Evidence of additive genetic variation for major milk proteins in dairy cows: A meta-analysis

In the past, there have been reports of genetic parameters for milk proteins in various dairy cattle populations. The high variability among genetic parameter estimates has been caused by this. This study aimed to use a random-effects meta-analysis model to compile published estimates of genetic parameter for major milk proteins of α-lactalbumin, β-lactoglobulin, sum of whey proteins, casein, αs1-casein, αs2-casein, β-casein, and κ-casein in dairy cows. The study used a total of 140 heritability and 256 genetic correlation estimates from 23 papers published between 2004 and 2022. The estimated range of milk protein heritability is from 0.284 (for α-lactalbumin in milk) to 0.596 (for sum of whey proteins). The genetic correlation estimates between casein and milk yield, milk fat and protein percentages were -0.461, 0.693, and 0.976, respectively (p < 0.05). The genetic correlation estimates between milk proteins expressed as a percentage of milk were significant and varied from 0.177 (between β-lactoglobulin and κ-casein) to 0.892 (between αs1-casein and αs2-casein). Moderate-to-high heritability estimates for milk proteins and their low genetic associations with milk yield and composition indicated the possibility for improving milk proteins in a genetic selection plan with negligible correlated effects on production traits in dairy cows.

Re-sequencing of the casein genes in Swedish Red cattle giving milk with diverse protein profiles and extreme rennet coagulation properties

Impaired rennet coagulation properties in milk could lead to prolonged processing times and production losses. Heritability for milk coagulation has previously been estimated to be 0.28 to 0.45, indicating that genetic selection can be used to manipulate this trait. The CN proteins are expressed by the genes CSN1S1, CSN2, CSN1S2, and CSN3 and are located on bovine chromosome 6. To better understand the effect of genetic variation in the CN genes on milk coagulation, blood and milk samples from 30 Swedish Red Dairy Cattle (RDC) with divergent coagulation properties were investigated. DNA from the 30 cows was sequenced for the CN genes to determine the theoretical AA sequence and to look for genetic variation in the untranslated regions. The aim is to confirm the protein genetic variants previously reported, while searching for additional genetic variation in the CN genes of 30 RDC. We observed genetic variation in 116 SNPs in the known CN genes where 10% of the SNPs are exon variants and the remaining 90% are intron variants. A total of 2.5% of the SNPs are found in the 5'- or 3'-untranslated region (UTR) regions of the exons; 2% are synonymous variants and 6% are missense variants that concurred with the known protein variants for CSN1S1, CSN2, and CSN3. Furthermore, 6% of the SNPs are splice polypyrimidine tract intron variants. The 2 genetic variants in the 5'- and 3'-UTR in CSN1S1 and CSN3 are found with protein variants CSN1S1C and CSN3B. Because both UTR variants are associated with gain and loss of micro RNA and transcription factors, this could explain differences in expression of the genetic protein variants. Preliminary chi-squared analysis and comparison with previous GWAS studies showed potential connections between the identified SNPs and coagulation properties of milk. By advancing the knowledge of the connection between the DNA sequence and the functional properties of the CN proteins, we hope to learn more about the cheese coagulation properties of milk from RDC.

Multivariate and Genome-Wide Analysis of Mid-Infrared Spectra of Non-Coagulating Milk of Sarda Sheep Breed

Milk coagulation ability is crucial for the dairy sheep industry since the whole amount of milk is processed into cheese. Non-coagulating milk (NCM) is defined as milk not forming a curd within the testing time. In sheep milk, it has been reported in literature that up to 20% of milk is NCM. Although the clotting properties of individual milk have been widely studied, little attention has been given to NCM and genomic dissection of this trait. Mid-infrared (MIR) spectra can be exploited both to predict cheese-making aptitude and to discriminate between coagulating milk and NCM. The main goals of this work were (i) to assess the predictivity of MIR spectra for NCM classification and (ii) to conduct a genome-wide association study on coagulation ability. Milk samples from 949 Sarda ewes genotyped and phenotyped for milk coagulation properties (MCPs) served as the training dataset. The validation dataset included 662 ewes. Three classical MCPs were measured: rennet coagulation time (RCT), curd firmness (a30), and curd firming time (k20). Moreover, MIR spectra were acquired and stored in the region between 925.92 and 5,011.54 cm−1. The probability of a sample to be NCM was modeled by step-wise logistic regression on milk spectral information (LR-W), logistic regression on principal component (LR-PC), and canonical discriminant analysis of spectral wave number (DA-W). About 9% of the samples did not coagulate at 30 min. The use of LR-W gave a poorer classification of NCM. The use of LR-PC improved the percentage of correct assignment (45 ± 9%). The DA-W method allows us to reach 75.1 ± 10.3 and 76.5 ± 18.4% of correct assignments of the inner and external validation datasets, respectively. As far as GWA of NCM, 458 SNP associations and 45 candidate genes were detected. The genes retrieved from public databases were mostly linked to mammary gland metabolism, udder health status, and a milk compound also known to affect the ability of milk to coagulate. In particular, the potential involvement of CAPNs deserves further investigation.

