Genome-wide association studies (GWAS) and post-GWAS analyses for technological traits in Assaf and Churra dairy breeds

This study aimed to perform a GWAS to identify genomic regions associated with milk and cheese-making traits in Assaf and Churra dairy sheep breeds; second, it aimed to identify possible positional and functional candidate genes and their interactions through post-GWAS studies. For 2,020 dairy ewes from 2 breeds (1,039 Spanish Assaf and 981 Churra), milk samples were collected and analyzed to determine 6 milk production and composition traits and 6 traits related to milk coagulation properties and cheese yield. The genetic profiles of the ewes were obtained using a genotyping chip array that included 50,934 SNP markers. For both milk and cheese-making traits, separate single-breed GWAS were performed using GCTA software. The set of positional candidate genes identified via GWAS was subjected to guilt-by-association-based prioritization analysis with ToppGene software. Totals of 84 and 139 chromosome-wise significant associations for the 6 milk traits and the 6 cheese-making traits were identified in this study. No significant SNPs were found in common between the 2 studied breeds, possibly due to their genetic heterogeneity of the phenotypes under study. Additionally, 63 and 176 positional candidate genes were located in the genomic intervals defined as confidence regions in relation to the significant SNPs identified for the analyzed traits for Assaf and Churra breeds. After the functional prioritization analysis, 71 genes were identified as promising positional and functional candidate genes and proposed as targets of future research to identify putative causative variants in relation to the traits under examination. In addition, this multitrait study allowed us to identify variants that have a pleiotropic effect on both milk production and cheese-related traits. The incorporation of variants among the proposed functional and positional candidate genes into genomic selection strategies represent an interesting approach for achieving rapid genetic gains, specifically for those traits difficult to measure, such as cheese-making traits.

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.

Integrated PTR-ToF-MS, GWAS and biological pathway analyses reveal the contribution of cow's genome to cheese volatilome

Volatile organic compounds (VOCs) are small molecules that contribute to the distinctive flavour of cheese which is an important attribute for consumer acceptability. To investigate whether cow's genetic background might contribute to cheese volatilome, we carried out genome-wide association studies (GWAS) and pathway-based analyses for 173 spectrometric peaks tentatively associated with several VOCs obtained from proton-transfer-reaction mass spectrometry (PTR-ToF-MS) analyses of 1,075 model cheeses produced using raw whole-milk from Brown Swiss cows. Overall, we detected 186 SNPs associated with 120 traits, several of which mapped close to genes involved in protein (e.g. CSN3, GNRHR and FAM169A), fat (e.g. AGPAT3, SCD5, and GPAM) and carbohydrate (e.g. B3GNT2, B4GALT1, and PHKB) metabolism. Gene set enrichment analysis showed that pathways connected with proteolysis/amino acid metabolism (purine and nitrogen metabolism) as well as fat metabolism (long-term potentiation) and mammary gland function (tight junction) were overrepresented. Our results provide the first evidence of a putative link between cow's genes and cheese flavour and offer new insights into the role of potential candidate loci and the biological functions contributing to the cheese volatilome.

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.

Pathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle

It is becoming common to complement genome-wide association studies (GWAS) with gene-set enrichment analysis to deepen the understanding of the biological pathways affecting quantitative traits. Our objective was to conduct a gene ontology and pathway-based analysis to identify possible biological mechanisms involved in the regulation of bovine milk technological traits: coagulation properties, curd firmness modeling, individual cheese yield (CY), and milk nutrient recovery into the curd (REC) or whey loss traits. Results from 2 previous GWAS studies using 1,011 cows genotyped for 50k single nucleotide polymorphisms were used. Overall, the phenotypes analyzed consisted of 3 traditional milk coagulation property measures [RCT: rennet coagulation time defined as the time (min) from addition of enzyme to the beginning of coagulation; k₂₀: the interval (min) from RCT to the time at which a curd firmness of 20 mm is attained; a₃₀: a measure of the extent of curd firmness (mm) 30 min after coagulant addition], 6 curd firmness modeling traits [RCT_eq: RCT estimated through the CF equation (min); CF_P: potential asymptotic curd firmness (mm); k_CF: curd-firming rate constant (% × min^-1); k_SR: syneresis rate constant (% × min^-1); CF_max: maximum curd firmness (mm); and t_max: time to CF_max (min)], 3 individual CY-related traits expressing the weight of fresh curd (%CY_CURD), curd solids (%CY_SOLIDS), and curd moisture (%CY_WATER) as a percentage of weight of milk processed and 4 milk nutrient and energy recoveries in the curd (REC_FAT, REC_PROTEIN, REC_SOLIDS, and REC_ENERGY calculated as the % ratio between the nutrient in curd and the corresponding nutrient in processed milk), milk pH, and protein percentage. Each trait was analyzed separately. In total, 13,269 annotated genes were used in the analysis. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases were queried for enrichment analyses. Overall, 21 Gene Ontology and 17 Kyoto Encyclopedia of Genes and Genomes categories were significantly associated (false discovery rate at 5%) with 7 traits (RCT, RCT_eq, k_CF, %CY_SOLIDS, REC_FAT, REC_SOLIDS, and REC_ENERGY), with some being in common between traits. The significantly enriched categories included calcium signaling pathway, salivary secretion, metabolic pathways, carbohydrate digestion and absorption, the tight junction and the phosphatidylinositol pathways, as well as pathways related to the bovine mammary gland health status, and contained a total of 150 genes spanning all chromosomes but 9, 20, and 27. This study provided new insights into the regulation of bovine milk coagulation and cheese ability that were not captured by the GWAS.

