SLICK1 is an allelic variant of the prolactin receptor (PRLR) that is found in Senepol beef cattle. The presence of a single copy of this allele produces a short hair coat and confers heat tolerance. We aimed to determine the effect of 2 copies of this allele on milking performance of dairy cattle. Data were obtained from a commercial dairy farm in Costa Rica where crossbreeding of the dairy herd with Senepol had been undertaken for more than 15 yr. Random regression modeling was used to generate lactation curves for individual cattle. Homozygous SLICK1 cows produced 9% more milk (356 L), predominantly over the first 200 d of a 305-d lactation. Milk production of wild-type cattle in the same herd was much lower than SLICK1 carriers, but the number of cows in this group was too low (n = 13) to provide a conclusive assessment of milking performance.

BACKGROUND:Bovine lactoferrin (Lf) is an iron absorbing whey protein with antibacterial, antiviral, and antifungal activity. Lactoferrin is economically valuable and has an extremely variable concentration in milk, partly driven by environmental influences such as milking frequency, involution, or mastitis. A significant genetic influence has also been previously observed to regulate lactoferrin content in milk. Here, we conducted genetic mapping of lactoferrin protein concentration in conjunction with RNA-seq, ChIP-seq, and ATAC-seq data to pinpoint candidate causative variants that regulate lactoferrin concentrations in milk. RESULTS:We identified a highly-significant lactoferrin protein quantitative trait locus (pQTL), as well as a cis lactotransferrin (LTF) expression QTL (cis-eQTL) mapping to the LTF locus. Using ChIP-seq and ATAC-seq datasets representing lactating mammary tissue samples, we also report a number of regions where the openness of chromatin is under genetic influence. Several of these also show highly significant QTL with genetic signatures similar to those highlighted through pQTL and eQTL analysis. By performing correlation analysis between these QTL, we revealed an ATAC-seq peak in the putative promotor region of LTF, that highlights a set of 115 high-frequency variants that are potentially responsible for these effects. One of the 115 variants (rs110000337), which maps within the ATAC-seq peak, was predicted to alter binding sites of transcription factors known to be involved in lactation-related pathways. CONCLUSIONS:Here, we report a regulatory haplotype of 115 variants with conspicuously large impacts on milk lactoferrin concentration. These findings could enable the selection of animals for high-producing specialist herds.

Additional file 2: Table S1. GWAS results for the top 250 SNPs in the region of the BLG gene that were associated with total BLG concentration in milk (n = 4140). Two SNPs that differentiate the A and B protein isoforms are highlighted. Table S2. GWAS results for the top 250 SNPs in the region of the BLG gene that were associated with BLG B isoform concentrations in milks from heterozygous AB animals (n = 1966). The top SNP (highlighted) is B’ (chr11: 103256256G > A).

Background Deleterious recessive conditions have been primarily studied in the context of Mendelian diseases. Recently, several deleterious recessive mutations with large effects were discovered via non-additive genome-wide association studies (GWAS) of quantitative growth and developmental traits in cattle, which showed that quantitative traits can be used as proxies of genetic disorders when such traits are indicative of whole-animal health status. We reasoned that lactation traits in cattle might also reflect genetic disorders, given the increased energy demands of lactation and the substantial stresses imposed on the animal. In this study, we screened more than 124,000 cows for recessive effects based on lactation traits. Results We discovered five novel quantitative trait loci (QTL) that are associated with large recessive impacts on three milk yield traits, with these loci presenting missense variants in the DOCK8 , IL4R , KIAA0556 , and SLC25A4 genes or premature stop variants in the ITGAL , LRCH4 , and RBM34 genes, as candidate causal mutations. For two milk composition traits, we identified several previously reported additive QTL that display small dominance effects. By contrasting results from milk yield and milk composition phenotypes, we note differing genetic architectures. Compared to milk composition phenotypes, milk yield phenotypes had lower heritabilities and were associated with fewer additive QTL but had a higher non-additive genetic variance and were associated with a higher proportion of loci exhibiting dominance. Conclusions We identified large-effect recessive QTL which are segregating at surprisingly high frequencies in cattle. We speculate that the differences in genetic architecture between milk yield and milk composition phenotypes derive from underlying dissimilarities in the cellular and molecular representation of these traits, with yield phenotypes acting as a better proxy of underlying biological disorders through presentation of a larger number of major recessive impacts.