Breed of goat affects the prediction accuracy of milk coagulation properties using Fourier-transform infrared spectroscopy

The prediction of traditional goat milk coagulation properties (MCP) and curd firmness over time (CF_t) parameters via Fourier-transform infrared (FTIR) spectroscopy can be of significant economic interest to the dairy industry and can contribute to the breeding objectives for the genetic improvement of dairy goat breeds. Therefore, the aims of this study were to (1) explore the variability of milk FTIR spectra from 4 goat breeds (Camosciata delle Alpi, Murciano-Granadina, Maltese, and Sarda), and to assess the possible discriminant power of milk FTIR spectra among breeds, (2) assess the viability to predict coagulation traits by using milk FTIR spectra, and (3) quantify the effect of the breed on the prediction accuracy of MCP and CF_t parameters. In total, 611 individual goat milk samples were used. Analysis of variance of measured MCP and CF_t parameters was carried out using a mixed model including the farm and pendulum as random factors, and breed, parity, and days in milk as fixed factors. Milk spectra for each goat were collected over the spectral range from wavenumber 5,011 to 925 × cm^-1. Discriminant analysis of principal components was used to assess the ability of FTIR spectra to identify breed of origin. A Bayesian model was used to calibrate equations for each coagulation trait. The accuracy of the model and the prediction equation was assessed by cross-validation (CRV; 80% training and 20% testing set) and stratified CRV (SCV; 3 breeds in the training set, one breed in the testing set) procedures. Prediction accuracy was assessed by using coefficient of determination of validation (R²_VAL), the root mean square error of validation (RMSE_VAL), and the ratio performance deviation. Moreover, measured and FTIR predicted traits were compared in the SCV procedure by assessing their least squares means for the breed effect, Pearson correlations, and variance heteroscedasticity. Results showed the feasibility of using FTIR spectra and multivariate analyses to correctly assign milk samples to their breeds of origin. The R²_VAL values obtained with the CRV procedure were moderate to high for the majority of coagulation traits, with RMSE_VAL and ratio performance deviation values increasing as the coagulation process progresses from rennet addition. Prediction accuracy obtained with the SCV were strongly influenced by the breed, presenting general low values restricting a practical application. In addition, the low Pearson correlation coefficients of Sarda breed for all the traits analyzed, and the heteroscedastic variances of Camosciata delle Alpi, Murciano-Granadina, and Maltese breeds, further indicated that it is fundamental to consider the differences existing among breeds for the prediction of milk coagulation traits.

Genetic parameters for noncoagulating milk, milk coagulation properties, and detailed milk composition in Swedish Red Dairy Cattle

The rennet-induced coagulation ability of milk is important in cheese production. For Swedish Red Dairy Cattle (RDC), this ability is reduced because of a high prevalence of noncoagulating (NC) milk. In this study, we simultaneously combined genetic parameters for NC milk, milk coagulation properties, milk composition, physical traits, and milk protein composition. Our aim was to estimate heritability and genetic and phenotypic correlations for NC milk and 24 traits (milk coagulation properties, milk composition, physical traits, and milk protein composition). Phenotypes and ∼7,000 SNP genotypes were available for all 600 Swedish RDC. The genotypes were imputed from ∼7,000 SNP to 50,000 SNP. Variance components and genetic parameters were estimated with an animal model. In Swedish RDC, a moderate heritability estimate of 0.28 was found for NC milk. For the other 24 traits, heritability estimates ranged from 0.12 to 0.77 (standard errors from 0.08 to 0.18). A total of 300 phenotypic and genetic correlations were estimated. For phenotypic and genetic correlations, 172 and 95 were significant, respectively. In general, most traits showing significant genetic correlations also showed significant phenotypic correlations. In this study, phenotypic and genetic correlations with NC milk suggest that many correlations between traits exist, making it difficult to predict the real consequences on the composition of milk, if selective breeding is applied on NC milk. We speculate that some of these consequences may lead to changes in the composition of milk, most likely affecting its physical and organoleptic properties. However, our results suggest that κ-casein could be used as an indicator trait to predict the occurrence of NC milk at the herd level.

