Estimation of genetic and crossbreeding parameters of fatty acid concentrations in milk fat predicted by mid-infrared spectroscopy in New Zealand dairy cattle

Effect of Breed on the Fatty Acid Composition of Milk from Dairy Cows Milked Once and Twice a Day in Different Stages of Lactation

The objective of this study was to evaluate the effect of breed on the overall composition and fatty acid composition of milk from cows milked once a day (OAD) and twice a day (TAD) in different stages of lactation. Milk samples were taken from 39 Holstein-Friesian (F), 27 Jersey (J), and 34 Holstein-Friesian × Jersey (F × J) crossbred cows from a OAD milking herd and 104 F and 83 F × J cows from a TAD milking herd in early (49 ± 15 days in milk), mid (129 ± 12 days in milk), and late (229 ± 13 days in milk) lactation. Calibration equations to predict the concentrations of individual fatty acids were developed using mid-infrared (MIR) spectroscopy. There was a significant interaction between breed within the milking frequency and stage of lactation for the production traits and composition traits. Holstein-Friesian cows milked OAD produced milk with lower concentrations of C18:0 in early and mid lactations compared to F × J and J cows. Holstein-Friesian cows milked TAD produced lower concentrations of C18:0 in early lactation and lower concentrations of C16:0 and C18:0 in late lactation compared to F × J. Lower concentrations of these fatty acids would reduce the hardness of the butter when the milk is processed. In the OAD milking herd, F cows were superior for daily milk yield compared to J cows, but Jersey cows produced significantly (p < 0.05) higher percentages of fat and a higher concentration of C18:0 fatty acid. The relative concentrations of C18:0 and C18 cis-9 in F and J cows milked OAD imply there is no breed effect on the activity of delta-9-desaturase, whereas stages of lactation likely have an effect. These results can be used to assist with selecting breeds and cows that are suitable for either OAD or TAD milking, allowing closer alignment with milk processing needs.

Genetic variants associated with two major bovine milk fatty acids offer opportunities to breed for altered milk fat composition

Background

Although bovine milk is regarded as healthy and nutritious, its high content of saturated fatty acids (FA) may be harmful to cardiovascular health. Palmitic acid (C16:0) is the predominant saturated FA in milk with adverse health effects that could be countered by substituting it with higher levels of unsaturated FA, such as oleic acid (C18:1cis-9). In this work, we performed genome-wide association analyses for milk fatty acids predicted from FTIR spectroscopy data using 1811 Norwegian Red cattle genotyped and imputed to a high-density 777k single nucleotide polymorphism (SNP)-array. In a follow-up analysis, we used imputed whole-genome sequence data to detect genetic variants that are involved in FTIR-predicted levels of C16:0 and C18:1cis-9 and explore the transcript profile and protein level of candidate genes.

Results

Genome-wise significant associations were detected for C16:0 on Bos taurus (BTA) autosomes 11, 16 and 27, and for C18:1cis-9 on BTA5, 13 and 19. Closer examination of a significant locus on BTA11 identified the PAEP gene, which encodes the milk protein β-lactoglobulin, as a particularly attractive positional candidate gene. At this locus, we discovered a tightly linked cluster of genetic variants in coding and regulatory sequences that have opposing effects on the levels of C16:0 and C18:1cis-9. The favourable haplotype, linked to reduced levels of C16:0 and increased levels of C18:1cis-9 was also associated with a marked reduction in PAEP expression and β-lactoglobulin protein levels. β-lactoglobulin is the most abundant whey protein in milk and lower levels are associated with important dairy production parameters such as improved cheese yield.

Conclusions

The genetic variants detected in this study may be used in breeding to produce milk with an improved FA health-profile and enhanced cheese-making properties.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00731-9.

Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle

BACKGROUND

Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk.

RESULTS

Separate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk.

CONCLUSIONS

This study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.