Aims To investigate the relationship between Zn concentrations in serum and those in milk or faeces, and to assess the ability of the Zn concentrations in milk, serum and faeces to predict intake of ZnO in dairy cattle. Method Seventy cows from one commercial farm in the Waikato region of New Zealand received one of seven dose rates (0, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5 g/100 kg bodyweight (BW)) of ZnO given by oral drench, every morning, for 7 consecutive days. Every afternoon, milk and blood samples were collected from all cows. Free-catch faecal samples were collected during the afternoon milking on 3 days throughout the trial. Linear mixed models were used to assess the relationship between the concentration of Zn in serum and that in milk, and in faeces, respectively, and the relationship between dose rate of ZnO and concentrations of Zn in serum, faeces and milk, respectively. Receiver operating characteristic curve analysis was used to determine the ability of the Zn concentration in serum, milk and faeces to predict that a cow had been treated with a dose of ZnO >= 2.5 g/100 kg, the industry-recommended dose rate needed to protect against facial eczema. Results A 1-mu mol/L increase in Zn concentration in milk was associated with a 0.14 (95% CI = 0.11-0.17) mu mol/L increase in Zn concentration in serum. Zn concentration in faeces was scaled by its SD; a 1 SD increase was associated with a 1.83 (95% CI = 0.54-3.12) mu mol/L increase in zinc concentration in serum. Zn concentrations in serum and faeces increased with increasing dose rates of ZnO. No differences in Zn concentrations in milk were noted between animals dosed with 1.5-3.5 g ZnO/100 kg BW, inclusive. At the optimal threshold of Zn concentration in serum to predict protective ZnO intake (22 mu mol/L), the sensitivity was 0.76 (95% CI = 0.69-0.82) and specificity 0.85 (95% CI = 0.80-0.89). For the concentration of Zn in faeces, the optimal threshold was 17.36 mmol/kg, with a corresponding sensitivity of 0.84 (95% CI = 0.84-0.85) and specificity of 0.85 (95% CI = 0.73-0.94). At the optimal threshold for the Zn concentration in milk (76.6 mu mol/L), the sensitivity was lower than the other two sample types at 0.59 (95% CI = 0.52-0.67), but with a similar specificity of 0.84 (95% CI = 0.79-0.88).

Abstract Background Milk samples from 10,641 dairy cattle were screened by a mass spectrometry method for extreme concentrations of the A or B isoforms of the whey protein, β-lactoglobulin (BLG), to identify causative genetic variation driving changes in BLG concentration. Results A cohort of cows, from a single sire family, was identified that produced milk containing a low concentration of the BLG B protein isoform. A genome-wide association study (GWAS) of BLG B protein isoform concentration in milk from AB heterozygous cows, detected a group of highly significant single nucleotide polymorphisms (SNPs) within or close to the BLG gene. Among these was a synonymous G/A variation at position + 78 bp in exon 1 of the BLG gene (chr11:103256256G > A). The effect of the A allele of this SNP (which we named B’) on BLG expression was evaluated in a luciferase reporter assay in transfected CHO-K1 and MCF-7 cells. In both cell types, the presence of the B’ allele in a plasmid containing the bovine BLG gene from -922 to + 898 bp (relative to the transcription initiation site) resulted in a 60% relative reduction in mRNA expression, compared to the plasmid containing the wild-type B sequence allele. Examination of a mammary RNAseq dataset (n = 391) identified 14 heterozygous carriers of the B’ allele which were homozygous for the BLG B protein isoform (BB’). The level of expression of the BLG B’ allele was 41.9 ± 1.0% of that of the wild-type BLG B allele. Milk samples from three cows, homozygous for the A allele at chr11:103,256,256 (B’B’), were analysed (HPLC) and showed BLG concentrations of 1.04, 1.26 and 1.83 g/L relative to a mean of 4.84 g/L in milk from 16 herd contemporaries of mixed (A and B) BLG genotypes. The mechanism by which B’ downregulates milk BLG concentration remains to be determined. Conclusions High-throughput screening and identification of outliers, enabled the discovery of a synonymous G > A mutation in exon 1 of the B allele of the BLG gene (B’), which reduced the milk concentration of β-lactoglobulin B protein isoform, by more than 50%. Milk from cows carrying the B’ allele is expected to have improved processing characteristics, particularly for cheese-making.