Identification of QTL on Chromosome 18 Associated with Non-Coagulating Milk in Swedish Red Cows

Non-coagulating (NC) milk, defined as milk not coagulating within 40 min after rennet-addition, can have a negative influence on cheese production. Its prevalence is estimated at 18% in the Swedish Red (SR) cow population. Our study aimed at identifying genomic regions and causal variants associated with NC milk in SR cows, by doing a GWAS using 777k SNP genotypes and using imputed sequences to fine map the most promising genomic region. Phenotypes were available from 382 SR cows belonging to 21 herds in the south of Sweden, from which individual morning milk was sampled. NC milk was treated as a binary trait, receiving a score of one in case of non-coagulation within 40 min. For all 382 SR cows, 777k SNP genotypes were available as well as the combined genotypes of the genetic variants of αs1-β-κ-caseins. In addition, whole-genome sequences from the 1000 Bull Genome Consortium (Run 3) were available for 429 animals of 15 different breeds. From these sequences, 33 sequences belonged to SR and Finish Ayrshire bulls with a large impact in the SR cow population. Single-marker analyses were run in ASReml using an animal model. After fitting the casein loci, 14 associations at -Log10(P-value) > 6 identified a promising region located on BTA18. We imputed sequences to the 382 genotyped SR cows using Beagle 4 for half of BTA18, and ran a region-wide association study with imputed sequences. In a seven mega base-pairs region on BTA18, our strongest association with NC milk explained almost 34% of the genetic variation in NC milk. Since it is possible that multiple QTL are in strong LD in this region, 59 haplotypes were built, genetically differentiated by means of a phylogenetic tree, and tested in phenotype-genotype association studies. Haplotype analyses support the existence of one QTL underlying NC milk in SR cows. A candidate gene of interest is the VPS35 gene, for which one of our strongest association is an intron SNP in this gene. The VPS35 gene belongs to the mammary gene sets of pre-parturient and of lactating cows.

Bovine chromosomal regions affecting rheological traits in acid-induced skim milk gels

The production of fermented milk products has increased worldwide during the last decade and is expected to continue to increase during the coming decade. The quality of these products may be optimized through breeding practices; however, the relations between cow genetics and technological properties of acid milk gels are not fully known. Therefore, the aim of this study was to identify chromosomal regions affecting acid-induced coagulation properties and possible candidate genes. Skim milk samples from 377 Swedish Red cows were rheologically analyzed for acid-induced coagulation properties using low-amplitude oscillation measurements. The resulting traits, including gel strength, coagulation time, and yield stress, were used to conduct a genome-wide association study. Single nucleotide polymorphisms (SNP) were identified using the BovineHD SNPChip (Illumina Inc., San Diego, CA), resulting in almost 621,000 segregating markers. The genome was scanned for putative quantitative trait loci (QTL) regions, haplotypes based on highly associated SNP were inferred, and the additive genetic effects of haplotypes within each QTL region were analyzed using mixed models. A total of 8 genomic regions were identified, with large effects of the significant haplotype explaining between 4.8 and 9.8% of the phenotypic variance of the studied traits. One major QTL was identified to overlap between gel strength and yield stress, the QTL identified with the most significant SNP closest to the gene coding for κ-casein (CSN3). In addition, a chromosome-wide significant region affecting yield stress on BTA 11 was identified to be colocated with PAEP, coding for β-lactoglobulin. Furthermore, the coagulation properties of the genetic variants within the 2 genes were compared with the coagulation properties identified by the patterns of the haplotypes within the regions, and it was discovered that the haplotypes were more diverse and in one case slightly better at explaining the phenotypic variance. Besides these significant QTL comprising the 2 milk proteins, 3 additional genes are proposed as possible candidates, namely RAB22A, CDH13, and STAT1, and all have previously been found to be expressed in the mammary gland. To our knowledge, this is the first attempt to map QTL regions for acid-induced coagulation properties.