Effects of different temperature-humidity indexes on milk traits of Holstein cows: a 10-yr retrospective study

Test-day records (n = 723,091) collected between 2012 and 2021 from 43,015 Holsteins in 157 farms located in Northern Italy were used to study the effects of heat load on milk production and composition a posteriori. The data consisted of milk yield (kg/d), traditional gross composition traits, somatic cell score (SCS), differential SCC (%), milk β-hydroxybutyrate (BHB, mmol/L), milk urea (mg/dL), and milk FA composition (g/100 g milk). Test-day records were then associated to their relative temperature-humidity indexes (THI) calculated using historical environmental data registered by weather stations. Indexes were created using either yearly or summer THI data: the first included the average daily THI (adTHI) and the maximum daily THI (mdTHI) measured throughout the whole year, whereas summer indexes were focused on 3 mo only (June to August) and included the average daily THI (adTHIs), the maximum daily THI (mdTHIs), and the average daily THI of the hottest 4 h of the day (adTHI4h, 12:00 to 4:00 p.m.). All indexes had significant effects on the majority of milk traits analyzed, with in particular adTHI and mdTHI being highly significant in explaining the variation of all traits. Milk yield started to decline at a higher THI compared with protein and fat content. The reduction in fat ceased in the elevated THI experienced during the summer months as demonstrated by adTHIs, mdTHIs, and adTHI4h. The cows had a tendency for increased BHB concentration with elevated THI, suggesting that there is a greater risk of negative energy balance in presence of heat stress. What is more, the concentration of the de-novo fatty acids C14:0 and C16:0 was reduced in higher THI, reflecting the altered mammary gland activity upon elevated heat load and stress. Milk SCS tended to increase with higher adTHIS, mdTHIS, and adTHI4h. The use of yearly indexes is recommended when investigating the effects of heat load on milk composition, while summer indexes are suggested when investigating traits influenced by extreme conditions, such as SCS and milk yield. With global temperatures set to further rise in the upcoming decades, early and easy identification of cows or herds suffering from heat stress, as through changes in milk composition, is crucial for timely intervention. Adapting measures to mitigate such effects of elevated THI on milk yield and composition is a necessity for the dairy industry to prevent detrimental impacts on dairy production.

Associations between subclinical intramammary infection and milk fatty acid profile at the quarter level in Holstein cattle

Mastitis, especially the subclinical form, is the most common economic and health problem in dairy cows. Little is known about changes in milk fatty acid (FA) composition according to infection/inflammation status of the mammary gland. The aim of this study was to investigate the associations between naturally occurring subclinical intramammary infection (IMI) from different pathogens, i.e. Streptococcus agalactiae, Staphylococcus aureus, Streptococcus uberis and Prototheca spp., and the detailed milk FA profile assessed at quarter level in Holstein cows. After an initial bacteriological screening (T0) on 450 Holstein cows reared in three dairy herds, we identified 78 cows positive at the bacteriological examination. These animals were followed up at the quarter level two weeks (T1) and six weeks (T2) after T0. In total, 600 single-quarter samples were obtained at T1 and T2. Individual FAs were determined using the gas chromatography analytical method. Investigated traits were 70 individual FAs, 12 FA groups, and six desaturation indices. The associations between subclinical IMI combined with somatic cell count (SCC) and milk FA profile were investigated using a hierarchical linear mixed model (i.e., observational unit was quarter within cow) with the following fixed effects: days in milk (DIM), parity, herd, SCC, bacteriological status (BACT, positive and negative), and the SCC × BACT interaction. The random effect of individual cow nested within herd, DIM and parity was used as the error term for the latter effects. The most significant associations were detected at T2. Notably, IMI reduced the proportions of individual short-chain FA, especially 4:0 and 6:0 (-14%), but increased the proportion of the most abundant medium-chain FA (MCFA), 16:0 (+4%). A reduction in the desaturation indices was observed mostly for 14:1 index (-9%), in line with the reduction in 14:1 (-10%). Somatic cell count significantly affected 14 individual FAs. In particular, samples with high SCC (≥200 000) had significantly lower proportions of 8:0, 10:0, 11:0, 12:0, and 13:0 compared with samples with low SCC (<200 000). Increasing SCC in animals positive at the bacteriological examination were associated with a reduction in total MCFA at T2 (while in negative animals, they remained constant across SCC classes), possible evidence that elongation of the FA chain from 11 to 16 carbons is affected by a combination of infection and SCC. This study showed that subclinical IMI and SCC are mainly associated with reductions in the synthesis of FA and the desaturation process in the mammary gland.

Comparative analysis of total protein, casein, lactose, and fat content in milk of cows suffering from subclinical and clinical mastitis caused by Streptococcus spp

Introduction

The aim of the study was to analyse the total protein (TP), casein (CAS), lactose (LAC), and fat content of milk from cows with subclinical (SCM) and clinical mastitis (CM) caused by Streptococcus spp.

Material and Methods

A total of 60 milk samples from diseased cows and 30 milk samples from healthy cows were included in the study. Milk samples were taken from Holstein-Friesian cows from four dairy farms in Lublin Province. The bacteriological examination of the milk was performed and the somatic cells count in 1 mL of milk was determined using a SomaCount FC automatic cell counter. Determination of TP, CAS, LAC, FAT and FA levels in milk was carried out using a DairySpec FT automated Fourier transform infrared spectrometer.

Results

Total protein in milk from HE was significantly higher than in milk from cows with mastitis (4.04% vs 3.57% in milk from SCM cows and 3.7% in milk from CM cows, P = 0.001). The CAS level was 2.73% in milk from CM cows and 2.92% in milk from SCM cows vs 3.30% in milk from HE cows, P = 0.001. The changes in CAS and TP in milk resulted in a significant difference in the CAS/TP ratio (81.7% in milk from HE cows vs 73.8% in milk from CM cows). A decrease in levels was also recorded for LAC (4.8% in milk from HE cows vs 4.51% in milk from SCM cows and 4.01% in milk from CM cows, P = 0.001). The fat level was significantly higher in milk from healthy cows than in milk from cows with mastitis (4.0% vs 2.3% in milk from SCM cows and 1.64% in milk from CM cows, P = 0.001).

Conclusion

It should be emphasised that the decrease in the levels of TP, LAC and FAT was significant not only in milk from CM cows but also in milk from SCM cows. This is very unfavourable, because the reduction in the main milk components results in poor quality dairy products and impairs line processes.

Role of functional fatty acids in modulation of reproductive potential in livestock

Fatty acids are not only widely known as energy sources, but also play important roles in many metabolic pathways. The significance of fatty acids in modulating the reproductive potential of livestock has received greater recognition in recent years. Functional fatty acids and their metabolites improve follicular development, oocyte maturation and embryo development, as well as endometrial receptivity and placental vascular development, through enhancing energy supply and precursors for the synthesis of their productive hormones, such as steroid hormones and prostaglandins. However, many studies are focused on the impacts of individual functional fatty acids in the reproductive cycle, lacking studies involved in deeper mechanisms and optimal fatty acid requirements for specific physiological stages. Therefore, an overall consideration of the combination and synergy of functional fatty acids and the establishment of optimal fatty acid requirement for specific stages is needed to improve reproductive potential in livestock.