Fourier-transform mid-infrared (FT-MIR) spectroscopy is a high-throughput and inexpensive methodology used to evaluate concentrations of fat and protein in dairy cattle milk samples. The objective of this study was to compare the genetic characteristics of FT-MIR predicted fatty acids and individual milk proteins with those that had been measured directly using gas and liquid chromatography methods. The data used in this study was based on 2,005 milk samples collected from 706 Holstein-Friesian × Jersey animals that were managed in a seasonal, pasture-based dairy system, with milk samples collected across 2 consecutive seasons. Concentrations of fatty acids and protein fractions in milk samples were directly determined by gas chromatography and high-performance liquid chromatography, respectively. Models to predict each directly measured trait based on FT-MIR spectra were developed using partial least squares regression, with spectra from a random selection of half the cows used to train the models, and predictions for the remaining cows used as validation. Variance parameters for each trait and genetic correlations for each pair of measured/predicted traits were estimated from pedigree-based bivariate models using REML procedures. A genome-wide association study was undertaken using imputed whole-genome sequence, and quantitative trait loci (QTL) from directly measured traits were compared with QTL from the corresponding FT-MIR predicted traits. Cross-validation prediction accuracies based on partial least squares for individual and grouped fatty acids ranged from 0.18 to 0.65. Trait prediction accuracies in cross-validation for protein fractions were 0.53, 0.19, and 0.48 for α-casein, β-casein, and κ-casein, 0.31 for α-lactalbumin, 0.68 for β-lactoglobulin, and 0.36 for lactoferrin. Heritability estimates for directly measured traits ranged from 0.07 to 0.55 for fatty acids; and from 0.14 to 0.63 for individual milk proteins. For FT-MIR predicted traits, heritability estimates were mostly higher than for the corresponding measured traits, ranging from 0.14 to 0.46 for fatty acids, and from 0.30 to 0.70 for individual proteins. Genetic correlations between directly measured and FT-MIR predicted protein fractions were consistently above 0.75, with the exceptions of C18:0 and C18:3 cis-3, which had genetic correlations of 0.72 and 0.74, respectively. The GWAS identified trait QTL for fatty acids with likely candidates in the DGAT1, CCDC57, SCD, and GPAT4 genes. Notably, QTL for SCD were largely absent in the FT-MIR predicted traits, and QTL for GPAT4 were absent in directly measured traits. Similarly, for directly measured individual proteins, we identified QTL with likely candidates in the CSN1S1, CSN3, PAEP, and LTF genes, but the QTL for CSN3 and LTF were absent in the FT-MIR predicted traits. Our study indicates that genetic correlations between directly measured and FT-MIR predicted fatty acid and protein fractions are typically high, but that phenotypic variation in these traits may be underpinned by differing genetic architecture.

Aims To describe the concentration of Zn in bulk tank milk (BTM) in a sample of New Zealand dairy farms, investigate the association between the method of Zn administration for facial eczema prophylaxis and Zn concentrations in BTM and investigate the relationship between the concentration of Zn in serum and that in BTM. Methods Multiple BTM samples (n = 3,330) collected during milk pick-up by the milk tanker driver were stored and tested for 121 farms, in Northland (n = 50), Waikato (n = 51) and Southland (n = 20) from February to May 2017. Enrolled farms provided retrospective information on the type of Zn supplementation (if any) used for the prevention of facial eczema and the timeframe over which supplementation occurred. In addition, the concentration of Zn in serum was measured in blood samples collected from >= 15 cattle per farm for 22 farms from Northland (n = 11) and Waikato (n = 11), and compared against the concentrations of Zn in BTM on the day of blood sampling. A linear mixed model was used to model log Zn concentrations in BTM using method of Zn supplementation, region, milk fat and protein percentage, volume of milk, and frequency of milk pick-up as risk factors. A mixed logistic regression model was used to assess the relationship between Zn concentrations in BTM and the presence of cows with a concentration of Zn in serum of >= 20 mu mol/L. Results The median Zn concentration in BTM was 67.9 (min 38.9, max 146.6) mu mol/L. The median range of Zn concentrations for repeated samples of BTM within farm was 22.6 mu mol/L. In comparison to farms that did not use any form of Zn supplementation, farms that supplemented Zn through a slow-release capsule, oral drench, in feed or a combination of in-feed and water were associated with increased concentrations of Zn in BTM (p < 0.001). There was no difference in Zn concentrations in BTM between farms that administered Zn through the water only and farms that did not administer Zn (p = 0.22). Every 15.3 mu mol/L increase in Zn concentration in BTM was associated with 2.2 times (95% CI=1.7-2.9) the odds of a cow having Zn concentration in serum >= 20 mu mol/L.

