Detecting genes associated with milk fat composition could provide valuable insights into the complex genetic networks of genes underling variation in fatty acids synthesis and point towards opportunities for changing milk fat composition via selective breeding. In this study, we conducted a genome-wide association study (GWAS) for 22 milk fatty acids in 784 Chinese Holstein cows with the PLINK software. Genotypes were obtained with the Illumina BovineSNP50 Bead chip and a total of 40,604 informative, high-quality single nucleotide polymorphisms (SNPs) were used. Totally, 83 genome-wide significant SNPs and 314 suggestive significant SNPs associated with 18 milk fatty acid traits were detected. Chromosome regions that affect milk fatty acid traits were mainly observed on BTA1, 2, 5, 6, 7, 9, 13, 14, 18, 19, 20, 21, 23, 26 and 27. Of these, 146 SNPs were associated with more than one milk fatty acid trait; most of studied fatty acid traits were significant associated with multiple SNPs, especially C18:0 (105 SNPs), C18 index (93 SNPs), and C14 index (84 SNPs); Several SNPs are close to or within the DGAT1, SCD1 and FASN genes which are well-known to affect milk composition traits of dairy cattle. Combined with the previously reported QTL regions and the biological functions of the genes, 20 novel promising candidates for C10:0, C12:0, C14:0, C14:1, C14 index, C18:0, C18:1n9c, C18 index, SFA, UFA and SFA/UFA were found, which composed of HTR1B, CPM, PRKG1, MINPP1, LIPJ, LIPK, EHHADH, MOGAT1, ECHS1, STAT1, SORBS1, NFKB2, AGPAT3, CHUK, OSBPL8, PRLR, IGF1R, ACSL3, GHR and OXCT1. Our findings provide a groundwork for unraveling the key genes and causal mutations affecting milk fatty acid traits in dairy cattle.

Paired-end RNA sequencing (RNA-Seq) was used to explore the bovine transcriptome from the mammary tissue of 12 Chinese Holstein cows with 6 extremely high and 6 low phenotypic values for milk protein percentage. We defined the differentially expressed transcripts between the two comparison groups, extremely high and low milk protein percentage during the peak lactation (HP vs LP) and during the non-lactating period (HD vs LD), respectively. Within the differentially expressed genes (DEGs), we detected 157 at peak lactation and 497 in the non-lactating period with a highly significant correlation with milk protein concentration. Integrated interpretation of differential gene expression indicated that SERPINA1, CLU, CNTFR, ERBB2, NEDD4L, ANG, GALE, HSPA8, LPAR6 and CD14 are the most promising candidate genes affecting milk protein concentration. Similarly, LTF, FCGR3A, MEGF10, RRM2 and UBE2C are the most promising candidates that in the non-lactating period could help the mammary tissue prevent issues with inflammation and udder disorders. Putative genes will be valuable resources for designing better breeding strategies to optimize the content of milk protein and also to provide new insights into regulation of lactogenesis.

