Estimation of genetic parameters for novel functional traits in Brown Swiss cattle

The aim of this study was to estimate genetic parameters and accuracies of breeding values for a set of functional, behavior, and conformation traits in Brown Swiss cattle. These traits were milking speed, udder depth, position of labia, rank order in herd, general temperament, aggressiveness, milking temperament, and days to first heat. Data of 1,799 phenotyped Brown Swiss cows from 40 Swiss dairy herds were analyzed taking the complete pedigree into account. Estimated heritabilities were within the ranges reported in literature, with results at the high end of the reported values for some traits (e.g., milking speed: 0.42 ± 0.06, udder depth: 0.42 ± 0.06), whereas other traits were of low heritability and heritability estimates were of low accuracy (e.g., milking temperament: 0.04 ± 0.04, days to first heat: 0.02 ± 0.04). For most behavior traits, we found relatively high heritabilities (general temperament: 0.38 ± 0.07, aggressiveness: 0.12 ± 0.08, and rank order in herd: 0.16 ± 0.06). Position of labia, arguably an indicator trait for pathological urovagina, was genetically analyzed in this study for the first time, and a moderate heritability (0.28 ± 0.06) was estimated.     

A first step toward genomic selection in the multi-breed French dairy goat population

The objectives of this study were to describe, using the goat SNP50 BeadChip (Illumina Inc., San Diego, CA), molecular data for the French dairy goat population and compare the effect of using genomic information on breeding value accuracy in different reference populations. Several multi-breed (Alpine and Saanen) reference population sizes, including or excluding female genotypes (from 67 males to 677 males, and 1,985 females), were used. Genomic evaluations were performed using genomic best linear unbiased predictor for milk production traits, somatic cell score, and some udder type traits. At a marker distance of 50 kb, the average r² (squared correlation coefficient) value of linkage disequilibrium was 0.14, and persistence of linkage disequilibrium as correlation of r-values among Saanen and Alpine breeds was 0.56. Genomic evaluation accuracies obtained from cross validation ranged from 36 to 53%. Biases of these estimations assessed by regression coefficients (from 0.73 to 0.98) of phenotypes on genomic breeding values were higher for traits such as protein yield than for udder type traits. Using the reference population that included all males and females, accuracies of genomic breeding values derived from prediction error variances (model accuracy) obtained for young buck candidates without phenotypes ranged from 52 to 56%. This was lower than the average pedigree-derived breeding value accuracies obtained at birth for these males from the official genetic evaluation (62%). Adding females to the reference population of 677 males improved accuracy by 5 to 9% depending on the trait considered. Gains in model accuracies of genomic breeding values ranged from 1 to 7%, lower than reported in other studies. The gains in breeding value accuracy obtained using genomic information were not as good as expected because of the limited size (at most 677 males and 1,985 females) and the structure of the reference population.     

Reliability of genomic evaluations in Holstein-Friesians using haplotypes based on the BovineHD BeadChip

The objectives of this study were to make subsets of high-density (HD) loci based on localized haplotype clusters, without loss of genomic information, to reduce computing time compared with the use of all HD loci and to investigate the effect on the reliability of the direct genomic value (DGV) when using this HD subset based on localized haplotype clusters in the genomic evaluation for Holstein-Friesians. The DNA was isolated from semen samples of 548 bulls (key ancestors) of the EuroGenomics Consortium, a collaboration between 4 European dairy cattle breeding organizations and scientific partners. These bulls were genotyped with the BovineHD BeadChip [~777,000 (777K) single nucleotide polymorphisms (SNP); Illumina Inc., San Diego, CA] and used to impute all 30,483 Holstein-Friesians from the BovineSNP50 BeadChip [~50,000 (50K) SNP; Illumina Inc.] to HD, using the BEAGLE software package. The final data set consisted of 30,483 animals and 603,145 SNP. For each locus, localized haplotype clusters (i.e., edges of the fitted graph model) identifications were obtained from BEAGLE. Three subsets [38,000 (38K), 116,000 (116K), and 322,000 (322K) loci] were made based on deleting obsolete loci (i.e., loci that do not give extra information compared with the neighboring loci). A fourth data set was based on 38K SNP, which is currently used for routine genomic evaluation at the Cattle Improvement Cooperative (CRV, Arnhem, the Netherlands). A validation study using the HD loci subsets based on localized haplotype clusters was performed for 9 traits (production, conformation, and functional traits). Error of imputation from 50K to HD averaged 0.78%. Three thresholds (0.17, 0.05, and 0.008%) were used for the identification of obsolete HD loci based on localized haplotype clusters to obtain a desired number of HD loci (38K, 116K, and 322K). On average, 46% (using threshold 0.008%) to 93% (using threshold 0.17%) of HD loci were eliminated. The computing time was about 9 d for 38K loci, 15.5 d for 116K loci, 21 d for 322K loci, and 7.5 d for 38K SNP. The increase in reliability of DGV compared with pedigree-based estimated breeding values for kilograms of protein was similar for 322K and 116K loci (30.7%), but was 1.5 to 2% higher compared with 38K loci and 38K SNP. Averaged over 9 traits, subset 116K loci resulted in a higher increase in reliability compared with 38K loci and 38K SNP. Eliminating obsolete loci enormously decreased the amount of data to be analyzed for genomic evaluations. The more HD loci used in a genomic evaluation, the higher the increase in reliability of DGV. It is possible to increase the reliability of DGV by 1 to 2% compared with the SNP currently used for routine genomic evaluation.     