Genome-wide association study for cheese yield and curd nutrient recovery in dairy cows

Cheese production and consumption are increasing in many countries worldwide. As a result, interest has increased in strategies for genetic selection of individuals for technological traits of milk related to cheese yield (CY) in dairy cattle breeding. However, little is known about the genetic background of a cow's ability to produce cheese. Recently, a relatively large panel (1,264 cows) of different measures of individual cow CY and milk nutrient and energy recoveries in the cheese (REC) became available. Genetic analyses showed considerable variation for CY and for aptitude to retain high proportions of fat, protein, and water in the coagulum. For the dairy industry, these characteristics are of major economic importance. Nevertheless, use of this knowledge in dairy breeding is hampered by high costs, intense labor requirement, and lack of appropriate technology. However, in the era of genomics, new possibilities are available for animal breeding and genetic improvement. For example, identification of genomic regions involved in cow CY might provide potential for marker-assisted selection. The objective of this study was to perform genome-wide association studies on different CY and REC measures. Milk and DNA samples from 1,152 Italian Brown Swiss cows were used. Three CY traits expressing the weight (wt) of fresh curd (%CY_CURD), curd solids (%CY_SOLIDS), and curd moisture (%CY_WATER) as a percentage of weight of milk processed, and 4 REC (REC_FAT, REC_PROTEIN, REC_SOLIDS, and REC_ENERGY, calculated as the % ratio between the nutrient in curd and the corresponding nutrient in processed milk) were analyzed. Animals were genotyped with the Illumina BovineSNP50 Bead Chip v.2. Single marker regressions were fitted using the GenABEL R package (genome-wide association using mixed model and regression-genomic control). In total, 103 significant associations (88 single nucleotide polymorphisms) were identified in 10 chromosomes (2, 6, 9, 11, 12, 14, 18, 19, 27, 28). For REC_FAT and REC_PROTEIN, high significance peaks were identified in Bos taurus autosome (BTA) 6 and BTA11, respectively. Marker ARS-BFGL-NGS-104610 (∼104.3 Mbp) was highly associated with REC_PROTEIN and Hapmap52348-rs29024684 (∼87.4 Mbp), closely located to the casein genes on BTA6, with REC_FAT. Genomic regions identified may enhance marker-assisted selection in bovine cheese breeding beyond the use of protein (casein) and fat contents, whereas new knowledge will help to unravel the genomic background of a cow's ability for cheese production.

Breed of cow and herd productivity affect milk composition and modeling of coagulation, curd firming, and syneresis