Summer Buffalo Milk Produced in China: A Desirable Diet Enriched in Polyunsaturated Fatty Acids and Amino Acids

The objective of the study was to compare and reveal differences in basic chemical parameters, fatty acids, amino acids, and lipid quality indices of crossbred buffalo (swamp x river type) milk produced in summer and winter. The buffalo milk samples were collected in summer (Jul-Aug) and winter (Dec-Jan) from Hubei province, China. The samples were detected by using CombiFoss apparatus, gas chromatography, and an automated specialized amino acid analyzer. The results showed that the basic chemical parameters, fatty acid profiles, lipid quality indices, and amino acid profiles of crossbred buffalo milk differed between summer and winter. Specifically, summer buffalo milk exhibited a higher content of MUFA (monounsaturated fatty acids) and PUFA (polyunsaturated fatty acids) than winter buffalo milk. Summer buffalo milk had a lower content of major SFA (saturated fatty acids), a higher content of ω-3 and DFA (hypocholesterolemic fatty acids), a lower ω-6/ω-3 ratio, a higher value of 3 unsaturated fatty acid indices (C14, C16, C18), and a lower value of IA (index of atherogenicity) and IT (index of thrombogenicity) than winter buffalo milk. Additionally, 17 amino acids, including 8 EAA (essential amino acids) and 9 NEAA (non-essential amino acids) were higher in summer buffalo milk. These results indicated that summer buffalo milk was more health-beneficial than winter buffalo milk. Therefore, summer buffalo milk might be a desirable diet option for human nutrition and health. Our findings provide valuable information for the research and development of buffalo dairy products in China or other Asian countries.

Dairy Sheep Grazing Management and Pasture Botanical Composition Affect Milk Macro and Micro Components: A Methodological Approach to Assess the Main Managerial Factors at Farm Level

Simple Summary

Studies on the management factors that affect milk components at the farm level are important for understanding how to transfer the results from experimental study. Plant phenological stages and partially fresh herbage intakes affect the lactose and milk fatty acid profile. The botanical composition of the grassland partially affects the milk’s phenol content. A few small relationships between plant phenols and milk colour could be of interest to explain the changes in milk colour parameters.

Abstract

The fatty acid profile, vitamins A and E, cholesterol, antioxidant power colour and the phenols profile of Sarda sheep milk from 11 commercial sheep flocks managed under permanent grassland were investigated. In each farm, the structural and managerial data and milk samples were collected during four periods (sampling dates, SD): January, March, May, and July. Data from the milk composition (fat, protein, casein, lactose, and somatic cell count), 68 fatty acids, 7 phenols, 1 total gallocatechin equivalent, ferric reducing antioxidant power, vitamins A and E, cholesterol, degree of antioxidant protection, and the colour (b *, a * and L *) were analyzed by multivariate factorial analysis using a principal component analysis approach. A proc mixed model for repeated measurement to point out the studied factors affecting significant macro and micro milk composition was also used. Only the first five components were detailed in this paper, with approximately 70% of the explained variance detected. PC1 presented the highest positive loadings for milk lactose, de novo FA synthesis and the BH intermediate, whereas OBCFA had negative loadings values. The PC2, LCFA, UFA, MUFA, vitamins E, and DAP showed positive loadings values, while SFA had a negative value. The PC3 showed a high positive loading for total phenols and non-flavonoids. PC4 presented a high positive loading for the milk macro-composition and negative values for n-3 FAs. The PC5 is characterized by high positive loadings for the a * and L * colour parameters whereas negative loadings were detected for the milk flavonoids content. These preliminary results could help to establish future threshold values for the biomarkers in milk sourced from grazing dairy sheep in natural, permanent pasture-based diets.

Effect of Flaxseed Supplementation on Milk and Plasma Fatty Acid Composition and Plasma Parameters of Holstein Dairy Cows

The objective of this study was to determine the effect of whole flaxseed and ground flaxseed supplementation on the composition of fatty acids in plasma and milk, particularly the content of omega-3 polyunsaturated fatty acids (n-3 PUFAs). Thirty Holstein dairy cows were randomly assigned to three treatment groups. Cows were fed a total mixed ration without flaxseed (CK), 1500 g of whole flaxseed (WF), and 1500 g of ground flaxseed (GF) supplementation. There were no differences observed in dry matter intake, milk yield, energy-corrected milk, and 4% fat-corrected milk (p > 0.05). Compared with the CK group, the contents of α-linolenic acid (ALA), eicosatrienoic acid, and eicosapentaenoic acid increased in the plasma and milk WF and GF groups, and the content of docosahexaenoic acid and total n-3 PUFA was higher in GF than the other groups (p < 0.001). The ALA yield increased to 232% and 360% in WF and GF, respectively, compared to the CK group. Compared with the WF group, GF supplementation resulted in an increased milk ALA/ALA intake ratio (p < 0.001). Flaxseed supplementation increased the activity of GSH-Px and decreased the concentration of MDA in milk (p < 0.001). Plasma parameters did not differ among the treatments (p > 0.05). This result indicated that compared with the WF group, GF supplementation in the diet showed higher efficiency in increasing the total n-3 PUFA levels and the milk ALA/ALA intake ratio, and decreased the ratio of n-6 PUFAs to n-3 PUFAs in milk.

Odd- and branched-chain fatty acids in milk fat from Holstein dairy cows are influenced by physiological factors

Dairy products are the major source of odd- and branched-chain fatty acids (OBCFAs), a group of nutrients with emerging health benefits. The animal diet is known to influence milk fat OBCFAs of dairy cows; however, little is known about the effects of physiological factors. The objective of this study was to investigate the effects of parity and lactation stage on OBCFAs in milk fat of dairy cows. Holstein dairy cows (n = 157) were selected according to parity (first, second, third, or greater) and days in milk (DIM) (≤21 DIM, 21 < DIM ≤ 100, 100 < DIM ≤ 200, >200 DIM). All cows were fed the same total mixed ration for three weeks. Milk samples were collected during the last three days of each lactation stage for fatty acid (FA) analyses via gas chromatography. Results showed that first- and second-parity cows displayed significantly higher proportions and yields of iso-14:0, iso-15:0, iso-16:0, total iso-FA, and total branched-chain FA (P < 0.05) compared with other parities. The proportions of C17:0 and C17:1 cis-9 were also greater in first-parity cows (P < 0.05), while the yields of C17:0 and C17:1 cis-9 were similar among different parities (P > 0.05). The proportions of total OBCFAs were greater in first- and second-parity cows (P < 0.05), whereas the highest yield was observed in second-parity cows. Lactation dairy cows in ≤ 21 DIM group displayed lower proportions of iso-13:0, anteiso-13:0, C13:0, iso-14:0, C15:0, iso-16:0, total iso-FA, and total OBCFAs compared with that of the other groups (P < 0.05), and also lower yields of iso-14:0 and iso-16:0 (P < 0.05). In contrast, C17:0 and C17:1 cis-9 proportions and yields were higher in dairy cows with ≤ 21 DIM (P < 0.05). Iso-17:0 and anteiso-17:0 were not affected by lactation stage (P > 0.05). Taken together, our data showed that both parity and lactation stage have considerable effects on milk fat OBCFAs of dairy cows. In summary, first- and second-parity cows had higher milk OBCFAs compared with later parity cows, and OBCFAs with medium chain lengths were lower in dairy cows with ≤ 21 DIM, while C17:0 and C17:1 cis-9 were higher. These findings show that milk OBCFA contents are differentially modulated by physiological state. They will be useful in future studies that seek to alter OBCFA composition of Holstein dairy cow milk fats.

