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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., (© 2024. The Author(s).)

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., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

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.

Background: 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., Results: 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., Conclusions: 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.

The main objectives of this study were to establish the relative value of milk yields under twice-daily milking (TDM) as a predictor of yield and yield loss under once-daily milking (ODM), and to understand the role of residual milk and udder storage capacity-related traits in regulating yield and yield loss during ODM. A Holstein-Friesian × Jersey crossbred herd was established over 2 seasons (years), as 2 individual cohorts on the same farm, managed on a pasture-based system over 4 lactations. Short-term (1-wk) ODM studies, with a starting total of 690 cows, were undertaken in mid- and late-lactation in lactation 2 and in mid-lactation in lactation 3 for each cohort. A 10-wk study of ODM performance began in mid-lactation in lactation 3, whereas lactation 4 was a full-lactation assessment of ODM. In the short-term studies, milk yield under ODM was well predicted (R(2)=0.7 to 0.8 in 5 of 6 studies) by the daily yield under TDM in the week before ODM. Yield loss (kg/d) increased with increasing milk yield and with increasing somatic cell count (SCC), although predictions were relatively poor (R(2)=0.09 to 0.30). Yield loss (%) decreased with increasing TDM yield in 3 of the 6 studies and was positively correlated with SCC during ODM. Nevertheless, ODM yield loss, in absolute or percentage terms, was a poorly repeatable trait in grazing cows. Part of the variation in yield loss percentage (30%) was positively associated with residual milk (%), measured pretrial, during measurement of functional udder capacity in lactation 3. Total production (kg of milk) over the full-lactation ODM study in lactation 4 was correlated with total production in the 10-wk trial in lactation 3 (r=0.72 and 0.63 for cohorts 1 and 2, respectively). Identifying the highest- and lowest-producing 10% of animals during the full lactation of ODM indicated that poor production was associated with high yields of residual milk (measured in lactation 3) and, conversely, high production was associated with low yields of residual milk, relative to the other 80% of animals. These same "high" and "low" production groups from lactation 4 had similar differences in performance in the earlier short-term studies and a larger or smaller percentage yield loss associated with the residual milk measurement. Breeding strategies for ODM may benefit, therefore, from greater emphasis on selecting for a low residual milk fraction to optimize milking performance. Nevertheless, the level of milk production under TDM is a strong phenotypic predictor of milk production under ODM., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Lactation, hair development and homeothermy are characteristic evolutionary features that define mammals from other vertebrate species. Here we describe the discovery of two autosomal dominant mutations with antagonistic, pleiotropic effects on all three of these biological processes, mediated through the prolactin signalling pathway. Most conspicuously, mutations in prolactin (PRL) and its receptor (PRLR) have an impact on thermoregulation and hair morphology phenotypes, giving prominence to this pathway outside of its classical roles in lactation.