Analysis of the relationship between workability traits and functional longevity in Canadian dairy breeds

The aim of this study was to assess the effect of workability traits like milking speed and temperament on functional longevity of Canadian dairy cattle using a Weibull proportional hazards model. First-lactation data consisted of the following: 1,728,289 and 2,426,123 Holstein cows for milking temperament and milking speed, respectively, from 18,401 herds and sired by 8,248 sires; 39,618 and 60,121 Jersey cows for milking temperament and milking speed, respectively, from 1,845 herds and sired by 2,413 sires; and 54,391 and 94,847 Ayrshire cows for milking temperament and milking speed, respectively, from 1,316 herds and sired by 2,779 sires. Functional longevity was defined as the number of days from the first calving to culling, death, or censoring adjusted for production. Milking temperament and milking speed were recorded on a 1- to 5-point scale from very nervous to very calm and from very slow to very fast, respectively. The statistical model included the effects of stage of lactation; season of production; the annual change in herd size; type of milk recording supervision; age at first calving; effects of milk, fat, and protein yields calculated as within herd-year-parity deviations; herd-year-season of calving; sire; and milking temperament or milking speed class. The relative culling rate was calculated for animals in each milking temperament or milking speed class after accounting for the above-mentioned effects. The study showed that there was a statistically significant association between workability traits and functional longevity. Very nervous cows were 26, 23, and 46% more likely to be culled than very calm cows in Holstein, Ayrshire, and Jersey breeds, respectively. Similarly, very slow milkers were 36, 33, and 28% more likely to be culled than average milkers in Holstein, Ayrshire, and Jersey breeds, respectively. Additionally, very fast milkers were 11, 13, and 15% more likely to be culled than average milkers in Holstein, Ayrshire, and Jersey breeds, respectively. Producers might want to avoid consequences associated with the fast milkers such as udder health problems.     

Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population

This study investigated the reliability of genomic estimated breeding values (GEBV) in the Danish Holstein population. The data in the analysis included 3,330 bulls with both published conventional EBV and single nucleotide polymorphism (SNP) markers. After data editing, 38,134 SNP markers were available. In the analysis, all SNP were fitted simultaneously as random effects in a Bayesian variable selection model, which allows heterogeneous variances for different SNP markers. The response variables were the official EBV. Direct GEBV were calculated as the sum of individual SNP effects. Initial analyses of 4 index traits were carried out to compare models with different intensities of shrinkage for SNP effects; that is, mixture prior distributions of scaling factors (standard deviation of SNP effects) assuming 5, 10, 20, or 50% of SNP having large effects and the others having very small or no effects, and a single prior distribution common for all SNP. It was found that, in general, the model with a common prior distribution of scaling factors had better predictive ability than any mixture prior models. Therefore, a common prior model was used to estimate SNP effects and breeding values for all 18 index traits. Reliability of GEBV was assessed by squared correlation between GEBV and conventional EBV (r²_{GEBV, EBV}), and expected reliability was obtained from prediction error variance using a 5-fold cross validation. Squared correlations between GEBV and published EBV (without any adjustment) ranged from 0.252 to 0.700, with an average of 0.418. Expected reliabilities ranged from 0.494 to 0.733, with an average of 0.546. Averaged over 18 traits, r²_{GEBV, EBV} was 0.13 higher and expected reliability was 0.26 higher than reliability of conventional parent average. The results indicate that genomic selection can greatly improve the accuracy of preselection for young bulls compared with traditional selection based on parent average information.     

