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Lipoprotein lipase (LPL) is a key enzyme for lipid metabolism, playing a fundamental role in the composition of fat in adipose tissue and milk. The LPL gene has been seldom investigated in dairy ruminants and barely studied in river buffalo (Bubalus bubalis). The aim of this work was to explore the genetic diversity of LPL and its promoter and to identify functional mutations, using a combined approach based on sequencing, dual-color electrophoretic mobility shift assay, and quantitative PCR. Thirteen consensus sequences for transcription factors were found in the promoter. Eleven SNP were detected, and the attention was focused on the SNP with potential functional effects: g.-446A>G, because the presence of G created a consensus motif for the transcription factor Sp1, and g.107A>G, which was the only exonic SNP. We developed PCR-RFLP methods for genotyping the 2 SNP and calculated the allele frequencies. A strong linkage disequilibrium (D' = 1; r2 = 0.903) was found between the 2 SNP. The dual-color electrophoretic mobility shift assay demonstrated that only genotype g.-446GG allowed the binding of the Sp1 transcription factor, resulting in overexpression of the gene (~2.5 fold), as confirmed by the quantitative PCR results. Haploinsufficiency is proposed as a regulation mechanism. This study adds further knowledge on the structure of the LPL gene and its expression in river buffalo, with potential effects on milk qualitative and quantitative production.

Milkability is a trait related to the milking efficiency of an animal, and it is a component of the herd profitability. Due to its economic importance, milkability is currently included in the selection index of the Italian Simmental cattle breed with a weight of 7.5%. This lowly heritable trait is measured on a subjective scale from 1 to 3 (1 = slow, 3 = fast), and genetic evaluations are performed by pedigree-based BLUP. Genomic information is now available for some animals in the Italian Simmental population, and its inclusion in the genetic evaluation system could increase accuracy of breeding values and genetic progress for milkability. The aim of this study was to test the feasibility and advantages of having a genomic evaluation for this trait in the Italian Simmental population. Phenotypes were available for 131,308 cows. A total of 9,526 animals had genotypes for 42,152 loci; among the genotyped animals, 2,455 were cows with phenotypes, and the other were their relatives. The youngest cows with both phenotypes and genotypes (n = 900) were identified as selection candidates. Variance components and heritability were estimated using pedigree information, whereas genetic and genomic evaluations were carried out using BLUP and single-step genomic BLUP (ssGBLUP), respectively. In addition, a weighted ssGBLUP was assessed using genomic regions from a genome-wide association study. Evaluation models were validated using theoretical and realized accuracies. The estimated heritability for milkability was 0.12 ± 0.01. The mean theoretical accuracies for selection candidates were 0.43 ± 0.08 (BLUP) and 0.53 ± 0.06 (ssGBLUP). The mean realized accuracies based on linear regression statistics were 0.29 (BLUP) and 0.40 (ssGBLUP). No genomic regions were significantly associated with milkability, thus no improvements in accuracy were observed when using weighted ssGBLUP. Results indicated that genomic information could improve the accuracy of breeding values and increase genetic progress for milkability in Italian Simmental.

Fatty acid (FA) profile is one of the most important aspects of the nutritional properties of milk. The FA content in milk is affected by several factors such as diet, physiology, environment, and genetics. Recently, principal component analysis (PCA) and multivariate factor analysis (MFA) have been used to summarize the complex correlation pattern of the milk FA profile by extracting a reduced number of new variables. In this work, the milk FA profile of a sample of 993 Sarda breed ewes was analyzed with PCA and MFA to compare the ability of these 2 multivariate statistical techniques in investigating the possible existence of latent substructures, and in studying the influence of physiological and environmental effects on the new extracted variables. Individual scores of PCA and MFA were analyzed with a mixed model that included the fixed effects of parity, days in milking, lambing month, number of lambs born, altitude of flock location, and the random effect of flock nested within altitude. Both techniques detected the same number of latent variables (9) explaining 80% of the total variance. In general, PCA structures were difficult to interpret, with only 4 principal components being associated with a clear meaning. Principal component 1 in particular was the easiest to interpret and agreed with the interpretation of the first factor, with both being associated with the FA of mammary origin. On the other hand, MFA was able to identify a clear structure for all the extracted latent variables, confirming the ability of this technique to group FA according to their function or metabolic origin. Key pathways of the milk FA metabolism were identified as mammary gland de novo synthesis, ruminal biohydrogenation, desaturation performed by stearoyl-coenzyme A desaturase enzyme, and rumen microbial activity, confirming previous findings in sheep and in other species. In general, the new extracted variables were mainly affected by physiological factors as days in milk, parity, and lambing month; the number of lambs born had no effect on the new variables, and altitude influenced only one principal component and factor. Both techniques were able to summarize a larger amount of the original variance into a reduced number of variables. Moreover, factor analysis confirmed its ability to identify latent common factors clearly related to FA metabolic pathways.