Accurate and timely pregnancy diagnosis is an important component of effective herd management in dairy cattle. Predicting pregnancy from Fourier-transform mid-infrared (FT-MIR) spectroscopy data is of particular interest because the data are often already available from routine milk testing. The purpose of this study was to evaluate how well pregnancy status could be predicted in a large data set of 1,161,436 FT-MIR milk spectra records from 863,982 mixed-breed pasture-based New Zealand dairy cattle managed within seasonal calving systems. Three strategies were assessed for defining the nonpregnant cows when partitioning the records according to pregnancy status in the training population. Two of these used records for cows with a subsequent calving only, whereas the third also included records for cows without a subsequent calving. For each partitioning strategy, partial least squares discriminant analysis models were developed, whereby spectra from all the cows in 80% of herds were used to train the models, and predictions on cows in the remaining herds were used for validation. A separate data set was also used as a secondary validation, whereby pregnancy diagnosis had been assigned according to the presence of pregnancy-associated glycoproteins (PAG) in the milk samples. We examined different ways of accounting for stage of lactation in the prediction models, either by including it as an effect in the prediction model, or by pre-adjusting spectra before fitting the model. For a subset of strategies, we also assessed prediction accuracies from deep learning approaches, utilizing either the raw spectra or images of spectra. Across all strategies, prediction accuracies were highest for models using the unadjusted spectra as model predictors. Strategies for cows with a subsequent calving performed well in herd-independent validation with sensitivities above 0.79, specificities above 0.91 and area under the receiver operating characteristic curve (AUC) values over 0.91. However, for these strategies, the specificity to predict nonpregnant cows in the external PAG data set was poor (0.002-0.04). The best performing models were those that included records for cows without a subsequent calving, and used unadjusted spectra and days in milk as predictors, with consistent results observed across the training, herd-independent validation and PAG data sets. For the partial least squares discriminant analysis model, sensitivity was 0.71, specificity was 0.54 and AUC values were 0.68 in the PAG data set; and for an image-based deep learning model, the sensitivity was 0.74, specificity was 0.52 and the AUC value was 0.69. Our results demonstrate that in pasture-based seasonal calving herds, confounding between pregnancy status and spectral changes associated with stage of lactation can inflate prediction accuracies. When the effect of this confounding was reduced, prediction accuracies were not sufficiently high enough to use as a sole indicator of pregnancy status.

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.

Mammalian species carry ~100 loss-of-function variants per individual 1 , 2 , where ~1–5 of these impact essential genes and cause embryonic lethality or severe disease when homozygous 3 . The functions of the remainder are more difficult to resolve, although the assumption is that these variants impact fitness in less manifest ways. Here we report one of the largest sequence-resolution screens of cattle to date, targeting discovery and validation of non-additive effects in 130,725 animals. We highlight six novel recessive loci with impacts generally exceeding the largest-effect variants identified from additive genome-wide association studies, presenting analogs of human diseases and hitherto-unrecognized disorders. These loci present compelling missense ( PLCD4 , MTRF1 and DPF2 ), premature stop ( MUS81 ) and splice-disrupting ( GALNT2 and FGD4 ) mutations, together explaining substantial proportions of inbreeding depression. These results demonstrate that the frequency distribution of deleterious alleles segregating in selected species can afford sufficient power to directly map novel disorders, presenting selection opportunities to minimize the incidence of genetic disease.

Abstract Context In New Zealand, cows are usually milked twice a day (TAD), but in ~8% of herds, cows are milked once a day (OAD) for the entire lactation. If a genetic correlation (rg) of the same trait expressed in two environments (such as TAD and OAD) is substantially <1.0, then the genetic merit assessed from TAD herds may not be reliable for predicting genetic merit in OAD herds. Initial evaluation of sires has been undertaken from progeny test herds with TAD milking, and the best sires have then been made widely available for use in TAD or OAD herds. Aim The study was designed to test formally whether sire re-rankings occur in widely used sires at different milking frequencies. Methods Regression coefficients and rank correlations (rS) for estimated breeding values (EBVs) of 242 widely used sires (86 Holstein-Friesian, 96 Jersey and 60 crossbred) were calculated for yields of milk, fat and protein, and for somatic cell score (SCS). The rS values were contrasted with expected rank correlations (rE) between TAD and OAD EBVs assuming rg = 1.0 between true BVs expressed at the two milking frequencies and accounting for the fact that sires were highly selected. Key results Within and across breeds, regression coefficients of OAD on TAD EBVs for the same traits were <1.0, but rS values between TAD and OAD EBVs of the same sires were strong (>0.75) for milk yield, moderate–strong for fat and protein yield (0.55–0.77), and moderate for SCS (0.41–0.65). Estimates of rS were higher than their critical rE values, indicating no significant sire re-ranking across milking frequencies. Conclusions On the basis of the results, a separate selection program to generate sires for use in OAD herds is not justified. Implications Farmers operating under OAD systems can rely on genetic evaluation of sires evaluated in TAD systems and used in the OAD population. However, producers should recognise that the realised productive and economic advantage of daughters of elite sires born in OAD herds is diminished relative to that expected in TAD herds.