Genetic Parameters of Bovine Milk Fatty Acid Profile, Yield, Composition, Total and Differential Somatic Cell Count

The growing interest of consumers for milk and dairy products of high nutritional value has pushed researchers to evaluate the feasibility of including fatty acids (FA) in selection programs to modify milk fat profile and improve its nutritional quality. Therefore, the aim of this study was to estimate genetic parameters of FA profile predicted by mid-infrared spectroscopy, milk yield, composition, and total and differential somatic cell count. Edited data included 35,331 test-day records of 25,407 Italian Holstein cows from 652 herds. Variance components and heritability were estimated using single-trait repeatability animal models, whereas bivariate repeatability animal models were used to estimate genetic and phenotypic correlations between traits, including the fixed effects of stage of lactation, parity, and herd-test-date, and the random effects of additive genetic animal, cow permanent environment and the residual. Heritabilities and genetic correlations obtained in the present study reflected both the origins of FA (extracted from the blood or synthesized de novo by the mammary gland) and their grouping according to saturation or chain length. In addition, correlations among FA groups were in line with correlation among individual FA. Moderate negative genetic correlations between FA and milk yield and moderate to strong positive correlations with fat, protein, and casein percentages suggest that actual selection programs are currently affecting all FA groups, not only the desired ones (e.g., polyunsaturated FA). The absence of association with differential somatic cell count and the weak association with somatic cell score indicate that selection on FA profile would not affect selection on resistance to mastitis and vice versa. In conclusion, our findings suggest that genetic selection on FA content is feasible, as FA are variable and moderately heritable. Nevertheless, in the light of correlations with other milk traits estimated in this study, a clear breeding goal should first be established.

Lactoferrin concentration and expression in New Zealand cows milked once or twice a day

This study evaluated the concentration and expression of lactoferrin (LF) in cows selected for once a day (OAD) milking compared to twice a day (TAD) milking. Milk samples were collected from the Massey University TAD and OAD herds. Milk traits and expression of LF and insulin-like growth factor 1 (IGF-1) were analyzed with a general linear model that included the fixed effects of milking frequency, lactation number, interaction between milking frequency and lactation number, and as covariates proportion of F, heterosis F × J and deviation from the herd median calving date. Cows milked OAD produced milk with higher (p < .01) concentrations of protein and lactose than TAD milked cows. Compared to TAD cows, cows milked OAD had higher expression of the LF gene (1.40 vs. 1.29 folds, p = .03) and the IGF-1 gene (1.69 vs. 1.48 folds, p = .007). The correlation between the expression of LF gene and the concentration of LF in milk was strong (r = .66 p < .001), but the correlation between the expression of the IGF-1 gene and LF concentration was stronger (r = .94, p < .001). These results suggest that milking frequency affects the milk composition and expression of milk composition genes at early lactation.

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

Genetic correlations of milk fatty acid contents predicted from milk mid-infrared spectra in New Zealand dairy cattle

The objective of this study was to estimate genetic correlations among milk fatty acid (FA) concentrations in New Zealand dairy cattle. Concentrations of each of the most common FA, expressed as a percentage of the total FA, were determined by gas chromatography on a specific cohort of animals. Using this data set, prediction equations were derived using mid-infrared (MIR) spectroscopy data collected from the same samples. These prediction equations were applied to a large data set of MIR measurements in 34,141 milk samples from 3,445 Holstein-Friesian, 2,935 Jersey, and 3,609 crossbred Holstein-Friesian × Jersey cows, sampled an average of 3.42 times during the 2007-2008 season. Data were analyzed using univariate and bivariate repeatability animal models. Heritability of predicted FA concentration in milk fat ranged from 0.21 to 0.42, indicating that genetic selection could be used to change the FA composition of milk. The de novo synthesized FA (C6:0, C8:0, C10:0, C12:0, and C14:0) showed strong positive genetic correlations with each other, ranging from 0.24 to 0.99. Saturated FA were negatively correlated with unsaturated (-0.93) and polyunsaturated (-0.84) FA. The saturated FA were positively correlated with milk fat yield and fat percentage, whereas the unsaturated FA were negatively associated with fat yield and fat percentage. Our results indicate that bovine milk FA composition can be changed through genetic selection using MIR as a phenotypic proxy.

