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See detailBayesian integration of external information into the single step approach for genomically enhanced prediction of breeding values
Vandenplas, Jérémie ULiege; Misztal, Ignacy; Faux, Pierre ULiege et al

in Journal of Dairy Science (2012), 95(Supplement 2),

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See detailBovine milk fat globule membrane affects virulence expression in Escherichia coli O157:H7
Tellez, Angela; Corredig, Milena; Guri, Anilda et al

in Journal of Dairy Science (2012)

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See detailUse of milk fatty acids to substitute for body condition score in breeding purposes
Bastin, Catherine ULiege; Berry, D.P.; Soyeurt, Hélène ULiege et al

in Journal of Dairy Science (2012), 95, Suppl. 2

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See detailRelationship between body condition score and health traits in first-lactation Canadian Holsteins.
Loker, S.; Miglior, F.; Koeck, A. et al

in Journal of Dairy Science (2012)

The objective of this research was to estimate daily genetic correlations between longitudinal body condition score (BCS) and health traits by using a random regression animal model in first-lactation ... [more ▼]

The objective of this research was to estimate daily genetic correlations between longitudinal body condition score (BCS) and health traits by using a random regression animal model in first-lactation Holsteins. The use of indicator traits may increase the rate of genetic progress for functional traits relative to direct selection for functional traits. Indicator traits of interest are those that are easier to record, can be measured early in life, and are strongly genetically correlated with the functional trait of interest. Several BCS records were available per cow, and only 1 record per health trait (1 = affected; 0 = not affected) was permitted per cow over the lactation. Two bivariate analyses were performed, the first between BCS and mastitis and the second between BCS and metabolic disease (displaced abomasum, milk fever, and ketosis). For the first analysis, 217 complete herds were analyzed, which included 28,394 BCS records for 10,715 cows and 6,816 mastitis records for 6,816 cows. For the second analysis, 350 complete herds were analyzed, which included 42,167 BCS records for 16,534 cows and 13,455 metabolic disease records for 13,455 cows. Estimation of variance components by a Bayesian approach via Gibbs sampling was performed using 400,000 samples after a burn-in of 150,000 samples. The average daily heritability (posterior standard deviation) of BCS was 0.260 (0.026) and the heritabilities of mastitis and metabolic disease were 0.020 (0.007) and 0.041 (0.012), respectively. Heritability estimates were similar to literature values. The average daily genetic correlation between BCS and mastitis was -0.730 (0.110). Cows with a low BCS during the lactation are more susceptible to mastitis, and mastitic cows are likely to have low BCS. Daily estimates of genetic correlations between BCS and mastitis were moderate to strong throughout the lactation, becoming stronger as the lactation progressed. The average daily genetic correlation between BCS and metabolic disease was -0.438 (0.125), and was consistent throughout the lactation. A lower BCS during the lactation is genetically associated with the occurrence of mastitis and metabolic disease. [less ▲]

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See detailGenetic and environmental relationships between body condition score and milk production traits in Canadian Holsteins.
Loker, S; Bastin, Catherine ULiege; Miglior, F et al

in Journal of Dairy Science (2012), 95(1), 410-9

The objective of this research was to estimate genetic parameters of first-lactation body condition score (BCS), milk yield, fat percentage (Fat%), protein percentage (Prot%), somatic cell score (SCS ... [more ▼]