In New Zealand cows are usually milked twice a day (TAD), however milking cows once-a-day (OAD) for the entire lactation is steadily increasing. This study aimed to estimate genetic correlations (r(g)) for milk production traits across TAD and OAD milking frequency (MF) environments from first lactation cows. Lactation yields of milk (MY), fat (FY) and protein (PY), and somatic cell score (SCS) of progeny from 1126 sires that were herd-tested during 2008-2012 were analysed. The r(g) estimates of the same trait expressed in the two MF were lower than unity but greater than 0.80. Genetic correlation between MY and PY across MF environments was high (0.79), moderate between MY and PY with FY (0.43-0.51), and low when the estimation involved SCS (<0.15). The results show that genetic evaluations made in TAD should include the GxMF effects, and although these effects are not expected to cause significant reductions in the efficiency of selection programmes in OAD systems for milk production traits in New Zealand dairy cattle. These estimates presented in this study can be used to predict genetic progress for the dairy population milked OAD when sires proven with daughters milked TAD are used in OAD herds.

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.

Background The DGAT1 gene encodes an enzyme responsible for catalysing the terminal reaction in mammary triglyceride synthesis, and underpins a well-known pleiotropic quantitative trait locus (QTL) with a large influence on milk composition phenotypes. Since first described over 15 years ago, a protein-coding variant K232A has been assumed as the causative variant underlying these effects, following in-vitro studies that demonstrated differing levels of triglyceride synthesis between the two protein isoforms. Results We used a large RNAseq dataset to re-examine the underlying mechanisms of this large milk production QTL, and hereby report novel expression-based functions of the chr14 g.1802265AA > GC variant that encodes the DGAT1 K232A substitution. Using expression QTL (eQTL) mapping, we demonstrate a highly-significant mammary eQTL for DGAT1, where the K232A mutation appears as one of the top associated variants for this effect. By conducting in vitro expression and splicing experiments in bovine mammary cell culture, we further show modulation of splicing efficiency by this mutation, likely through disruption of an exon splice enhancer as a consequence of the allele encoding the 232A variant. Conclusions The relative contributions of the enzymatic and transcription-based mechanisms now attributed to K232A remain unclear; however, these results suggest that transcriptional impacts contribute to the diversity of lactation effects observed at the DGAT1 locus.

ABSTRACT In New Zealand, a specific selection scheme for the dairy cattle population milked once-a-day (OAD) has not been implemented. This study aims to investigate estimated genetic progress for different selection objectives based on selection scheme designs using progeny testing (PT) and genomic selection (GS) in OAD herds. Under a PT-scheme, estimated genetic progress ranged from 7 to 45 kg/year (milk), 2.18–2.24 kg/year (fat), and 0.83–1.71 kg/year (protein). Conversely, genetic gains resulting from selecting bulls generated in twice-a-day (TAD) milking systems and used to inseminate cows in OAD herds, ranged between 12 and 52 kg/year (milk), 2.34 and 3.22 kg/year (fat) and 0.83 and 2.29 kg/year (protein). Correlated responses tend to be greater in the yield traits when the emphasis on fertility, residual survival and body condition score were reduced in the selection objective. Further correlated responses in yield traits were reduced when type-related traits were included in the selection objective, in particular when bulls were tested in TAD systems. A more immediate and profitable alternative might be the implementation of a GS-scheme, that would result in a faster genetic gain in the aggregate breeding value or merit for all the traits included in the selection objective (0.310–0.368 vs 0.191–0.196 σg/yr).

