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ABSTRACT: As the proportion of prime carcasses originating from dairy herds increases, the focus is shifting to the beef merit of the progeny from dairy herds. Several dairy cow total merit indexes include a negative weight on measures of cow size. However, there is a lack of knowledge on the effect of genetic selection, solely for lighter or smaller-sized dairy cows, on the beef performance of their progeny. Therefore, the objective of this study was to quantify the genetic correlations among cow size traits (i.e., cow BW, cow carcass weight [CW]), cow BCS, cow carcass conformation (CC), and cow carcass fat cover (CF), as well as the correlations between these cow traits and a series of beef performance slaughter-related traits (i.e., CW, CC, CF, and age at slaughter [AS]) in their progeny. After data editing, there were 52,950 cow BW and BCS records, along with 57,509 cow carcass traits (i.e., CW, CC, and CF); carcass records from 346,350 prime animals along with AS records from 316,073 prime animals were also used. Heritability estimates ranged from moderate to high (0.18–0.62) for all cow and prime animal traits. The same carcass trait in cows and prime animals were strongly genetically correlated with each other (0.76–0.85), implying that they are influenced by very similar genomic variants. Selecting exclusively for cows with higher BCS (i.e., fatter) will, on average, produce more conformed prime animal carcasses, owing to a moderate genetic correlation (0.30) between both traits. Genetic correlations revealed that selecting exclusively for lighter BW or CW cows will, on average, result in lighter prime animal carcasses of poor CC, while also delaying slaughter age. Nonetheless, selective breeding through total merit indexes should be successful in breeding for smaller dairy cows and desirable prime animal carcass traits concurrently, because of the nonunity genetic correlations between the cow and prime animal traits; this will help to achieve a more ethical, environmentally sustainable, and economically viable dairy-beef industry.

The objective was to explore if the regression of phenotypic performance for six milk production traits on the respective estimate of genetic merit for that trait differed by herd milking frequency; variance components for each trait in the two milking frequency environments were also estimated as well as the genetic correlation between the same trait in both environments. The data used included 12,581 lactations from 5,456 cows in 32 spring-calving once-a-day (OAD) milking herds. Each OAD herd was matched with three contemporary twice-a-day (TAD)-milking herds; 35,823 lactations from 15,188 cows in 96 TAD herds were used. Mean yield was 20% (fat yield) to 31% (milk yield) lower in OAD herds. Milk protein concentration was 11% higher in OAD herds, while milk fat concentration was 16% higher in OAD herds. The mean back-transformed somatic cell score (SCS) was 100,390 cells/mL in OAD herds and 72,493 cells/mL in TAD herds. The association between each milk production trait and its respective estimate of genetic merit differed by herd milking frequency; the estimated regression coefficients were larger in TAD for just milk yield and SCS. The genetic correlation between the same trait in OAD versus TAD was all ≥0.73 with the exception of SCS (genetic correlation of 0.48) which suggests some re-ranking of sires between environments. In conclusion, differences in the scale of the genetic variance were evident for both milking frequencies and possible re-ranking was evident for SCS.

Many sport horse studbooks worldwide use microsatellite markers for parentage verification. However, many have expressed a desire to introduce genomic selection using genome-wide dense single nucleotide polymorphism (SNP) genotypes to complement their current breeding programmes. Hence, it does not make sense to genotype the same animal for both microsatellite markers and SNP markers. Transitioning to SNP-based parentage verification is an obvious solution but one barrier to this transition is the lack of SNP data on parents from which to verify parentage against. Therefore, the objective of this study was to assess the ability to impute the SNP genotype of a stallion from the genotypes of its progeny, with or without the consideration of the genotype of the progeny’s dam. Genotype information from 55 935 SNPs was available on 13 327 horses. A total of 98 stallions had genotype data on 10 progeny and the genotypes of these stallions were used as a test population. Genome-wide genotype imputation was undertaken by combining a family- and population-based imputation approach. Several different scenarios were assessed to quantify the ability to accurately impute the genotype of a stallion based on genotype data of incrementally more half-sibling progeny. Using genomic information from four progeny the average genotype concordance of the imputed sire genotype compared to the actual sire’s genotype was 0.932. The average genotype concordance rate increased to 0.960 when the genotypes of 10 progeny were included in the imputation process. The inclusion of the genotypes of the dams of the progeny improved the concordance rate from 0.932 to 0.977 when based on the genotype of just four progeny and their dams and from 0.960 to 0.996 when based on the genotype of 10 progeny and their dams. These results suggest it is possible to impute the genotype of a non-genotyped horse from the genotypes of its progeny and that the inclusion of the genotypes of the dams of the progeny improves this imputation accuracy further.

