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Vincent Ducrocq - One of the best experts on this subject based on the ideXlab platform.
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Estimation of Genetic Parameters for test day records of dairy traits in the first three lactations
Genetics Selection Evolution, 2005Co-Authors: Tom Druet, Florence Jaffrezic, Vincent DucrocqAbstract:Application of test-day models for the Genetic evaluation of dairy populations requires the solution of large mixed model equations. The size of the (co)variance matrices required with such models can be reduced through the use of its first eigenvectors. Here, the first two eigenvectors of (co)variance matrices estimated for dairy traits in first lactation were used as covariables to jointly estimate Genetic Parameters of the first three lactations. These eigenvectors appear to be similar across traits and have a biological interpretation, one being related to the level of production and the other to persistency. Furthermore, they explain more than 95% of the total Genetic variation. Variances and heritabilities obtained with this model were consistent with previous studies. High correlations were found among production levels in different lactations. Persistency measures were less correlated. Genetic correlations between second and third lactations were close to one, indicating that these can be considered as the same trait. Genetic correlations within lactation were high except between extreme parts of the lactation. This study shows that the use of eigenvectors can reduce the rank of (co)variance matrices for the test-day model and can provide consistent Genetic Parameters.
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modeling lactation curves and estimation of Genetic Parameters for first lactation test day records of french holstein cows
Journal of Dairy Science, 2003Co-Authors: Tom Druet, Florence Jaffrezic, Didier Boichard, Vincent DucrocqAbstract:Abstract Several functions were used to model the fixed part of the lactation curve and Genetic Parameters of milk test-day records to estimate using French Holstein data. Parametric curves (Legendre polynomials, Ali-Schaeffer curve, Wilmink curve), fixed classes curves (5-d classes), and regression splines were tested. The latter were appealing because they adjusted the data well, were relatively insensitive to outliers, were flexible, and resulted in smooth curves without requiring the estimation of a large number of Parameters. Genetic Parameters were estimated with an Average Information REML algorithm where the average information matrix and the first derivatives of the likelihood functions were pooled over 10 samples. This approach made it possible to handle larger data sets. The residual variance was modeled as a quadratic function of days in milk. Quartic Legendre polynomials were used to estimate (co)variances of random effects. The estimates were within the range of most other studies. The greatest Genetic variance was in the middle of the lactation while residual and permanent environmental variances mostly decreased during the lactation. The resulting heritability ranged from 0.15 to 0.40. The Genetic correlation between the extreme parts of the lactation was 0.35 but Genetic correlations were higher than 0.90 for a large part of the lactation. The use of the pooling approach resulted in smaller standard errors for the Genetic Parameters when compared to those obtained with a single sample.
Tom Druet - One of the best experts on this subject based on the ideXlab platform.
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Estimation of Genetic Parameters for test day records of dairy traits in the first three lactations
Genetics Selection Evolution, 2005Co-Authors: Tom Druet, Florence Jaffrezic, Vincent DucrocqAbstract:Application of test-day models for the Genetic evaluation of dairy populations requires the solution of large mixed model equations. The size of the (co)variance matrices required with such models can be reduced through the use of its first eigenvectors. Here, the first two eigenvectors of (co)variance matrices estimated for dairy traits in first lactation were used as covariables to jointly estimate Genetic Parameters of the first three lactations. These eigenvectors appear to be similar across traits and have a biological interpretation, one being related to the level of production and the other to persistency. Furthermore, they explain more than 95% of the total Genetic variation. Variances and heritabilities obtained with this model were consistent with previous studies. High correlations were found among production levels in different lactations. Persistency measures were less correlated. Genetic correlations between second and third lactations were close to one, indicating that these can be considered as the same trait. Genetic correlations within lactation were high except between extreme parts of the lactation. This study shows that the use of eigenvectors can reduce the rank of (co)variance matrices for the test-day model and can provide consistent Genetic Parameters.