Use of a single-step approach for integrating foreign information into national genomic evaluation in Holstein cattle

The use of multi-trait across-country evaluation (MACE) and the exchange of genomic information among countries allows national breeding programs to combine foreign and national data to increase the size of the training populations and potentially increase accuracy of genomic prediction of breeding values. By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (GBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. A single-step genomic BLUP approach, which enables integration of data from MACE evaluations, can be used to obtain genomic predictions while avoiding double-counting of information. The objectives of this study were to apply a single-step approach that simultaneously includes domestic and MACE information for genomic evaluation of workability traits in Canadian Holstein cattle, and compare the results obtained with this methodology with those obtained using a multi-step approach (msGBLUP). By including MACE bulls in the training population, msGBLUP led to an increase in reliability of genomic predictions of 4.8 and 15.4% for milking temperament and milking speed, respectively, compared with a traditional evaluation using only pedigree and phenotypic information. Integration of MACE data through a single-step approach (ssGBLUP_IM) yielded the highest reliabilities compared with other considered methods. Integration of MACE data also helped reduce bias of genomic predictions. When using ssGBLUP_IM, the bias of genomic predictions decreased by half compared with msGBLUP using domestic and MACE information. Therefore, the reliability and bias of genomic predictions for both traits improved substantially when a single-step approach was used for evaluation compared with a multi-step approach. The use of a single-step approach with integration of MACE information provides an alternative to the current method used in Canadian genomic evaluations.     

Genomic predictions based on a joint reference population for the Nordic Red cattle breeds

The main aim of this study was to compare accuracies of imputation and genomic predictions based on single and joint reference populations for Norwegian Red (NRF) and a composite breed (DFS) consisting of Danish Red, Finnish Ayrshire, and Swedish Red. The single nucleotide polymorphism (SNP) data for NRF consisted of 2 data sets: one including 25,000 markers (NRF25K) and the other including 50,000 markers (NRF50K). The NRF25K data set had 2,572 bulls, and the NRF50K data set had 1,128 bulls. Four hundred forty-two bulls were genotyped in both data sets (double-genotyped bulls). The DFS data set (DSF50K) included 50,000 markers of 13,472 individuals, of which around 4,700 were progeny-tested bulls. The NRF25K data set was imputed to 50,000 density using the software Beagle. The average error rate for the imputation of NRF25K decreased slightly from 0.023 to 0.021, and the correlation between observed and imputed genotypes changed from 0.935 to 0.936 when comparing the NRF50K reference and the NRF50K–DFS50K joint reference imputations. A genomic BLUP (GBLUP) model and a Bayesian 4-component mixture model were used to predict genomic breeding values for the NRF and DFS bulls based on the single and joint NRF and DFS reference populations. In the multiple population predictions, accuracies of genomic breeding values increased for the 3 production traits (milk, fat, and protein yields) for both NRF and DFS. Accuracies increased by 6 and 1.3 percentage points, on average, for the NRF and DFS bulls, respectively, using the GBLUP model, and by 9.3 and 1.3 percentage points, on average, using the Bayesian 4-component mixture model. However, accuracies for health or reproduction traits did not increase from the multiple population predictions. Among the 3 DFS populations, Swedish Red gained most in accuracies from the multiple population predictions, presumably because Swedish Red has a closer genetic relationship with NRF than Danish Red and Finnish Ayrshire. The Bayesian 4-component mixture model performed better than the GBLUP model for most production traits for both NRF and DFS, whereas no advantage was found for health or reproduction traits. In general, combining NRF and DFS reference populations was useful in genomic predictions for both the NRF and DFS bulls.     

Incorporating heifer feed efficiency in the Australian selection index using genomic selection