Associations between Milk Fatty Acid Profile and Body Condition Score, Ultrasound Hepatic Measurements and Blood Metabolites in Holstein Cows

Dairy cows have high incidences of metabolic disturbances, which often lead to disease, having a subsequent significant impact on productivity and reproductive performance. As the milk fatty acid (FA) profile represents a fingerprint of the cow’s nutritional and metabolic status, it could be a suitable indicator of metabolic status at the cow level. In this study, we obtained milk FA profile and a set of metabolic indicators (body condition score, ultrasound liver measurements, and 29 hematochemical parameters) from 297 Holstein–Friesian cows. First, we applied a multivariate factor analysis to detect latent structure among the milk FAs. We then explored the associations between these new synthetic variables and the morphometric, ultrasonographic and hematic indicators of immune and metabolic status. Significant associations were exhibited by the odd-chain FAs, which were inversely associated with β-hydroxybutyrate and ceruloplasmin, and positively associated with glucose, albumin, and γ-glutamyl transferase. Short-chain FAs were inversely related to predicted triacylglycerol liver content. Rumen biohydrogenation intermediates were associated with glucose, cholesterol, and albumin. These results offer new insights into the potential use of milk FAs as indicators of variations in energy and nutritional metabolism in early lactating dairy cows.

Study of the Fatty Acid Profile of Milk in Different Sheep Breeds: Evaluation by Multivariate Factorial Analysis

Simple Summary

The quality of milk is strongly influenced by its lipid profile. The increase in fats with nutraceutical properties at the expense of those negative for human health, has always been a goal to improve the functional properties of milk. To achieve this goal, it is essential to know the metabolism of the mammary gland and the relationship between the various lipid components. Much is known about bovine milk, while the aspect relating to the sheep species has not been developed. The present work aims to investigate the relationships between the various fatty acids in sheep’s milk through a multivariate approach, which can highlight the mammary role of lipid synthesis.

Abstract

A multivariate analysis was used to investigate the fatty acid (FA) profile in three different Italian sheep breeds: Comisana, Massese, and Sarda. A sample of 852 animals was considered: 118 Massese, 303 Comisana, 431 Sarda. Sarda sheep were divided into two groups, based on their breeding origin (298 and 133 reared in Sardinia and Tuscany, respectively). Sarda sheep, bred both in Sardinia and in Tuscany, were considered in different groups, both because in these two regions most of the sheep of this breed are reared, and because they differ in geographical characteristics and in the farming system. The individual milk FA composition of dairy ewes was analyzed with multivariate factor analysis. The extracted factors were representative of the following eight groups of fatty acids or functions: factor 1 (odd branched fatty acids and long-chain fatty acids), factor 2 (sn3_position), factor 3 (alternative biohydrogenation), factor 4 (SCD_1), factor 5 (SCD_2), factor 6 (SCD_3), factor 7 (fat secretion) and factor 8 (omega-3). A factor analysis suggested the presence of different metabolic pathways for de novo short- and medium-chain fatty acids and Δ9-desaturase products. The ANOVA of factor scores highlighted the significant effects of the breed. The results of the present study showed that breed is an important factor in defining the fatty acid profile of milk, combined with the effect of the diet. Breeds reared in the same farming system (Comisana, Massese and Sarda reared in Tuscany) showed significant differences for all the factors extracted. At the same time, we found differences between the Sarda sheep reared in Sardinia and Tuscany, two different regions of Italy.

Can FT-Mid-Infrared Spectroscopy of Milk Samples Discriminate Different Dietary Regimens of Sheep Grazing With Restricted Access Time?

Milk obtained from sheep grazing natural pastures and some forage crops may be worth a plus value as compared to milk obtained from stall-fed sheep, due to their apparently higher content of beneficial fatty acids (FAs). Fourier transformed mid-infrared (FT-MIR) analysis of FA can help distinguish milk from different areas and diverse feeding systems. The objective was to discriminate milk from sheep and milk from dairy sheep rotationally grazing Italian ryegrass or berseem clover for 2, 4, or 6 h/day. To test this hypothesis, a data-mining study was undertaken using a database of 1,230 individual milk spectra. Data were elaborated by principal component analysis (PCA) and analyzed by linear discriminant analysis (LDA) with or without the use of genetic algorithm (GA) as a variable selection tool with the primary aim to discriminate grazed forages (grass vs. legume), access time (2, 4, or 6 h/day), grazing day (first vs. last grazing day during the 7-day grazing period), and the milking time (morning vs. afternoon milking). The best-fitting discriminant models of FT-MIR spectra were able to correctly predict 100% of the samples differing for the pasture forage, 91.9% of the samples differing for grazing day, and 97.1% of the samples regarding their milking time. The access time (AT) to pasture was correctly predicted by the model in 60.3% of the samples, and the classification ability was improved to 77.0% when considering only the 2 and 6 h/day classes.

Genetic correlations between fertility traits and milk composition and fatty acids in Holstein-Friesian, Brown Swiss, and Simmental cattle using recursive models

This study aimed to investigate the genetic and putative causal relationships between fertility traits [i.e., days open (DO) and calving rate (CR)] and milk quality, composition, and fatty acid contents (milk composition traits) in Holstein-Friesian, Brown Swiss, and Simmental cattle, using recursive models within a Bayesian framework. Trivariate animal models were run, each including one fertility trait, one milk composition trait, and, in all models, milk yield. The DO and CR data were merged with the test days closest to the insemination date for milk composition traits. After editing, 16,468 to 23,424 records for Holstein-Friesian, 23,424 to 46,660 for Brown Swiss, and 26,105 to 35,574 for Simmental were available for the analyses. Recursive animal models were applied to investigate the possible causal influences of milk composition traits on fertility and the genetic relationships among these traits. The results suggested a potential cause-and-effect relationship between milk composition traits and fertility traits, with the first trait influencing the latter. We also found greater recursive effects of milk composition traits on DO than on CR, the latter with some putative differences among breeds in terms of sensitivity. For instance, the putative causal effects of somatic cell score on CR (on the observed scale, %) varied from -0.96 to -1.39%, depending on the breed. Concerning fatty acids, we found relevant putative effects of C18:0 on CR, with estimates varying from -7.8 to -9.9%. Protein and casein percentages, and short-chain fatty acid showed larger recursive effects on CR, whereas fat, protein, and casein percentages, C16:0, C18:0, and long-chain fatty acid had larger effects on DO. The results obtained suggested that these milk traits could be considered as effective indicators of the effects of animal metabolic and physiological status on fertility.