Although milk fat depression (MFD) has been observed and described since the beginning of the last century, all the molecular and biochemical mechanisms involved are still not completely understood. Some fatty acids (FA) originating during rumen biohydrogenation have been proposed as causative elements of MFD. However, contradictory results were obtained when studying the effect of single FA on MFD. An alternative could be the simultaneous evaluation of the effect of many FA using a multivariate approach. The aim of this study was to evaluate the relationship between individual milk FA of ruminal origin and MFD using canonical discriminant analysis, a multivariate technique able to distinguish 2 or more groups on the basis of a pool of variables. In a commercial dairy herd, a diet containing 26% starch on a DM basis induced an unintentional MFD syndrome in 14 cows out of 40. Milk yielded by these 14 animals showed a fat content lower than 50% of the ordinary value, whereas milk production and protein content were normal. The remaining 26 cows secreted typical milk fat content and therefore were considered the control group, even though they ate the same diet. The stepwise discriminant analysis selected 14 milk FA of ruminal origin most able to distinguish the 2 groups. This restricted pool of FA was used, as variables, in a run of the canonical discriminant analysis that was able to significantly discriminate between the 2 groups. Out of the 14 FA, 5 conjugated linoleic acid isomers (C18:2 trans-10,trans-12, C18:2 trans-8,trans-10, C18:2 trans-11,cis-13, C18:2 cis-9,cis-11, C18:2 cis-10,cis-12) and C15:0 iso were more related to the control group, whereas C18:2 trans-10,cis-12, C16:1 trans-6-7, C16:1 trans-9, C18:1 trans-6-8, C18:1 trans-9, C18:1 trans-10, C18:1 cis-11, and C18:3n-3 were positively associated with the MFD group, allowing a complete discrimination. On the basis of these results, we can conclude that (1) the shift of ruminal biohydrogenation from C18:1 trans-11 to C18:1 trans-10 seemed to be strongly associated with MFD; (2) at the same time, other C18:1 trans isomers showed a similar association; (3) on the contrary, conjugated linoleic acid isomers other than C18:2 trans-10,cis-12 seemed to be associated with a normal fat secretion. Results confirmed that MFD is the consequence of a combined effect of the outflow of many ruminal FA, which collectively affect mammary fat synthesis. Because the animals of the 2 groups were fed the same diet, these results suggested that factors other than diet are involved in the MFD syndrome. Feeding behavior (i.e., ability to select dietary ingredients in a total mixed ration), rumen environment and the composition of ruminal bacteria are additional factors able to modify the products of rumen biohydrogenation. Results of the present work confirmed that the multivariate approach can be a useful tool to evaluate a metabolic pathway that involves several parameters, providing interesting suggestions about the role of some FA involved in MFD. However, results about the MFD syndrome obtained in the present research require a deep molecular investigation to be confirmed.

