressed breeding values (pseudo-phenotypes) based on the performances of their crossbred offspring at the test station. The effective population size was 223, the genetic diversity parameter was 97.96%, while the mean inbreeding coefficient was 2.74%. The MDS identified four main clusters of boars. Two principal components indicated two major directions of selection: growth or meat traits. Genetically close boars did not necessarily show similar performances in their offspring. Different performances for genetically linked animals should reflect the breeding objectives of their owner, a practice that was confirmed by most owners during interviews. Pedigree, phenotypes and genotypes provide complementary information and therefore should be used simultaneously in the implementation of conservation programmes. These first results also showed that the genetic diversity of the Walloon Piétrain population is so far well preserved. However, recommendations need to be developed in order to maintain it. For example, boars provided to the progeny-testing scheme should come from equally contributing breeders, allowing the Belgian Piétrain programme to sample boars from a larger variety of animals taking into account genetic and phenotypic diversity. Finally, in-situ preservation of Piétrain diversity will require the development of new tools and mating schemes. [less ▲]

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See detailAssessment of Walloon dairy farms eco-efficiency using Data Envelopment Analysis and easily-accessible environmental and economic indicators: a preliminary study
Delhez, Pauline ULiege; Reding, Edouard; Gengler, Nicolas ULiege et al

Poster (2020, January 31)

Achieving economically viable and environmentally friendly food production is a key challenge today. In this context, the aim of this study was to (i) analyse the economic and environmental efficiency (i ... [more ▼]

Achieving economically viable and environmentally friendly food production is a key challenge today. In this context, the aim of this study was to (i) analyse the economic and environmental efficiency (i.e., eco-efficiency) of a sample of specialised dairy farms in the Walloon region of Belgium; and (ii) to identify key management factors that differ between efficient and inefficient farms. Eco-efficiency was estimated with the productive efficiency benchmarking method Data Envelopment Analysis (DEA). DEA is a well-known technique for measuring the relative efficiency of comparable decision-making units using several inputs to produce one or more outputs. In our study, input and output variables were selected based on their economic and environmental relevance, as well as on their availability in the accounting database of the Walloon Breeding Association (awé). The chosen DEA inputs and output included economic-oriented variables such as fat and protein corrected milk yield and simple environmental indicators like land use, livestock units, fertiliser and pesticide application, purchased feed and on-farm energy use. Preliminary results on 174 dairy farms in 2017 suggested contrasting levels of eco-efficiency in our sample. Hypotheses concerning the determinants of eco-efficiency will be tested. The findings of this study will help inform policy-making towards dairy farm management that can increase dairy production at the least environmental costs. [less ▲]

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See detailBeyond studying genetic diversity: how can pedigree and genomic data help us assigning individuals to breeds?
Wilmot, Hélène ULiege; Bormann, Jeanne; Gengler, Nicolas ULiege

Conference (2020)

Determining to which breed an individual belongs is not always an unbiased choice. Definition of breeds is not always clear and takes some subjective elements into account (e.g. phenotypes or ... [more ▼]

Determining to which breed an individual belongs is not always an unbiased choice. Definition of breeds is not always clear and takes some subjective elements into account (e.g. phenotypes or administrative rules). Moreover, insufficient pedigree deepness worsens this issue. This explains the need for development of breed assignment tools and their routine use. This kind of tools supposes a known "Reference population" containing maximum genetic diversity of the breed considered. Moreover, "Candidate individuals" have to be close enough to this "Reference population" to allow correct individual assignment. Tools based on classification methods allow breed assignment and subsequently subsidy payment schemes in Wallonia (Southern Belgium) and Luxembourg. Currently, a principal component analysis (PCA) based on genotypes is used as a routine to determine if individuals belong to two local dual-purpose cattle breeds (i.e. East Belgian Red and White, Belgium, and Ösling Red Pied, Luxembourg). This analysis relies on the position of individuals on the PCA compared to those of reference individuals from different breeds (East Belgian Red and White and Ösling Red Pied but also “sister breeds” and (Red-)Holstein). However, the continuum of Red-Pied breeds in Western Europe makes it difficult to choose to which breed the animal belongs. One example is the overlapping observed on the PCA between East Belgian Red and White and Ösling Red Pied. One solution is maybe to include these animals in both herdbooks. This would allow exchange program between so closely related breeds. Furthermore, the question arises to what extent peripherical individuals, but potentially phenotypically interesting, should be included, as they could provide more diversity to the current gene pool. In these cases, the study of different parameters that inform us about inbreeding (e.g. runs of homozygosity or effective population size) and admixture within breeds or differentiation levels (e.g. fixation index) between breeds, can help to assign individuals to breeds. [less ▲]