Principal component and multivariate factor analysis of detailed sheep milk fatty acid profile

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.

Genomic prediction for latent variables related to milk fatty acid composition in Holstein, Simmental and Brown Swiss dairy cattle breeds

Genomic selection (GS) reports on milk fatty acid (FA) profiles have been published quite recently and are still few despite this trait represents the most important aspect of milk nutritional and sensory quality. Reasons for this can be found in the high costs of phenotype recording but also in issues related to its nature of complex trait constituted by multiple genetically correlated variables with low heritabilities. One possible strategy to deal with such constraint is represented by the use of dimension reduction methods. We analysed 40 individual FAs from Italian Brown Swiss, Holstein and Simmental milk through multivariate factor analysis (MFA) to study the genetics of milk FA-related latent variables (factors) and assess their potential use in breeding. A total of nine factors were obtained, and their genetic parameters were inferred under a Bayesian framework using two statistical approaches: the classical pedigree best linear unbiased prediction (ABLUP) and the single-step genomic BLUP (ssGBLUP). The resulting factorial solutions were able to represent groups of FAs with common origin and function and can be considered concise pathway-level phenotypes. The heritability (h² ) values showed relevant variations across different factors in each breed (0.03 ≤ h²  ≤ 0.38). The accuracies of breeding values predicted were low to high, ranging from 0.13 to 0.72 and from 0.18 to 0.74 considering the pedigree and the genomic model, respectively. The gain in accuracy in genetic prediction due to the addition of genomic information was ~30% and ~5% in validation and training groups respectively, confirming the contribution of genomic information in yielding more accurate predictions compared to the traditional ABLUP methodology. Our results suggest that MFA in combination with GS can be a valuable tool in dairy cattle breeding and deserves to be further investigated for use in future breeding programs to improve cow's milk FA-related traits.

Integration of Wet-Lab Measures, Milk Infrared Spectra, and Genomics to Improve Difficult-to-Measure Traits in Dairy Cattle Populations

The objective of this study was to evaluate the contribution of Fourier-transformed infrared spectroscopy (FTIR) data for dairy cattle breeding through two different approaches: (i) estimating the genetic parameters for 30 measured milk traits and their FTIR predictions and investigating the additive genetic correlation between them and (ii) evaluating the effectiveness of FTIR-derived phenotyping to replicate a candidate bull’s progeny testing or breeding value prediction at birth. Records were available from 1,123 cows phenotyped using gold standard laboratory methodologies (LAB data). This included phenotypes related to fine milk composition and milk technological characteristics, milk acidity, and milk protein fractions. The dataset used to generate FTIR predictions comprised 729,202 test-day records from 51,059 Brown Swiss cows (FIELD data). A first approach consisted of estimating genetic parameters for phenotypes available from LAB and FIELD datasets. To do so, a set of bivariate animal models were run, and genetic correlations between LAB and FIELD phenotypes were estimated using FIELD information obtained at the population level. Heritability estimates were generally higher for FIELD predictions than for the corresponding LAB measures. The additive genetic correlations (r_a) between LAB and FIELD phenotypes had different magnitudes across traits but were generally strong. Overall, these results demonstrated the potential of using FIELD information as indicator traits for the indirect genetic improvement of LAB measures. In the second approach, we included genotype information for 1,011 cows from the LAB dataset, 1,493 cows from the FIELD dataset, 181 sires with daughters in both LAB and FIELD datasets, and 540 sires with daughters in the FIELD dataset only. Predictions were obtained using the single-step GBLUP method. A four fold cross-validation was used to assess the predictive ability of the different models, assessed as the ability to predict masked LAB records from daughters of progeny testing bulls. The correlation between observed and predicted LAB measures in validation was averaged over the four training-validation sets. Different sets of phenotypic information were used sequentially in cross-validation schemes: (i) LAB cows from the training set; (ii) FIELD cows from the training set; and (iii) FIELD cows from the validation set. Models that included FIELD records showed an improvement for the majority of traits. This study suggests that breeding programs for difficult-to-measure traits could be implemented using FTIR information. While these programs should use progeny testing, acceptable values of accuracy can be achieved also for bulls without phenotyped progeny. Robust calibration equations are, deemed as essential.

Genetic Parameters of Different FTIR-Enabled Phenotyping Tools Derived from Milk Fatty Acid Profile for Reducing Enteric Methane Emissions in Dairy Cattle

This study aimed to infer the genetic parameters of five enteric methane emissions (EME) predicted from milk infrared spectra (13 models). The reference values were estimated from milk fatty acid profiles (chromatography), individual model-cheese, and daily milk yield of 1158 Brown Swiss cows (85 farms). Genetic parameters were estimated, under a Bayesian framework, for EME reference traits and their infrared predictions. Heritability of predicted EME traits were similar to EME reference values for methane yield (CH₄/DM: 0.232-0.317) and methane intensity per kg of corrected milk (CH₄/CM: 0.177-0.279), smaller per kg cheese solids (CH₄/SO: 0.093-0.165), but greater per kg fresh cheese (CH₄/CU: 0.203-0.267) and for methane production (dCH₄: 0.195-0.232). We found good additive genetic correlations between infrared-predicted methane intensities and the reference values (0.73 to 0.93), less favorable values for CH₄/DM (0.45-0.60), and very variable for dCH₄ according to the prediction method (0.22 to 0.98). Easy-to-measure milk infrared-predicted EME traits, particularly CH₄/CM, CH₄/CU and dCH₄, could be considered in breeding programs aimed at the improvement of milk ecological footprint.

Use of multivariate factor analysis of detailed milk fatty acid profile to perform a genome-wide association study in Italian Simmental and Italian Holstein

Milk fatty acid (FA) profile is a clear example of complex and multiple correlated traits whose genetic basis is difficult to assess. Although genome-wide association (GWA) studies have been successful in the identification of significant genetic variants for complex traits, when correlated phenotypes are analysed separately, the outcomes are difficult to compare and interpret in a metabolic context. Here, we performed a multivariate factor analysis (MFA) on Italian Simmental and Italian Holstein milk fat profiles to extract latent unobserved factors able to explain correlation structure and common metabolic function among different FAs. Individual factor scores obtained by MFA were used to perform a single-SNP based GWA. In both breeds, MFA was able to extract ten latent factors with specific biological meaning, notably: de novo synthesis, desaturation activity and biohydrogenation. The GWA result confirmed the increased power of joint association analysis on multiple correlated traits and allowed us to identify major candidate genes with well-documented function consistent with the metabolic classification of factors obtained, such as DGAT1, FASN and SCD.