The economic benefit of expanding the Australian Profit Ranking (APR) index to include residual feed intake (RFI) was evaluated using a multitrait selection index. This required the estimation of genetic parameters for RFI and genetic correlations using single nucleotide polymorphism data (genomic) correlations with other traits. Heritabilities of RFI, dry matter intake (DMI), and all the traits in the APR (milk, fat, and protein yields; somatic cell count; fertility; survival; milking speed; and temperament), and genomic correlations between these traits were estimated using a Bayesian framework, using data from 843 growing Holstein heifers with phenotypes for DMI and RFI, and bulls with records for the other traits. Heritability estimates of DMI and RFI were 0.44 and 0.33, respectively, and the genomic correlation between them was 0.03 and nonsignificant. The genomic correlations between the feed-efficiency traits and milk yield traits were also close to zero, ranging between −0.11 and 0.10. Positive genomic correlations were found for DMI with stature (0.16) and with overall type (0.14), suggesting that taller cows eat more as heifers. One issue was that the genomic correlation estimates for RFI with calving interval (ClvI) and with body condition score were both unfavorable (−0.13 and 0.71 respectively), suggesting an antagonism between feed efficiency and fertility. However, because of the relatively small numbers of animals in this study, a large 95% probability interval existed for the genomic correlation between RFI and ClvI (−0.66, 0.36). Given these parameters, and a genetic correlation between heifer and lactating cow RFI of 0.67, inclusion of RFI in the APR index would reduce RFI by 1.76 kg/cow per year. Including RFI in the APR would result in the national Australian Holstein herd consuming 1.73 × 10⁶ kg less feed, which is worth 0.55 million Australian dollars (A$) per year and is 3% greater than is currently possible to achieve. Other traits contributing to profitability, such as milk production and fertility, will also improve through selection on this index; for example, ClvI would be reduced by 0.53d/cow per year, which is 96% of the gain for this trait that is achieved without RFI in the APR.     

Genomic evaluation for calf wellness traits in Holstein cattle

Reducing calf morbidity and mortality is important for attaining financial sustainability and improving animal welfare on commercial dairy operations. Zoetis (Kalamazoo, MI) has developed genomic predictions for calf wellness traits in Holsteins that include calf respiratory disease (RESP; recorded between 0 and 365 d of age), calf scours (DIAR; recorded between 2 and 50 d of age), and calf livability (DEAD; recorded between 2 and 365 d of age). Phenotype and pedigree data were from commercial dairies and provided directly by producers upon obtaining their permission. The number of records ranged from 741,484 for DIAR to 1,926,261 for DEAD. The number of genotyped animals was 325,025. All traits were analyzed using a univariate threshold animal model including fixed effect of year of birth × calving season × region, and random effects of herd × year of birth and animal. A total of 45,425 SNP were used in genomic analyses. Animals genotyped with low-density chips were imputed to the required number of SNP. All analyses were conducted using single-step genomic BLUP implementing the “algorithm for proven and young” (APY) animals designed to accommodate very large numbers of genotypes. Estimated heritabilities were 0.042, 0.045, and 0.060 for RESP, DIAR, and DEAD, respectively. The genomic predicted transmitting abilities ranged between ?8.0 and 24.0, ?11.5 and 28.5, and ?6.5 to 22.8 for RESP, DIAR, and DEAD, respectively. Reliabilities of breeding values were obtained by approximation based on partitioning of a function of reliability into contributions from records, pedigree, and genotypes, where the genotype contribution was approximated using the diagonal value of the genomic relationship matrix. The average reliabilities for the genotyped animals were 41.9, 42.6, and 47.3% for RESP, DIAR, and DEAD, respectively. Estimated genomic predicted transmitting abilities and reliabilities were approximately normally distributed for all analyzed traits. Approximated genetic correlations of calf wellness with Zoetis dairy wellness traits and traits included in the US national genetic evaluation were low to moderate. The results indicate that direct evaluation of calf wellness traits under a genomic threshold model is feasible and offers predictions with average reliabilities comparable to other lowly heritable traits. Genetic selection for calf wellness traits presents a compelling opportunity for dairy producers to help manage herd replacement costs and improve overall profitability.     

Mastitis control in Swedish dairy herds

The aim of this study was to investigate which preventive measures targeting mastitis are implemented in Swedish dairy herds with different housing and milking systems. Data were collected through a self-administered postal questionnaire sent to 898 dairy farmers, stratified by housing and milking system, in May 2011. The questionnaire contained general questions about the herd and the person responsible for the udder health of the cows, and specific questions about perceived udder health and the implementation of preventive measures. The response rate was 48%. The median herd size of participating herds was 80 cows, and the median herd average milk yield per cow was 9,586 kg of milk. External validity was assessed by comparing participating herds with nonresponders in respect to key performance indicators in the Swedish official milk recording system; no significant differences were found. When herds with combined systems had been removed, 400 herds with tiestalls and pipeline milking, freestalls and parlor milking, and freestalls with an automatic milking system remained. Differences between herd types were analyzed using the Kruskal-Wallis test and Fisher’s exact test. The results showed that herd types differed in their rates of implementation of different preventive measures. Freestall herds with milking parlors implemented more preventive measures related to milking hygiene and milking routines than did tiestall herds. A milking order based on the udder health status of the cows was frequently implemented in tiestall herds, but not in most herds with an automatic milking system or most freestall herds with milking parlors. Irrespective of herd type, the proportion of herds in which cows were kept standing for at least 30 min after milking was low. A substantial proportion of herds ignored the udder health status of lactating cows when grouping them, and few herds grouped dry cows according to udder health status, although this occurred more frequently in tiestall herds. A large proportion of herds, especially those with tiestalls, did not allow cows and heifers to calve in single pens that were cleaned between animal occupations. These findings can be used to tailor advice on mastitis specifically to different herd types and thus improve the efficiency of mastitis control.     