ABSTRACT: Improved nitrogen utilization of dairy production systems should improve not only the economic output of the systems but also the environmental metrics. One strategy to improve efficiency is through breeding programs. Improving a trait through breeding is conditional on the presence of exploitable genetic variability. Using a database of 1,291 deeply phenotyped grazing dairy cows, the genetic variability for 2 definitions of nitrogen utilization was studied: nitrogen use efficiency (i.e., nitrogen output in milk and meat divided by nitrogen available) and nitrogen balance (i.e., nitrogen available less nitrogen output in milk and meat). Variance components for both variables were estimated using animal repeatability linear mixed models. Genetic variability was detected for both nitrogen utilization metrics, even though their heritability estimates were low (

Abstract:: The growing demand among dairy producers for suitable beef sires to mate to their females creates the possibility of separate breeding programs to generate beef sires for the dairy sector versus those for the beef sector. Informing such a decision is the extent of the genetic differences among beef sires used by dairy producers relative to those used by beef producers. The objective therefore of the present study was to use a large national database of artificial insemination (AI) records in dairy and beef cow herds to establish the difference in mean genetic merit of beef AI sires used by dairy producers versus those used by cow-calf beef producers. The traits explored were gestation length, calving difficulty, and perinatal mortality as well as the 3 carcass traits of carcass weight, conformation, and fat score. Carcass conformation and fat score are mechanically assessed on a scale of 1 (poor conformation and low fat cover) to 15 (excellent conformation and high fat cover). Sire genetic merit differences for feed intake and docility were also examined. Estimates of genetic merit for all 8 traits on individual AI sires available at the time of service were used. A total of 1,230,622 AI records comprised 909,719 services from dairy herds and 320,903 services from beef herds were used. Of the 1,802 beef AI sires represented in the entire dataset, over half were used by both dairy and beef herds representing ≥98% of the services in each production system. However, the usage rate of individual AI sires differed between dairy and beef herds with the Spearman rank correlation between the quantity of inseminations per sire in dairy and beef herds being just 0.38. This correlation means that beef AI sires used heavily in the beef herd were not always those heavily used in dairy herds. A clear difference in the mean genetic merit of beef AI sires selected by dairy producers relative to those selected by beef cow-calf producers was obvious with the extent of the difference being a function of whether the female served was a nulliparous heifer or a cow. Much of the differences in genetic merit of chosen beef AI sires between dairy and beef producers was actually attributable to differences in breed choice, albeit some within-breed selection was also evident. Irrespective, dairy producers, on average, chose shorter gestation length sires whose progeny were genetically less predisposed to require intervention during the birthing process; these sires had genetic merit estimates expected to result in lighter and less conformed progeny carcasses relative to the beef AI sires used by beef producers. Results point to large differences in genetic merit of the beef AI sires chosen by dairy versus beef producers, much of which actually reflected differences in breed choice among dairy and beef producers.

Methane (CH4) is a potent gas produced by ruminants, and new measurement techniques are required to generate large datasets suitable for genetic analysis. One such technique are portable accumulation chambers (PAC), a short-term sampling method. The objectives of the current study were to explore the relationship between CH4 and carbon dioxide (CO2) output measured using both PAC and respiration chambers (RC) in growing lambs, and separately investigate the relationship among CH4, CO2 and measured ad libitum DM intake (DMI). Methane, CO2 and DMI were measured on 30 Suffolk and 30 Texel ewe lambs (age 253 ± 12 days) using the RC and PAC sequentially. The experiment was conducted over a 14-day period, with DMI measured from days 1 to 14; measurements in RC were conducted from days 10 to 12, while measurements in PAC were taken twice, the day immediately prior to the lambs entering the RC (day 9; PAC Pre-RC) and on the day lambs exited the RC (day 13; PAC Post-RC). Greater CH4 and CO2 output was measured in the RC than in the PAC (P 0.05). Moderate to strong correlations were found between CH4 and CO2 per kg of live weight and CH4 and CO2 yield. Results from this study highlight the suitability of PAC as a ranking tool to rank animals based on their gaseous output when compared to the RC. However, repeated measurements separated by several days may be beneficial if precise rankings are required. Given the close to unity regression coefficient of CH4 output measured in the RC regressed on CH4 output measured in PAC Pre-RC suggests that PAC could also be potentially used to estimate absolute CH4 output; however, further research is required to substantiate this claim. When DMI is unknown, CH4 and CO2 per kg of live weight are a suitable alternative to the measurement of CH4 and CO2 yield.