Multivariate factor analysis of milk fatty acid composition in relation to the somatic cell count of single udder quarters

The present study investigated whether the fatty acid composition of milk changes in relation to an increase in the milk somatic cell count (SCC) of separate udder quarters. We investigated the potential of multivariate factor analysis to extract metabolic evidence from data on the quantity and quality of milk of quarters characterized by different SCC levels. We collected data from individual milk samples taken from single quarters of 49 Italian Holstein cows from the same dairy farm. Factor analysis was carried out on 64 individual fatty acids. In line with a previous study on multivariate factor analysis, a variable was considered to be associated with a specific factor if the absolute value of its correlation with the factor was ≥0.60. Seven factors were extracted that explained the following groups of fatty acids or functions: de novo synthesis, energy balance, uptake of dietary fatty acids, biohydrogenation, short-chain fatty acids, very long chain fatty acids, and odd- and branched-chain fatty acids. An ANOVA of factor scores highlighted the significant effects of the SCC level on de novo fatty acids and biohydrogenation. The de novo fatty acid factor decreased significantly with a high level of SCC, from just 10,000 cells/mL, whereas the biohydrogenation factor showed a significantly higher level in quarters with SCC levels greater than 400,000 cells/mL. This statistical approach enabled us to reduce the number of variables to a few latent factors with biological significance and to represent groups of fatty acids with a common origin and function. Multivariate factor analysis could therefore be key to studying the influence of SCC on the lipid metabolism of single quarters. This approach also demonstrated the metabolic differences between quarters of the same animal showing a different level of SCC.

Structural equation modeling for investigating multi-trait genetic architecture of udder health in dairy cattle

Mastitis is one of the most prevalent and costly diseases in dairy cattle. It results in changes in milk composition and quality which are indicators of udder inflammation in absence of clinical signs. We applied structural equation modeling (SEM) - GWAS aiming to explore interrelated dependency relationships among phenotypes related to udder health, including milk yield (MY), somatic cell score (SCS), lactose (%, LACT), pH and non-casein N (NCN, % of total milk N), in a cohort of 1,158 Brown Swiss cows. The phenotypic network inferred via the Hill-Climbing algorithm was used to estimate SEM parameters. Integration of multi-trait models-GWAS and SEM-GWAS identified six significant SNPs for SCS, and quantified the contribution of MY and LACT acting as mediator traits to total SNP effects. Functional analyses revealed that overrepresented pathways were often shared among traits and were consistent with biological knowledge (e.g., membrane transport activity for pH and MY or Wnt signaling for SCS and NCN). In summary, SEM-GWAS offered new insights on the relationships among udder health phenotypes and on the path of SNP effects, providing useful information for genetic improvement and management strategies in dairy cattle.

Negative Energy Balance Influences Nutritional Quality of Milk from Czech Fleckvieh Cows due Changes in Proportion of Fatty Acids

The objective of this study was to evaluate the influence of negative energy balance on fatty acids proportion in the milk of Czech Fleckvieh cows after calving. Milk quality was determined based on fatty acid group proportion. Milk quality was evaluated in relation to selected negative energy balance (NEB) traits: body condition change (DEC) and milk citric acid content (CAC) after calving. Milk samples (n = 992) were collected once per week from 248 Czech Fleckvieh cows during the first month of lactation. Fatty acid content (%) in milk samples was determined and results were grouped as saturated (SFA) (hypercholesterolemic or volatile fatty acids) or unsaturated (UFA) (monounsaturated or polyunsaturated). Our results showed that cows with a deep NEB produce milk that is healthier for human consumption. Cows with a more significant DEC or the highest level of CAC in milk had the lowest proportion of SFA and the highest proportion of UFA (p < 0.01). These cows experienced higher physiological stress after calving; however, they produced milk of higher nutritional quality. Nowadays, we can see preventive efforts to mitigate NEB periods as a result of modern breeding trends regarding vitality, robustness, or longevity.

Genetic Analysis of Milk Production Traits and Mid-Infrared Spectra in Chinese Holstein Population

Simple Summary

Usually, spectral data are used as predictors to predict milk components, animal characteristics, and even reproductive status. Another innovative way to use spectral data involves considering spectral wavenumbers as traits and then analyzing from the genetic perspective. In this study, we considered milk spectral data directly as traits, then detected the influence of some non-genetic factors on spectral wavenumbers and estimated the genetic parameters of spectral points. The result of the present study could be used as a management tool for dairy farm and also provides a further understanding of genetic background of milk mid-infrared (MIR) spectra. In future, milk spectral data could be applied more effective. For example, some sub-clinical diseases might be detected based on the difference between the expected and observed values of the spectral traits. In addition, we could also use genetic correlation between wavenumbers and a trait of interest, which are difficult and expensive to measure, to apply for the genetic improvement of dairy species.

Abstract

Milk composition always serves as an indicator for the cow’s health status and body condition. Some non-genetic factors such as parity, days in milk (DIM), and calving season, which obviously affect milk performance, therefore, need to be considered in dairy farm management. However, only a few milk compositions are used in the current animal selection programs. The mid-infrared (MIR) spectroscopy can reflect the global composition of milk, but this information is currently underused. The objectives of this study were to detect the effect of some non-genetic factors on milk production traits as well as 1060 individual spectral points covering from 925.92 cm⁻¹ to 5011.54 cm⁻¹, estimate heritabilities of milk production traits and MIR spectral wavenumbers, and explore the genetic correlations between milk production traits and 1060 individual spectral points in a Chinese Holstein population. The mixed models procedure of SAS software was used to test the non-genetic factors. Single-trait animal models were used to estimate heritabilities and bivariate animal models were used to estimate genetic correlations using the package of ASReml in R software. The results showed that herd, parity, calving season, and lactation stage had significant effects on the percentages of protein and lactose, whereas herd and lactation stage had significant effects on fat percentage. Moreover, the herd showed a significant effect on all of the 1060 individual wavenumbers, whereas lactation stage, parity, and calving season had significant effect on most of the wavenumbers of the lactose-region (925 cm⁻¹ to 1200 cm⁻¹), protein-region (1240 cm⁻¹ to 1600 cm⁻¹), and fat-regions (1680 cm⁻¹ to 1770 cm⁻¹ and 2800 cm⁻¹ to 3015 cm⁻¹). The estimated heritabilities for protein percentage (PP), fat percentage (FP), and lactose percentage (LP) were 0.08, 0.05, and 0.09, respectively. Further, the milk spectrum was heritable but low for most individual points. Heritabilities of 1060 individual spectral points were 0.04 on average, ranging from 0 to 0.11. In particular, heritabilities for wavenumbers of spectral regions related to water absorption were very low and even null, and heritabilities for wavenumbers of specific MIR regions associated with fat-I, fat-II, protein, and lactose were 0.04, 0.06, 0.05, and 0.06 on average, respectively. The genetic correlations between PP and FP, PP and LP, FP, and LP were 0.78, −0.29, and −0.14, respectively. In addition, PP, FP, and LP shared the similar patterns of genetic correlations with the spectral wavenumbers. The genetic correlations between milk production traits and spectral regions related to important milk components varied from weak to very strong (0.01 to 0.94, and −0.01 to −0.96). The current study could be used as a management tool for dairy farms and also provides a further understanding of the genetic background of milk MIR spectra.