Genetic parameters for handling and milking temperament in Danish first-parity Holstein cows

The human-animal relationship in dairy cattle is reflected in the trait “temperament” in breeding programs and is mainly based on observations by farmers. However, farmers' knowledge of an individual cow's temperament decreases with an increased herd size, and this has been the case in many countries during the last decades. The aim of this study was to investigate if temperament recorded by classifiers and automatic milking systems is heritable, and estimate the genetic relationship with farmer-assessed temperament. Farmer-assessed temperament is defined as the overall assessment of the individual cows' temperament at milking and handling. Data on handling temperament were recorded by Danish classifiers from October 2016 to April 2017 on a 1 to 9 scale specially designed for this purpose. Data from automatic milking systems were recorded from January 2010 until April 2017, where connection time and number of attachments per teat were classified as milking temperament traits. Estimated heritabilities were relatively low for handling temperament (0.13) and farmer-assessed temperament (0.10). For milking temperament traits, connection time showed higher heritability than number of attachments per teat (0.36 and 0.26, respectively). The genetic correlation between farmer-assessed temperament and handling temperament was highly favorable (0.84). The genetic correlations between handling temperament and the 2 milking temperament traits, connection time and number of attachments per teat, were low (?0.02 and ?0.10, respectively). Moderate genetic correlations were estimated between farmer-assessed temperament and connection time (?0.29) and between farmer-assessed temperament and number of attachments per teat (?0.37). The genetic correlations and heritabilities suggest a basis for further investigations of the possibility of including handling or milking temperament traits (or both) in the breeding program for temperament in dairy cattle.     

Bimodality and the genetics of milk flow traits in the Italian Holstein-Friesian breed

The overall goal of this study was to investigate milk flow traits in Italian Holstein-Friesian cows and, in particular, the bimodality of milk flow, defined as delayed milk ejection at the start of milking. Using a milkometer, 2,886 records were collected from 133 herds in northern Italy from 2001 to 2007. All records included 5 time-period measurements for milk flow, somatic cell score (SCS), milk yield, 8 udder type traits, and the presence or absence of bimodality in milk flow. Genetic parameters were estimated using linear animal models for continuous traits such as milk flow, udder type, SCS, and milk production, whereas bimodality was analyzed as a categorical trait. With the exception of decreasing time (which had a very small heritability value of 0.06), heritability values for milk flow traits were moderate, ranging from 0.10 (ascending time) to 0.41 (maximum milk flow). In addition, moderate to high genetic correlations were estimated between total milking time and other time measures (from 0.78 to 0.87), and among time flow traits (from 0.62 to 0.91). The decreasing time was the trait most genetically correlated with udder type traits, with correlation values of 0.92 with rear udder height, 0.85 with rear udder width, and 0.73 with teat placement. Large udders with strong attachments were also associated with greater milk production. Heritability estimated for bimodality was 0.43, and its genetic correlation with milk flow traits and SCS indicated a sizable genetic component underlying this trait. Bimodality was negatively associated with milk production; shorter milking times and greater peak milk levels were genetically correlated with more frequent bimodal flows, indicating that faster milk release would result in an increase in bimodal patterns. The negative genetic correlation of bimodality with SCS (−0.30) and the genetic correlation between milk flow traits and SCS suggest that the relationship between milkability and SCS is probably nonlinear and that intermediate flow rates are optimal with respect to mastitis susceptibility. Quicker milk flow over a shorter period would increase the frequency of bimodal curves in milking, whereas the correlation between bimodality and both ascending and descending time was less clear.     

