The objective of this study was to estimate genetic parameters for major milk fatty acids and milk production traits. One morning milk sample was collected from 1,918 Holstein-Friesian heifers located in 398 commercial herds in The Netherlands. Each sample was analyzed for total percentages of fat and protein, and for detailed fatty acid percentages (computed as fatty acid weight as a proportion of total fat weight). Intraherd heritabilities were high for C4:0 to C16:0, ranging from 0.42 for C4:0 to 0.71 for C10:0. Saturated and unsaturated C18 fatty acids had intraherd heritability estimates of approximately 0.25, except for C18:2 cis-9, trans-11, which was 0.42. Standard errors of the heritabilities were between 0.07 and 0.12. Genetic correlations were high and positive among C4:0 to C14:0, as well as among unsaturated C18, but correlations of C4:0 to C14:0 with unsaturated C18 were generally weak. The genetic correlation of C16:0 with fat percentage was positive (0.65), implying that selection for fat percentage should result in a correlated increase of C16:0, whereas unsaturated C18 fatty acids decreased with increasing fat percentage (-0.74). Milk fat composition can be changed by means of selective breeding, which offers opportunities to meet consumer demands regarding health and technological aspects.

Dietary fat may play a role in the aetiology of many chronic diseases. Milk and milk-derived foods contribute substantially to dietary fat, but have a fat composition that is not optimal for human health. We measured the fat composition of milk samples in 1918 Dutch Holstein Friesian cows in their first lactation and estimated genetic parameters for fatty acids. Substantial genetic variation in milk-fat composition was found: heritabilities were high for short- and medium-chain fatty acids (C4:0-C16:0) and moderate for long-chain fatty acids (saturated and unsaturated C18). We genotyped 1762 cows for the DGAT1 K232A polymorphism, which is known to affect milk-fat percentage, to study the effect of the polymorphism on milk-fat composition. We found that the DGAT1 K232A polymorphism has a clear influence on milk-fat composition. The DGAT1 allele that encodes lysine (K) at position 232 (232K) is associated with more saturated fat; a larger fraction of C16:0; and smaller fractions of C14:0, unsaturated C18 and conjugated linoleic acid (P < 0.001). We conclude that selective breeding can make a significant contribution to change the fat composition of cow's milk.

The effects of lactation stage, negative energy balance (NEB), and milk fat depression (MFD) were estimated on detailed milk fat composition in primiparous Holstein-Friesian cows. One morning milk sample was collected from each of 1,933 cows from 398 commercial Dutch herds in winter 2005. Milk fat composition was measured using gas chromatography, and fat and protein percentage were measured using infrared spectrometry. Each fatty acid changed 0.5 to 1 phenotypic standard deviation over lactation, except odd-chain C5:0 to C15:0, branched-chain fatty acids, and trans-10, cis-12 conjugated linoleic acid (CLA). The greatest change was an increase from 31.2 to 33.3% (wt/wt) for C16:0 from d 80 to 150 of lactation. Energy status was estimated for each cow as the deviation from each average lactation fat-to-protein ratio (FPdev). A high FPdev (>0.12) indicated NEB. Negative energy balance was associated with an increase in C16:0 (0.696 +/- 0.178) and C18:0 (0.467 +/- 0.093), which suggested mobilization of body fat reserves. Furthermore, NEB was associated with a decrease in odd-chain C5:0 to C15:0 (-0.084 +/- 0.020), which might reflect a reduced allocation of C3 components to milk fat synthesis. A low FPdev indicated MFD (<-0.12) and was associated with a decrease in C16:0 (-0.681 +/- 0.255) and C18:0 (-0.128 +/- 0.135) and an increase in total unsaturated fatty acids (0.523 +/- 0.227). The study showed that both lactation stage and energy balance significantly contribute to variation in milk fat composition and alter the activity of different fatty acid pathways.