The objective of this research was to estimate genetic parameters of first-lactation body condition score (BCS), milk yield, fat percentage (Fat%), protein percentage (Prot%), somatic cell score (SCS), milk urea nitrogen (MUN), lactose percentage (Lact%), and fat to protein ratio (F:P) using multiple-trait random regression animal models. Changes in covariances between BCS and milk production traits on a daily basis have not been investigated before and could be useful for determining which BCS estimated breeding values (EBV) might be practical for selection in the future. Field staff from Valacta milk recording agency (Sainte-Anne-de-Bellevue, QC, Canada) collected BCS from Quebec herds several times per cow throughout the lactation. Average daily heritabilities and genetic correlations among the various traits were similar to literature values. On an average daily basis, BCS was genetically unfavorably correlated with milk yield (i.e., increased milk yield was associated with lower body condition). The unfavorable genetic correlation between BCS and milk yield became stronger as lactation progressed, but was equivalent to zero for the first month of lactation. Favorable genetic correlations were found between BCS with Prot%, SCS, and Lact% (i.e., greater BCS was associated with greater Prot%, lower SCS, and greater Lact%). These correlations were strongest in early lactation. On an average daily basis, BCS was not genetically correlated with Fat% or MUN, but was negatively correlated with F:P. Furthermore, BCS at 5 and 50 d in milk (DIM) had the most favorable genetic correlations with milk production traits over the lactation (at 5, 50, 150, and 250 DIM). Thus, early lactation BCS EBV shows potential for selection. Regardless, this study showed that the level of association BCS has with milk production traits is not constant over the lactation. Simultaneous selection for both BCS and milk production traits should be considered, mainly due to the unfavorable genetic correlation between BCS with milk yield. [less ▲]

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See detailImputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle.
Mulder, H. A.; Calus, M. P. L.; Druet, Tom ULiege et al

in Journal of Dairy Science (2012), 95(2), 876-89

Genomic selection using 50,000 single nucleotide polymorphism (50k SNP) chips has been implemented in many dairy cattle breeding programs. Cheap, low-density chips make genotyping of a larger number of ... [more ▼]

Genomic selection using 50,000 single nucleotide polymorphism (50k SNP) chips has been implemented in many dairy cattle breeding programs. Cheap, low-density chips make genotyping of a larger number of animals cost effective. A commonly proposed strategy is to impute low-density genotypes up to 50,000 genotypes before predicting direct genomic values (DGV). The objectives of this study were to investigate the accuracy of imputation for animals genotyped with a low-density chip and to investigate the effect of imputation on reliability of DGV. Low-density chips contained 384, 3,000, or 6,000 SNP. The SNP were selected based either on the highest minor allele frequency in a bin or the middle SNP in a bin, and DAGPHASE, CHROMIBD, and multivariate BLUP were used for imputation. Genotypes of 9,378 animals were used, from which approximately 2,350 animals had deregressed proofs. Bayesian stochastic search variable selection was used for estimating SNP effects of the 50k chip. Imputation accuracies and imputation error rates were poor for low-density chips with 384 SNP. Imputation accuracies were higher with 3,000 and 6,000 SNP. Performance of DAGPHASE and CHROMIBD was very similar and much better than that of multivariate BLUP for both imputation accuracy and reliability of DGV. With 3,000 SNP and using CHROMIBD or DAGPHASE for imputation, 84 to 90% of the increase in DGV reliability using the 50k chip, compared with a pedigree index, was obtained. With multivariate BLUP, the increase in reliability was only 40%. With 384 SNP, the reliability of DGV was lower than for a pedigree index, whereas with 6,000 SNP, about 93% of the increase in reliability of DGV based on the 50k chip was obtained when using DAGPHASE for imputation. Using genotype probabilities to predict gene content increased imputation accuracy and the reliability of DGV and is therefore recommended for applications of imputation for genomic prediction. A deterministic equation was derived to predict accuracy of DGV based on imputation accuracy, which fitted closely with the observed relationship. The deterministic equation can be used to evaluate the effect of differences in imputation accuracy on accuracy and reliability of DGV. [less ▲]

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See detailGenomic breeding value estimation using genetic markers, inferred ancestral haplotypes, and the genomic relationship matrix
de Roos, A. P. W.; Schrooten, C.; Druet, Tom ULiege

in Journal of Dairy Science (2011), 94(9), 4708-4714

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See detailEstimating daily yield of major fatty acids from single milking: first approach.
Arnould, Valérie ULiege; NGuyen, N. H.; Froidmont et al

in Journal of Dairy Science (2011, July), 94(E-Suppl. 1), 29

Reducing the frequency of milk recording and the number of recorded samples per test-day could be a solution in order to reduce costs of milk recording. However, this solution leads to decrease also the ... [more ▼]