Although interest in beef-on-dairy breeding strategies is intensifying, little is actually known of the performance differences between beef-sired male and female progeny of dairy cows. The objective was therefore to use a large cross-sectional database of up to 1,389,670 animals to investigate if performance differences existed between male and female progeny generated from beef-on-dairy matings; the focus was on characteristics of interest to both the dairy producer (i.e., gestation length, calving performance, perinatal mortality, and calf sale value) and the beef producer (i.e., slaughter-related traits). While statistical differences existed between both sexes, the observed differences were not always biologically large, with some favoring females (e.g., calving traits and age at slaughter) and some favoring males (i.e., carcass weight). Beef-sired male calves had, on average, a 0.8 d longer gestation than their female counterparts; the sex difference in dairy-sired calves was, on average, 1.1 d, with the advantage to females. The odds of a difficult calving was 2.2 times greater for beef-sired male calves relative to beef-sired female calves; this translated to a difference in predicted probability of dystocia between the sexes of 1.8 percentage units. Male beef-sired calves sold at auctions

ABSTRACT: Directly measuring individual cow energy balance is not trivial. Other traits such as body condition score (BCS) and BCS change (ΔBCS) can, however, be used as an indicator of cow energy status. Body condition score is a metric used worldwide to estimate cow body reserves, but the estimation of ΔBCS was, until now, conditional on the availability of multiple BCS assessments. The aim of the present study was to estimate ΔBCS from milk mid-infrared (MIR) spectra and days in milk (DIM) in intensively fed dairy cows using statistical prediction methods. Daily BCS was interpolated from cubic splines fitted through the BCS records and daily ΔBCS was calculated from these splines. The ΔBCS records were merged with milk MIR spectra recorded on the same week. The dataset comprised 37,077 ΔBCS phenotypes across 9,403 lactations from 6,988 cows in 151 herds based in Quebec, Canada. Partial least squares regression (PLSR) and a neural network (NN) were then used to estimate ΔBCS from (1) MIR spectra only, (2) DIM only, or (3) MIR spectra and DIM together. The ΔBCS data in both the first 120 and 305 DIM of lactation were used to develop the estimates. Daily ΔBCS had a standard deviation of 4.40 × 10−3 BCS units in the 120-d dataset and of 3.63 × 10−3 BCS units in the 305-d dataset. A 4-fold cross-validation was used to calibrate and test the prediction equations. External validation was also conducted using more recent years of data. Irrespective of whether based on the first 120 or 305 DIM, or when MIR spectra only, DIM only or MIR spectra and DIM were jointly used as prediction variables, NN produced the lowest root mean square error (RMSE) of cross-validation (1.81 × 10−3 BCS units and 1.51 × 10−3 BCS units, respectively, using the 120-d and 305-d dataset). Relative to predictions for the entire 305 DIM, the RMSE of cross-validation was 15.4% and 1.5% lower in the first 120 DIM when using PLSR and NN, respectively. Predictions from DIM only were more accurate than those using just MIR spectra data but, irrespective of the dataset and of the prediction model used, combining DIM information with MIR spectral data as prediction variables reduced the RMSE compared with the inclusion of DIM alone, albeit the benefit was small (the RMSE from cross-validation reduced by up to 5.5% when DIM and spectral data were jointly used as model features instead of DIM only). However, when predicting extreme ΔBCS records, the MIR spectral data were more informative than DIM. Model performance when predicting ΔBCS records in future years was similar to that from cross-validation demonstrating the ability of MIR spectra of milk and DIM combined to estimate ΔBCS, particularly in early lactation. This can be used to routinely generate estimates of ΔBCS to aid in day-to-day individual cow management.