Enteric Methane Emissions of Dairy Cows Predicted from Fatty Acid Profiles of Milk, Cream, Cheese, Ricotta, Whey, and Scotta

Enteric methane emissions (EME) of ruminants contribute to global climate change, but any attempt to reduce it will need an easy, inexpensive, and accurate method of quantification. We used a promising indirect method for estimating EMEs of lactating dairy cows based on the analysis of the fatty acid (FA) profile of their milk. The aim of this preliminary study was to assess milk from four single samplings (morning whole, evening whole, evening partially skimmed, and vat milks) as alternatives to reference whole milk samples from two milkings. Three fresh products (cream, cheese, and ricotta), two by-products (whey and scotta), and two long-ripened cheeses (6 and 12 months) were also assessed as alternative sources of information to reference milk. The 11 alternative matrices were obtained from seven experimental cheese- and ricotta-making sessions carried out every two weeks following the artisanal Malga cheese-making procedure using milk from 148 dairy cows kept on summer highland pastures. A total of 131 samples of milk, dairy products, and by-products were analyzed to determine the milk composition and to obtain detailed FA profiles using bi-dimensional gas-chromatography. Two equations taken from a published meta-analysis of methane emissions measured in the respiration chambers of cows on 30 different diets were applied to the proportions of butyric, iso-palmitic, iso-oleic, vaccenic, oleic, and linoleic acids out of total FAs to predict methane yield per kg of dry matter ingested and methane intensity per kg of fat and protein corrected milk produced by the cows. Methane yield and intensity could be predicted from single milk samples with good accuracy (trueness and precision) with respect to those predicted from reference milk. The fresh products (cream, cheese and ricotta) generally showed good levels of trueness but low precision for predicting both EME traits, which means that a greater number of samples needs to be analyzed. Among by-products, whey could be a viable alternative source of information for predicting both EME traits, whereas scotta overestimated both traits and showed low precision (due also to its very low fat content). Long-ripened cheeses were found to be less valuable sources of information, although six-month cheese could, with specific correction factors, be acceptable sources of information for predicting the methane yield of lactating cows. These preliminary results need to be confirmed by further study on different dairy systems and cheese-making technologies but offer new insight into a possible easy method for monitoring the EME at the field level along the dairy chain.

Chemometric authentication of farming systems of origin of food (milk and ripened cheese) using infrared spectra, fatty acid profiles, flavor fingerprints, and sensory descriptions

Milk samples from 1264 cows in 85 farms were authenticated for different farming-systems using a 10-fold cross-validated linear-discriminant-analysis using Fourier-transform infrared spectra (FTIRS) and gas-chromatographic fatty-acid (FA) profiles. FTIRS gave correct classification greater than FAs (97.4% vs. 81.1%) during calibration, but slightly worse in validation (73.5% vs 77.3%) and their combination improved the results. All milk samples were processed into ripened model-cheeses, and analyzed by near-infrared-spectrometry (NIRS), by proton-transfer-reaction time-of-flight mass-spectrometry for their volatile organic compound (VOCs) fingerprint and by panel sensory profiling (SENS). Farming-system authentication on cheese samples was less efficient than on milk, but still possible. The instrumental methods yielded similar validation results, better than SENS, and their combination improved the correct classification rate. The efficiency of the different technics was affected by specific farming systems. In conclusion, dairy products could be discriminated for farming-systems with acceptable accuracy, but the methods tested differ in sampling procedure, rapidity and costs.

Genetic and genomic analyses of latent variables related to the milk fatty acid profile, milk composition, and udder health in dairy cattle

The aim of this study was to perform genetic, genome-wide association (GWAS), and gene-set enrichment analyses with latent variables related to milk fatty acid profile (i.e., fatty acids factor scores; FAF), milk composition, and udder health in a cohort of 1,158 Italian Brown Swiss cows. The phenotypes under study were 12 FAF previously identified through factor analysis and classified as follows: de novo FA (F1), branched-chain FA-milk yield (F2), biohydrogenation (F3), long-chain fatty acids (F4), desaturation (F5), short-chain fatty acids (F6), milk protein and fat contents (F7), odd fatty acids (F8), conjugated linoleic acids (F9), linoleic acid (F10), udder health (F11) and vaccelenic acid (F12). (Co)variance components were estimated for factor scores using a Bayesian linear animal model via Gibbs sampling. The animals were genotyped with the Illumina BovineSNP50 BeadChip v.2 (Illumina Inc., San Diego, CA). A single marker regression model was fitted for GWAS analysis. The gene-set enrichment analysis was run on the GWAS results using the Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway databases to identify the ontologies and pathways associated with the FAF. Marginal posterior means of the heritabilities of the aforementioned FAF ranged from 0.048 for F12 to 0.310 for F5. Factors F1 and F6 had the highest number of relevant genetic correlations with the other traits. The genomic analysis detected a total of 39 significant SNP located on 17 Bos taurus autosomes. All latent variables produced signals except for F2 and F10. The traits with the highest number of significant associations were F11 (17) and F12 (7). Gene-set enrichment analyses identified significant pathways (false discovery rate 5%) for F3 and F7. In particular, systemic lupus erythematosus was enriched for F3, whereas the MAPK (mitogen-activated protein kinase) signaling pathway was overrepresented for F7. The results support the existence of important and exploitable genetic and genomic variation in these latent explanatory phenotypes. Information acquired might be exploited in selection programs and when designing further studies on the role of the putative candidate genes identified in the regulation of milk composition and udder health.

A Study on the Effects of Rumen Acidity on Rumination Time and Yield, Composition, and Technological Properties of Milk from Early Lactating Holstein Cows

Simple Summary

The increase in milk yield achieved in recent decades by the dairy sector has been sustained by feeding dairy cows with more concentrates and less forage. This leads to increasing rumen acidity, a status widespread in high-producing dairy cows that may affect feed intake, impair ruminal digestion, and cause diarrhea, laminitis, inflammation, and liver abscesses. The effects of rumen acidity on milk yield and composition are controversial, while those on milk coagulation properties and cheese yield have not yet been explored. This study investigated whether the rumen acidity status affects rumination time, and the production, composition, coagulation properties and cheese yield of milk obtained by 100 early-lactating Holstein cows. The variation in rumen acidity was associated with changes in the cows’ rumen fluid composition and circadian pattern of rumination time. Moreover, daily milk yield linearly decreased as the rumen acidity increased. Conversely, the composition and technological properties of milk were unaffected, even when there were differences in rumen acidity, suggesting that variation in rumen acidity has little impact on cheese-making traits.

Abstract

The use of high grain rations in dairy cows is related to an increase in rumen acidity. This study investigated whether the rumen acidity status affects rumination time (RT), and the production, composition, coagulation properties (MCPs) and cheese yield (CY) of milk. One hundred early-lactating Holstein cows with no clinical signs of disease and fed total mixed rations were used. Rumen fluid was collected once from each cow by rumenocentesis to determine pH and volatile fatty acid (VFA) content. The cows were classified according to the quartile of rumen acidity (QRA), a factor defined by multivariate analysis and associated with VFA and pH. Rumen fluid pH averaged 5.61 in the first quartile and 6.42 in the fourth, and total VFA content increased linearly with increasing rumen acidity. In addition, RT increased as rumen acidity increased, but only in the daily time interval from 08:00 to 12:00. Milk yield linearly decreased as rumen acidity increased, whereas QRA did not affect pH, fat or protein contents of milk. Furthermore, the MCPs, assessed by lactodynamograph, and CY were unaffected by QRA. It is suggested that differences in rumen acidity have little influence on the nutrient content, coagulation properties and CY of milk.

