Fourier transform infrared milk spectral data are routinely used for milk quality control and have been revealed to be driven by genetics. This study aimed to (1) estimate heritability for 1,060 wavenumbers in the infrared region from 5,008 to 925 cm −1 , (2) estimate genomic correlations between wavenumbers with increased heritability, and (3) compare results between Danish Holstein and Danish Jersey cows. For Danish Holstein, 3,275 cows and 19,656 milk records were available. For Danish Jersey, 3,408 cows and 20,228 milk records were available. We used a hierarchical mixed model, with a Bayesian approach. Heritability of individual wavenumbers ranged from 0.00 to 0.31 in Danish Holstein, and from 0.00 to 0.30 in Danish Jersey. Genomic correlation was calculated between 15 selected wavenumbers, and varied from weak to very strong, in both Danish Holstein and Danish Jersey (0.03 to 0.97, and −0.11 to −0.97). Within the 15 selected wavenumbers, a subdivision into 2 groups of wavenumbers was observed, where genomic correlations were negative between groups, and positive within groups. Heritability and genomic correlations were higher in Danish Holstein compared with Danish Jersey, but followed a similar pattern in both breeds. Breed differences were most pronounced in the mid-infrared region that interacts with lactose and the spectral region that interacts with protein.In conclusion, heritability for individual wavenumbers of Fourier transform milk spectra was moderate, and strong genomic correlations were observed between wavenumbers across the spectrum. Heritability and genomic correlations were higher in Danish Holstein, with the strongest breed differences showing in spectral regions interacting with protein or lactose. bers in breeding programs, or use genetic correlations between wavenumbers and a trait of interest.The aims of this study were to (1) calculate heritability (h 2 ) for 1,060 wavenumbers in the MIR region from 5,008 to 925 cm −1 , (2) calculate genomic correlations between transmittance values of wavenumbers with high h 2 , and (3) compare results between Danish Holstein (DH) and Danish Jersey (DJ) cows. MATERIALS AND METHODS Study PopulationPhenotypes. Milk records were provided by the Danish milk recording organization (RYK, Aarhus, Denmark). The study population consisted of 3,275 DH cows from 354 farms, and 3,408 DJ cows from 175 farms. On 4 farms, both DH and DJ cows were present. From farms with only DH, 1 to 112 cows were sampled, and from farms with only DJ, 1 to 217 cows were sampled. Milk records were sent to Eurofins-Steins laboratory (Vejen, Denmark) for FT-IR spectral analyses using MilkoScan FT+ (Foss, Hillerød, Denmark) and for analysis of the infrared region 5,008 to 925 cm −1 , including 1,060 individual wavenumbers.Visual inspection of spectra was performed using PLSR Hotelling T 2 versus Q-residual plots and leverage versus studentized residual plots, and outliers were removed. Milk records with outlying fat percentage (fat%), and protein percentage (protein%) were excluded fr...

Fourier transform infrared spectral analysis is a cheap and fast method to predict milk composition. A not very well studied milk component is orotic acid. Orotic acid is an intermediate in the biosynthesis pathway of pyrimidine nucleotides and is an indicator for the metabolic cattle disorder deficiency of uridine monophosphate synthase. The function of orotic acid in milk and its effect on calf health, health of humans consuming milk or milk products, manufacturing properties of milk, and its potential as an indicator trait are largely unknown. The aims of this study were to determine if milk orotic acid can be predicted from infrared milk spectra and to perform a large-scale phenotypic and genetic analysis of infrared-predicted milk orotic acid. An infrared prediction model for orotic acid was built using a training population of 292 Danish Holstein and 299 Danish Jersey cows, and a validation population of 381 Danish Holstein cows. Milk orotic acid concentration was determined with nuclear magnetic resonance spectroscopy. For genetic analysis of infrared orotic acid, 3 study populations were used: 3,210 Danish Holstein cows, 3,360 Danish Jersey cows, and 1,349 Dutch Holstein Friesian cows. Using partial least square regression, a prediction model for orotic acid was built with 18 latent variables. The error of the prediction for the infrared model varied from 1.0 to 3.2 mg/L, and the accuracy varied from 0.68 to 0.86. Heritability of infrared orotic acid predicted with the standardized prediction model was 0.18 for Danish Holstein, 0.09 for Danish Jersey, and 0.37 for Dutch Holstein Friesian. We conclude that milk orotic acid can be predicted with moderate to good accuracy based on infrared milk spectra and that infrared-predicted orotic acid is heritable. The availability of a cheap and fast method to predict milk orotic acid opens up possibilities to study the largely unknown functions of milk orotic acid.

Background: Infrared spectral analysis of milk is cheap, fast, and accurate. Infrared light interacts with chemical bonds present inside the milk, which means that Fourier transform infrared milk spectra are a reflection of the chemical composition of milk. Heritability of Fourier transform infrared milk spectra has been analysed previously. Further genetic analysis of Fourier transform infrared milk spectra could give us a better insight in the genes underlying milk composition. Breed influences milk composition, yet not much is known about the effect of breed on Fourier transform infrared milk spectra. Improved understanding of the effect of breed on Fourier transform infrared milk spectra could enhance efficient application of Fourier transform infrared milk spectra. The aim of this study is to perform a genome wide association study on a selection of wavenumbers for Danish Holstein and Danish Jersey. This will improve our understanding of the genetics underlying milk composition in these two dairy cattle breeds.Results: For each breed separately, fifteen wavenumbers were analysed. Overall, more quantitative trait loci were observed for Danish Jersey compared to Danish Holstein. For both breeds, the majority of the wavenumbers was most strongly associated to a genomic region on BTA 14 harbouring DGAT1. Furthermore, for both breeds most quantitative trait loci were observed for wavenumbers that interact with the chemical bond C-O. For Danish Jersey, wavenumbers that interact with C-H were associated to genes that are involved in fatty acid synthesis, such as AGPAT3, AGPAT6, PPARGC1A, SREBF1, and FADS1. For wavenumbers which interact with -OH, associations were observed to genomic regions that have been linked to alpha-lactalbumin. Conclusions: The current study identified many quantitative trait loci that underlie Fourier transform infrared milk spectra, and thus milk composition. Differences were observed between groups of wavenumbers that interact with different chemical bonds. Both overlapping and different QTL were observed for Danish Holstein and Danish Jersey.

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0.63, and a relative error of 18.8%. Prediction accuracies of milk minerals based on infrared-predicted fat, protein, and lactose content were considerably lower than those based on the infrared milk spectra. This shows that the milk infrared spectrum contains valuable information on milk minerals, which is currently not used. Heritability for infrared-predicted Ca, Na, and P varied from low (0.13) to moderate (0.36). Several QTL for infrared-predicted milk minerals were observed that have been associated with gold standard milk minerals previously. In conclusion, this study has shown infrared milk spectra were good at predicting Ca, Na, and P in milk. Infrared-predicted Ca, Na, and P had low to moderate heritability estimates.