Reducing the frequency of milk recording and the number of recorded samples per test-day could be a solution in order to reduce costs of milk recording. However, this solution leads to decrease also the accuracy of predicting daily yield. According to the literature, several authors have already worked on this problematic. Unfortunately, some effects used in previous studies are not often available or reliable in used databases. This study was aimed to enlarge these investigations to milk fatty acids (FA) production: saturated FA, mono-unsaturated FA, unsaturated FA, medium-chain FA, and long chain FA and to propose a simple, robust and practical method for estimating accurate daily major FA yield from single milking. To do this, five dairy cows were followed between January 2007 and December 2010. FA contents were predicted by mid-infrared spectrometry. The final database contained 1,440 records. The first step was to ensure that used effects were available in most used databases. According to the availability of data, height models were tested to estimate daily yields from both morning and evening milking. These models were compared on the basis of the coefficient of determination values between estimated and observed daily yields and the mean square error. The proposed models included progressively several effects such as the milk yield, the fat and protein content, some classes of stage in lactation, of month of test or of month of calving. As expected, R² values were higher when these effects are introduced in the model and were comprised between 0.87 and 0.88 when daily yield were estimated from morning milking, and from 0.75 and 0.86 when daily yield were estimated from evening milking. It was concluded that the introduction of these effects did highly improve the daily predictability of all trait yield and can partially replace the milking interval effect. It was also observed that daily yields estimated from evening milkings are less accurate than those estimated from morning milkings. Finally, the applied model will depend on the availability of the data and to the convenience of the applied model to the studied population. Keywords: Milk recording, Fatty acids, prediction [less ▲]

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See detailMid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries
Soyeurt, Hélène ULiege; Dehareng, F.; Gengler, Nicolas ULiege et al

in Journal of Dairy Science (2011), 94(4), 1657-1667

Increasing consumer concern exists over the relationship between food composition and human health. Because of the known effects of fatty acids on human health, the development of a quick, inexpensive ... [more ▼]

Increasing consumer concern exists over the relationship between food composition and human health. Because of the known effects of fatty acids on human health, the development of a quick, inexpensive, and accurate method to directly quantify the fatty acid (FA) composition in milk would be valuable for milk processors to develop a payment system for milk pertinent to their customer requirements and for farmers to adapt their feeding systems and breeding strategies accordingly. The aim of this study was (1) to confirm the ability of mid-infrared spectrometry (MIR) to quantify individual FA content in milk by using an innovative procedure of sampling (i.e., samples were collected from cows belonging to different breeds, different countries, and in different production systems); (2) to compare 6 mathematical methods to develop robust calibration equations for predicting the contents of individual FA in milk; and (3) to test interest in using the FA equations developed in milk as basis to predict FA content in fat without corrections for the slope and the bias of the developed equations. In total, 517 samples selected based on their spectral variability in 3 countries (Belgium, Ireland, and United Kingdom) from various breeds, cows, and production systems were analyzed by gas chromatography (GC). The samples presenting the largest spectral variability were used to calibrate the prediction of FA by MIR. The remaining samples were used to externally validate the 28 FA equations developed. The 6 methods were (1) partial least squares regression (PLS); (2) PLS + repeatability file (REP); (3) first derivative of spectral data + PLS; (4) first derivative + REP + PLS; (5) second derivative of spectral data + PLS; and (6) second derivative + REP + PLS. Methods were compared on the basis of the crossvalidation coefficient of determination (R2cv), the ratio of standard deviation of GC values to the standard error of cross-validation (RPD), and the validation coefficient of determination (R2v). The third and fourth methods had, on average, the highest R2cv, RPD, and R2v. The final equations were built using all GC and the best accuracy was observed for the infrared predictions of C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, C16:0, C18:0, C18:1 trans, C18:1 cis-9, C18:1 cis, and for some groups of FA studied in milk (saturated, monounsaturated, unsaturated, short-chain, medium-chain, and long-chain FA). These equations showed R2cv greater than 0.95. With R2cv equal to 0.85, the MIR prediction of polyunsaturated FA could be used to screen the cow population. As previously published, infrared predictions of FA in fat are less accurate than those developed from FA content in milk (g/dL of milk) and no better results were obtained by using milk FA predictions if no corrections for bias and slope based on reference milk samples with known contents of FA were used. These results indicate the usefulness of equations with R2cv greater than 95% in milk payment systems and the usefulness of equations with R2cv greater than 75% for animal breeding purposes. [less ▲]