Evaluation of Breed and Parity Order Effects on the Lipid Composition of Porcine Colostrum

Porcine colostrum lipid classes and fatty acids (FA) were characterized in 6 pools (from 69 samples) from 3 sow breeds (Italian Large White, Italian Landrace, and Italian Duroc) and different parity orders (only Large White). Triacylglycerols (TAG; 94.44 expressed as g/100 g of fat) were the most abundant lipid class, followed by diacylglycerols (DAG; 3.36 g/100 g of fat), free fatty acids (FFA; 0.98 g/100 g of fat), and cholesterol (0.84 g/100 g of fat). The main FAs found in swine colostrum were palmitic (27.29%, expressed as g/100 g of total FA), oleic (28.81%), and linoleic (23.39%) acids. Both the breed of sow and parity order affected the FA and lipid composition. The results suggest that the FA composition of swine colostrum is similar to that of human colostrum and could represent a new source of nutrients for human infants, after further assessment of hygienic and quality aspects. The swine model could be an opportunity for a better understanding of colostrum effects on newborns.

Effects of dietary concentrate composition and linseed oil supplementation on the milk fatty acid profile of goats

Milk fat composition can be modulated by the inclusion of lipid supplements in ruminant diets. An interaction between the lipid supplement and the forage to concentrate ratio or the type of forage in the rations may affect milk fat composition. However, little is known about the effects of the starch-to-non-forage NDF ratio in the concentrate and lipid supplementation of goat diets. The aim of this work was to determine the role of dietary carbohydrates in goats rations supplemented with linseed oil on animal performance and milk fatty acid (FA) profile. A total of 16 dairy goats were allocated to two simultaneous experiments (two treatments each), in a crossover design with four animals per treatment and two experimental periods of 25 days. In both experiments alfalfa hay was the sole forage and the forage to concentrate ratio (33:67) remained constant. The concentrate in experiment 1 consisted of barley, maize and soybean meal (concentrate rich in starch), whereas it included soybean hulls replacing 25% of barley and 25% maize in experiment 2 (concentrate rich in NDF). As a result, the starch-to-non-forage NDF ratio was 3.1 in experiment 1 and it decreased to 0.8 in experiment 2. Both concentrates were administered either alone or in combination with 30 g/day of linseed oil. Animal performance parameters were not affected by experimental treatments. In contrast, major changes were observed in milk FA profile due to lipid supplementation and the type of concentrate. Linseed oil significantly raised vaccenic and rumenic acids as well as α-linolenic acid and its biohydrogenation intermediates while decreased medium-chain saturated FA (12:0 to 16:0) in milk fat. Milk fat contents of odd and branched-chain FA and trans-10 18:1 responded differently to linseed oil supplementation according to the concentrate fed.

Genetic variation in serum protein pattern and blood β-hydroxybutyrate and their relationships with udder health traits, protein profile, and cheese-making properties in Holstein cows

The aim of this study was to investigate in Holstein cows the genetic basis of blood serum metabolites [i.e., total protein, albumin, globulin, albumin:globulin ratio (A:G), and blood β-hydroxybutyrate (BHB)], a set of milk phenotypes related to udder health, milk quality technological characteristics, and genetic relationships among them. Samples of milk were collected from 498 Holstein cows belonging to 28 herds. All animal welfare and milk phenotypes were assessed using standard analytical methodology. A set of Bayesian univariate and bivariate animal models was implemented via Gibbs sampling, and statistical inference was based on the marginal posterior distributions of parameters of concern. We observed a small additive genetic influence for serum albumin concentrations, moderate heritability (≥0.20) for total proteins, globulins, and A:G, and high heritability (0.37) for blood BHB. Udder health traits (somatic cell score, milk lactose, and milk pH) showed low or moderate heritabilities (0.15-0.20), whereas variations in milk protein fraction concentrations were confirmed as mostly under genetic control (heritability: 0.21-0.71). The moderate and high heritabilities observed for milk coagulation properties and curd firming modeling parameters provided confirmation that genetic background exerts a strong influence on the cheese-making ability of milk, largely due to genetic polymorphisms in the major milk protein genes. Blood BHB showed strong negative genetic correlations with globulins (-0.619) but positive correlations with serum albumin (0.629) and A:G (0.717), which suggests that alterations in the serum protein pattern and BHB blood levels are likely to be genetically related. Strong relationships were found between albumin and fat percentages (-0.894), between globulin and α_S2-CN (-0.610), and, to a lesser extent, between serum protein pattern and milk technological characteristics. Genetic relationships between blood BHB and traits related to udder health and milk quality and technological characteristics were mostly weak. This study provides evidence that there is exploitable additive genetic variation for traits related to animal health and welfare and throws light on the shared genetic basis of these traits and the phenotypes related to the quality and cheese-making ability of milk.

SNP co-association and network analyses identify E2F3, KDM5A and BACH2 as key regulators of the bovine milk fatty acid profile

The fatty acid (FA) profile has a considerable impact on the nutritional and technological quality of milk and dairy products. The molecular mechanism underlying the regulation of fat metabolism in bovine mammary gland have been not completely elucidated. We conducted genome-wide association studies (GWAS) across 65 milk FAs and fat percentage in 1,152 Brown Swiss cows. In total, we identified 175 significant single nucleotide polymorphism (SNPs) spanning all chromosomes. Pathway analyses revealed that 12:0 was associated with the greatest number of overrepresented categories/pathways (e.g. mitogen-activated protein kinase (MAPK) activity and protein phosphorylation), suggesting that it might play an important biological role in controlling milk fat composition. An Associated Weight Matrix approach based on SNP co-associations predicted a network of 791 genes related to the milk FA profile, which were involved in several connected molecular pathways (e.g., MAPK, lipid metabolism and hormone signalling) and undetectable through standard GWAS. Analysis of transcription factors and their putative target genes within the network identified BACH2, E2F3 and KDM5A as key regulators of milk FA metabolism. These findings contribute to increasing knowledge of FA metabolism and mammary gland functionality in dairy cows and may be useful in developing targeted breeding practices to improve milk quality.

