Identification of candidate novel production variants on the Bos taurus chromosome X.

Chromosome X is often excluded from bovine genetic studies due to complications caused by the sex specific nature of the chromosome. As chromosome X is the second largest cattle chromosome and makes up approximately 6% of the female genome, finding ways to include chromosome X in dairy genetic studies is important. Using female animals and treating chromosome X as an autosome, we performed X chromosome inclusive genome-wide association studies in the selective breeding environment of the New Zealand dairy industry, aiming to identify chromosome X variants associated with milk production traits. We report on the findings of these genome-wide association studies and their potential effect within the dairy industry. We identify missense mutations in the MOSPD1 and CCDC160 genes that are associated with decreased milk volume and protein production and increased fat production. Both of these mutations are exonic SNP that are more prevalent in the Jersey breed than in Holstein-Friesians. Of the 2 candidates proposed it is likely that only one is causal, though we have not been able to identify which is more likely.

High male specific contribution of the X-chromosome to individual global recombination rate in dairy cattle.

BACKGROUND: Meiotic recombination plays an important role in reproduction and evolution. The individual global recombination rate (GRR), measured as the number of crossovers (CO) per gametes, is a complex trait that has been shown to be heritable. The sex chromosomes play an important role in reproduction and fertility related traits. Therefore, variants present on the X-chromosome might have a high contribution to the genetic variation of GRR that is related to meiosis and to reproduction. RESULTS: We herein used genotyping data from 58,474 New Zealand dairy cattle to estimate the contribution of the X-chromosome to male and female GRR levels. Based on the pedigree-based relationships, we first estimated that the X-chromosome accounted for 30% of the total additive genetic variance for male GRR. This percentage was equal to 19.9% when the estimation relied on a SNP-BLUP approach assuming each SNP has a small contribution. We then carried out a haplotype-based association study to map X-linked QTL, and subsequently fine-mapped the identified QTL with imputed sequence variants. With this approach we identified three QTL with large effect accounting for 7.7% of the additive genetic variance of male GRR. The associated effects were equal to + 0.79, - 1.16 and + 1.18 CO for the alternate alleles. In females, the estimated contribution of the X-chromosome to GRR was null and no significant association with X-linked loci was found. Interestingly, two of the male GRR QTL were associated with candidate genes preferentially expressed in testis, in agreement with a male-specific effect. Finally, the most significant QTL was associated with PPP4R3C, further supporting the important role of protein phosphatase in double-strand break repair by homologous recombination. CONCLUSIONS: Our study illustrates the important role the X-chromosome can have on traits such as individual recombination rate, associated with testis in males. We also show that contribution of the X-chromosome to such a trait might be sex dependent.

Pregnancy status predicted using milk mid-infrared spectra from dairy cattle.

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.

Heifers with positive genetic merit for fertility traits reach puberty earlier and have a greater pregnancy rate than heifers with negative genetic merit for fertility traits.

This study investigated the hypothesis that dairy heifers divergent in genetic merit for fertility traits differ in the age of puberty and reproductive performance. New Zealand's fertility breeding value (FertBV) is the proportion of a sire's daughters expected to calve in the first 42 d of the seasonal calving period. We used the New Zealand national dairy database to identify and select Holstein-Friesian dams with either positive (POS, +5 FertBV, n = 1,334) or negative FertBV (NEG, -5% FertBV, n = 1,662) for insemination with semen from POS or NEG FertBV sires, respectively. The resulting POS and NEG heifers were predicted to have a difference in average FertBV of 10 percentage points. We enrolled 640 heifer calves (POS, n = 324; NEG, n = 316) at 9 d ± 5.4 d (± standard deviation; SD) for the POS calves and 8 d ± 4.4 d old for the NEG calves. Of these, 275 POS and 248 NEG heifers were DNA parent verified and retained for further study. The average FertBV was +5.0% (SD = 0.74) and -5.1% (SD = 1.36) for POS and NEG groups, respectively. Heifers were reared at 2 successive facilities as follows: (1) calf rearing (enrollment to ∼13 wk of age) and (2) grazier, after 13 wk until 22 mo of age. All heifers wore a collar with an activity sensor to monitor estrus events starting at 8 mo of age, and we collected weekly blood samples when individual heifers reached 190 kg of body weight (BW) to measure plasma progesterone concentrations. Puberty was characterized by plasma progesterone concentrations >1 ng/mL in at least 2 of 3 successive weeks. Date of puberty was defined when the first of these samples was >1 ng/mL. Heifers were seasonally bred for 98 d starting at ∼14 mo of age. Transrectal ultrasound was used to confirm pregnancy and combined with activity data to estimate breeding and pregnancy dates. We measured BW every 2 wk, and body condition and stature at 6, 9, 12, and 15 mo of age. The significant FertBV by day interaction for BW was such that the NEG heifers had increasingly greater BW with age. This difference was mirrored with the significant FertBV by month interaction for average daily gain, with the NEG heifers having a greater average daily gain between 9 and 18 mo of age. There was no difference in heifer stature between the POS and NEG heifers. The POS heifers were younger and lighter at puberty, and were at a lesser mature BW, compared with the NEG heifers. As a result, 94 ± 1.6% of the POS and 82 ± 3.2% of the NEG heifers had reached puberty at the start of breeding. The POS heifers were 20% and 11% more likely to be pregnant after 21 d and 42 d of breeding than NEG heifers (relative risk = 1.20, 95% confidence interval of 1.03-1.34; relative risk = 1.11, 95% confidence interval of 1.01-1.16). Results from this experiment support an association between extremes in genetic merit for fertility base on cow traits and heifer reproduction. Our results indicate that heifer puberty and pregnancy rates are affected by genetic merit for fertility traits, and these may be useful phenotypes for genetic selection.