Short communication: Modification of genetic evaluation of herd life from a three-trait to a five-trait model in Canadian dairy cattle

The national genetic evaluation of herd life for Canadian dairy breeds was modified from a 3-trait to a 5-trait animal model. The genetic evaluation incorporates information from daughter survival (direct herd life) and information from conformation, fertility, and udder health traits that are related to longevity (indirect herd life). Genetic evaluations for direct herd life were based on cows’ survival from first calving to 120 days in milk (DIM), from 120 to 240 DIM, from 240 DIM to second calving, survival to third calving, and survival to fourth calving, which were analyzed using a multiple-trait animal model. Sire evaluations obtained for each of the 5 survival traits were combined into an overall sire evaluation for direct herd life. Sire evaluations for indirect herd life were based on an index of sire evaluations for dairy strength, feet and legs, overall mammary, rump angle, somatic cell score, milking speed, nonreturn rate in cows, and interval from calving to first service. A multiple-trait sire model based on multiple-trait across-country evaluation methodology was used to combine direct and indirect genetic evaluations for herd life into an overall genetic evaluation for herd life. Sire evaluations for herd life were expressed as an estimated transmitting ability for the number of lactations. The transmitting ability represents expected differences among daughters for herd life; and the average herd life was set to 3 lactations.     

Genomic information allows for more accurate breeding values for milkability in dual-purpose Italian Simmental cattle

Milkability is a trait related to the milking efficiency of an animal, and it is a component of the herd profitability. Due to its economic importance, milkability is currently included in the selection index of the Italian Simmental cattle breed with a weight of 7.5%. This lowly heritable trait is measured on a subjective scale from 1 to 3 (1 = slow, 3 = fast), and genetic evaluations are performed by pedigree-based BLUP. Genomic information is now available for some animals in the Italian Simmental population, and its inclusion in the genetic evaluation system could increase accuracy of breeding values and genetic progress for milkability. The aim of this study was to test the feasibility and advantages of having a genomic evaluation for this trait in the Italian Simmental population. Phenotypes were available for 131,308 cows. A total of 9,526 animals had genotypes for 42,152 loci; among the genotyped animals, 2,455 were cows with phenotypes, and the other were their relatives. The youngest cows with both phenotypes and genotypes (n = 900) were identified as selection candidates. Variance components and heritability were estimated using pedigree information, whereas genetic and genomic evaluations were carried out using BLUP and single-step genomic BLUP (ssGBLUP), respectively. In addition, a weighted ssGBLUP was assessed using genomic regions from a genome-wide association study. Evaluation models were validated using theoretical and realized accuracies. The estimated heritability for milkability was 0.12 ± 0.01. The mean theoretical accuracies for selection candidates were 0.43 ± 0.08 (BLUP) and 0.53 ± 0.06 (ssGBLUP). The mean realized accuracies based on linear regression statistics were 0.29 (BLUP) and 0.40 (ssGBLUP). No genomic regions were significantly associated with milkability, thus no improvements in accuracy were observed when using weighted ssGBLUP. Results indicated that genomic information could improve the accuracy of breeding values and increase genetic progress for milkability in Italian Simmental.     

Accuracy of genomic evaluation with weighted single-step genomic best linear unbiased prediction for milk production traits,udder type traits,and somatic cell scores in French dairy goats

Genomic evaluation of French dairy goats is routinely conducted using the single-step genomic BLUP (ssGBLUP) method. This method has the advantage of simultaneously using all phenotypes, pedigrees, and genotypes. However, ssGBLUP assumes that all SNP explain the same amount of genetic variance, which is unlikely in the case of traits whose major genes or QTL are segregating. In this study, we investigated the effect of weighted ssGBLUP and its alternatives, which give more weight to SNP associated with the trait, on the accuracy of genomic evaluation of milk production, udder type traits, and somatic cell scores. The data set included 2,955 genotyped animals and 2,543,680 pedigree animals. The number of phenotypes varied with the trait. The accuracy of genomic evaluation was assessed on 205 genotyped Alpine and 146 genotyped Saanen goats born between 2009 and 2012. For traits with unknown QTL, weighted ssGBLUP was less accurate than, or as accurate as, ssGBLUP. For traits with identified QTL (i.e., QTL only present in the Saanen breed), weighted ssGBLUP outperformed ssGBLUP by between 2 and 14%.     