The aim of this study was to estimate genetic parameters for test-day milk urea nitrogen (MUN) and its relationships with milk production traits. Three test-day morning milk samples were collected from 1,953 Holstein-Friesian heifers located on 398 commercial herds in The Netherlands. Each sample was analyzed for somatic cell count, net energy concentration, MUN, and the percentage of fat, protein, and lactose. Genetic parameters were estimated using an animal model with covariates for days in milk and age at first calving, fixed effects for season of calving and effect of test or proven bull, and random effects for herd-test day, animal, permanent environment, and error. Coefficient of variation for MUN was 33%. Estimated heritability for MUN was 0.14. Phenotypic correlation of MUN with each of the milk production traits was low. The genetic correlation was close to zero for MUN and lactose percentage (-0.09); was moderately positive for MUN and net energy concentration of milk (0.19), fat yield (0.41), protein yield (0.38), lactose yield (0.22), and milk yield (0.24), and percentage of fat (0.18), and percentage of protein (0.27); and was high for MUN and somatic cell score (0.85). Herd-test day explained 58% of the variation in MUN, which suggests that management adjustments at herd-level can reduce MUN. This study shows that it is possible to influence MUN by herd practice and by genetic selection.

A genome-wide scan was performed to identify quantitative trait loci (QTL) for short- and medium-chain fatty acids (expressed in wt/wt %). Milk samples were available from 1,905 cows from 398 commercial herds in the Netherlands, and milk-fat composition was measured by gas chromatography. DNA was available from 7 of the paternal half-sib families: 849 cows and their 7 sires. A genetic map was constructed comprising 1,341 SNP and 2,829 cM, with an average information content of 0.83. Multimarker interval mapping was used in an across-family regression on corrected phenotypes for the 7 half-sib families. Four QTL were found: on Bos taurus autosome (BTA) 6, a QTL was identified for C6:0 and C8:0; on BTA14, a QTL was identified for fat percentage, all odd-chain fatty acids, and C14:0, C16:0, C16:1, and their unsaturation indices; on BTA19, a QTL affected C14:0; and on BTA26, a QTL was identified for the monounsaturated fatty acids and their unsaturation indices. The QTL explained 3 to 19% of phenotypic variance. Furthermore, 49 traits with suggestive evidence for linkage were found on 21 chromosomes. Additional analyses revealed that the QTL on BTA14 was most likely caused by a mutation in DGAT1, whereas the QTL on BTA26 was most likely caused by a mutation in the SCD1 gene. Quantitative trait loci that affect specific fatty acids might increase the understanding of physiological processes regarding fat synthesis and the position of the causal genes.

Milk fat composition shows substantial seasonal variation, most of which is probably caused by differences in the feeding of dairy cows. The present study aimed to know whether milk fat composition in winter is genetically the same trait as milk fat composition in summer. For this purpose, we estimated heritabilities, genetic correlations, effects of acyl-coenzyme A:diacylglycerol acyltransferase 1 (DGAT1) K232A, and stearoyl-coenzyme A desaturase 1 (SCD1) A293V polymorphisms for milk fat composition in winter and summer, and tested for genotype by season interactions of DGAT1 K232A and SCD1 A293V polymorphisms. Milk samples were obtained from 2,001 first-lactation Dutch Holstein-Friesian cows, most with records in both winter and summer. Summer milk contained higher amounts of unsaturated fatty acids (FA) and lower amounts of saturated FA compared with winter milk. Heritability estimates were comparable between seasons: moderate to high for short- and medium-chain FA (0.33 to 0.74) and moderate for long-chain FA (0.19 to 0.43) in both seasons. Genetic correlations between winter and summer milk were high, indicating that milk fat composition in winter and in summer can largely be considered as genetically the same trait. Effects of DGAT1 K232A and SCD1 A293V polymorphisms were similar across seasons for most FA. Allele DGAT1 232A in winter as well as in summer milk samples was negatively associated with most FA with less than 18 carbons, saturated FA, saturated FA to unsaturated FA ratio, and C10 to C16 unsaturation indices, and was positively associated with C14:0, unsaturated C18, unsaturated FA, and C18 and conjugated linoleic acid unsaturation indices. Allele SCD1 293V in winter as well as in summer milk samples was negatively associated with C18:0, C10:1 to cis-9 C14:1, trans-11 C18:1, and C10 to C14 unsaturation indices, and positively associated with C8:0 to C14:0, cis-9 C16:1, and C16 to conjugated linoleic acid unsaturation indices. In addition, significant DGAT1 K232A by season interaction was found for some FA and SCD1 A293V by season interaction was only found for trans-11 C18:1. These interactions were due to scaling of genotype effects.