Genetic correlations of milk fatty acid contents predicted from milk mid-infrared spectra in New Zealand dairy cattle.

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.

Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle.

The use of Fourier-transform mid-infrared (FTIR) spectroscopy is of interest to the dairy industry worldwide for predicting milk composition and other novel traits that are difficult or expensive to measure directly. Although there are many valuable applications for FTIR spectra, noise from differences in spectral responses between instruments is problematic because it reduces prediction accuracy if ignored. The purpose of this study was to develop strategies to reduce the impact of noise and to compare methods for standardizing FTIR spectra in order to reduce between-instrument variability in multiple-instrument networks. Noise levels in bands of the infrared spectrum caused by the water content of milk were characterized, and a method for identifying and removing outliers was developed. Two standardization methods were assessed and compared: piecewise direct standardization (PDS), which related spectra on a primary instrument to spectra on 5 other (secondary) instruments using identical milk-based reference samples (n = 918) analyzed across the 6 instruments; and retroactive percentile standardization (RPS), whereby percentiles of observed spectra from routine milk test samples (n = 2,044,094) were used to map and exploit primary- and secondary-instrument relationships. Different applications of each method were studied to determine the optimal way to implement each method across time. Industry-standard predictions of milk components from 2,044,094 spectra records were regressed against predictions from spectra before and after standardization using PDS or RPS. The PDS approach resulted in an overall decrease in root mean square error between industry-standard predictions and predictions from spectra from 0.190 to 0.071 g/100 mL for fat, from 0.129 to 0.055 g/100 mL for protein, and from 0.143 to 0.088 g/100 mL for lactose. Reductions in prediction error for RPS were similar but less consistent than those for PDS across time, but similar reductions were achieved when PDS coefficients were updated monthly and separate primary instruments were assigned for the North and South Islands of New Zealand. We demonstrated that the PDS approach is the most consistent method to reduce prediction errors across time. We also showed that the RPS approach is sensitive to shifts in milk composition but can be used to reduce prediction errors, provided that secondary-instrument spectra are standardized to a primary instrument with samples of broadly equivalent milk composition. Appropriate implementation of either of these approaches will improve the quality of predictions based on FTIR spectra for various downstream applications.

Short communication: Identification of the pseudoautosomal region in the Hereford bovine reference genome assembly ARS-UCD1.2.

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.

Mating strategies to maximize genetic merit in dairy cattle herds.

The genetic merit of a herd is a key determinant in productivity for dairy farmers. However, making breeding decisions to maximize the rate of genetic gain can be complex because there is no certainty about which cows will become pregnant with a heifer calf. In this study, breeding worth (BrW) was used as a measure of genetic merit, and several mating strategies were evaluated. These strategies included randomly mating whole herds to the entire bull team, excluding low-ranked cows from producing replacement heifers, and nominating high-ranked cows to the most highly ranked bulls. Simulations were undertaken using 4 bull teams generated from bulls currently marketed in New Zealand and a selection of New Zealand dairy herds. Average replacement heifer BrW was calculated for 1,000 iterations of each combination of mating strategy, herd, and bull team (scenario). Variation in resulting average replacement heifer BrW within scenarios was due to random sampling of which cows became pregnant with a heifer calf. Relative to mating the whole herd to an entire bull team, excluding the lowest ranked cows from producing replacements resulted in the greatest increase in average replacement heifer BrW across all herds and bull teams, with a gain of approximately 0.4 BrW point for each 1% of cows excluded. Nominating top-ranking cows to the highest ranking bulls in the team had little effect (0.06-0.13 BrW increase for each 1% of top cows nominated) in improving BrW of replacement heifers. The number of top bulls nominated had a variable effect depending on the BrW spread of the entire bull team. Although excluding cows with the lowest BrW from producing replacement heifers is most effective for improving BrW, it is important to ensure that the number of heifers born is sufficient to replace cows leaving the herd. It is likely that optimal strategies for improving BrW will vary from farm to farm depending not only on the BrW structure of the herd, the bull team available, and the reproduction success on farm but also on farm management practices. This simulation study provides expected outcomes from a variety of mating strategies to allow informed decision making on farm.