Longitudinal study of herd udder hygiene and its association with clinical mastitis in pasture-based dairy cows

The objectives of this exploratory study were to (1) describe the association between herd-level udder hygiene and clinical mastitis and (2) investigate how sample size and milking stage affect the accuracy and precision of herd udder hygiene assessments made at milking time. A prospective longitudinal study was conducted in a dairy herd in Northern Australia as part of a previously published clinical trial of premilking teat disinfection. Video footage from 35 afternoon milkings was used to conduct 12,544 udder hygiene scores from 504 cows during an 89-d period and measure udder hygiene of the herd (proportion of cows with udder hygiene ≥3 out of 4). Linear interpolation was used to estimate herd udder hygiene on the days that were not scored, such that a herd-level udder hygiene measure was available for all cow-days in the study. Clinical mastitis events occurring during the study period were detected and recorded by farm staff according to a standardized definition. The relationship between herd udder hygiene on each of 1, 2, and 3 d before each study day (d ?1, ?2, and ?3, respectively) and clinical mastitis at the cow level on each study day (each in turn being set as d 0) was determined using multivariable generalized estimating equations (family = Poisson, link = log), with the unit of analysis being the cow-day, adjusting for potential confounders and the clustering within the data. In addition, sampling strategies were evaluated by simulating herd udder hygiene assessments using a subset of cows in the herd. Herd udder hygiene from d ?1, ?2, and ?3 was positively associated with clinical mastitis on d 0 (incidence rate ratio = 1.4 per 10-point increase in the percentage of cows with poor udder hygiene). Sampling strategy simulation found that at least 80 cows needed to be scored to achieve sufficiently precise estimations of herd udder hygiene. Furthermore, cows scored later during milking were slightly more likely to have poor udder hygiene than those scored earlier (risk ratio = 1.02 for cows that were 10% later in the milking order). More research is needed to evaluate risk factors for poor udder hygiene and potential interventions in pasture-based dairy cows.     

Predicting energy balance for dairy cows using high-density single nucleotide polymorphism information

The objective of this study was to investigate the genetic basis of energy balance (EB) and the potential use of genomic selection to enable EB to be incorporated into selection programs. Energy balance provides an essential link between production and nonproduction traits because both depend on a common source of energy. A small number (527) of Dutch Holstein-Friesian heifers with phenotypes for EB were genotyped. Direct genomic values were predicted for these heifers using a model that included the genotypic information. A polygenic model was also applied to predict estimated breeding values using only pedigree information. A 10-fold cross-validation approach was employed to assess the accuracies of the 2 sets of predicted breeding values by correlating them with phenotypes. Because of the small number of phenotypes, accuracies were relatively low (0.29 for the direct genomic values and 0.21 for the estimated breeding values), where the maximum possible accuracy was the square root of heritability (0.57). Despite this, the genomic model produced breeding values with reliability double that of the breeding values produced by the polygenic model. To increase the accuracy of the genomic breeding values and make it possible to select for EB, measurement and recording of EB would need to improve. The study suggests that it may be possible to select for minimally recorded traits; for instance, those measured on experimental farms, using genomic selection. Overall, the study demonstrated that genomic selection could be used to select for EB, confirming its genetic background.     

Improving parlor efficiency in block calving pasture-based dairy systems through the application of a fixed milking time determined by daily milk yield and milking frequency

Adjusting end-of-milking criteria, in particular applying a maximum milking time determined by expected milk yield at an individual milking session, is one strategy to optimize parlor efficiency. However, this strategy can be difficult to apply practically on farm due to large differences in session milk yield, driven by milking interval, which affects milking routines and can be limited by in-parlor technology. The objective of this study was to test the hypothesis that a single fixed milking time (duration) could be applied at all milking sessions without compromising milk production or udder health for a range of milking intervals. To test the hypothesis, 4 experimental herds were established: (1) herd milked twice a day (TAD) using a 10- and 14-h interval, (2) herd milked TAD using an 8- and 16-h interval, (3) herd milked 3 times in 2 d using a 10–19–19-h interval, and (4) herd milked once a day (OAD). Herds consisted of 40 cows each, and were established for two 6-wk experimental periods, one in peak lactation and the other in mid-late lactation. Within each herd, half the cows had an end-of-milking criterion of 0.35 kg/min (Flow), and the other half had milking ended after a fixed period of time (FixedT) based on the average milking session yield, the daily milk yield divided by average number of milkings per day, irrespective of milking interval. We found no differences in daily milk yield between end-of-milking criteria due to residual milk from one milking likely increasing the proportion of milk in the udder cistern at the next milking session for the FixedT treatment. However, fat yield was compromised when the percentage of the herd with a truncated milking exceeded an estimated 33% at a milking session, which occurred in the TAD 8–16 herd due to the divergence from the average milking interval (in the case of TAD, 12–12 h). Applying a fixed milking time had no detrimental effects on udder health, except in the OAD herd in mid-late lactation, which had both a higher cell count and new intramammary infection rate. This warrants further investigation, although the majority of cultured bacteria were coagulase-negative staphylococci (CNS). Consequently, we conclude that, in general, with appropriate monitoring (e.g., weekly inspection) to ensure the proportion of the herd with truncated milkings does not exceed 33%, farmers in pasture-based dairy systems can use a fixed milking time to improve parlor efficiency.     