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modeling lactation curves and estimation of Genetic Parameters for first lactation test day records of french holstein cows
Journal of Dairy Science, 2003Co-Authors: Tom Druet, Florence Jaffrezic, Didier Boichard, Vincent DucrocqAbstract:Abstract Several functions were used to model the fixed part of the lactation curve and Genetic Parameters of milk test-day records to estimate using French Holstein data. Parametric curves (Legendre polynomials, Ali-Schaeffer curve, Wilmink curve), fixed classes curves (5-d classes), and regression splines were tested. The latter were appealing because they adjusted the data well, were relatively insensitive to outliers, were flexible, and resulted in smooth curves without requiring the estimation of a large number of Parameters. Genetic Parameters were estimated with an Average Information REML algorithm where the average information matrix and the first derivatives of the likelihood functions were pooled over 10 samples. This approach made it possible to handle larger data sets. The residual variance was modeled as a quadratic function of days in milk. Quartic Legendre polynomials were used to estimate (co)variances of random effects. The estimates were within the range of most other studies. The greatest Genetic variance was in the middle of the lactation while residual and permanent environmental variances mostly decreased during the lactation. The resulting heritability ranged from 0.15 to 0.40. The Genetic correlation between the extreme parts of the lactation was 0.35 but Genetic correlations were higher than 0.90 for a large part of the lactation. The use of the pooling approach resulted in smaller standard errors for the Genetic Parameters when compared to those obtained with a single sample.
J.a.m. Van Arendonk - One of the best experts on this subject based on the ideXlab platform.
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From Calibration of a FTIR Model for Milk Fat Composition to the Estimation of Genetic Parameters
2010Co-Authors: M.j.m. Rutten, Henk Bovenhuis, J.a.m. Van ArendonkAbstract:Fourier transform infrared (IR) spectroscopy is a suitable method to determine bovine milk fat composition (Soyeurt et al., 2006; Rutten et al., 2009). In this way, fat composition data for a large number of animals can be generated. Hence, IR determined fat composition data could be used by dairy breeding organizations to estimate Genetic Parameters and breeding values. Genetic Parameters, i.e. specifically Genetic correlations between observations determined by gas chromatography (GC) and predicted by means of IR, are required to reveal the potential Genetic gain that can be achieved from selection on IR predicted fat composition instead of observations determined by GC, which is expensive and time consuming. Rutten et al. (2009) showed that the number of observations used for calibration of an IR prediction model for fat composition is strongly related to accuracy of prediction. The relation between the number of calibration samples, and therewith accuracy of prediction, and the accuracy of estimated Genetic Parameters, however, needs to be established. A guideline with respect to the number of observations required for calibration of an IR prediction model for fat composition and its effect on estimated Genetic Parameters is indispensable for animal breeding organizations if estimation of Genetic Parameters on IR predicted fat composition is targeted.
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Genetic Parameters for major milk proteins in dutch holstein friesians
Journal of Dairy Science, 2009Co-Authors: G C B Schopen, H Bovenhuis, J M L Heck, M H P W Visker, H J F Van Valenberg, J.a.m. Van ArendonkAbstract:Abstract The objective of this study was to estimate Genetic Parameters for major milk proteins. One morning milk sample was collected from 1,940 first-parity Holstein-Friesian cows in February or March 2005. Each sample was analyzed with capillary zone electrophoresis to determine the relative concentrations of the 6 major milk proteins. The results show that there is considerable Genetic variation in milk protein composition. The intraherd heritabilities for the relative protein concentrations were high and ranged from 0.25 for β-casein to 0.80 for β-lactoglobulin. The intraherd heritability for the summed whey fractions (0.71) was higher than that for the summed casein fractions (0.41). Further, there was relatively more variation in the summed whey fraction (coefficient of variation was 11% and standard deviation was 1.23) compared with the summed casein fraction (coefficient of variation was 2% and standard deviation was 1.72). For the caseins and α-lactalbumin, the proportion of phenotypic variation explained by herd was approximately 14%. For β-lactoglobulin, the proportion of phenotypic variation explained by herd was considerably lower (5%). Eighty percent of the Genetic correlations among the relative contributions of the major milk proteins were between −0.38 and +0.45. The Genetic correlations suggest that it is possible to change the relative proportion of caseins in milk. Strong negative Genetic correlations were found for β-lactoglobulin with the summed casein fractions (−0.76), and for β-lactoglobulin with casein index (−0.98). This study suggests that there are opportunities to change the milk protein composition in the cow's milk using selective breeding.