Milk coagulation properties (MCP) have been widely investigated in the past using milk collected from different cattle breeds and herds. However, to our knowledge, no previous studies have assessed MCP in individual milk samples from several multi-breed herds characterized by either high or low milk productivity, thereby allowing the effects of herd and cow breed to be evaluated independently. Multi-breed herds (n=41) were classified into 2 categories based on milk productivity (high vs. low), defined according to the average milk net energy yielded daily by lactating cows. Milk samples were taken from 1,508 cows of 6 different breeds: 3 specialized dairy (Holstein-Friesian, Brown Swiss, Jersey) and 3 dual-purpose (Simmental, Rendena, Alpine Grey) breeds, and analyzed in duplicate (3,016 tests) using 2 lactodynamographs to obtain 240 curd firming (CF) measurements over 60min (1 every 15 s) for each duplicate. The 5 traditional single-point MCP (RCT, k₂₀, a₃₀, a₄₅, and a₆₀) were yielded directly by the instrument from the available CF measures. All 240 CF measures of each replicate were also used to estimate 4 individual equation parameters: RCT estimated according to curd firm change over time modeling (RCT_eq), asymptotic potential curd firmness (CF_P), curd firming instant rate constant (k_CF), and syneresis instant rate constant (k_SR) and 2 derived traits: maximum curd firmness achieved within 45min (CF_max) and time at achievement of CF_max (t_max) by curvilinear regression using a nonlinear procedure. Results showed that the effect of herd-date on traditional and modeled MCP was modest, ranging from 6.1% of total variance for k₂₀ to 10.7% for RCT, whereas individual animal variance was the highest, ranging from 32.0% for t_max to 82.5% for RCT_eq. The repeatability of MCP was high (>80%) for all traits except those associated with the last part of the lactodynamographic curve (i.e., a₆₀, k_SR, k_CF, and t_max: 57 to 71%). Reproducibility, taking into account the effect of instrument, was equal to or slightly lower than repeatability. Milk samples collected in farms characterized by high productivity exhibited delayed coagulation (RCT_eq: 18.6 vs. 16.3min) but greater potential curd firmness (CF_P: 76.8 vs. 71.9mm) compared with milk samples collected from low-productivity herds. Parity and days in milk influenced almost all MCP. Large differences in all MCP traits were observed among breeds, both between specialized and dual-purpose breeds and within these 2 groups of breeds, even after adjusting for milk quality and yield. Milk quality and MCP of samples from Jersey cows, and coagulation time of samples from Rendena cows were better than in milk from Holstein-Friesian cows, and intermediate results were found with the other breeds of Alpine origin. The results of this study, taking into account the intrinsic limitation of this technique, show that the effects of breed on traditional and modeled MCP are much greater than the effects of herd productivity class, parity, and DIM. Moreover, the variance in individual animals is much greater than the variance in individual herds within herd productivity class. It seems that improvement in MCP depends more on genetics (e.g., breed, selection) than on environmental and management factors.

Estimation of genetic parameters and detection of chromosomal regions affecting the major milk proteins and their post translational modifications in Danish Holstein and Danish Jersey cattle

Background

In the Western world bovine milk products are an important protein source in human diet. The major proteins in bovine milk are the four caseins (CN), α_S1-, α_S2-, β-, and k-CN and the two whey proteins, β-LG and α-LA. It has been shown that both the amount of specific CN and their isoforms including post-translational modifications (PTM) influence technological properties of milk. Therefore, the aim of this study was to 1) estimate genetic parameters for individual proteins in Danish Holstein (DH) (n = 371) and Danish Jersey (DJ) (n = 321) milk, and 2) detect genomic regions associated with specific milk protein and their different PTM forms using a genome-wide association study (GWAS) approach.

Results

For DH, high heritability estimates were found for protein percentage (0.47), casein percentage (0.43), k-CN (0.77), β-LG (0.58), and α-LA (0.40). For DJ, high heritability estimates were found for protein percentage (0.70), casein percentage (0.52), and α-LA (0.44). The heritability for G-k-CN, U-k-CN and GD was higher in the DH compared to the DJ, whereas the heritability for the PD of α_S1-CN was lower in DH compared to DJ, whereas the PD for α_S2-CN was higher in DH compared to DJ. The GWAS results for the main milk proteins were in line what has been earlier published. However, we showed that there were SNPs specifically regulating G-k-CN in DH. Some of these SNPs were assigned to casein protein kinase genes (CSNK1G3, PRKCQ).

Conclusion

The genetic analysis of the major milk proteins and their PTM forms revealed that these were heritable in both DH and DJ. In DH, genomic regions specific for glycosylation of k-CN were detected. Furthermore, genomic regions for the major milk proteins confirmed the regions on BTA6 (casein cluster), BTA11 (PEAP), and BTA14 (DGAT1) as important regions influencing protein composition in milk. The results from this study provide confidence that it is possible to breed for specific milk protein including the different PTM forms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0421-2) contains supplementary material, which is available to authorized users.

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.