The stearoyl-CoA desaturase (SCD) gene has been investigated in depth in ruminants because of its effect on milk fat composition. In river buffalo, the single nucleotide polymorphism (SNP) g.133A>C in the gene promoter has been associated with milk quality and yield. However, the biological reason for such effects remains unexplored. In this study, we combined mRNA profile analysis, an electromobility shift assay, and quantitative PCR to elucidate the role of this SNP on gene transcription and its effects on milk fat traits. A preliminary genotyping of g.133A>C was carried out on a group of 303 river buffaloes to choose individuals for the downstream applications. Analysis of allele frequencies showed an increase in the minor allele C (0.25) compared with previous findings (0.16). Six animals (2 for each genotype) were chosen for cloning and 216 positive cDNA recombinant clones for SCD (72 per genotype) were analyzed by PCR. All clones showed the same length on agarose gel; therefore, random clones were chosen for sequencing. No qualitative differences were found and all gene transcripts assembled correctly. An electrophoretic mobility shift assay was performed to evaluate the binding of the transcription factor Sp1 to DNA sequences including g.133A>C. Genotype CC showed a higher binding affinity (mean ± standard error of the mean) than genotype AA in 2 different conditions [Enzo buffer (EB), Enzo Life Science Inc., Farmingdale, NY: 201.77 ± 4.06 vs. 141.65 ± 3.77 band intensity values and Poletto buffer (PB): 95.90 ± 1.15 vs. 67.30 ± 2.14 band intensity values]. The subsequent quantitative PCR confirmed the upregulation of the CC genotype compared with the AA and AC genotypes. The association study with milk fat traits revealed a favorable effect of allele C. The heterozygous genotype had the highest values for monounsaturated fatty acids, oleic acid (C18:1 cis-9), polyunsaturated fatty acids, and odd- and branched-chain fatty acids, and the lowest values for saturated fatty acids and atherogenic and thrombogenic indices; the heterozygous genotype differed significantly from the AA genotype. The AC genotype has previously been associated with higher milk yield. Therefore, the g.133A>C SNP is a marker with dual effects and is an interesting candidate for assisted selection programs in river buffalo. These data clarified the biological role of the SNP g.133A>C in the SCD promoter and how it affects gene function, providing important knowledge on the genetic background of lipid metabolism, including the future possibility of selecting alleles with quantitatively or qualitatively favorable effects.

Functional traits, such as fertility and lactation persistency, are becoming relevant breeding goals for dairy cattle. Fertility is a key element for herd profitability and animal welfare; in particular, calving interval (CIN) is an indicator of female fertility that can be easily recorded. Lactation persistency (LPE; i.e., the ability of a cow to maintain a high milk yield after the lactation peak) is economically important and is related to several other traits, such as feed efficiency, health, and reproduction. The selection of these functional traits is constrained by their low heritability. In this study, variance components for CIN and LPE in the Italian Simmental cattle breed were estimated using genomic and pedigree information under the single-step genomic framework. A data set of 594,257 CIN records (from 275,399 cows) and 285,213 LPE records (from 1563,389 cows) was considered. Phenotypes were limited up to the third parity. The pedigree contained about 2 million animals, and 7,246 genotypes were available. Lactation persistency was estimated using principal component analysis on test day records, with higher values of the second extracted principal component (PC2) values associated with lower LPE, and lower PC2 values associated with higher LPE. Heritability of CIN and LPE were estimated using single-trait repeatability models. A multiple-trait analysis using CIN and production traits (milk, fat, and protein yields) was performed to estimate genetic correlations among these traits. Heritability for CIN in the single-trait model was low (0.06 ± 0.002). Unfavorable genetic correlations were found between CIN and production traits. A measure of LPE was derived using principal component analysis on test day records. The heritability and repeatability of LPE were 0.11 ± 0.004 and 0.20 ± 0.02, respectively. Genetic correlation between CIN and LPE was weak but had a favorable direction. Despite the low heritability estimates, results of the present work suggest the possibility of including these traits in the Italian Simmental breeding program. The use of a single-step approach may provide better results for young genotyped animals without their own phenotypes.

Milk yield and composition are of great economic importance for the dairy goat industry. The identification of genes associated with phenotypic differences for these traits could allow for the implementation of gene-assisted selection programs in goats. Associations between polymorphisms at 3 candidate genes and milk production traits in Alpine goats farmed in Italy were investigated in the present research. Considered genes were acetyl-coenzyme A carboxylase α (ACACA), the major regulatory enzyme of fatty acid biosynthesis; stearoyl-coenzyme A desaturase (SCD), involved in the biosynthesis of monounsaturated fatty acids in the mammary gland; and lipoprotein lipase (LPL), which plays a central role in plasma triglyceride metabolism. An approach somewhat similar to the granddaughter design for detecting quantitative trait loci in dairy cattle was followed. Effects of genotypes of a sample of 59 Alpine bucks on phenotypes of their 946 daughters raised in 75 flocks were investigated. Data comprised 13,331 daily records for milk yields (L/d), fat and protein yields (kg/d), and fat and protein contents (%) of 2,200 lactations. Population genetics parameters were calculated and associations between milk production traits and 10 single nucleotide polymorphisms (SNP) at the 3 genes were tested. Two markers at the ACACA, 1 for the SCD and 1 at the LPL locus, deviated significantly from the Hardy-Weinberg equilibrium, with an observed heterozygosity lower than expected. Flock, age of the goat, kidding season, and stage of lactation affected all traits considered, except fat percentage. Three SNP were found to be significantly associated with milk production traits. The SNP located on the ACACA gene showed an effect on milk yield, with daughters of TT bucks having an average test-day milk yield of about 0.3 to 0.25 L/d lower than the other 2 genotypes. The marker on the LPL locus was highly associated with milk yield, with the largest values for CC daughters (about 0.50L more than GG). The TGT deletion located on the untranslated region of the SCD gene showed significant effects on average milk and protein yields. The homozygote-deleted genotype had values about 0.5 L/d and 16 g/d lower for milk and protein daily yield, respectively, compared with the TGT/TGT genotype. Differences between genotypes were quite constant across most of the lactation. Associations found in the present study, which should be tested in a larger sample, especially for those markers that show rare genotypes, may offer useful indications for the genetic improvement of dairy traits in goats.