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Genetic Parameters for milk urea nitrogen in relation to milk production traits
Journal of Dairy Science, 2007Co-Authors: W M Stoop, H Bovenhuis, J.a.m. Van ArendonkAbstract:Abstract The aim of this study was to estimate Genetic Parameters for test-day milk urea nitrogen (MUN) and its relationships with milk production traits. Three test-day morning milk samples were collected from 1,953 Holstein-Friesian heifers located on 398 commercial herds in the Netherlands. Each sample was analyzed for somatic cell count, net energy concentration, MUN, and the percentage of fat, protein, and lactose. Genetic Parameters were estimated using an animal model with covariates for days in milk and age at first calving, fixed effects for season of calving and effect of test or proven bull, and random effects for herd-test day, animal, permanent environment, and error. Coefficient of variation for MUN was 33%. Estimated heritability for MUN was 0.14. Phenotypic correlation of MUN with each of the milk production traits was low. The Genetic correlation was close to zero for MUN and lactose percentage (−0.09); was moderately positive for MUN and net energy concentration of milk (0.19), fat yield (0.41), protein yield (0.38), lactose yield (0.22), and milk yield (0.24), and percentage of fat (0.18), and percentage of protein (0.27); and was high for MUN and somatic cell score (0.85). Herd-test day explained 58% of the variation in MUN, which suggests that management adjustments at herd-level can reduce MUN. This study shows that it is possible to influence MUN by herd practice and by Genetic selection.
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estimation of Genetic Parameters for fat deposition and carcass traits in broilers
Poultry Science, 2004Co-Authors: Saeed Zerehdaran, J.a.m. Van Arendonk, A L J Vereijken, E H Van Der WaaijAbstract:Abdominal and subcutaneous fat are regarded as the main sources of waste in the slaughterhouse. Fat stored intramuscularly is regarded a favorite trait related to meat quality. The objective of current study was to estimate Genetic Parameters for fat deposition in the 3 different parts of body and their relationships with other carcass traits. Traits were recorded for 1,752 females and 1,526 males from a meat-type chicken line. Heritability estimates for abdominal fat percentage, skin percentage as a measure of subcutaneous fat, and intramuscular fat percentage were 0.71, 0.24, and 0.08, respectively. Heritabilities of the other carcass traits were moderate to high (0.28 to 0.73). There was a high Genetic correlation between abdominal fat weight and skin weight (0.54), whereas the Genetic correlation between abdominal fat weight and intramuscular fat percentage was almost zero (0.02). The BW at 7 wk showed a positive Genetic correlation with fat production traits, which were high for intramuscular fat percentage (0.87) and moderate for skin percentage (0.17) and abdominal fat percentage (0.13). Therefore carcass traits could be improved by selection for increased breast muscle and reduced abdominal fat without decreased intramuscular fat.
E F Knol - One of the best experts on this subject based on the ideXlab platform.
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Genetic Parameters for carcass composition and pork quality estimated in a commercial production chain
Journal of Animal Science, 2005Co-Authors: H J Van Wijk, D J G Arts, J O Matthews, M Webster, B J Ducro, E F KnolAbstract:Breeding goals in pigs are subject to change and are directed much more toward retail carcass yield and meat quality because of the high economic value of these traits. The objective of this study was to estimate Genetic Parameters of growth, carcass, and meat quality traits. Carcass components included ham and loin weights as primal cuts, which were further dissected into boneless subprimal cuts. Meat quality traits included pH, drip loss, purge, firmness, and color and marbling of both ham and loin. Phenotypic measurements were collected on a commercial crossbred pig population (n = 1,855). Genetic Parameters were estimated using REML procedures applied to a bivariate animal model. Heritability estimates for carcass traits varied from 0.29 to 0.51, with 0.39 and 0.51 for the boneless subprimals of ham and loin, respectively. Heritability estimates for meat quality traits ranged from 0.08 to 0.28, with low estimates for the water holding capacity traits and higher values for the color traits: Minolta b*(0.14), L* (0.15), a* (0.24), and Japanese color scale (0.25). Heritability estimates differed for marbling of ham (0.14) and loin (0.31). Neither backfat nor ADG was correlated with loin depth (rg = 0.0), and their mutual Genetic correlation was 0.27. Loin primal was moderately correlated with ham primal (rg = 0.31) and more strongly correlated with boneless ham (rg = 0.58). Backfat was negatively correlated with (sub)primal cut values. Average daily gain was unfavorably correlated with subprimals and with most meat quality characteristics measured. Genetic correlations among the color measurements and water-holding capacity traits were high (average rg = 0.70), except for Minolta a* (average rg = 0.17). The estimated Genetic Parameters indicate that meat quality and valuable cut yields can be improved by Genetic selection. The estimated Genetic Parameters make it possible to predict the response to selection on performance, carcass, and meat quality traits and to design an effective breeding strategy fitting pricing systems based on retail carcass and quality characteristics