BREEDING AND GENETICS SYMPOSIUM:Breeding heat tolerant dairy cattle: the case for introgression of the "slick" prolactin receptor variant into dairy breeds.

Increasing environmental temperatures are a threat to the sustainability of livestock production and, because of the high metabolic demands of lactation, to dairy production in particular. Summer heat waves in temperate climates reduce feed intake, milk production, and cow comfort. In extreme heat events, there is an increase in cow mortality. In tropical climates, dairy cattle are mostly (zebu) type or zebu crossbred with temperate dairy breeds. Crossbreeding is undertaken to combine the heat tolerance and tick resistance of zebu with the productivity of temperate dairy breeds. In the absence of improved heat tolerance, milk production and fertility of temperate cattle is severely impaired. We have recently identified a key role for the prolactin pathway in regulating heat tolerance. A de novo mutation in prolactin that impairs prolactin activity was discovered in hairy and heat intolerant, New Zealand dairy cattle. The phenotypes produced were remarkably similar to those seen in fescue toxicosis, a syndrome seen in grazing cattle in the U.S. where ingestion of ergovaline, a fungal toxin from infected pasture, inhibits prolactin secretion. Recognition of the role of prolactin in hairy cattle led us to identify a deletion in exon 10 of the long-form of the prolactin receptor in Senepol cattle that causes truncation of the protein and determines the slick coat and heat tolerance traits found in this , beef breed. The short form of the prolactin receptor is predicted to be unaffected by the deletion. Knowledge of this dominant mutation has provided the impetus to begin a crossbreeding program to investigate performance and heat tolerance of temperate dairy cattle carrying the slick, prolactin receptor variant. The perceived opportunity is to introgress this variant into temperate dairy cattle to enable performance and welfare improvement in hot climates. Heat tolerance of cattle with slick coats appears to be mostly associated with coat type although sweating ability may also be enhanced. Further investigation is required of performance traits in cows homozygous for the slick variant because the published data are almost exclusively from heterozygous animals. Combination of the slick mutation with other favorable genes for heat tolerance, especially those for coat color, will be particularly enabled by gene editing technologies, offering opportunities for further improvement in bovine thermotolerance.

Detection and assessment of copy number variation using PacBio long-read and Illumina sequencing in New Zealand dairy cattle.

Single nucleotide polymorphisms have been the DNA variant of choice for genomic prediction, largely because of the ease of single nucleotide polymorphism genotype collection. In contrast, structural variants (SV), which include copy number variants (CNV), translocations, insertions, and inversions, have eluded easy detection and characterization, particularly in nonhuman species. However, evidence increasingly shows that SV not only contribute a substantial proportion of genetic variation but also have significant influence on phenotypes. Here we present the discovery of CNV in a prominent New Zealand dairy bull using long-read PacBio (Pacific Biosciences, Menlo Park, CA) sequencing technology and the Sniffles SV discovery tool (version 0.0.1; https://github.com/fritzsedlazeck/Sniffles). The CNV identified from long reads were compared with CNV discovered in the same bull from Illumina sequencing using CNVnator (read depth-based tool; Illumina Inc., San Diego, CA) as a means of validation. Subsequently, further validation was undertaken using whole-genome Illumina sequencing of 556 cattle representing the wider New Zealand dairy cattle population. Very limited overlap was observed in CNV discovered from the 2 sequencing platforms, in part because of the differences in size of CNV detected. Only a few CNV were therefore able to be validated using this approach. However, the ability to use CNVnator to genotype the 557 cattle for copy number across all regions identified as putative CNV allowed a genome-wide assessment of transmission level of copy number based on pedigree. The more highly transmissible a putative CNV region was observed to be, the more likely the distribution of copy number was multimodal across the 557 sequenced animals. Furthermore, visual assessment of highly transmissible CNV regions provided evidence supporting the presence of CNV across the sequenced animals. This transmission-based approach was able to confirm a subset of CNV that segregates in the New Zealand dairy cattle population. Genome-wide identification and validation of CNV is an important step toward their inclusion in genomic selection strategies.