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See detailMid-infrared predictions of lactoferrin content in bovine milk
Soyeurt, Hélène ULiege; Bastin, Catherine ULiege; Colinet, Frédéric ULiege et al

in Journal of Dairy Science (2011), 94(E-suppl.1), 714

Lactoferrin (LF) is a glycoprotein present in milk and active in the immune system of cows and humans. Therefore, an inexpensive and rapid analysis to quantify this protein is desirable. A previous study ... [more ▼]

Lactoferrin (LF) is a glycoprotein present in milk and active in the immune system of cows and humans. Therefore, an inexpensive and rapid analysis to quantify this protein is desirable. A previous study reported the potential to quantify LF from the mid-infrared (MIR) spectrometry from 69 milk samples. Through the European RobustMilk project (www.robustmilk.eu), 3,606 milk samples were collected in Belgium, Ireland, and Scotland from individual cows and analyzed using a MIR MilkoScanFT6000 spectrometer. Milk LF content was quantified using ELISA in duplicate. Average ELISA data with a CV lower than 5% were used. After the detection of spectral and ELISA outliers, the calibration set contained 2,499 samples. An equation to predict LF content from MIR was developed using partial least squared regression. A first derivative pre-treatment of spectra was used to correct the baseline drift. To improve the repeatability of the spectral data, a file which contained the spectra of samples analyzed on 5 spectrometers was used during the calibration. The lactoferrin mean was 159.28 mg/l of milk with a SD of 97.21 mg/l of milk. The calibration (C) coefficient of determination (R2) was equal to 0.73 with a standard error (SE) of calibration of 50.54 mg/l of milk. A cross-validation (CV) was used to assess the robustness of the equation. R2 CV was 0.72 with a SE-CV of 51.16 mg/l of milk. An external validation (V) was conducted on 150 milk samples collected in Belgium. The SE of prediction (SEP) was 59.17 mg/L of milk. The similarity between R2 C and R2CV as well as between SE-C and SE-CV and between SE-CV and SEP confirms the equations developed are robust. The correlation between predicted and measured LF values was 0.71. This lower value compared with the one obtained from the calibration set (0.85) could be explained by the low ELISA reproducibility (16.24% ± 25.51%). If the developed equation is used to clean the validation data set, a total of 16 samples can be deleted. The validation coefficient for these 134 samples increased to 0.82. From these results, the developed equation could be used for screening the dairy cow population for breeding purposes. [less ▲]

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See detailPhenotypic and genetic variability of production traits and milk fatty acid contents across days in milk for Walloon Holstein first-parity cows.
Bastin, Catherine ULiege; Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege

in Journal of Dairy Science (2011), 94(8), 4152-63

The objective of this study was to assess the phenotypic and genetic variability of production traits and milk fatty acid (FA) contents throughout lactation. Genetic parameters for milk, fat, and protein ... [more ▼]

