Capitalizing on fine milk composition for breeding and management of dairy cows

The aim of this study was to compare the common method of exploiting infrared spectral data in animal breeding; that is, estimating the breeding values for the traits predicted by infrared spectroscopy, and an alternative approach based on the direct use of spectral information (direct prediction, DP) to predict the estimated breeding values (EBV). Traits were pH, milk coagulation properties, contents of the main casein and whey protein fractions, cheese yield measured by micro-cheese making, lactoferrin, Ca, and fat composition. For the DP method, the number of spectral variables was reduced by principal components analysis to 8 latent traits that explained 99% of the original spectral variation. Restricted maximum likelihood was used to estimate variance components of the latent traits. (Co)variance components of the original spectral traits were obtained by back-transformation and EBV of all derived milk traits were then predicted as traits correlated with the genetic information of the spectra. The rank correlation between the EBV obtained for the infrared-predicted traits and those obtained from the DP method was variable across traits. Rank correlations ranged from 0.07 (for the content of saturated fatty acids expressed as g/100 g of fat) to 0.96 (for dry matter cheese yield, %) and, for most traits, was <0.5. This result can be explained by the nature of the principal components analysis: it does not take into account the covariance between the spectral variables and the reference traits but produces latent traits that maximize the spectral variance explained. Thus, the direct approach is more likely to be effective for traits more related to the main sources of spectral variation (i.e., protein and fat). More research is required to study spectral genetic variation and to determine the best way to choose spectral regions and the type and number of considered latent traits for potential applications.

Section: Comparison Between Direct and Indirect Methods For Exploitingmentioning

confidence: 99%

Comparison between direct and indirect methods for exploiting Fourier transform spectral information in estimation of breeding values for fine composition and technological properties of milk

Bonfatti

Vicario²,

Degano³

et al. 2017

“…A study of SNP associated with subclinical ketosis in US Jerseys (Fugate et al, 2014) reported that genotypes could be useful for predicting predisposition for ketosis. Another promising avenue is the use of mid-infrared (MIR) data to predict ketosis because of its association with energy balance (Gengler et al, 2015). In the United States, data from on-farm herd management systems may be sufficient for routine calculation of genetic and genomic evaluations for most common health disorders of US Holsteins (Clay et al, 2013;Cole et al, 2013).…”

Section: Other Health Traitsmentioning

confidence: 99%

Using genomics to enhance selection of novel traits in North American dairy cattle,

Chesnais¹,

Cooper

Wiggans

et al. 2016

The objectives of this paper were to briefly review progress in the genetic evaluation of novel traits in Canada and the United States, assess methods to predict selection accuracy based on cow reference populations, and illustrate how the use of indicator traits could increase genomic selection accuracy. Traits reviewed are grouped into the following categories: udder health, hoof health, other health traits, feed efficiency and methane emissions, and other novel traits. The status of activities expected to lead to national genetic evaluations is indicated for each group of traits. For traits that are more difficult to measure or expensive to collect, such as individual feed intake or immune response, the development of a cow reference population is the most effective approach. Several deterministic methods can be used to predict the reliability of genomic evaluations based on cow reference population size, trait heritability, and other population parameters. To provide an empirical validation of those methods, predicted accuracies were compared with observed accuracies for several cow reference populations and traits. Reference populations of 2,000 to 20,000 cows were created through random sampling of genotyped Holstein cows in Canada and the United States. The effects of single nucleotide polymorphisms (SNP) were estimated from those cow records, after excluding the dams of validation bulls. Bulls that were first progeny tested in 2013 and 2014 were then used to carry out a validation and estimate the observed accuracy of genomic selection based on those SNP effects. Over the various cow population sizes and traits considered in the study, even the best prediction methods were found, on average, to either under-evaluate observed accuracy by 0.20 or over-evaluate it by 0.22, depending on the approach used to estimate the number of independently segregating chromosome segments. In some instances, differences between observed and predicted accuracies were as large as 0.47. Indicator traits can be very useful for the selection of novel traits. To illustrate this, protein yield, body weight, and mid-infrared data were used as indicator traits for feed efficiency. Using those traits in conjunction with 5,000 cow records for dry matter intake increased the reliability of genomic predictions for young animals from 0.20 to 0.50.

“…Mid-infrared (MIR) spectroscopy is routinely used by worldwide milk recording organizations to quantify the concentration of fat, protein, and lactose in milk samples. Moreover, an increasing number of studies have reported a good capability of MIR to predict traits related to cow metabolic status, such as energy balance, milk and serum fatty acids, and BHB [see reviews by De Marchi et al (2014) and Gengler et al (2016)], which are known to affect fertility (Ribeiro et al, 2013). Also, Toledo-Alvarado et al (2018b) reported that milk fatty acid profiles predicted by MIR could be used to identify cows in proestrus or estrus.…”

Section: Introductionmentioning

confidence: 99%

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

Bonfatti

Luke

et al. 2019

The objective of this study was to investigate the potential of milk mid-infrared (MIR) spectroscopy, MIR-derived traits including milk composition, milk fatty acids, and blood metabolic profiles (fatty acids, β-hydroxybutyrate, and urea), and other on-farm data for discriminating cows of good versus poor likelihood of conception to first insemination (i.e., pregnant vs. open). A total of 6,488 spectral and milk production records of 2,987 cows from 19 commercial dairy herds across 3 Australian states were used. Seven models, comprising different explanatory variables, were examined. Model 1 included milk production; concentrations of fat, protein, and lactose; somatic cell count; age at calving; days in milk at herd test; and days from calving to insemination. Model 2 included, in addition to the variables in model 1, milk fatty acids and blood metabolic profiles. The MIR spectrum collected before first insemination was added to model 2 to form model 3. Fat, protein, and lactose percentages, milk fatty acids, and blood metabolic profiles were removed from model 3 to create model 4. Model 5 and model 6 comprised model 4 and either fertility genomic estimated breeding value or principal components obtained from a genomic relationship matrix derived using animal genotypes, respectively. In model 7, all previously described sources of information, but not MIR-derived traits, were used. The models were developed using partial least squares discriminant analysis. The performance of each model was evaluated in 2 ways: 10-fold random cross-validation and herd-by-herd external validation. The accuracy measures were sensitivity (i.e., the proportion of pregnant cows that were correctly classified), specificity (i.e., the proportion of open cows that were correctly classified), and area under the curve (AUC) for the receiver operating curve. The results showed that in all models, prediction accuracy obtained through 10fold random cross-validation was higher than that of herd-by-herd external validation, with the difference in AUC ranging between 0.01 and 0.09. In the herd-byherd external validation, using basic on-farm information (model 1) was not sufficient to classify good-and poor-fertility cows; the sensitivity, specificity, and AUC were around 0.66. Compared with model 1, adding milk fatty acids and blood metabolic profiles (model 2) increased the sensitivity, specificity, and AUC by 0.01, 0.02, and 0.02 unit, respectively (i.e., 0.65, 0.63, and 0.678). Incorporating MIR spectra into model 2 resulted in sensitivity, specificity, and AUC values of 0.73, 0.63, and 0.72, respectively (model 3). The comparable prediction accuracies observed for models 3 and 4 mean that useful information from MIR-derived traits is already included in the spectra. Adding the fertility genomic estimated breeding value and animal genotypes (model 7) produced the highest prediction accuracy, with sensitivity, specificity, and AUC values of 0.75, 0.66, and 0.75, respectively. However, removing either the fertility estimated breeding value or ...