The evolving role of Fourier-transform mid-infrared spectroscopy in genetic improvement of dairy cattle

Over the last 100 years, significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs. Technological progress has enabled a shift from labour intensive, on-farm collection and processing of samples that assess yield and fat levels in milk, to large-scale processing of samples through centralised laboratories, with the scope extended to include quantification of other traits. Fourier-transform mid-infrared (FT-MIR) spectroscopy has had a significant role in the transformation of milk composition phenotyping, with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally. Increasingly, there is interest in analysing the individual FT-MIR wavenumbers, and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs. This includes traits related to the nutritional value of milk, the processability of milk into products such as cheese, and traits relevant to animal health and the environment. The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits, and the genetic correlations between the FT-MIR predicted and actual trait values. Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis. The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes. Additionally, there are other molecular phenotypes such as those related to the metabolome, chromatin accessibility, and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest. Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets, and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs.

Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle

The use of Fourier-transform mid-infrared (FTIR) spectroscopy is of interest to the dairy industry worldwide for predicting milk composition and other novel traits that are difficult or expensive to measure directly. Although there are many valuable applications for FTIR spectra, noise from differences in spectral responses between instruments is problematic because it reduces prediction accuracy if ignored. The purpose of this study was to develop strategies to reduce the impact of noise and to compare methods for standardizing FTIR spectra in order to reduce between-instrument variability in multiple-instrument networks. Noise levels in bands of the infrared spectrum caused by the water content of milk were characterized, and a method for identifying and removing outliers was developed. Two standardization methods were assessed and compared: piecewise direct standardization (PDS), which related spectra on a primary instrument to spectra on 5 other (secondary) instruments using identical milk-based reference samples (n = 918) analyzed across the 6 instruments; and retroactive percentile standardization (RPS), whereby percentiles of observed spectra from routine milk test samples (n = 2,044,094) were used to map and exploit primary- and secondary-instrument relationships. Different applications of each method were studied to determine the optimal way to implement each method across time. Industry-standard predictions of milk components from 2,044,094 spectra records were regressed against predictions from spectra before and after standardization using PDS or RPS. The PDS approach resulted in an overall decrease in root mean square error between industry-standard predictions and predictions from spectra from 0.190 to 0.071 g/100 mL for fat, from 0.129 to 0.055 g/100 mL for protein, and from 0.143 to 0.088 g/100 mL for lactose. Reductions in prediction error for RPS were similar but less consistent than those for PDS across time, but similar reductions were achieved when PDS coefficients were updated monthly and separate primary instruments were assigned for the North and South Islands of New Zealand. We demonstrated that the PDS approach is the most consistent method to reduce prediction errors across time. We also showed that the RPS approach is sensitive to shifts in milk composition but can be used to reduce prediction errors, provided that secondary-instrument spectra are standardized to a primary instrument with samples of broadly equivalent milk composition. Appropriate implementation of either of these approaches will improve the quality of predictions based on FTIR spectra for various downstream applications.

Unravelling genetic variation underlying de novo-synthesis of bovine milk fatty acids

The relative abundance of specific fatty acids in milk can be important for consumer health and manufacturing properties of dairy products. Understanding of genes controlling milk fat synthesis may contribute to the development of dairy products with high quality and nutritional value. This study aims to identify key genes and genetic variants affecting de novo synthesis of the short- and medium-chained fatty acids C4:0 to C14:0. A genome-wide association study using 609,361 SNP markers and 1,811 animals was performed to detect genomic regions affecting fatty acid levels. These regions were further refined using sequencing data to impute millions of additional genetic variants. Results suggest associations of PAEP with the content of C4:0, AACS with the content of fatty acids C4:0-C6:0, NCOA6 or ACSS2 with the longer chain fatty acids C6:0-C14:0, and FASN mainly associated with content of C14:0. None of the top-ranking markers caused amino acid shifts but were mostly situated in putatively regulating regions and suggested a regulatory role of the QTLs. Sequencing mRNA from bovine milk confirmed the expression of all candidate genes which, combined with knowledge of their roles in fat biosynthesis, supports their potential role in de novo synthesis of bovine milk fatty acids.