The objective of this study was to assess the phenotypic and genetic variability of production traits and milk fatty acid (FA) contents throughout lactation. Genetic parameters for milk, fat, and protein yields, fat and protein contents, and 19 groups and individual FA contents in milk were estimated for first-parity Holstein cows in the Walloon Region of Belgium using single-trait, test-day animal models and random regressions. Data included 130,285 records from 26,166 cows in 531 herds. Heritabilities indicated that de novo synthesized FA were under stronger genetic control than FA originating from the diet and from body fat mobilization. Estimates for saturated short- and medium-chain individual FA ranged from 0.35 for C4:0 to 0.44 for C8:0, whereas those for monounsaturated long-chain individual FA were lower (around 0.18). Moreover, de novo synthesized FA were more heritable in mid to late lactation. Approximate daily genetic correlations among traits were calculated as correlations between daily breeding values for days in milk between 5 and 305. Averaged daily genetic correlations between milk yield and FA contents did not vary strongly among FA (around -0.35) but they varied strongly across days in milk, especially in early lactation. Results indicate that cows selected for high milk yield in early lactation would have lower de novo synthesized FA contents in milk but a slightly higher content of C18:1 cis-9, indicating that such cows might mobilize body fat reserves. Genetic correlations among FA emphasized the combination of FA according to their origin: contents in milk of de novo FA were highly correlated with each other (from 0.64 to 0.99). Results also showed that genetic correlations between C18:1 cis-9 and other FA varied strongly during the first 100 d in milk and reinforced the statement that the release of long-chain FA inhibits FA synthesis in the mammary gland while the cow is in negative energy balance. Finally, results showed that the FA profile in milk changed during the lactation phenotypically and genetically, emphasizing the relationship between the physiological status of cow and milk composition. [less ▲]

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See detailEffect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations.
Dassonneville, Romain; Brondum, R. F.; Druet, Tom ULiege et al

in Journal of Dairy Science (2011), 94(7), 3679-86

The purpose of this study was to investigate the imputation error and loss of reliability of direct genomic values (DGV) or genomically enhanced breeding values (GEBV) when using genotypes imputed from a ... [more ▼]

The purpose of this study was to investigate the imputation error and loss of reliability of direct genomic values (DGV) or genomically enhanced breeding values (GEBV) when using genotypes imputed from a 3,000-marker single nucleotide polymorphism (SNP) panel to a 50,000-marker SNP panel. Data consisted of genotypes of 15,966 European Holstein bulls from the combined EuroGenomics reference population. Genotypes with the low-density chip were created by erasing markers from 50,000-marker data. The studies were performed in the Nordic countries (Denmark, Finland, and Sweden) using a BLUP model for prediction of DGV and in France using a genomic marker-assisted selection approach for prediction of GEBV. Imputation in both studies was done using a combination of the DAGPHASE 1.1 and Beagle 2.1.3 software. Traits considered were protein yield, fertility, somatic cell count, and udder depth. Imputation of missing markers and prediction of breeding values were performed using 2 different reference populations in each country: either a national reference population or a combined EuroGenomics reference population. Validation for accuracy of imputation and genomic prediction was done based on national test data. Mean imputation error rates when using national reference animals was 5.5 and 3.9% in the Nordic countries and France, respectively, whereas imputation based on the EuroGenomics reference data set gave mean error rates of 4.0 and 2.1%, respectively. Prediction of GEBV based on genotypes imputed with a national reference data set gave an absolute loss of 0.05 in mean reliability of GEBV in the French study, whereas a loss of 0.03 was obtained for reliability of DGV in the Nordic study. When genotypes were imputed using the EuroGenomics reference, a loss of 0.02 in mean reliability of GEBV was detected in the French study, and a loss of 0.06 was observed for the mean reliability of DGV in the Nordic study. Consequently, the reliability of DGV using the imputed SNP data was 0.38 based on national reference data, and 0.48 based on EuroGenomics reference data in the Nordic validation, and the reliability of GEBV using the imputed SNP data was 0.41 based on national reference data, and 0.44 based on EuroGenomics reference data in the French validation. [less ▲]

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See detailThe use of mid-infrared spectrometry to predict body energy status of Holstein cows
McParland, Sinead; Banos, Giorgios; Wall, Eileen et al

in Journal of Dairy Science (2011), 94

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See detailShort communication: estimates of genetic parameters of body condition score in the first 3 lactations using a random regression animal model.
Loker, S.; Bastin, Catherine ULiege; Miglior, F. et al

in Journal of Dairy Science (2011), 94(7), 3693-9

The objective of this research was to estimate the genetic parameters of body condition score (BCS) in the first 3 lactations in Canadian Holstein dairy cattle using a multiple-lactation random regression ... [more ▼]