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estimates of Genetic Parameters for reproduction traits at different parities in dutch landrace pigs
Livestock Production Science, 2001Co-Authors: E H A T Hanenberg, E F Knol, J W M MerksAbstract:Data from Dutch Landrace sows were used to estimate Genetic Parameters for reproduction traits in the first six parities. Analyses were performed with DFREML using a model with equal design and herd-year-season of first parity as a fixed effect. Estimates of Genetic Parameters were calculated for different traits and parities using data from 58 194 sows. Heritabilities were found to be low for farrowing after first insemination (FFI), mothering ability (MA) and number of still born piglets (NSB); moderate for number of piglets born in total (NBT) or alive (NBA) and interval from weaning to first insemination (IWI); and high for gestation length (GL) and age at first insemination (AFI). Heritability increased slightly with parity number for NBT and NBA, increased markedly for NSB and MA, and decreased for IWI. Genetic correlations between the same traits measured in different parities were close to unity for parities higher than 2, for all traits. Genetic correlations below 0.70 were found between parity 1 and higher parities, for NBT, NBA, NSB, MA and FFI. Undesirable correlations were found between NBT and NSB (0.53) and NBT and MA (0.49). Indirect selection on MA would be possible using GL (r 5 0.40). IWI was positively correlated with AFI (0.31). It is concluded that selection on litter size, g piglet mortality and also number of litters per year would be worthwhile. © 2001 Elsevier Science B.V. All rights reserved. ) berg). influencing the number of pigs weaned per sow per
Florence Jaffrezic - One of the best experts on this subject based on the ideXlab platform.
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Estimation of Genetic Parameters for test day records of dairy traits in the first three lactations
Genetics Selection Evolution, 2005Co-Authors: Tom Druet, Florence Jaffrezic, Vincent DucrocqAbstract:Application of test-day models for the Genetic evaluation of dairy populations requires the solution of large mixed model equations. The size of the (co)variance matrices required with such models can be reduced through the use of its first eigenvectors. Here, the first two eigenvectors of (co)variance matrices estimated for dairy traits in first lactation were used as covariables to jointly estimate Genetic Parameters of the first three lactations. These eigenvectors appear to be similar across traits and have a biological interpretation, one being related to the level of production and the other to persistency. Furthermore, they explain more than 95% of the total Genetic variation. Variances and heritabilities obtained with this model were consistent with previous studies. High correlations were found among production levels in different lactations. Persistency measures were less correlated. Genetic correlations between second and third lactations were close to one, indicating that these can be considered as the same trait. Genetic correlations within lactation were high except between extreme parts of the lactation. This study shows that the use of eigenvectors can reduce the rank of (co)variance matrices for the test-day model and can provide consistent Genetic Parameters.
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modeling lactation curves and estimation of Genetic Parameters for first lactation test day records of french holstein cows
Journal of Dairy Science, 2003Co-Authors: Tom Druet, Florence Jaffrezic, Didier Boichard, Vincent DucrocqAbstract:Abstract Several functions were used to model the fixed part of the lactation curve and Genetic Parameters of milk test-day records to estimate using French Holstein data. Parametric curves (Legendre polynomials, Ali-Schaeffer curve, Wilmink curve), fixed classes curves (5-d classes), and regression splines were tested. The latter were appealing because they adjusted the data well, were relatively insensitive to outliers, were flexible, and resulted in smooth curves without requiring the estimation of a large number of Parameters. Genetic Parameters were estimated with an Average Information REML algorithm where the average information matrix and the first derivatives of the likelihood functions were pooled over 10 samples. This approach made it possible to handle larger data sets. The residual variance was modeled as a quadratic function of days in milk. Quartic Legendre polynomials were used to estimate (co)variances of random effects. The estimates were within the range of most other studies. The greatest Genetic variance was in the middle of the lactation while residual and permanent environmental variances mostly decreased during the lactation. The resulting heritability ranged from 0.15 to 0.40. The Genetic correlation between the extreme parts of the lactation was 0.35 but Genetic correlations were higher than 0.90 for a large part of the lactation. The use of the pooling approach resulted in smaller standard errors for the Genetic Parameters when compared to those obtained with a single sample.