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See detailPredicting milk mid-infrared spectra from first parity Holstein cows using a test-day mixed model with the perspective of herd management
Delhez, Pauline ULiege; Colinet, Frédéric ULiege; Vanderick, Sylvie ULiege et al

in Journal of Dairy Science (2020)

The use of test-day models to model milk mid-infrared (MIR) spectra for genetic purposes has already been explored; however, little attention has been given to their use to predict milk MIR spectra for ... [more ▼]

The use of test-day models to model milk mid-infrared (MIR) spectra for genetic purposes has already been explored; however, little attention has been given to their use to predict milk MIR spectra for management purposes. The aim of this paper was to study the ability of a test-day mixed model to predict milk MIR spectra for management purposes. A data set containing 467,496 test-day observations from 53,781 Holstein dairy cows in first lactation was used for model building. Principal component analysis was implemented on the selected 311 MIR spectral wavenumbers to reduce the number of traits for modeling; 12 principal components (PC) were retained, explaining approximately 96% of the total spectral variation. Each of the retained PC was modeled using a single trait test-day mixed model. The model solutions were used to compute the predicted scores of each PC, followed by a back-transformation to obtain the 311 predicted MIR spectral wavenumbers. Four new data sets, containing altogether 122,032 records, were used to test the ability of the model to predict milk MIR spectra in 4 distinct scenarios with different levels of information about the cows. The average correlation between observed and predicted values of each spectral wavenumber was 0.85 for the modeling data set and ranged from 0.36 to 0.62 for the scenarios. Correlations between milk fat, protein, and lactose contents predicted from the observed spectra and from the modeled spectra ranged from 0.83 to 0.89 for the modeling set and from 0.32 to 0.73 for the scenarios. Our results demonstrated a moderate but promising ability to predict milk MIR spectra using a test-day mixed model. Current and future MIR traits prediction equations could be applied on the modeled spectra to predict all MIR traits in different situations instead of developing one test-day model separately for each trait. Modeling MIR spectra would benefit farmers for cow and herd management, for instance through prediction of future records or comparison between observed and expected wavenumbers or MIR traits for the detection of health and management problems. Potential resulting tools could be incorporated into milk recording systems. [less ▲]

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See detailAdvanced monitoring of milk quality to address the demand of added-value dairy products
Bastin, Catherine; Dehareng, Frédéric; Gengler, Nicolas ULiege et al

in Book of Abstracts of the 71st Annual Meeting of the European Federation of Animal Science (2020)

Consumers are seeking for local, healthy and direct-from-producers dairy products. Hence, assessing the suitability of milk to be processed in dairy products either directly in the farms or through local ... [more ▼]