The large number of markers available compared with phenotypes represents one of the main issues in genomic selection. In this work, principal component analysis was used to reduce the number of predictors for calculating genomic breeding values (GEBV). Bulls of 2 cattle breeds farmed in Italy (634 Brown and 469 Simmental) were genotyped with the 54K Illumina beadchip (Illumina Inc., San Diego, CA). After data editing, 37,254 and 40,179 single nucleotide polymorphisms (SNP) were retained for Brown and Simmental, respectively. Principal component analysis carried out on the SNP genotype matrix extracted 2,257 and 3,596 new variables in the 2 breeds, respectively. Bulls were sorted by birth year to create reference and prediction populations. The effect of principal components on deregressed proofs in reference animals was estimated with a BLUP model. Results were compared with those obtained by using SNP genotypes as predictors with either the BLUP or Bayes_A method. Traits considered were milk, fat, and protein yields, fat and protein percentages, and somatic cell score. The GEBV were obtained for prediction population by blending direct genomic prediction and pedigree indexes. No substantial differences were observed in squared correlations between GEBV and EBV in prediction animals between the 3 methods in the 2 breeds. The principal component analysis method allowed for a reduction of about 90% in the number of independent variables when predicting direct genomic values, with a substantial decrease in calculation time and without loss of accuracy.

Associations of allelic variants of the β-lactoglobulin ( LGB ), stearoyl-coenzyme A desaturase 1 ( SCD ), and sterol regulatory element binding protein ( SREBP-1 ) genes with milk production, composition (fat, protein, and casein content), acidity (pH and titratable acidity), and coagulation properties (rennet coagulation time and curd firmness) were investigated in Brown Swiss cows. In total, 294 animals (progeny of 15 sires) reared in 16 herds were milk-sampled once. The additive effects of LGB (rs109625649:C>T), SCD (ss469414220:C>T), and SREBP-1 (AB355704: g.101_185ins) polymorphisms on the aforementioned traits were analyzed through Bayesian linear models. The LGB genotype affected rennet coagulation time, with TT (or BB) alleles showing longer rennet coagulation time compared with CC (or AA) cows. The SCD gene allelic variants were found to be associated with protein and casein contents and curd firmness: CC animals had the lowest values for the aforementioned traits. An association was found between SREBP-1 alleles and fat content, with the highest values for cows carrying the 84-bp insertion (or L) allele. Results suggest a possible use of these loci in gene-assisted selection programs for the improvement of milk quality traits and coagulation properties in Brown Swiss cattle.

Genome-wide selection aims to predict genetic merit of individuals by estimating the effect of chromosome segments on phenotypes using dense single nucleotide polymorphism (SNP) marker maps. In the present paper, principal component analysis was used to reduce the number of predictors in the estimation of genomic breeding values for a simulated population. Principal component extraction was carried out either using all markers available or separately for each chromosome. Priors of predictor variance were based on their contribution to the total SNP correlation structure. The principal component approach yielded the same accuracy of predicted genomic breeding values obtained with the regression using SNP genotypes directly, with a reduction in the number of predictors of about 96% and computation time of 99%. Although these accuracies are lower than those currently achieved with Bayesian methods, at least for simulated data, the improved calculation speed together with the possibility of extracting principal components directly on individual chromosomes may represent an interesting option for predicting genomic breeding values in real data with a large number of SNP. The use of phenotypes as dependent variable instead of conventional breeding values resulted in more reliable estimates, thus supporting the current strategies adopted in research programs of genomic selection in livestock.

