Prediction accuracy of direct and indirect approaches, and their relationships with prediction ability of calibration models.

Milk infrared spectra are routinely used for phenotyping traits of interest through links developed between the traits and spectra. Predicted individual traits are then used in genetic analyses for estimated breeding value (EBV) or for phenotypic predictions using a single-trait mixed model; this approach is referred to as indirect prediction (IP). An alternative approach [direct prediction (DP)] is a direct genetic analysis of (a reduced dimension of) the spectra using a multitrait model to predict multivariate EBV of the spectral components and, ultimately, also to predict the univariate EBV or phenotype for the traits of interest. We simulated 3 traits under different genetic (low: 0.10 to high: 0.90) and residual (zero to high: ±0.90) correlation scenarios between the 3 traits and assumed the first trait is a linear combination of the other 2 traits. The aim was to compare the IP and DP approaches for predictions of EBV and phenotypes under the different correlation scenarios. We also evaluated relationships between performances of the 2 approaches and the accuracy of calibration equations. Moreover, the effect of using different regression coefficients estimated from simulated phenotypes (ßp), true breeding values (ßg), and residuals (ßr) on performance of the 2 approaches were evaluated. The simulated data contained 2,100 parents (100 sires and 2,000 cows) and 8,000 offspring (4 offspring per cow). Of the 8,000 observations, 2,000 were randomly selected and used to develop links between the first and the other 2 traits using partial least square (PLS) regression analysis. The different PLS regression coefficients, such as ßp, ßg, and ßr, were used in subsequent predictions following the IP and DP approaches. We used BLUP analyses for the remaining 6,000 observations using the true (co)variance components that had been used for the simulation. Accuracy of prediction (of EBV and phenotype) was calculated as a correlation between predicted and true values from the simulations. The results showed that accuracies of EBV prediction were higher in the DP than in the IP approach. The reverse was true for accuracy of phenotypic prediction when using ßp but not when using ßg and ßr, where accuracy of phenotypic prediction in the DP was slightly higher than in the IP approach. Within the DP approach, accuracies of EBV when using ßg were higher than when using ßp only at the low genetic correlation scenario. However, we found no differences in EBV prediction accuracy between the ßp and ßg in the IP approach. Accuracy of the calibration models increased with an increase in genetic and residual correlations between the traits. Performance of both approaches increased with an increase in accuracy of the calibration models. In conclusion, the DP approach is a good strategy for EBV prediction but not for phenotypic prediction, where the classical PLS regression-based equations or the IP approach provided better results.

An attempt at predicting blood ß-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle.

Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood ß-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross-validation for untransformed blood BHB were from 0.21 to 0.32, whereas that for the log-transformed BHB were from 0.31 to 0.38. The corresponding estimates in validation were from 0.29 to 0.37 and 0.21 to 0.43, respectively, for untransformed and logarithmic BHB. Contrary to expectation, slightly better predictions of BHB were found when univariate variance structure was used (IP) than when multivariate covariance structures were used (DP). Conclusive remarks on the importance of keeping spectral data in multivariate form for prediction of phenotypes may be found in data sets where the trait of interest has strong relationships with spectral variables.

Genetic contributions and their optimization.

Genetic contributions were first formalized in 1958 by James and McBride (Journal of Genetics, 56, 55-62) and have since been shown to provide a unifying framework for theories of gain and inbreeding. As such they have underpinned the development of methods that provide the most effective combination of maximizing gain whilst managing inbreeding and loss of genetic variation. It is shown how this optimum contribution technology can be developed from theory and adapted to provide practical selection protocols for a wide variety of situations including overlapping generations and multistage selection. The natural development of the theory to incorporate genomic selection and genomic control of inbreeding is also shown.

Grazing season and forage type influence goat milk composition and rennet coagulation properties.

Two different types of pasture (cultivated and rangeland) and 2 different hay qualities (high and low quality) were examined for their effects on goat milk composition and rennet coagulation properties. Furthermore, the effect of dietary treatments in both the early and late grazing season was studied. As lactation stage is known to influence milk composition, the goats in the early and late grazing season were in the same lactation stage at the start of the experiment. The milk composition was influenced both by dietary treatment and season. Milk from goats on pasture was superior to those on hay by containing a higher content of protein and casein, and the goats on cultivated pasture had the highest milk yield. Casein composition was significantly influenced by forage treatment. Goats grazing on cultivated pasture had higher contents of αs1-casein and also of κ-casein compared with the other treatments, whereas goats grazing on rangeland had the highest content of ß-casein. Factors such as milk yield, casein micelle size, αs2-casein, and calcium content were reduced in late compared with early season. More favorable rennet coagulation properties were achieved in milk from the early grazing season, with shorter firming time and higher curd firmness compared with milk from the late grazing season, but the firming time and curd firmness were not prominently influenced by forage treatment. The content of αs2-casein and calcium in the milk affected the firming time and the curd firmness positively. The influence of season and forage treatment on especially milk yield, casein content, and rennet coagulation properties is of economic importance for both the dairy industry and goat milk farmers.