Indirect genomic predictions for milk yield in crossbred Holstein-Jersey dairy cattle

The objective of this study was to predict genomic breeding values for milk yield of crossbred dairy cattle under different scenarios using single-step genomic BLUP (ssGBLUP). The data set included 13,880,217 milk yield measurements on 6,830,415 cows. Genotypes of 89,558 Holstein, 40,769 Jersey, and 22,373 Holstein-Jersey crossbred animals were used, of which all Holstein, 9,313 Jersey, and 1,667 crossbred animals had phenotypic records. Genotypes were imputed to 45K SNP markers. The SNP effects were estimated from single-breed evaluations for Jersey (JE), Holstein (HO) and crossbreds (CROSS), and multibreed evaluations including all Jersey and Holstein (JE_HO) or approximately equal proportions of Jersey, Holstein, and crossbred animals (MIX). Indirect predictions (IP) of the validation animals (358 crossbred animals with phenotypes excluded from evaluations) were calculated using the resulting SNP effects. Additionally, breed proportions (BP) of crossbred animals were applied as a weight when IP were estimated based on each pure breed. The predictive ability of IP was calculated as the Pearson correlation between IP and phenotypes of the validation animals adjusted for fixed effects in the model. Regression of adjusted phenotypes on IP was used to assess the inflation of IP. The predictive ability of IP for CROSS, JE, HO, JE_HO, and MIX scenario was 0.50, 0.50, 0.47, 0.50, and 0.46, respectively. Using BP was the least successful, with a predictive ability of 0.32. The inflation of the IP for crossbred animals using CROSS, JE, HO, JE_HO, MIX, and BP scenarios were 1.17, 0.65, 0.55, 0.78, 1.00, and 0.85, respectively. The IP of crossbred animals can be predicted using single-step GBLUP under a scenario that includes purebred genotypes.     

Single-step genomic evaluation of milk production traits in Canadian Alpine and Saanen dairy goats

Genomic evaluations are routine in most plant and livestock breeding programs but are used infrequently in dairy goat breeding schemes. In this context, the purpose of this study was to investigate the use of the single-step genomic BLUP method for predicting genomic breeding values for milk production traits (milk, protein, and fat yields; protein and fat percentages) in Canadian Alpine and Saanen dairy goats. There were 6,409 and 12,236 Alpine records and 3,434 and 5,008 Saanen records for each trait in first and later lactations, respectively, and a total of 1,707 genotyped animals (833 Alpine and 874 Saanen). Two validation approaches were used, forward validation (i.e., animals born after 2013 with an average estimated breeding value accuracy from the full data set ≥0.50) and forward cross-validation (i.e., subsets of all animals included in the forward validation were used in successive replications). The forward cross-validation approach resulted in similar validation accuracies (0.55 to 0.66 versus 0.54 to 0.61) and biases (?0.01 to –0.07 versus ?0.03 to 0.11) to the forward validation when averaged across traits. Additionally, both single and multiple-breed analyses were compared, and similar average accuracies and biases were observed across traits. However, there was a small gain in accuracy from the use of multiple-breed models for the Saanen breed. A small gain in validation accuracy for genomically enhanced estimated breeding values (GEBV) relative to pedigree-based estimated breeding values (EBV) was observed across traits for the Alpine breed, but not for the Saanen breed, possibly due to limitations in the validation design, heritability of the traits evaluated, and size of the training populations. Trait-specific gains in theoretical accuracy of GEBV relative to EBV for the validation animals ranged from 17 to 31% in Alpine and 35 to 55% in Saanen, using the cross-validation approach. The GEBV predicted from the full data set were 12 to 16% more accurate than EBV for genotyped animals, but no gains were observed for nongenotyped animals. The largest gains were found for does without lactation records (35–41%) and bucks without daughter records (46–54%), and consequently, the implementation of genomic selection in the Canadian dairy goat population would be expected to increase selection accuracy for young breeding candidates. Overall, this study represents the first step toward implementation of genomic selection in Canadian dairy goat populations.