Associations between stearoyl-CoA desaturase (SCD) gene polymorphisms and milk production traits (milk, fat, and protein yields, fat and protein contents, somatic cell score) were investigated on a sample of 701 lactations of 313 Italian Holsteins. Test-day records (5,097) were analyzed with a mixed linear model that included the fixed effects of herd, date of test, parity, genotype at the SCD locus, and lactation interval nested within SCD genotype, and the random effect of cow. An effect of the SCD genotype on milk and protein yields was detected, with VV cows producing more milk (about 2 kg/d) and protein (about 0.07 kg/d) compared with AA cows. The contribution of the SCD locus to the phenotypic variance of the 2 traits was about 0.015. These results suggest a possible use of the SCD locus in gene-assisted selection programs for the improvement of milk production traits in dairy cattle, although large-scale studies in different breeds are required.

Milk composition and its technological properties are traits of interest for the dairy sheep industry because almost all milk produced is processed into cheese. However, several variables define milk technological properties and a complex correlation pattern exists among them. In the present work, we measured milk composition, coagulation properties, and individual cheese yields in a sample of 991 Sarda breed ewes in 47 flocks. The work aimed to study the correlation pattern among measured variables and to obtain new synthetic indicators of milk composition and cheese-making properties. Multivariate factor analysis was carried out on individual measures of milk coagulation parameters; cheese yield; fat, protein, and lactose percentages; somatic cell score; casein percentage; NaCl content; pH; and freezing point. Four factors that were able to explain about 76% of the original variance were extracted. They were clearly interpretable: the first was associated with composition and cheese yield, the second with udder health status, the third with coagulation, and the fourth with curd characteristics. Factor scores were then analyzed by using a mixed linear model that included the fixed effect of parity, lambing month, and lactation stage, and the random effect of flock-test date. The patterns of factor scores along lactation stages were coherent with their technical meaning. A relevant effect of flock-test date was detected, especially on the 2 factors related to milk coagulation properties. Results of the present study suggest the existence of a simpler latent structure that regulates relationships between variables defining milk composition and coagulation properties in sheep. Heritability estimates for the 4 extracted factors were from low to moderate, suggesting potential use of these new variables as breeding goals.

The study of relationships between mathematical properties of functions used to model lactation curves is usually limited to the evaluation of the goodness of fit. Problems related to the existence of different lactation curve shapes are usually neglected or solved drastically by considering shapes markedly different from the standard as biologically atypical. A deeper investigation could yield useful indications for developing technical tools aimed at modifying the lactation curve in a desirable fashion. Relationships between mathematical properties and lactation curve shapes were analyzed by fitting several common functions (Wood incomplete gamma, Wilmink's exponential, Ali and Schaeffer's polynomial regression, and fifth-order Legendre polynomials) to 229,518 test-day records belonging to 27,837 lactations of Italian Simmental cows. Among the best fits (adjusted r 2 higher than 0.75), the 3-parameter models (Wood and Wilmink) were able to detect 2 main groups of curve shape: standard and atypical. Five-parameter models (Ali and Schaeffer function and the Legendre polynomials) were able to recognize a larger number of curve shapes. The higher flexibility of 5-parameter models was accompanied by increased sensitivity to local random variation as evidenced by the bias in estimated test-day yields at the beginning and end of lactation (border effect). Meaning of parameters, range of their values and of their (co) variances are clearly different among groups of curves. Our results suggest that analysis based on comparisons between parameter values and (co)variances should be done carefully. Comparisons among parameter values and (co)variances could yield more robust, reliable, and easy to interpret results if performed within groups based on curve shape.

Autoregressive Moving Average (ARMA) models, originally developed in the context of time series analysis, were used to predict Test Day (TD) yields of milk production traits in dairy cows. ARMA models are able to take into account both the average lactation curve of homogeneous groups of animals and the residual individual variability that may be explained in terms of probability models, such as Autoregressive (AR) and Moving Average (MA) processes. Milk, fat, and protein yields of 6000 Italian Simmental cows with 8 TD records per lactation were analyzed. Data were grouped according to parity (1st, 2nd, and 3rd calving) and fitted to a Box-Jenkins ARMA model in order to predict TD yields in five situations of incomplete lactations. Reasonable accuracies have been obtained for a limited horizon of prediction: average correlations among actual and predicted data were 0.85, 0.72, and 0.80 for milk, fat and protein yields when the first predicted TD was one step ahead (on average 42d) of the last actual record available. Cumulative 305-d yields were calculated using all actual (actual yields) or actual plus forecasted (estimated yields) daily yields. Accuracy of lactation predictions was remarkable even when only a few actual TD records were available, with values of 0.88 for milk and protein and 0.84 for fat for the correlations between actual and estimated yields when 6 out of 8 TD records were predicted. Accuracy rapidly increases with the number of actual TD available: correlations were about 0.96 for milk and protein and 0.93 for fat when 4 out of 8 TD records were predicted. In comparison with other prediction methods, ARMA models are very simple and can be easily implemented in data recording software, even at the farm level.