ABSTRACT: Reducing nitrogen pollution while maintaining milk production is a major challenge of dairy production. One of the keys to delivering on this challenge is to improve the efficiency of how dairy cows use nitrogen. Thus, estimating the nitrogen utilization of lactating grazing dairy cows and exploring the association between animal factors and productivity with nitrogen utilization are the first steps to understanding the nitrogen utilization complex in dairy cows. Nitrogen utilization metrics were derived from milk and body weight records from 1,291 grazing dairy cows of multiple breeds and crossbreeds; all cows had sporadic information on nitrogen intake concurrent with information on nitrogen sinks (and other nitrogen sources, such as body tissue mobilization). Several nitrogen utilization metrics were investigated, including nitrogen use efficiency (nitrogen output as products such as milk and meat divided by nitrogen intake) and nitrogen excreted (nitrogen intake less the nitrogen output as products such as milk and meat). In the present study, a primiparous Holstein-Friesian used, on average, 20.6% of the nitrogen it ate, excreting the surplus as feces and urine, representing 402 g of nitrogen per day. Intercow variability existed, with a between-cow standard deviation of 0.0094 for nitrogen use efficiency and 24 g of nitrogen per day for nitrogen excretion. As lactation progressed, nitrogen use efficiency declined and nitrogen excretion increased. Nevertheless, nitrogen use efficiency improved (i.e., decreased) from first to second parity, even though it did not improve from second to third parity or greater. Furthermore, nitrogen excretion continued to increase from first to third parity or greater. Nitrogen use efficiency and nitrogen excretion were negatively correlated (−0.56 to −0.40), signifying that dairy cows who partition more of the ingested nitrogen into products such as milk and meat, on average, also excrete less nitrogen. Milk urea nitrogen was, at best, weakly correlated with nitrogen use efficiency and nitrogen excretion; the correlations were between −0.01 and 0.06. In conclusion, several cow-level factors such as parity, stage of lactation, and breed were associated with the range of different nitrogen efficiency metrics investigated; moreover, even after accounting for such effects, 4.8% to 6.3% of the remaining variation in the nitrogen use efficiency and nitrogen balance metrics were attributable to intercow differences.

ABSTRACT: Gains through breeding can be achieved through a combination of both between-breed and within-breed selection. Two suites of traits of particular interest to dairy producers when selecting beef bulls for mating to dairy females are calving-related attributes and the expected value of the subsequent calf, the latter usually being a function of expected carcass value. Estimated breed effects can be informative, particularly in the absence of across-breed genetic evaluations. The objective of the present study was to use a large national database of the progeny from beef-on-dairy matings to estimate the mean breed effects of the used beef sires. Calving performance (i.e., gestation length, calving difficulty score, and perinatal morality) as well as calf value were investigated; a series of slaughter-related traits (i.e., carcass metrics and age at slaughter) of the prime progeny were also investigated. Phenotypic data on up to 977,037 progeny for calving performance, 79,903 for calf price and 103,175 for carcass traits (including dairy × dairy progeny for comparative purposes) were used; sire breeds represented were Holstein-Friesian, Angus, Aubrac, Belgian Blue, Charolais, Hereford, Limousin, Salers, and Simmental. Large interbreed differences existed. The mean gestation length of male calves from beef sires varied from 282.3 d (Angus) to 287.4 d (Limousin) which were all longer than the mean of 280.9 d for Holstein-Friesian sired male calves. Relative to a Holstein-Friesian sire, the odds of dystocia varied from 1.43 (Angus) to 4.77 (Belgian Blue) but, once adjusted for both the estimated maternal genetic merit of the dam and direct genetic merit of the calf for calving difficulty, the range in odds ratios shrunk. A difference of €125.4 existed in calf sale price between the progeny of the different beef breeds investigated which represented over twice the residual standard deviation in calf price within the day of sale—Angus was the cheapest while Charolais calves were, on average, the most expensive calves. Mean carcass weight of steers, not adjusted for age at slaughter or carcass fat, varied from 327.1 kg (Angus) to 363.2 kg (Belgian Blue) for the beef breeds with the mean carcass weight of Holstein-Friesian steer progeny being 322.4 kg. Belgian Blues had, on average, the best carcass conformation with the Herefords and Angus having the worst of all beef breeds. Angus and Hereford steers were slaughtered the youngest of all beef breeds but just 9 d younger than the average of all other beef breeds yet 24 d younger than Holstein-Friesian sired progeny. Clear breed differences in calving and carcass performance exist among beef breeds mated to dairy females. Those breeds excelling in calving performance were not necessarily the best for carcass merit.