Dairy system, parity, and lactation stage affect enteric methane production, yield, and intensity per kilogram of milk and cheese predicted from gas chromatography fatty acids

Ruminants (and milk production) contribute to global climate change through enteric methane emissions (EME), and any attempt to reduce them is complicated by the fact that they are difficult and expensive to measure directly. In the case of dairy cows, a promising indirect method of estimating EME is to use the milk fatty acid profile as a proxy, as a relationship exists between microbial activity in the rumen and the molecules available for milk synthesis in the mammary gland. In the present study, we analyzed the detailed fatty acid profiles (through gas chromatography) of a large number of milk samples from 1,158 Brown Swiss cows reared on 85 farms with the aim of testing in the field 2 equations for estimating EME taken from a published meta-analysis. The average estimated methane yield (CH₄ emission per kg of dry matter intake, 21.34 ± 1.60 g/kg) and methane intensity (per kg of corrected milk, 14.17 ± 1.78 g/kg), and the derived methane production (CH₄ emissions per day per cow, 357 ± 109 g/d) were similar to those previously published. Using data from model cheese makings from individual cows, we also calculated estimated methane intensity per kilogram of fresh cheese (99.7 ± 16.4 g/kg) and cheese solids (207.5 ± 30.9 g/kg). Dairy system affected all EME estimates. Traditional dairy farms, and modern farms including corn silage in the TMR exhibited greater estimated methane intensities. We found very wide variability in estimated EME traits among different farms within dairy system (0.33 to 0.61 of total variance), suggesting the need to modify the farms' feeding regimens and management practices to mitigate emissions. Among the individual factors, parity order affected all estimated EME traits excepted methane yield, with an increase from first lactation to the following ones. Lactation stage exhibited more favorable estimated EME traits during early lactation, concomitant with the availability of nutrients from body tissue mobilization for mammary synthesis of milk. Our results showed a coherence between the EME traits estimated from the analysis of milk fatty acids and the expectations according to current knowledge. Further research is needed to validate the results obtained in this study in other breeds and populations, to assess the magnitude of the genetic variation and the potential of these phenotypes to be exploited in breeding programs with the aim to mitigate emissions.

Inferring individual cow effects, dairy system effects and feeding effects on latent variables underlying milk protein composition and cheese-making traits in dairy cattle

We examined the latent structure of 26 cheese related phenotypes in dairy cattle. Traits related to milk yield and quality (8 traits), milk protein fractions (8 traits), coagulation and curd firmness indicators (CF, 5 traits) and cheese-making phenotypes (cheese yields (%CY) and nutrient recoveries in the curd (REC), 5 traits) were analysed through multivariate factor analysis (MFA) using a varimax rotation. All phenotypes were measured in 1264 Brown Swiss cows. Ten mutual orthogonal, latent variables (factors; Fs) were obtained explaining 74% of the original variability. These Fs captured basic concepts of the cheese-making process. More precisely, the first 4 Fs, sorted by variance explained, were able to capture the underlying structure of the CY percentage (F1: %CY), the CF process with time (F2: CFt), the milk and solids yield (F3: Yield) and the presence of nitrogen (N) in the cheese (F4: Cheese N). Moreover, 4 Fs (F5: as1-β-CN, F7: κ-β-CN, F8: as2-CN and F9: as1-CN-Ph) were related to the basic milk caseins and 1 factor was associated with the α-LA whey protein (F10: α-LA). A factor describing udder health status (F6: Udder health), mainly loaded on lactose, other nitrogen compounds in the milk and SCS, was also obtained. Further, we inferred the effects of some potential sources of variation (e.g. stage of lactation and parity) including feeding and management systems. Stage of lactation had a significant effect for 7 of the 10 Fs, followed by parity of the cow (3 Fs), dairy system and feeding (3 Fs). Our work demonstrates the usefulness of MFA in reducing a large number of variables to a few latent factors with biological meaning and representing groups of traits that describe a complex process like cheese-making. Such an approach would be a valuable tool for studying the influence of different production environments and individual animal factors on protein composition and cheese-making related traits.

Genome-wide association and pathway-based analysis using latent variables related to milk protein composition and cheesemaking traits in dairy cattle

The aim of this study was to perform genome-wide associations (GWAS) and gene-set enrichment analyses with protein composition and cheesemaking-related latent variables (factors; F) in a cohort of 1,011 Italian Brown Swiss cows. Factor analysis was applied to identify latent structures of 26 phenotypes related to bovine milk quantity and quality, protein fractions [α_S1-, α_S2-, β-, and κ-casein (CN), β-lactoglobulin, and α-lactalbumin (α-LA)], coagulation and curd firming at time t (CF_t) measures, and cheese properties [cheese yield (%CY) and nutrients recovery in the curd] of individual cows. Ten orthogonal F were extracted, explaining 74% of the original variability. Factor 1_%CY underlined the %CY characteristics, F2_CFt was related to the CF_t process parameters, F3_Yield was considered as descriptor of milk and solids yield, whereas F4_{Cheese N} underscored the presence of nitrogenous compounds (N) into the cheese. Four more F were related to the milk caseins (F5_αS1-β-CN, F7_β-κ-CN, F8_αS2-CN, and F9_αS1-CN-Ph) and 1 F was linked to the whey protein (F10_α-LA); 1 F underlined the udder health status (F6_{Udder health}). All cows were genotyped with the Illumina BovineSNP50 Bead Chip v.2 (Illumina Inc., San Diego, CA). Single marker regression GWAS were fitted. Gene-set enrichment analysis was run on GWAS results, using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases, to reveal ontologies or pathways associated with the F. All F but F3_Yield showed significance in GWAS. Signals in 10 Bos taurus autosomes (BTA) were detected. High peaks on BTA6 (∼87 Mbp) were found for F6_β-κ-CN, F5_αS1-β-CN, and at the tail of BTA11 (∼104 Mbp) for F4_{Cheese N}. Gene-set enrichment analyses showed significant results (false discovery rate at 5%) for F8_αS2-CN, F1_%CY, F4_{Cheese N}, and F10_α-LA. For F8_αS2-CN, 33 Gene Ontology terms and 3 Kyoto Encyclopedia of Genes and Genomes categories were enriched, including terms related to ion transport and homeostasis, neuron function or part, and GnRH signaling pathway. Our results support the feasibility of factor analysis as a dimension reduction technique in genomic studies and evidenced a potential key role of α_S2-CN in milk quality and composition.

Genome-wide comparative analyses of correlated and uncorrelated phenotypes identify major pleiotropic variants in dairy cattle

While single nucleotide polymorphisms (SNPs) associated with multiple phenotype have been reported, the knowledge of pleiotropy of uncorrelated phenotype is minimal. Principal components (PCs) and uncorrelated Cholesky transformed traits (CT) were constructed using 25 raw traits (RTs) of 2841 dairy bulls. Multi-trait meta-analyses of single-trait genome-wide association studies for RT, PC and CT in bulls were validated in 6821 cows. Most PCs and CTs had substantial estimates of heritability, suggesting that genes affect phenotype via diverse pathways. Phenotypic orthogonalizations did not eliminate pleiotropy: the meta-analysis achieved an agreement of significant pleiotropic SNPs (p < 1 × 10⁻⁵, n = 368) between RTs (416), PCs (466) and CTs (425). From this overlap we identified 21 lead SNPs with 100% validation rate containing two clusters: one consisted of DGAT1 (chr14:1.8 M+), MGST1 (chr5:93 M+), PAEP (chr11:103 M+) and GPAT4 (chr27:36 M+) affecting protein, milk and fat yield and the other included CSN2 (chr6:87 M+), MUC1 (chr3:15.6 M), GHR (chr20:31.2 M+) and SDC2 (chr14:70 M+) affecting protein and milk yield. Combining beef cattle data identified correlated SNPs representing CAPN1 (chr29:44 M+) and CAST (chr 7:96 M+) loci affecting beef tenderness, showing pleiotropic effects in dairy cattle. Our findings show that SNPs with a large effect on one trait are likely to have small effects on other uncorrelated traits.