In recent years, an increase in the number of donkeys farmed in Italy as a consequence of the growing demand for donkey milk for direct consumption has been observed. Some research has been carried out on jenny milk composition and on its nutritional properties, whereas milk production features are scarcely described for this species. In this work, the lactation curve shape of donkeys for milk yield and composition was investigated. A total of 453 test-day records for milk yield, fat and protein percentage, and somatic cell count of 62 lactations measured on 46 multiparous jennies of the Ragusano breed were considered. Effects of herd, age, and foaling season were assessed by using a mixed model analysis. Average and individual lactation curves were fitted using the Wood incomplete gamma function, the Cappio-Borlino modified gamma, and a third-order Legendre orthogonal polynomial model. Donkeys foaling between 6- and 10-yr-old had the highest test-day milk yield (about 1.85 kg/d). Donkeys foaling in winter and autumn had a higher daily milk yield compared with those foaling in summer and spring. Less defined results were obtained for composition traits. The general pattern of the donkey lactation curve is similar to the standard shape reported for the main dairy ruminant species, with a peak yield occurring at about 5 wk from parturition. Younger jennies tended to have lower production peaks and higher lactation persistency. Similarly to what is reported for dairy cattle, a large variability in individual patterns has been observed. No differences in goodness of fit have been observed between the models in the case of average lactation curves, whereas orthogonal polynomials were more efficient in fitting individual patterns.

The aim of this study was to elucidate the structure of relationships between milk yield, composition, and coagulation properties of Brown Swiss cattle. Multivariate factor analysis was used to derive new synthetic variables that can be used for selection purposes. For this reason, genetic parameters of these new variables were estimated. Individual records on milk yield, fat and protein percentages, casein content, lactose percentage, somatic cell count, titratable acidity, and pH were taken on 1,200 Italian Brown Swiss cows located in 38 herds. Factor analysis was able to extract 4 latent variables with an associated communality equal to 70% of the total original variance. The 4 latent factors were interpreted as indicators of milk composition, coagulation, acidity, and mammary gland health, respectively. Factor scores calculated for each animal exhibited coherent patterns along the lactation and across different parities. Estimation of genetic parameters of factor scores carried out with a multiple-trait Bayesian hierarchical model showed moderate to low heritabilities (raging from 0.10 to 0.23) and genetic correlations (from −0.15 to 0.46). Results of the present study support the hypothesis of a simpler structure that controls, at least in part, the covariance of milk composition and coagulation properties. Moreover, extracted variables may be useful for both breeding and management purposes, being able to represent, with a single value for each animal, complex traits such as milk coagulation properties or health status of the mammary gland.

Milk fatty acid composition is a parameter of great interest for evaluation of nutritional quality of milk. Stearoyl-CoA desaturase (SCD) is a key enzyme in mammary lipid metabolism because it is able to add a double bond in the cis Δ 9 -position in a large spectrum of medium- and long-chain fatty acids. A polymorphism with 2 alleles (A and V) in the fifth exon of the SCD gene has been reported. The effect of SCD genotype on individual milk fatty acid composition and on cis -9 unsaturated/saturated fatty acid ratios of 297 Holstein Italian Friesian cows was investigated in this paper. The SCD genotypes were determined by using a single strand conformation polymorphism method. Relative frequencies of SCD genotypes were 27, 60, and 13% for AA, AV, and VV, respectively. Milk of AA cows had a greater content of cis -9 C18:1 and total monounsaturated fatty acids and a higher C14:1/C14 ratio than did milk of VV cows. The relative contribution of SCD genotype to variation of monounsaturated fatty acids, cis -9 C18:1, and cis -9 C14:1 was 5, 4, and 7.7%, respectively. No significant differences were detected between SCD genotypes in the milk content of cis -9, trans -11 C18:2. Results of the present work provide some indication of an association between SCD locus and the fatty acid profile in the examined sample of Italian Holsteins, thus suggesting a possible role of this gene in the genetic variation of milk nutritional properties.