ABSTRACT: Subclinical mastitis in cows affects their health, well-being, longevity, and performance, leading to reduced productivity and profit. Early prediction of subclinical mastitis can enable dairy farmers to perform interventions to mitigate its effect. The present study investigated how well predictive models built using machine learning techniques can detect subclinical mastitis up to 7 d before its occurrence. The data set used consisted of 1,346,207 milk-day (i.e., a day when milk was collected on both morning and evening) records spanning 9 yr from 2,389 cows producing on 7 Irish research farms. Individual cow composite milk yield and maximum milk flow were available twice daily, whereas milk composition (i.e., fat, lactose, protein) and somatic cell count (SCC) were collected once per week. Other features describing parity, calving dates, predicted transmitting ability for SCC, body weight, and history of subclinical mastitis were also available. The results of the study showed that a gradient boosting machine model trained to predict the onset of subclinical mastitis 7 d before a subclinical case occurs achieved a sensitivity and specificity of 69.45 and 95.64%, respectively. Reduced data collection frequency, where milk composition and SCC were recorded only every 15, 30, 45, and 60 d was simulated by masking data, to reflect the frequency of recording of this data on commercial dairy farms in Ireland. The sensitivity and specificity scores reduced as recording frequency reduced with respective scores of 66.93 and 80.43% when milk composition and SCC were recorded just every 60 d. Results demonstrate that models built on data that could be recorded routinely available on commercial dairy farms, can achieve useful predictive ability of subclinical mastitis even with reduced frequency of milk composition and SCC recording.

ABSTRACT: Body condition score (BCS) is a subjective estimate of body reserves in cows. Body condition score and its change in early lactation have been associated with cow fertility and health. The aim of the present study was to estimate change in BCS (ΔBCS) using mid-infrared spectra of the milk, with a particular focus on estimating ΔBCS in cows losing BCS at the fastest rate (i.e., the cows most of interest to the producer). A total of 73,193 BCS records (scale 1 to 5) from 6,572 cows were recorded. Daily BCS was interpolated from cubic splines fitted through the BCS records, and subsequently used to calculate daily ΔBCS. Body condition score change records were merged with milk mid-infrared spectra recorded on the same week. Both morning (a.m.) and evening (p.m.) spectra were available. Two different statistical methods were used to estimate ΔBCS: partial least squares regression and a neural network (NN). Several combinations of variables were included as model features, such as days in milk (DIM) only, a.m. spectra only and DIM, p.m. spectra only and DIM, and a.m. and p.m. spectra as well as DIM. The data used to estimate ΔBCS were either based on the first 120 DIM or all 305 DIM. Daily ΔBCS had a standard deviation of 1.65 × 10−3 BCS units in the 305 DIM data set and of 1.98 × 10−3 BCS units in the 120 DIM data set. Each data set was divided into 4 sub-data sets, 3 of which were used for training the prediction model and the fourth to test it. This process was repeated until all the sub-data sets were considered as the test data set once. Using all 305 DIM, the lowest root mean square error of validation (RMSEV; 0.96 × 10−3 BCS units) and the strongest correlation between actual and estimated ΔBCS (0.82) was achieved with NN using a.m. and p.m. spectra and DIM. Using the 120 DIM data, the lowest RMSEV (0.98 × 10−3 BCS units) and the strongest correlation between actual and estimated ΔBCS (0.87) was achieved with NN using DIM and either a.m. spectra only or a.m. and p.m. spectra together. The RMSEV for records in the lowest 2.5% ΔBCS percentile per DIM in early lactation was reduced up to a maximum of 13% when spectra and DIM were both considered in the model compared with a model that considered just DIM. The performance of the NN using DIM and a.m. spectra only with the 120 DIM data was robust across different strata of farm, parity, year of sampling, and breed. Results from the present study demonstrate the ability of mid-infrared spectra of milk coupled with machine learning techniques to estimate ΔBCS; specifically, the inclusion of spectral data reduced the RMSEV over and above using DIM alone, particularly for cows losing BCS at the fastest rate. This approach can be used to routinely generate estimates of ΔBCS that can subsequently be used for farm decisions.