The rapid advances in technology for both evaluating and understanding the structure of animal genomes and their functional significance have presented opportunities for scientists to more clearly understand the complexity of bovine milk proteins and the control of their expression. Used in conjunction with proteomic databases, we can start to expand our knowledge of how milk proteins are processed into peptides, which represent much of the biological activity residing within colostrum and milk. The challenge then remains to translate this information into products that form the basis of a functional foods industry, helping to underpin the commercial viability of the dairy industry. In this chapter, we present a review of the current status of bovine milk genomics and functional genomics, and describe the roles, characteristics and key bioactivities of the major bovine milk proteins and their encrypted peptides. The application of these analytical tools to the full spectrum of lactation strategies adopted by eutherians, marsupials and monotremes to improve our understanding of the milk proteome is discussed.

Background White spotting of the coat is a characteristic trait of various domestic species including cattle and other mammals. It is a hallmark of Holstein-Friesian cattle, and several previous studies have detected genetic loci with major effects for white spotting in animals with Holstein-Friesian ancestry. Here, our aim was to better understand the underlying genetic and molecular mechanisms of white spotting, by conducting the largest mapping study for this trait in cattle, to date. Results Using imputed whole-genome sequence data, we conducted a genome-wide association analysis in 2973 mixed-breed cows and bulls. Highly significant quantitative trait loci (QTL) were found on chromosomes 6 and 22, highlighting the well-established coat color genes KIT and MITF as likely responsible for these effects. These results are in broad agreement with previous studies, although we also report a third significant QTL on chromosome 2 that appears to be novel. This signal maps immediately adjacent to the PAX3 gene, which encodes a known transcription factor that controls MITF expression and is the causal locus for white spotting in horses. More detailed examination of these loci revealed a candidate causal mutation in PAX3 (p.Thr424Met), and another candidate mutation (rs209784468) within a conserved element in intron 2 of MITF transcripts expressed in the skin. These analyses also revealed a mechanistic ambiguity at the chromosome 6 locus, where highly dispersed association signals suggested multiple or multiallelic QTL involving KIT and/or other genes in this region. Conclusions Our findings extend those of previous studies that reported KIT as a likely causal gene for white spotting, and report novel associations between candidate causal mutations in both the MITF and PAX3 genes. The sizes of the effects of these QTL are substantial, and could be used to select animals with darker, or conversely whiter, coats depending on the desired characteristics.

Over many years, artificial selection has substantially improved milk production by cows. However, the genes that underlie milk production quantitative trait loci (QTL) remain relatively poorly characterised. Here, we investigate a previously reported QTL located at the CSF2RB locus on chromosome 5, for several milk production phenotypes, to better understand its underlying genetic and molecular causes. Using a population of 29,350 taurine dairy cows, we conducted association analyses for milk yield and composition traits, and identified highly significant QTL for milk yield, milk fat concentration, and milk protein concentration. Strikingly, protein concentration and milk yield appear to show co-located yet genetically distinct QTL. To attempt to understand the molecular mechanisms that might be mediating these effects, gene expression data were used to investigate eQTL for 11 genes in the broader interval. This analysis highlighted genetic impacts on CSF2RB and NCF4 expression that share similar association signatures to those observed for lactation QTL, strongly implicating one or both of these genes as responsible for these effects. Using the same gene expression dataset representing 357 lactating cows, we also identified 38 novel RNA editing sites in the 3′ UTR of CSF2RB transcripts. The extent to which two of these sites were edited also appears to be genetically co-regulated with lactation QTL, highlighting a further layer of regulatory complexity that involves the CSF2RB gene. This locus presents a diversity of molecular and lactation QTL, likely representing multiple overlapping effects that, at a minimum, highlight the CSF2RB gene as having a causal role in these processes.

In cattle, the X chromosome accounts for approximately 3 and 6% of the genome in bulls and cows, respectively. In spite of the large size of this chromosome, very few studies report analysis of the X chromosome in genome-wide association studies and genomic selection. This lack of genetic interrogation is likely due to the complexities of undertaking these studies given the hemizygous state of some, but not all, of the X chromosome in males. The first step in facilitating analysis of this gene-rich chromosome is to accurately identify coordinates for the pseudoautosomal boundary (PAB) to split the chromosome into a region that may be treated as autosomal sequence (pseudoautosomal region) and a region that requires more complex statistical models. With the recent release of ARS-UCD1.2, a more complete and accurate assembly of the cattle genome than was previously available, it is timely to fine map the PAB for the first time. Here we report the use of SNP chip genotypes, short-read sequences, and long-read sequences to fine map the PAB (X chromosome:133,300,518) and simultaneously determine the neighboring regions of reduced homology and true pseudoautosomal region. These results greatly facilitate the inclusion of the X chromosome in genome-wide association studies, genomic selection, and other genetic analysis undertaken on this reference genome.