Consumers are seeking for local, healthy and direct-from-producers dairy products. Hence, assessing the suitability of milk to be processed in dairy products either directly in the farms or through local dairy plants is of great interest. Moreover, several studies demonstrated the usefulness of mid-infrared (MIR) spectrometry for the prediction of various traits related to the nutritional and technological properties of milk. This study presents 5 groups of MIR predicted traits related to various aspects of dairy products: (1) milk coagulation traits, (2) cheese and butter yields, (3) nutritional quality, (4) texture and, (5) sensory quality. The MIR prediction equations of these 5 groups of traits were applied on standardized spectra from individual milk samples collected in the frame of the Walloon milk recording scheme. After edits, more than 780,000 records collected between 2017 and 2019 were used. The MIR predictions for coagulation time and curd firmness were combined to define a new trait with 5 levels assessing the overall milk coagulation property, from poorly coagulating milk to optimal milk for coagulation. Casein and calcium contents and titrable acidity were also studied in relation to milk coagulation properties. The nutritional quality of milk was assessed through the content in fat of PUFA and the health promoting index (i.e., UFA / (C12 + C14 x 4 + C16). The spreadability of butter was defined as the ratio of C18:1 cis-9 to C16:0. The sensory quality of milk was assessed through SCC and the content of free fatty acids. Results showed that about 10% of the records were classified as poorly coagulating milk; these records had lower calcium and casein contents with lower MIR predicted cheese yield. Also, 60% of the cows were never classified in the poorly coagulating milk class while 5% of the cow produced poorly coagulating milk at least half of the time. Moreover, cheese yield was highly correlated with the protein and fat contents. The results also showed the influence of days in milk, parity, herd and breed on all traits. Local breeds (i.e., Dual Purpose Belgian Blue and Eastern Red) showed favourable milk fat profile for nutritional quality of milk and texture of dairy products even if these breeds tend to have higher proportion of suboptimal milk for coagulation. These results indicates that dairy farmers have the opportunities to monitor and improve milk quality for the production of added-value dairy products. [less ▲]

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See detailBetween- and within-herd variation in blood and milk biomarkers in Holstein cows in early lactation
Krogh, M. A.; Hostens, M.; Salavati, M. et al

in Animal (2020), 14(5), 1-9

Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically ... [more ▼]

Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (β-hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N-acetyl-β-D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites’ levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring. [less ▲]

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See detailThe use of mid-infrared spectral data to predict traits for genetic selection in dairy cattle
Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege

in van der Werf, Julius; Pryce, Jennie (Eds.) Advances in breeding of dairy cattle (2019)

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See detailThe CaLiCCo project
Wilmot, Hélène ULiege; Gengler, Nicolas ULiege

Conference (2019, November 28)

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See detailMastitis detection from milk mid-infrared (MIR) spectroscopy in dairy cows
Rienesl, Lisa; Khayatzadeh, Negar; Köck, Astrid et al

in Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis (2019), 67(5), 12211226

Mid-infrared (MIR) spectroscopy is the method of choice for the standard milk recording system, to determine milk components including fat, protein, lactose and urea. Since milk composition is related to ... [more ▼]

Mid-infrared (MIR) spectroscopy is the method of choice for the standard milk recording system, to determine milk components including fat, protein, lactose and urea. Since milk composition is related to health and metabolic status of a cow, MIR spectra could be potentially used for disease detection. In dairy production, mastitis is one of the most prevalent diseases. The aim of this study was to develop a calibration equation to predict mastitis events from routinely recorded MIR spectra data. A further aim was to evaluate the use of test day somatic cell score (SCS) as covariate on the accuracy of the prediction model. The data for this study is from the Austrian milk recording system and its health monitoring system (GMON). Test day data including MIR spectra data was merged with diagnosis data of Fleckvieh, Brown Swiss and Holstein Friesian cows. As prediction variables, MIR absorbance data after first derivatives and selection of wavenumbers, corrected for days in milk, were used. The data set contained roughly 600,000 records and was split into calibration and validation sets by farm. Calibration sets were made to be balanced (as many healthy as mastitis cases), while the validation set was kept large and realistic. Prediction was done with Partial Least Squares Discriminant Analysis, key indicators of model fit were sensitivity and specificity. Results were extracted for association between spectra and diagnosis with different time windows (days between diagnosis and test days) in validation. The comparison of different sets of predictor variables (MIR, SCS, MIR + SCS) showed an advantage in prediction for MIR + SCS. For this prediction model, specificity was 0.79 and sensitivity was 0.68 in time window -7 to +7 days (calibration and validation). Corresponding values for MIR were 0.71 and 0.61, for SCS they were 0.81 and 0.62. In general, prediction of mastitis performed better with a shorter distance between test day and mastitis event, yet even for time windows of -21 to +21 days, prediction accuracies were still reasonable, with sensitivities ranging from 0.50 to 0.57 and specificities remaining unchanged (0.71 to 0.85). Additional research to further improve prediction equation, and studies on genetic correlations among clinical mastitis, SCS and MIR predicted mastitis are planned. [less ▲]