Genetic components of milk Fourier-transform infrared spectra used to predict breeding values for milk composition and quality traits in dairy goats.

The usual practice today is that milk component phenotypes are predicted using Fourier-transform infrared (FTIR) spectra and they are then, together with pedigree information, used in BLUP for calculation of individual estimated breeding values. Here, this is referred to as the indirect prediction (IP) approach. An alternative approach-a direct prediction (DP) method-is proposed, where genetic analyses are directly conducted on the milk FTIR spectral variables. Breeding values of all derived milk traits (protein, fat, fatty acid composition, and coagulation properties, among others) can then be predicted as traits correlated only to the genetic information of the spectra. For the DP, no need exists to predict the phenotypes before calculating breeding values for each of the traits-the genetic analysis is done once for the spectra, and is applicable to all traits derived from the spectra. The aim of the study was to compare the effects of DP and IP of milk composition and quality traits on prediction error variance (PEV) and genetic gain. A data set containing 27,927 milk FTIR spectral observations and milk composition phenotypes (fat, lactose, and protein) belonging to 14,869 goats of 271 herds was used for training and evaluating models. Partial least squares regression was used for calibrating prediction models for fat, protein, and lactose percentages. Restricted maximum likelihood was used to estimate variance components of the spectral variables after principal components analysis was applied to reduce the spectral dimension. Estimated breeding values were predicted for fat, lactose, and protein percentages using DP and IP methods. The DP approach reduced the mean PEV by 3.73, 4.07, and 7.04% for fat, lactose, and protein percentages, respectively, compared with the IP method. Given the reduction in PEV, relative genetic gains were 2.99, 2.78, and 4.85% for fat, lactose, and protein percentages, respectively. We concluded that more accurate estimated breeding values could be found using genetic components of milk FTIR spectra compared with single-trait animal model analyses on phenotypes predicted from the spectra separately. The potential and application is not only limited to milk FTIR spectra, but could also be extended to any spectroscopy techniques implemented in other species and for other traits.

Genetic and environmental information in goat milk Fourier transform infrared spectra.

Fourier transform infrared (FTIR) spectroscopy is often used in prediction of major milk components in genetic evaluation of dairy animals. Until now genetic variability of goat milk FTIR spectra has only been known indirectly through their contribution to the major milk components. In this study, genetic and environmental components of goat milk FTIR spectra were examined directly. A data set containing 83,858 milk FTIR spectral observations belonging to 29,320 Norwegian dairy goats of 271 herds was used for the study. Principal components analysis was applied on both unprocessed and preprocessed spectral data, and new traits (latent traits) were defined because a multitrait analysis of all spectral variables for variance components could not be done. Eight and 7 latent variables, explaining approximately 99% of the total unprocessed and preprocessed spectral variation, respectively, were kept from the principal components analysis for genetic analysis. Genetic and environmental variance components were estimated for the latent traits using restricted maximum likelihood. Genetic-to-total phenotypic variance ratios (heritabilities) of the latent traits were between 0.011 and 0.285 for the unprocessed spectra and between 0.135 and 0.262 for the preprocessed spectra. The estimated variance components for the latent traits were back transformed to the spectral variables. Heritabilities of these spectral variables ranged from 0.018 to 0.408 and variance ratios of the permanent environmental effects of goats were between 0.002 and 0.184 of the phenotypic spectral variation. High-to-moderate heritabilities were observed in particular in spectral regions related to major milk components (fat, lactose, and protein): between 1,030 and 1,300 cm(-1), 1,500 and 1,600 cm(-1), 1,700 and 1,800 cm(-1), and 2,800 and 3,000 cm(-1). Our results confirmed that a substantial amount of genetic variation exists in goat milk FTIR spectra. Not all spectral variations are of genetic origin; some FTIR regions are highly influenced by herd test-day variation. The study also pointed out the possibility of using FTIR spectra as a monitoring tool in herd management.