A genomic prediction for residual feed intake (RFI) developed in growing dairy heifers (RFIgro) was used to predict and test breeding values for RFI in lactating cows (RFIlac) from an independent, industry population. A selection of 3,359 cows, in their third or fourth lactation during the study, of above average genetic merit for milk production, and identified as at least 15/16ths Holstein-Friesian breed, were selected for genotyping from commercial dairy herds. Genotyping was carried out using the bovine SNP50 BeadChip (Illumina Inc., San Diego, CA) on DNA extracted from ear-punch tissue. After quality control criteria were applied, genotypes were imputed to the 624,930 single nucleotide polymorphisms used in the growth study. Using these data, genomically estimated breeding values (GEBV) for RFIgro were calculated in the selected cow population based on a genomic prediction for RFIgro estimated in an independent group of growing heifers. Cows were ranked by GEBV and the top and bottom 310 identified for possible purchase. Purchased cows (n=214) were relocated to research facilities and intake and body weight (BW) measurements were undertaken in 99 "high" and 98 "low" RFIgro animals in 4 consecutive groups [beginning at d 61 ± 1.0 standard error (SE), 91 ± 0.5 SE, 145 ± 1.3 SE, and 191 ± 1.5 SE d in milk, respectively] to measure RFI during lactation (RFIlac). Each group of ~50 cows (~25 high and ~25 low RFIgro) was in a feed intake facility for 35 d, fed pasture-alfalfa cubes ad libitum, milked twice daily, and weighed every 2 to 3 d. Milk composition was determined 3 times weekly. Body weight change and BW at trial mid-point were estimated by regression of pre- and posttrial BW measurements. Residual feed intake in lactating cows was estimated from a linear model including BW, BW change, and milk component yield (as MJ/d); RFIlac differed consistently between the high and low selection classes, with the overall means for RFIlac being +0.32 and -0.31 kg of dry matter (DM) per day for the high and low classes, respectively. Further, we found evidence of sire differences for RFIlac, with one sire, in particular, being highly represented in the low RFIgro class, having a mean RFIlac of -0.83 kg of DM per day in 47 daughters. In conclusion, genomic prediction of RFIgro based on RFI measured during growth will discriminate for RFIlac in an independent group of lactating cows., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Residual feed intake (RFI), as a measure of feed conversion during growth, was estimated for around 2,000 growing Holstein-Friesian heifer calves aged 6 to 9 mo in New Zealand and Australia, and individuals from the most and least efficient deciles (low and high RFI phenotypes) were retained. These animals (78 New Zealand cows, 105 Australian cows) were reevaluated during their first lactation to determine if divergence for RFI observed during growth was maintained during lactation. Mean daily body weight (BW) gain during assessment as calves had been 0.86 and 1.15 kg for the respective countries, and the divergence in RFI between most and least efficient deciles for growth was 21% (1.39 and 1.42 kg of dry matter, for New Zealand and Australia, respectively). At the commencement of evaluation during lactation, the cows were aged 26 to 29 mo. All were fed alfalfa and grass cubes; it was the sole diet in New Zealand, whereas 6 kg of crushed wheat/d was also fed in Australia. Measurements of RFI during lactation occurred for 34 to 37 d with measurements of milk production (daily), milk composition (2 to 3 times per week), BW and BW change (1 to 3 times per week), as well as body condition score (BCS). Daily milk production averaged 13.8 kg for New Zealand cows and 20.0 kg in Australia. No statistically significant differences were observed between calf RFI decile groups for dry matter intake, milk production, BW change, or BCS; however a significant difference was noted between groups for lactating RFI. Residual feed intake was about 3% lower for lactating cows identified as most efficient as growing calves, and no negative effects on production were observed. These results support the hypothesis that calves divergent for RFI during growth are also divergent for RFI when lactating. The causes for this reduced divergence need to be investigated to ensure that genetic selection programs based on low RFI (better efficiency) are robust., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Recently, DGAT1 was identified as the gene that underlies the QTL for bovine milk production on chromosome 14. This study investigated the effect of the reported polymorphism in three dairy breeds in New Zealand. Statistically significant results were identified for milk fat, milk protein, and volume for Jersey and Holstein-Friesian breeds, and only milk volume for Ayrshires. The average allele substitution effects were 2 to 3 kg of protein and 120 to 130 l milk for both the Jersey and Holstein-Friesian breeds. For milk fat, the average allele substitution effect was 6 kg for Holstein-Friesians and 3 kg for Jerseys. In all breeds, where the polymorphism increased milk fat yield, it decreased milk protein yield and milk volume.

The effects of selection and crossbreeding on the New Zealand dairy industry net income were evaluated with a deterministic model over a 25 yr planning horizon. Several mating strategies involving Holstein-Friesian, Jersey and Ayrshire cattle were evaluated. These strategies were straight breeding, upgrading to Holstein-Friesian, upgrading to Jersey, upgrading to Ayrshire, use of the best bulls irrespective of breed and two- and three-breed rotational crossbreeding. Industry productions of milk, fat, protein, and lactose were calculated assuming that 12,000 kg of dry matter per hectare was utilized from 1,224,911 hectares of pasture. Profitability was the difference between income (international sale of whole milk powder, casein, butter, and beef from salvage animals) and costs (farm expenses, milk collection, manufacture, and marketing). Casein and whole milk powder were valued at NZ$8.345 and NZ$3.306/ per kilogram, respectively, over 25 yr. Butter was valued at NZ$2.995/kilogram for base year production levels and NZ$0.45/kilogram for marginal increases in production. Upgrading to Holstein-Friesian resulted in the highest industry net income (NZ$1119 million) followed by straight breeding (NZ$1086 million) and two-breed rotational Holstein-Friesian x Jersey (NZ$1076 million). However, if the marginal value of extra butter production was assumed equal to the average base value, then upgrading to Jersey resulted in the highest industry net income (NZ$1185 million) followed by two-breed rotational Holstein-Friesian x Jersey (NZ$1177 million) and use of the best bulls (NZ$1173 million). Future costs and prices of dairy products have major impact on mating strategies.