Phenotypic and Genetic Variation of Milk Fatty Acid Composition Predicted by Mid-Infrared Spectroscopy in Dairy Cattle

The potential of mid-infrared spectroscopy (MIRS) has been well assessed in the literature. Several authors tested the capability of MIRS to predict milk fatty acid (FA) composition with the aim of applying this technology at population level to collect phenotypes for genetic purposes. The accuracy of prediction models largely differs across groups and individual FA, and comparisons among studies are difficult due to several factors such as the unit of measure to express the FA content (milk or fat basis), the computational algorithm used to construct the model, the spectra pretreatments, the number of samples and the biological variability of the trait in the population used to build the prediction equation. In general, prediction models for FA groups (in particular SFA and UFA) are quite reliable whereas those for individual FA are less accurate (especially for FA that are present in low concentration). The overall aim of the present thesis was to study the phenotypic and genetic aspects of FA composition of bovine milk predicted by MIRS. Chapter 1 demonstrated the feasibility of improving calibration models for both FA groups (SFA, UFA, MUFA and PUFA) and individual FA (C14:0, C16:0, C18:0 and C18:1) through the use of a variable selection method (UVE, uninformative variable elimination) and a heuristic optimization algorithm (GA, genetic algorithm). Both methods were able to discriminate the spectrum areas closely related with FA in milk enhancing the fitting statistics of all the analyzed traits, with GA providing more accurate models than UVE. Methods like UVE and GA have great potential to be used both as “screening tools” for a qualitative analysis and as “selection methods” for the construction of robust and conservative calibration models. Further research is needed to understand how well these algorithms perform in larger dataset in both calibration and validation processes. Chapter 2 investigated the sources of variation of milk FA profile routinely predicted by MIRS in four Italian cattle breeds reared in mountain area: Holstein-Friesian, Brown Swiss, Simmental and Alpine Grey. Results underlined that a phenotypic variation is present and the four breeds differed in terms of milk FA composition. Among the studied effects, breed and herd were the most important to explain the variation of FA profile followed by days in milk and parity. In particular, Alpine Grey presented the “healthier” milk FA profile with lower content of SFA and greater content of UFA, MUFA, PUFA and C18:1. The saturated fraction of FA increased from calving until approximately 120 days in milk. Furthermore, milk of first parity cows presented lower concentration of C14:0 and C16:0 than milk of multiparous cows. Chapter 3 reported the existence of an exploitable additive genetic variation for predicted milk FA groups (g/100 mL milk) in Italian Holstein-Friesian cows. Heritability estimates for SFA, UFA, MUFA and PUFA were moderate to low (0.246, 0.069, 0.082 and 0.078, respectively) and the genetic correlations between these traits suggested that an increase of fat content leads to an increase of SFA with undesirable effects on the healthy quality of milk. Also, if the content of UFA is predisposed to be higher, it will be mostly its MUFA component to increase due to the strong positive genetic correlation between these 2 traits (0.95). Finally, Chapter 4 estimated genetic parameters of the major FA (expressed as g/100 g of total identified FA) predicted by MIRS in milk of four cattle breeds: Holstein-Friesian, Brown Swiss, Simmental and Alpine Grey. Overall, all the estimated parameters were comparable across breeds and heritability estimates were moderate to low ranging from 0.18 to 0.30 and from 0.16 to 0.30 for groups and individual FA, respectively. The positive genetic correlations between milk fat, SFA, C14:0 and C16:0, and the negative correlations between these FA and C18:0 confirmed findings of Chapter 3 suggesting that genetic selection to address milk FA to a better healthy profile is possible.