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See detailContribution of milk mid-infrared spectrum to improve the accuracy of test-day body weight predicted from stage, lactation number, month of test and milk yield
Soyeurt, Hélène ULiege; Froidmont, Éric; Dufrasne, Isabelle ULiege et al

in Livestock Science (2019), 227(2019), 82-89

A regular and repeated recording of body weight (BW) is useful information for herd management. BW can bepredicted regularly from animal characteristics such as age, lactation number, or lactation stage ... [more ▼]

A regular and repeated recording of body weight (BW) is useful information for herd management. BW can bepredicted regularly from animal characteristics such as age, lactation number, or lactation stage. Those traits areunfortunately animal unspecific. Adding animal specific information, which can be easily obtained on a largescale, to the BW prediction would be of utmost importance. There are good scientific reasons to suspect linksbetween BW and animal specific characteristics, available in a repeated fashion, such as milk yield and milkcomposition. This study aimed to demonstrate the feasibility of predicting test-day BW from stage, lactationnumber, month of test, milk yield and mid-infrared spectra, representing milk composition. Five models weretested initially from 721 BW records collected in 6 herds: day in milk + number of lactation (equation 1a);equation1a + milk yield (equation 1b); only spectral data (equation 1c); equation 1c + equation 1a (equation2); equation 2 + milk yield (equation 3). Then 3 other equations included the same explicative variables, exceptthat the spectral data were regressed using second order Legendre Polynomials (PL) to take into account changesof spectral data within lactation. Equation 1a and 1b were built using linear regressions and equation 1c until 3were built using partial least square regressions. These 3 last equations had a higher number of factors. Adding ofMIR data in the equation increased of 7% the values of cross-validation R² (R²cv). Potential BW outliers werediscarded using a residual analysis based on equation 3. From 662 records, the following statistical parameterswere obtained: the calibration coefficient of determination (R²c) = 0.65, R²cv = 0.61, calibration root meansquared error of prediction (RMSEP)=38 kg, and RMSEPcv=40 kg. Low variation of R²c and RMSEPc valuesobtained from the herd validation confirmed the herd independence of predictions. However, large variabilitywas observed for RMSEPv (37 to 64 kg) suggesting the need to increase the dataset in order to improve therobustness of the equation. By applying the equations on a large spectral database, it was confirmed that theaddition of MIR data allows to better model the BW evolution within lactation. Based on these preliminaryresults, and if a larger validation confirms thesefindings, this approach could be used to develop equations thatare better able to assess BW throughout lactation(s), BW being an important element for management andselection tools. [less ▲]

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See detailGplusE: Mid-infrared milk analysis based technologies adding value to gene banks
Gengler, Nicolas ULiege; Grelet, Clément ULiege; Soyeurt, Hélène ULiege et al

in Book of Abstracts of the 70th Annual Meeting of the European Federation of Animal Science (2019, August)

Fitter farms through the use of animal gene banks need an excellent level of characterization of the genetic material represented in these banks. If genomic characterization is straight forward the ... [more ▼]

Fitter farms through the use of animal gene banks need an excellent level of characterization of the genetic material represented in these banks. If genomic characterization is straight forward the phenotypic characterization of specific traits of interest is less. Even worse, some traits may even not be known as of interest when the material was conserved. Here mid-infrared (MIR) spectra based milk analysis strategies as studied during the recently finished EU FP7 project GplusE would allow to add value to gene bank collection. Required for this would be the recording of spectra for individuals of preserved breeds, if possible from spectrometers which were at the same time participating to a standardization procedure, and the preservation for at least a part of these MIR records the corresponding preserved (frozen) milk. There are at least four ways MIR based technologies can add value. First, MIR prediction equations developed well after the conservation of the MIR data can be used to determine a posteriori novel phenotypes for these old animals. Prediction would be more reliable when made on standardized spectra and can be done with the sole knowledge of the spectra so even for larger groups of animals then those preserved (e.g. daughters of preserved sires). Also, second, preserved frozen samples associated to some MIR records, can be used to validate, or even improve these equations adding variability that has maybe disappeared since preservation. These frozen samples can also be useful because novel reference methods (e.g. proteomics) may appear and these samples can contribute to better novel equations because they increase variability of reference calibration dataset. Finally, MIR spectra data, but also MIR based predictions can be used to establish breed differences. Using directly MIR has the advantage of a phenotypic characterization (i.e. milk phenome) close the genome. As for the use of the genome for selection of candidates for gene banks, the use of this milk phenome could be a strategy to assess and to cover the existing variability in a given breed and among breeds to preserve. [less ▲]