The objective of this research was to estimate the genetic parameters of body condition score (BCS) in the first 3 lactations in Canadian Holstein dairy cattle using a multiple-lactation random regression animal model. Field staff from Valacta milk recording agency (Sainte-Anne-de-Bellevue, QC, Canada) collected BCS from Quebec herds several times throughout each lactation. Approximately 32,000, 20,000, and 11,000 first-, second-, and third-parity BCS were analyzed, respectively, from a total of 75 herds. Body condition score was a moderately heritable trait over the lactation for parity 1, 2, and 3, with average daily heritabilities of 0.22, 0.26, and 0.30, respectively. Daily heritability ranged between 0.14 and 0.26, 0.19 and 0.28, and 0.24 and 0.33 for parity 1, 2, and 3, respectively. Genetic variance of BCS increased with days in milk within lactations. The low genetic variance in early lactation suggests that the evolution of the ability to mobilize tissue reserves in early lactation provided cattle with a major advantage, and is, therefore, somewhat conserved. The increasing genetic variance suggests that more genetic differences were related to how well cows recovered from the negative energy balance state. More specifically, increasing genetic variation as lactation progressed could be a reflection of genetic differences in the ability of cows to efficiently control the rate of mobilization of tissue reserves, which would not be crucial in early lactation. The shape of BCS curves was similar across parities. From first to third parity, differences included the progressively deeper nadir and faster rate of recovery of condition. Daily genetic correlations between parities were calculated from 5 to 305 DIM, and were summed and divided by 301 to obtain average daily genetic correlations. The average daily genetic correlations were 0.84 between parity 1 and 2, 0.83 between parity 1 and 3, and 0.86 between parity 2 and 3. Although not 1, these genetic correlations are still strong, so much of the variation observed in BCS was controlled by the same genes for each of the first 3 lactations. If a genetic evaluation for BCS is developed, regular collection of first-lactation BCS records should be sufficient for genetic evaluation. [less ▲]

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See detailIs there value in maintaining small populations ? Example of the Dual-Purpose Belgian Blue breed.
Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege; Bastin, Catherine ULiege et al

in Journal of Dairy Science (2011), 94(E-suppl.1), 664

Current status of thinking on genomic selection in dairy cattle is mostly major breed centric (e.g., Holstein) and only for traditional traits (e.g., milk yields). Once you depart from this, it becomes ... [more ▼]

Current status of thinking on genomic selection in dairy cattle is mostly major breed centric (e.g., Holstein) and only for traditional traits (e.g., milk yields). Once you depart from this, it becomes obvious that different, often related, issues appear (e.g., lack of large training populations, need for expensive recording of new phenotypes). Also, there is an urgent need to rethink issues that are important for sustainability of dairy production (e.g., added value foods, animal robustness). In this context, small populations (breeds/lines) could represent a potential source of extra information to justify their maintenance. As marker densities increase, efficient dissection of different selection histories of divergent breeds or lines, potentially identifying pockets of unexploited variability will increase. A current example from the Belgian (Walloon) perspective is the Dual Purpose (DP) line of the Belgian Blue Breed (BBB), with presently around 4500 breeding females, for historical reason of which only 1500 have good pedigrees, and which is present in Belgium and northern France. Recent research, done on this line, showed its tendency to produce less saturated milk fat and to have better fertility. Results indicated that it could stay competitive in specific markets, especially because of largely increased meat value. Currently, the myostatin mutation is largely used for breeding purposes. To assess the genetic diversity of the breed, recently, over 200 genotypes (SNP50K) for nearly all breeding bulls of the last 20 years became available. HD genotypes should be available in the near future, also allowing to access selection history of this breed as being in between the 2 extreme breeds: Beef BBB (with which it shares a recent history) and Holstein-Friesian (which is related through its geographic proximity over centuries). Finally, genomic selection for DP-BBB will need to consider a single step type approach without the need of reference population and potentially relying heavily on SNP3K of cows, also with the objective to recreate relationships between animals of interest. [less ▲]