Fatty acid profile of ghee derived from two genotypes (cattle–yak vs yak) grazing different alpine Himalayan pasture sites

The fatty acid (FA) profile of ghee produced from milk of cattle–yak hybrids grazing five mountain pasture sites along a high-alpine transhumance route in Nepal was analysed. Pastures differed in altitude above sea level (2600–4500 m), time period of being grazed and phytochemical composition of the swards. Additionally, a comparison of ghee from purebred yak and hybrid was performed, with samples produced at two of the sites. Pasture site had a strong effect on almost all FAs. Proportions of oleic, linoleic and α-linolenic acid in ghee were smallest on the highest pasture at 4500 m where the largest condensed tannin concentrations in the forages were found. No systematic site effects were found for c9,t11 conjugated linoleic acid and total polyunsaturated FAs. Ghee produced from the hybrids’ milk was richer in major functional FAs such as α-linolenic and linoleic acid, while yak ghee contained more saturated FAs and eicosapentaenoic, docosapentaenoic and docosahexaenoic acids.

Could predicting fatty acid profile by mid-infrared reflectance spectroscopy be used as a method to increase the value added by milk production chains?

The aims of this work were (1) to develop prediction equations from mid-infrared spectroscopy (MIRS) to establish a detailed fatty acid (FA) composition of milk; (2) to propose a milk FA index, utilizing MIRS-developed equations, in which the precision of the FA-prediction equations is taken into account to increase the value of milk; and (3) to show application examples. A total of 651 bulk cow milk samples were collected from 245 commercial farms in northwest Italy. The results of the 651 gas chromatography analyses were used to establish (421 samples) and to validate (230 samples) the outcomes of the FA composition prediction that had been obtained by MIRS. A class-based approach, in which the obtained MIRS equations were used, was proposed to define a milk classification. The method provides a numerical index [milk FA index (MFAI)] that allows a premium price to be quantified to increase the value of a favorable FA profile of milk. Ten FA were selected to calculate MFAI, according to their relevance for human health and potential cheese sensory properties, and animal welfare and environmental sustainability were also considered. These factors were selected as dimensions of MFAI. A statistical analysis and expert judgment aggregation were performed on the selected FA by weighting the FA and normalizing the dimensions to reduce redundancy. A class approach was applied, using the precision of the MIRS equations to establish the classes. The median FA concentration of the data set was set as a reference value of class 0. The width, number, and limits of classes above and below the median were calculated using the 95% confidence level of the standard error of prediction, corrected with the bias of each FA. A progressive number and a positive or negative sign were assigned to each FA class above or below the median according to their role in the above mentioned dimensions. The sum of the numbers of each class, associated with its sign for each FA, was used to generate MFAI. The MFAI was applied to dairy farms characterized by different feeding strategies, all of which deliver milk to a commercial dairy plant. The MFAI values ranged from 0.7 to 4.2, and large variations, which depended on the cows' diet and forage quality, were observed for each feeding system. The proposed method has been found to be flexible and adaptable to several contexts on both intensive and extensive dairy farms.

Prediction of milk fatty acid content with mid-infrared spectroscopy in Canadian dairy cattle using differently distributed model development sets

The fatty acid profile of milk is a prevailing issue due to the potential negative or positive effects of different fatty acids to human health and nutrition. Mid-infrared spectroscopy can be used to obtain predictions of otherwise costly fatty acid phenotypes in a widespread and rapid manner. The objective of this study was to evaluate the prediction of fatty acid content for the Canadian dairy cattle population from mid-infrared spectral data and to compare the results produced by altering the partial least squares (PLS) model development set used. The PLS model development sets used to develop the predictions were reference fatty acids expressed as (1) grams per 100 g of fatty acid, (2) grams per 100 g of milk, (3) the natural logarithmic transform of grams per 100 g of milk, and (4) subsets of samples randomly selected by removing excess records around the mean to present a more uniform distribution, repeated 10 times. Gas chromatography measured fatty acid concentration and spectral data for 2,023 milk samples of 373 cows from 4 breeds and 44 herds were used in the model development. The coefficient of determination of cross-validation (R_cv²) increased when fatty acids were expressed on a per 100 g of milk basis compared with on a per 100 g of fat basis for all examined fatty acids. The logarithmic transformation used to create a more Gaussian distribution in the development set had little effect on the prediction accuracy. The individual fatty acids C12:0, C14:0, C16:0, C18:0, C18:1n-9 cis, and saturated, monounsaturated, unsaturated, short-chain, medium-chain, and long-chain fatty acid groups had (R_cv²) greater than 0.70. When model development was performed with subsets of the original samples, slight increases in (R_cv²) values were observed for the majority of fatty acids. The difference in (R_cv²) between the top- and bottom-performing prediction equation across the different subsets for a single predicted fatty acid was on average 0.055 depending on which samples were randomly selected to be used in the PLS model development set. Predictions for fatty acids with high accuracies can be used to monitor fatty acid contents for cows in milk recording programs and possibly for genetic evaluation.