Genome-wide association studies (GWASs) have been widely used to determine the genetic architecture of quantitative traits in dairy cattle. In this study, with the aim of identifying candidate genes that affect milk protein composition traits, we conducted a GWAS for nine such traits (α_s1-casein, α_s2-casein, β-casein, κ-casein, α-lactalbumin, β-lactoglobulin, casein index, protein percentage, and protein yield) in 614 Chinese Holstein cows using a single-step strategy. We used the Illumina BovineSNP50 Bead chip and imputed genotypes from high-density single-nucleotide polymorphisms (SNPs) ranging from 50 to 777 K, and subsequent to genotype imputation and quality control, we screened a total of 586,304 informative high-quality SNPs. Phenotypic observations for six major milk proteins (α_s1-casein, α_s2-casein, β-casein, κ-casein, α-lactalbumin, and β-lactoglobulin) were evaluated as weight proportions of the total protein fraction (wt/wt%) using a commercial enzyme-linked immunosorbent assay kit. Informative windows comprising five adjacent SNPs explaining no < 0.5% of the genomic variance per window were selected for gene annotation and gene network and pathway analyses. Gene network analysis performed using the STRING Genomics 10.0 database revealed a co-expression network comprising 46 interactions among 62 of the most plausible candidate genes. A total of 178 genomic windows and 194 SNPs on 24 bovine autosomes were significantly associated with milk protein composition or protein percentage. Regions affecting milk protein composition traits were mainly observed on chromosomes BTA 1, 6, 11, 13, 14, and 18. Of these, several windows were close to or within the CSN1S1, CSN1S2, CSN2, CSN3, LAP3, DGAT1, RPL8, and HSF1 genes, which have well-known effects on milk protein composition traits of dairy cattle. Taken together with previously reported quantitative trait loci and the biological functions of the identified genes, we propose 19 novel candidate genes affecting milk protein composition traits: ARL6, SST, EHHADH, PCDHB4, PCDHB6, PCDHB7, PCDHB16, SLC36A2, GALNT14, FPGS, LARP4B, IDI1, COG4, FUK, WDR62, CLIP3, SLC25A21, IL5RA, and ACADSB. Our findings provide important insights into milk protein synthesis and indicate potential targets for improving milk quality.

A previous genome-wide association study deduced that one (ARS-BFGL-NGS-39328), two (Hapmap26001-BTC-038813 and Hapmap31284-BTC-039204), two (Hapmap26001-BTC-038813 and BTB-00246150), and one (Hapmap50366-BTA-46960) genome-wide significant single nucleotide polymorphisms (SNPs) associated with milk fatty acids were close to or within the fatty acid synthase (FASN), peroxisome proliferator-activated receptor gamma, coactivator 1 alpha (PPARGC1A), ATP-binding cassette, sub-family G, member 2 (ABCG2) and insulin-like growth factor 1 (IGF1) genes. To further confirm the linkage and reveal the genetic effects of these four candidate genes on milk fatty acid composition, genetic polymorphisms were identified and genotype-phenotype associations were performed in a Chinese Holstein cattle population. Nine SNPs were identified in FASN, among which SNP rs41919985 was predicted to result in an amino acid substitution from threonine (ACC) to alanine (GCC), five SNPs (rs136947640, rs134340637, rs41919992, rs41919984 and rs41919986) were synonymous mutations, and the remaining three (rs41919999, rs132865003 and rs133498277) were found in FASN introns. Only one SNP each was identified for PPARGC1A, ABCG2 and IGF1. Association studies revealed that FASN, PPARGC1A, ABCG2 and IGF1 were mainly associated with medium-chain saturated fatty acids and long-chain unsaturated fatty acids, especially FASN for C10:0, C12:0 and C14:0. Strong linkage disequilibrium was observed among ARS-BFGL-NGS-39328 and rs132865003 and rs134340637 in FASN (D´ > 0.9), and among Hapmap26001-BTC-038813 and Hapmap31284-BTC-039204 and rs109579682 in PPARGC1A (D´ > 0.9). Subsequently, haplotype-based analysis revealed significant associations of the haplotypes encompassing eight FASN SNPs (rs41919999, rs132865003, rs134340637, rs41919992, rs133498277, rs41919984, rs41919985 and rs41919986) with C10:0, C12:0, C14:0, C18:1n9c, saturated fatty acids (SFA) and unsaturated fatty acids (UFA) (P = 0.0204 to P < 0.0001). Our study confirmed the linkage between the significant SNPs in our previous genome-wide association study and variants in FASN and PPARGC1A. SNPs within FASN, PPARGC1A, ABCG2 and IGF1 showed significant genetic effects on milk fatty acid composition in dairy cattle, indicating their potential functions in milk fatty acids synthesis and metabolism. The findings presented here provide evidence for the selection of dairy cows with healthier milk fatty acid composition by marker-assisted breeding or genomic selection schemes, as well as furthering our understanding of technological processing aspects of cows’ milk.