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See detailShort communication: Genetic variation of saturated fatty acids in Holsteins in the Walloon region of Belgium
Arnould, Valérie ULiege; Hammami, Hedi ULiege; Soyeurt, Hélène ULiege et al

in Journal of Dairy Science (2010), 93

Random regression test-day models using Legendre polynomials are commonly used for the estimation of genetic parameters and genetic evaluation for test-day milk production traits. However, some ... [more ▼]

Random regression test-day models using Legendre polynomials are commonly used for the estimation of genetic parameters and genetic evaluation for test-day milk production traits. However, some researchers have reported that these models present some undesirable properties such as the overestimation of variances at the edges of lactation. Describing genetic variation of saturated fatty acids expressed in milk fat might require the testing of different models. Therefore, 3 different functions were used and compared to take into account the lactation curve: (1) Legendre polynomials with the same order as currently applied for genetic model for production traits; 2) linear splines with 10 knots; and 3) linear splines with the same 10 knots reduced to 3 parameters. The criteria used were Akaike’s information and Bayesian information criteria, percentage square biases, and log-likelihood function. These criteria indentified Legendre polynomials and linear splines with 10 knots reduced to 3 parameters models as the most useful. Reducing more complex models using eigenvalues seemed appealing because the resulting models are less time demanding and can reduce convergence difficulties, because convergence properties also seemed to be improved. Finally, the results showed that the reduced spline model was very similar to the Legendre polynomials model. [less ▲]

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See detailFeasibility of a genetic evaluation for milk fatty acids in dairy cattle
Soyeurt, Hélène ULiege; Arnould, Valérie ULiege; Vanderick, Sylvie ULiege et al

in Journal of Dairy Science (2010, July), 88/93/89(E-Suppl. 2/ E-Suppl. 1/ E-Suppl. 1), 744

Recent development of equations based on mid-infrared (MIR) spectrometry for the prediction of milk fatty acid (FA) contents allows their measurement on a large scale during performance recording. The ... [more ▼]

Recent development of equations based on mid-infrared (MIR) spectrometry for the prediction of milk fatty acid (FA) contents allows their measurement on a large scale during performance recording. The objective was to show that a genetic evaluation for milk FA in dairy cattle is feasible in the Walloon region of Belgium and to report first results. Estimated breeding values (EBV) and associated reliabilities (REL) were computed using a multi-trait test-day animal model similar to the one used for the routine genetic evaluation for yield traits. Studied traits were first lactation test-day milk, fat and protein yields, fat (FAT) and protein contents, and content of saturated fatty acids in milk (g/100g of milk, SAT). More than 6,700,000 records were available for common production and content traits and 194,000 records were used for SAT. Used variance components were estimated using REML. The average SAT content was 2.79% with a standard deviation (SD) of 0.50%. A total of 1,707 Holstein bulls used in Walloon Region had REL superior to 0.49 for all studied traits. REL for SAT ranged from 0.53 to 0.99. A total of 1,217 bulls had REL superior to 0.74. SD of EBV for SAT was 0.20%. The maximum and minimum SAT EBV values were 0.89% and -0.69%, respectively. In order to have a direct measure of the part of FAT that is not due to SAT, a new trait (dSAT) was post-evaluated and defined as difference between expected SAT EBV for a given FAT EBV and the estimated EBV for SAT. This new trait can be assumed to be a direct predictor of the content of unsaturated fatty acids in fat. The interest is that this trait cannot be accurately predicted directly by MIR. The maximum and minimum EBV for dSAT for the 1,707 bulls were -0.28% and 0.24%, respectively. Based on these results, a genetic evaluation for milk fatty acids is feasible. In the bull population used recently, a genetic variability for dSAT exists and could be used to improve the milk fat composition. [less ▲]

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