Interest in the fatty acid composition of dairy products is increasing; however, the measurement of fatty acids requires using gas-liquid chromatography. Although this method is suitable, it involves a time-consuming procedure, expensive reagents, and qualified staff. By comparison, the mid-infrared (MIR) spectrometry method could be a good alternative for assessing the fatty acid profile of dairy products. The objective of this study was to explore the calibration of MIR spectrometry for estimating fatty acid concentrations in milk and milk fat. Estimated concentrations in milk fat were less reliable than those for the same fatty acids in milk. Results also showed that when the fatty acid concentrations in milk increased, the efficiency of the infrared analysis method in predicting these values simultaneously increased. Selected prediction equations must have a high cross-validation coefficient of determination, a high ratio of standard error of cross-validation to standard deviation, and good repeatability of chromatographic data. Results from this study showed that the calibration equations predicting 12:0, 14:0, 16:0, 16:1cis-9, 18:1, and saturated and monounsaturated fatty acids in milk could be used. Thus, with its potential for use in regular milk recording, this infrared analysis method offers the possibility of assessing and improving the quality of milk produced. Indeed, it enables the fatty acid composition in milk to be estimated for each cow and the estimates to be used as indicator traits to determine the genetic values of underlying fatty acid concentrations. The knowledge of these genetic values
The current cattle selection program for dairy cattle in the Walloon region of Belgium does not consider the relative content of the different fatty acids (FA) in milk. However, interest by the local dairy industry in differentiated milk products is increasing. Therefore, farmers may be interested in selecting their animals based on the fat composition. The aim of this study was to evaluate the feasibility of genetic selection to improve the nutritional quality of bovine milk fat. The heritabilities and correlations among milk yield, fat, protein, and major FA contents in milk were estimated. Heritabilities for FA in milk and fat ranged from 5 to 38%. The genetic correlations estimated among FA reflected the common origin of several groups of FA. Given these results, an index including FA contents with the similar metabolic process of production in the mammary gland could be used, for example, to increase the monounsaturated and conjugated fatty acids in milk. Moreover, the genetic correlations between the percentage of fat and the content of C14:0, C12:0, C16:0, and C18:0 in fat were −0.06, 0.55, 0.60, and 0.84, respectively. This result demonstrates that an increase in fat content is not directly correlated with undesirable changes in FA profile in milk for human health. Based on the obtained genetic parameters, a future selection program to improve the FA composition of milk fat could be initiated.
Gene content is the number of copies of a particular allele in a genotype of an animal. Gene content can be used to study additive gene action of candidate gene. Usually genotype data are available only for a part of population and for the rest gene contents have to be calculated based on typed relatives. Methods to calculate expected gene content for animals on large complex pedigrees are relatively complex. In this paper we proposed a practical method to calculate gene content using a linear regression. The method does not estimate genotype probabilities but these can be approximated from gene content assuming Hardy-Weinberg proportions. The approach was compared with other methods on multiple simulated data sets for real bovine pedigrees of 1 082 and 907 903 animals. Different allelic frequencies (0.4 and 0.2) and proportions of the missing genotypes (90, 70, and 50%) were considered in simulation. The simulation showed that the proposed method has similar capability to predict gene content as the iterative peeling method, however it requires less time and can be more practical for large pedigrees. The method was also applied to real data on the bovine myostatin locus on a large dual-purpose Belgian Blue pedigree of 235 133 animals. It was demonstrated that the proposed method can be easily adapted for particular pedigrees.
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