We present the results of a genome-wide scan to identify quantitative trait loci (QTL) that contribute to genetic variation in long-chain milk fatty acids. Milk-fat composition phenotypes were available on 1,905 Dutch Holstein-Friesian cows. A total of 849 cows and their 7 sires were genotyped for 1,341 single nucleotide polymorphisms across all Bos taurus autosomes (BTA). We detected significant QTL on BTA14, BTA15, and BTA16: for C18:1 cis-9, C18:1 cis-12, C18:2 cis-9,12, CLA cis-9,trans-11, C18:3 cis-9,12,15, the C18 index, the total index, total saturated fatty acids, total unsaturated fatty acids (UFA), and the ratio of saturated fatty acids:unsaturated fatty acids on BTA14; for C18:1 trans fatty acids on BTA15; and for the C18 and CLA indices on BTA16. The QTL explained 3 to 19% of the phenotypic variance. Suggestive QTL were found on 16 other chromosomes. The diacylglycerol acyltransferase 1 (DGAT1) K232A polymorphism on BTA14, which is known to influence fatty acid composition, most likely explains the QTL that was detected on BTA14.

The objectives were to investigate the accuracy of genomic evaluations obtained for a small dairy cattle population (Israeli Holsteins) via joint evaluation with a larger population (Dutch Holsteins), and to evaluate the use of pedigree data from foreign bulls computed by Interbull without daughter records in Israel. The training population included 4,010 Dutch bulls and 713 Israeli bulls. The validation population included 185 Israeli bulls with daughter records for milk production traits and slightly fewer bulls for the nonproduction traits. Milk, fat, and protein yields, somatic cell score, longevity, female fertility, direct and maternal calving ease, direct and maternal stillbirth, and the Israeli breeding index were analyzed. The genomic prediction model was based on the Bayesian multi-QTL model of Meuwissen and Goddard, where the effects of dense single nucleotide polymorphisms across the whole genome are fitted directly, without the use of haplotypes or identical-by-descent probabilities. Correlations of May 2014 estimated breeding values (EBV14) with genomic EBV (GEBV) were higher than the correlations of EBV14 with parent averages (PA) computed from the June 2009 evaluation for all traits. For the Israel selection index, the difference between EBV14 and GEBV correlation on the one hand and EBV14 and PA computed using Interbull data on the other hand was 15 percentage points. For protein, the difference between the corresponding correlations was 14 percentage points. Generally, correlations of EBV14 with PA based on Israeli EBV only were similar to correlations of EBV14 with PA including Interbull evaluations. Relative to EBV14, milk production traits were biased upwards for both GEBV and PA, but the bias was greater for PA. The Y-intercepts of regressions of EBV14 were significantly different from zero for regression on GEBV for all 3 milk production traits and the Israeli selection index. This was not the case for regression of EBV14 on PA. The regression line intersected with the line of unbiased estimation near the EBV of the bulls with highest values. Because only bulls with high evaluations are of interest for selection, GEBV for these bulls were less biased compared with that of bulls with lower evaluations. The difference in mean EBV14 between bulls born during 2007-2008 selected by GEBV and PA was 65 units. If half of all inseminations are by young bulls, then the annual genetic gain obtained by implementation of genomic evaluation will be 8 units per year (65/8). Because annual gain is currently 107 units, this is a gain of 7%.