Genetic analysis of groups of mid-infrared predicted fatty acids in milk

The objective of this study was to investigate genetic variability of mid-infrared predicted fatty acid groups in Canadian Holstein cattle. Genetic parameters were estimated for 5 groups of fatty acids: short-chain (4 to 10 carbons), medium-chain (11 to 16 carbons), long-chain (17 to 22 carbons), saturated, and unsaturated fatty acids. The data set included 49,127 test-day records from 10,029 first-lactation Holstein cows in 810 herds. The random regression animal test-day model included days in milk, herd-test date, and age-season of calving (polynomial regression) as fixed effects, herd-year of calving, animal additive genetic effect, and permanent environment effects as random polynomial regressions, and random residual effect. Legendre polynomials of the third degree were selected for the fixed regression for age-season of calving effect and Legendre polynomials of the fourth degree were selected for the random regression for animal additive genetic, permanent environment, and herd-year effect. The average daily heritability over the lactation for the medium-chain fatty acid group (0.32) was higher than for the short-chain (0.24) and long-chain (0.23) fatty acid groups. The average daily heritability for the saturated fatty acid group (0.33) was greater than for the unsaturated fatty acid group (0.21). Estimated average daily genetic correlations were positive among all fatty acid groups and ranged from moderate to high (0.63-0.96). The genetic correlations illustrated similarities and differences in their origin and the makeup of the groupings based on chain length and saturation. These results provide evidence for the existence of genetic variation in mid-infrared predicted fatty acid groups, and the possibility of improving milk fatty acid profile through genetic selection in Canadian dairy cattle.

Genome-wide association mapping for milk fat composition and fine mapping of a QTL for de novo synthesis of milk fatty acids on bovine chromosome 13

Background

Bovine milk is widely regarded as a nutritious food source for humans, although the effects of individual fatty acids on human health is a subject of debate. Based on the assumption that genomic selection offers potential to improve milk fat composition, there is strong interest to understand more about the genetic factors that influence the biosynthesis of bovine milk and the molecular mechanisms that regulate milk fat synthesis and secretion. For this reason, the work reported here aimed at identifying genetic variants that affect milk fatty acid composition in Norwegian Red cattle. Milk fatty acid composition was predicted from the nation-wide recording scheme using Fourier transform infrared spectroscopy data and applied to estimate heritabilities for 36 individual and combined fatty acid traits. The recordings were used to generate daughter yield deviations that were first applied in a genome-wide association (GWAS) study with 17,343 markers to identify quantitative trait loci (QTL) affecting fatty acid composition, and next on high-density and sequence-level datasets to fine-map the most significant QTL on BTA13 (BTA for Bos taurus chromosome).

Results

The initial GWAS revealed 200 significant associations, with the strongest signals on BTA1, 13 and 15. The BTA13 QTL highlighted a strong functional candidate gene for de novo synthesis of short- and medium-chained saturated fatty acids; acyl-CoA synthetase short-chain family member 2. However, subsequent fine-mapping using single nucleotide polymorphisms (SNPs) from a high-density chip and variants detected by resequencing showed that the effect was more likely caused by a second nearby gene; nuclear receptor coactivator 6 (NCOA6). These findings were confirmed with results from haplotype studies. NCOA6 is a nuclear receptor that interacts with transcription factors such as PPARγ, which is a major regulator of bovine milk fat synthesis.

Conclusions

An initial GWAS revealed a highly significant QTL for de novo-synthesized fatty acids on BTA13 and was followed by fine-mapping of the QTL within NCOA6. The most significant SNPs were either synonymous or situated in introns; more research is needed to uncover the underlying causal DNA variation(s).

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-017-0294-5) contains supplementary material, which is available to authorized users.