The physiologic and metabolic stresses that dairy cows experience during the transition into early lactation can promote oxidative stress, inflammation, and immune dysfunction. Optimal supply of micronutrients such as trace minerals (e.g., Zn, Mn, Cu, and Co) via more bioavailable forms (e.g., AA complexes) might minimize these negative effects. Multiparous Holstein cows were enrolled at 60 d before dry-off (~110 d before calving) and remained on experiment until 30 d in milk (DIM). Cows were offered a common diet supplemented entirely with inorganic trace minerals (INO) from -110 to -30 d before calving. From -30 to calving cows received a common prepartal [1.5 Mcal/kg of dry matter (DM), 15% crude protein] diet, and from calving to 30 DIM a common postpartal (1.76 Mcal/kg of DM, 18% crude protein) diet. Both diets were partially supplemented with an INO mix of Zn, Mn, and Cu to supply 35, 45, and 6 mg/kg, respectively, of the total diet DM. Cows were assigned to treatments in a randomized complete block design to receive an oral bolus with a mix of INO (n=21) or organic AA complexes (AAC; n=16) of Zn, Mn, Cu, and Co to achieve supplemental levels of 75, 65, 11, and 1mg/kg, respectively, in the total diet DM. Inorganic trace minerals were provided in sulfate form and AAC were supplied via Availa Zn, Availa Mn, Availa Cu, and COPRO (Zinpro Corp., Eden Prairie, MN). Liver tissue was harvested on -30, -15, 10, and 30 d, and blood samples for biomarker analyses were obtained more frequently from -30 to 30 DIM. Short-term changes in blood ketones were measured via Precision Xtra (Abbott Diabetes Care, Alameda, CA) every other day from 1 to 15 d postpartum. Prepartal DM intake was lower in AAC cows. In contrast, a tendency for a diet by time (D × T) interaction resulted in greater postpartal DM intake of approximately 2 kg/d in cows fed AAC. Milk and milk protein yield had a D × T interaction because AAC cows produced approximately 3.3 kg/d more milk and 0.14 kg/d more protein during the first 30 DIM. Although blood glucose, fatty acids, and liver triacylglycerol were not affected by diet, the Precision Xtra ketones (1.44 vs. 2.18 mmol/L) and γ-glutamyltransferase (liver function biomarker) were lower in AAC than INO. Furthermore, feeding AAC increased (D × T) polymorphonuclear neutrophilic lymphocyte phagocytosis, antioxidant capacity postpartum, and overall concentration of liver tissue Co and Cu. Overall, the positive response in milk yield and milk protein in AAC cows might be partly explained by the beneficial effects of AAC on postpartal DM intake driven at least in part by better liver and immune function as a result of improved antioxidant status.

Long non-coding RNAs (lncRNAs) have emerged as a novel class of regulatory molecules involved in various biological processes. However, their role in milk performance is unknown. Here, whole transcriptome RNA sequencing was used to generate the lncRNA transcriptome profiles in mammary tissue samples from 6 Chinese Holstein cows with 3 extremely high and 3 low milk protein percentage phenotypes. In this study, 6,450 lncRNA transcripts were identified through 5 stringent steps and filtration by coding potential. In total, 31 lncRNAs and 18 novel genes were identified to be differentially expressed in high milk protein samples (HP) relative to low milk protein samples (LP), respectively. Differentially expressed lncRNAs were selected to predict target genes through bioinformatics analysis, followed by the integration of differentially expressed mRNA data, gene function, gene ontology (GO) and pathway, genome wide association study (GWAS) and quantitative trait locus (QTL) information, as well as network analysis to further characterize potential interactions. Several lncRNAs were found (such as XLOC_059976) that could be used as candidate markers for milk protein content prediction. This is the first study to perform global expression profiling of lncRNAs and mRNAs related to milk protein traits in dairy cows. These results provide important information and insights into the synthesis of milk proteins, and potential targets for the future improvement of milk quality.