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See detailDoes the Walloon Piétrain pig breed require preservation measures?
Wilmot, Hélène ULiege; Vanderick, Sylvie ULiege; REIS MOTA, Rodrigo ULiege et al

in Book of Abstracts of the 70th Annual Meeting of the European Federation of Animal Science (2019, August)

Piétrain pigs are used worldwide as terminal sires, and they are not expected to require preservation measures. However, the number of pure Piétrain breeders is dwindling everywhere and also in Wallonia ... [more ▼]

Piétrain pigs are used worldwide as terminal sires, and they are not expected to require preservation measures. However, the number of pure Piétrain breeders is dwindling everywhere and also in Wallonia (southern Belgium), the region where the breed is originated from. As a first step, the objective of this study was to assess the genetic diversity in Walloon Piétrain pig populations by using pedigree information. A total of 199 boars, whose breeders could be identified and which passed through performances testing using crossbred progeny at the performances recording station during the last ten years, were used for pedigree extraction. Kinship coefficients were determined and a classical multi-dimensional scaling (MDS) was performed on those boars for herd comparison purposes. In addition, breeders who have stopped their activity were identified in order to check genetic diversity loss overtime. Four groups were identified: a first cluster of Walloon Brabant breeders; a second core cluster, which suggests high levels of inbreeding, composed mainly by Hainaut breeders; a third cluster with great diversity, represented by a breeder whose animals may had German boars influence; and the last cluster formed by two breeders. The breeders from all four clusters are in ongoing pig breeding activities. However, due to their aging as well as the lack of new breeders, conservation measures establishment may be urgent in order to preserve genetic resources. As Walloon Piétrain breeders tend to keep very different phenotypes, complementary principal components analysis will be performed by using pseudo-phenotypes, by using deregressed estimated breeding values (EBV), to be compared with previous MDS results. Finally, single nucleotide polymorphisms (SNP) markers from different European Piétrain pig populations will be used to determine genomic relationships to further verify if Walloon Piétrain population(s) have intrinsic particularities, which would justify conservation measure [less ▲]

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See detailChromosome regions affecting MIR spectra indirect predictors of cheese-making properties in cattle
REIS MOTA, Rodrigo ULiege; Hammami, Hedi ULiege; Vanderick, Sylvie ULiege et al

in Book of Abstracts of the 70th Annual Meeting of the European Federation of Animal Science (2019, August)

The current study aimed to identify single nucleotide polymorphism (SNP) markers and candidate genes associated with titratable acidity (TA), total caseins (TC) and dry cheese yield (DC) predicted from ... [more ▼]

