Valérie Arnould

Genetic variability of milk fatty acids.

The milk fatty acid (FA) profile is far from the optimal fat composition in regards to human health. The natural sources of variation, such as feeding or genetics, could be used to increase the concentrations of unsaturated fatty acids. The impact of feeding is well described. However, genetic effects on the milk FA composition begin to be extensively studied. This paper summarizes the available information about the genetic variability of FAs. The greatest breed differences in FA composition are observed between Holstein and Jersey milk. Milk fat of the latter breed contains higher concentrations of saturated FAs, especially short-chain FAs. The variation of the delta-9 desaturase activity estimated from specific FA ratios could explain partly these breed differences. The choice of a specific breed seems to be a possibility to improve the nutritional quality of milk fat. Generally, the proportions of FAs in milk are more heritable than the proportions of these same FAs in fat. Heritability estimates range from 0.00 to 0.54. The presence of some single nucleotide polymorphisms could explain partly the observed individual genetic variability. The polymorphisms detected onSCD1 andDGAT1 genes influence the milk FA composition. TheSCD1 V allele increases the unsaturation of C16 and C18. TheDGAT1 A allele is related to the unsaturation of C18. So, a combination of the molecular and quantitative approaches should be used to develop tools helping farmers in the selection of their animals to improve the nutritional quality of the produced milk fat.

Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis

Lactoferrin (LTF) is a milk glycoprotein favorably associated with the immune system of dairy cows. Somatic cell count is often used as an indicator of mastitis in dairy cows, but knowledge on the milk LTF content could aid in mastitis detection. An inexpensive, rapid and robust method to predict milk LTF is required. The aim of this study was to develop an equation to quantify the LTF content in bovine milk using mid-infrared (MIR) spectrometry. LTF was quantified by enzyme-linked immunosorbent assay (ELISA), and all milk samples were analyzed by MIR. After discarding samples with a coefficient of variation between 2 ELISA measurements of more than 5% and the spectral outliers, the calibration set consisted of 2499 samples from Belgium (n = 110), Ireland (n = 1658) and Scotland (n = 731). Six statistical methods were evaluated to develop the LTF equation. The best method yielded a cross-validation coefficient of determination for LTF of 0.71 and a cross-validation standard error of 50.55 mg/l of milk. An external validation was undertaken using an additional dataset containing 274 Walloon samples. The validation coefficient of determination was 0.60. To assess the usefulness of the MIR predicted LTF, four logistic regressions using somatic cell score (SCS) and MIR LTF were developed to predict the presence of mastitis. The dataset used to build the logistic regressions consisted of 275 mastitis records and 13 507 MIR data collected in 18 Walloon herds. The LTF and the interaction SCS × LTF effects were significant (P < 0.001 and P = 0.02, respectively). When only the predicted LTF was included in the model, the prediction of the presence of mastitis was not accurate despite a moderate correlation between SCS and LTF (r = 0.54). The specificity and the sensitivity of models were assessed using Walloon data (i.e. internal validation) and data collected from a research herd at the University of Wisconsin - Madison (i.e. 5886 Wisconsin MIR records related to 93 mastistis events - external validation). Model specificity was better when LTF was included in the regression along with SCS when compared with SCS alone. Correct classification of non-mastitis records was 95.44% and 92.05% from Wisconsin and Walloon data, respectively. The same conclusion was formulated from the Hosmer and Lemeshow test. In conclusion, this study confirms the possibility to quantify an LTF indicator from milk MIR spectra. It suggests the usefulness of this indicator associated to SCS to detect the presence of mastitis. Moreover, the knowledge of milk LTF could also improve the milk nutritional quality.

Genetic analysis of lactoferrin content in bovine milk

Bovine lactoferrin (LF) is mainly present in milk and shows important physiological and biological functions. The aim of this study was to estimate the heritability and correlation values of LF content in bovine milk with different economic traits as milk yield (MY), fat and protein percentages, and somatic cell score (SCS). Variance components of the studied traits were estimated by REML using a multiple-trait mixed model. The obtained heritability (0.22) for LF content predicted using mid-infrared spectrometry (pLF) suggested the possibility of animal selection based on the increase of LF content in milk. The phenotypic and genetic correlation values calculated between pLF and SCS were moderate (0.31 and 0.24, respectively). Furthermore, a preliminary study of bovine LF gene polymorphism effects was performed on the same production traits. By PCR, all exons of the LF gene were amplified and then sequenced. Three new polymorphisms were detected in exon 2, exon 11, and intron 8. We examined the effects of LF gene polymorphisms of exons 2, 4, 9, 11, and 15, and intron 8 on pLF, MY, fat and protein percentages, and SCS. The different observed effects did not reach a significant level probably because of the characteristics of the studied population. However, the results were promising, and LF may be a potential indicator of mastitis. Further studies are necessary to evaluate the effect of genetic selection based on LF content on the improvement of mastitis resistance.

Short communication: Genetic variation of saturated fatty acids in Holsteins in the Walloon region of Belgium

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 Akaikes 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.

Predictions of Daily Milk and Fat Yields, Major Groups of Fatty Acids, and C18:1 cis-9 from Single Milking Data without a Milking Interval

Simple Summary Reducing the frequency of milk recording decreases the costs of official milk recording. However, this approach can negatively affect the accuracy of predicting daily yields. Equations to predict daily yield from morning or evening data were developed in this study for fatty milk components from traits recorded easily by milk recording organizations. The correlation values ranged from 96.4% to 97.6% (96.9% to 98.3%) when the daily yields were estimated from the morning (evening) milkings. The simplicity of the proposed models which do not include the milking interval should facilitate their use by breeding and milk recording organizations. Abstract Reducing the frequency of milk recording would help reduce the costs of official milk recording. However, this approach could also negatively affect the accuracy of predicting daily yields. This problem has been investigated in numerous studies. In addition, published equations take into account milking intervals (MI), and these are often not available and/or are unreliable in practice. The first objective of this study was to propose models in which the MI was replaced by a combination of data easily recorded by dairy farmers. The second objective was to further investigate the fatty acids (FA) present in milk. Equations to predict daily yield from AM or PM data were based on a calibration database containing 79,971 records related to 51 traits [milk yield (expected AM, expected PM, and expected daily); fat content (expected AM, expected PM, and expected daily); fat yield (expected AM, expected PM, and expected daily; g/day); levels of seven different FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk), and the corresponding FA yields for these seven FA types/groups (expected AM, expected PM, and expected daily; g/day)]. These equations were validated using two distinct external datasets. The results obtained from the proposed models were compared to previously published results for models which included a MI effect. The corresponding correlation values ranged from 96.4% to 97.6% when the daily yields were estimated from the AM milkings and ranged from 96.9% to 98.3% when the daily yields were estimated from the PM milkings. The simplicity of these proposed models should facilitate their use by breeding and milk recording organizations.