The current study aimed to identify single nucleotide polymorphism (SNP) markers and candidate genes associated with titratable acidity (TA), total caseins (TC) and dry cheese yield (DC) predicted from MIR spectra as indirect predictors of milk cheese-making properties in early lactation cows. These predicted MIR novel phenotypes were obtained by using equations developed through GplusE and former EU projects. Only autosomes SNPs (n=29) with call rates >0.95, minor allele frequencies (MAF) >0.05 and significant deviations from Hardy-Weinberg equilibrium (P>10-7) were used for the genome-wide association study. After quality control edits, 32,687 SNPs remained for further analysis. The detection of SNPs affecting TA, TC and DC were obtained based on the proportion of variation explained by each SNP. The SNPs were considered as outliers if the proportion of variance explained was higher than 5×IQR+Q3, where IQR is the interquartile range and Q3 is the third quartile of the distribution. The putative genes located within or close to an outlier SNP were further identified. A total of 1,109 SNPs were considered as outliers with 8, 13, and 4% in common to all, between TC and DC, and between TC and TA traits, respectively. The remaining 833 were trait specific SNPs, with 18, 28, and 29% affecting TC, TA and DC, respectively. The 84 common SNPs to all traits were identified on BTA14 (75%), 1 (19%), 19 (5%) and 6 (1%). The trait specific markers were located on 26 out of 29 chromosomes. For TA, 86% of SNPs were located on chromosomes 1, 20, 7, and 14. On the other hand, TC showed the majority of SNPs on BTA6, BTA20 and BTA10 whereas for DC, SNPs were mainly on BTA5, BTA14 and BTA27. The top 5% outlier SNPs were located on BTA14, BTA1 and BTA20 with 20 (RPL8; FOXH1; CYHR1; OPLAH; HSF1; CPSF1; DGAT1; CYC1; GRINA; PARP10; NRBP2; PUF60; RNF19A; RRM2B; C8orf33; FBXO43; LY6H; ANKRD46; NAPRT; LY6E), 11 (FAM3B; MX1; HSF2BP; RRP1B; CSTB; C21orf2; LRRC3; DSCAM; ADARB1; ITGB2; MX2) and 3 (ANKH; TRIO; OTULIN) potential candidate genes affecting MIR indirect predictors (TA, TC and DC) of cheese-making properties in early lactation, respectively. [less ▲]

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See detailUsing milk MIR spectra to identify candidate genes associated with climate-smart traits in cattle
Hammami, Hedi ULiege; REIS MOTA, Rodrigo ULiege; Vanderick, Sylvie ULiege et al

in Book of Abstracts of the 70th Annual Meeting of the European Federation of Animal Science (2019, August)

Enhancing the resilience to extreme climate events and reducing greenhouse gases (mitigation), while improving productivity are of large importance in future breeding objectives. However, gaps to get ... [more ▼]

Enhancing the resilience to extreme climate events and reducing greenhouse gases (mitigation), while improving productivity are of large importance in future breeding objectives. However, gaps to get large-scale phenotyping as well as limited knowledge about the biological basis are still challenges. This study tried to exploit the milk MIR spectra jointly with genomics to identify potential candidate genes affecting mitigation and resilience abilities. For mitigation, methane emissions (CH4) and phosphorus (P) could be viewed as major sources of environmental pollution. Biomarkers reflecting the equilibrium between mobilisation and intake and body ketones associated with energy status are potential indicator traits for resilience to thermal stress. Predicted phenotypes form MIR data, CH4 and P (mitigation), and C18:1cis-9, Acetone, BHB in addition to milk yield (resilience) were used in this study. The proportion of variance explained by SNPs was evaluated for all studied traits. The putative genes located within or close to outliers SNPs were further identified. Concerning CH4 and P, 31; 28 and 7% of the what we called outliers SNPs were located at BTA14; BTA1 and BTA15, respectively. The markers in the most informative windows on BTA1 were close or within several genes such as RIPK4, PRDM15, ABCG1, SLC37A1, UBASH3A and FAM3B. On BTA14, the top outlier SNPs were located in LD block containing 3 genes (DGAT1, CYHR1, and PLEC). For resilience traits, the two outlier markers associated with acetone slope were located on BTA7 at approximately 45.5-45.7 Mb. Possible candidate genes located within this interval are UQC411 and ATP5F1D. The SNPs ARSBFGL- NGS-4939 and ARS-BFGL-NGS-34135, located on BTA14 were top SNPs and were also detected to affect milk, C18:1cis9, acetone, and BHB slopes. Interesting candidate genes such as DGAT1, HSP1 and SLC52A2 were identified surrounding those two markers. In conclusion, markers identified for resilience and mitigation traits could prove useful in genomic selection for climate-smart breeding programs. [less ▲]

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