Access to high-dimensional genomic information in many livestock species is accelerating. This has been greatly aided not only by continual reductions in genotyping costs but also an expansion in the services available that leverage genomic information to create a greater return-on-investment. Genomic information on individual animals has many uses including (1) parentage verification and discovery, (2) traceability, (3) karyotyping, (4) sex determination, (5) reporting and monitoring of mutations conferring major effects or congenital defects, (6) better estimating inbreeding of individuals and coancestry among individuals, (7) mating advice, (8) determining breed composition, (9) enabling precision management, and (10) genomic evaluations; genomic evaluations exploit genome-wide genotype information to improve the accuracy of predicting an animal’s (and by extension its progeny’s) genetic merit. Genomic data also provide a huge resource for research, albeit the outcome from this research, if successful, should eventually be realised through one of the ten applications already mentioned. The process for generating a genotype all the way from sample procurement to identifying erroneous genotypes is described, as are the steps that should be considered when developing a bespoke genotyping panel for practical application.

Carcass value is one of the main contributors to revenue in meat sheep enterprises, while age at slaughter is also a major component to the cost of production. Despite the contribution of such traits to overall profit, little is actually known on the extent of exploitable genetic variability in the traits that govern carcass value (i.e. carcass weight, carcass conformation, carcass fat) and age at slaughter, especially independent of each other. The objective of the present study was to estimate genetic (co)variances for and among carcass weight, carcass conformation, carcass fat, kill-out percentage and age at slaughter as well as their genetic (co)variances with traits measured earlier in life. Data consisted of slaughter records from 15 714 lambs, with 12 630 of these lambs having at least one live weight measure. The heritability (SE) of carcass weight, carcass conformation, carcass fat, kill-out percentage, and age at slaughter was 0.14 (0.02), 0.19 (0.02), 0.08 (0.01), 0.22 (0.03), and 0.16 (0.02), respectively. The maternal heritability for age at slaughter was 0.07 (0.02); no maternal genetic influence was found on any of the other slaughter traits. The coefficient of genetic variation for carcass weight and age at slaughter was 3 and 8%, respectively. The correlations between the direct genetic effects for live weight throughout life, and carcass weight were weak up to weaning but were strong (0.83) thereafter. The correlation between the direct genetic effects of birth weight and age at slaughter was zero, but varied from −0.91 to −0.56 between live weight measured later in life and age at slaughter. Results demonstrate significant exploitable genetic variability in a range of slaughter traits with the prediction of genetic merit for carcass traits and age at slaughter being possible using live weight measures taken on live animals. For example, the accuracy of selection for slaughter traits (comprising of age at slaughter, carcass conformation and carcass fat) from weaning weight records available on 100 progeny was 0.37; when slaughter data were also available for 10 progeny, the accuracy of selection increased to 0.56.

The objective of the present study was to investigate the genetic trends of 18 subjectively scored linear type traits describing animal morphology, as well as udder, teat, feet and leg conformation. The analysis was undertaken using 2,932,700 Holstein-Friesian females born in the Republic of Ireland between the years 2000 and 2020, inclusive. The results indicate that Holstein-Friesian females have progressively become shorter in stature as well as shallower (i.e. body depth) and less angular. The reduction in genetic merit for stature score since the year 2004 was, however, only observed in non-herdbook-registered heifers. Furthermore, the reducing score in body depth (i.e. narrower) and angularity (i.e. less angular) was approximately twice as fast in non-herdbook-registered heifers as it was in herdbook-registered heifers. Differences in the genetic merit of the body-related traits for calves born versus those that became cows only existed prior to 2010 with little biological differences thereafter; this observation was common across most of the linear type traits. Genetic merit for locomotion in non-herdbook-registered animals has deteriorated over the 20-yr period, while the foot angle over that period is becoming lower; no such trends were observed for the herdbook-registered animals. Large differences not only in the trends themselves, but also in the mean genetic merit for udder traits existed when comparing herdbook-registered calves versus non-registered calves. In conclusion, genetic merit for many of the traits evaluated has trended relatively consistent in a given direction, albeit the cumulative change in genetic s.d. units per traits over the 20-yr period was very small.

ABSTRACT: The cumulative improvement achieved in the genetic merit for reproductive performance in dairy populations will likely improve dairy cow longevity; therefore, it is time to reassess whether linear type traits are still suitable predictors of survival in an aging dairy cow population. The objective of the present study was therefore to estimate the genetic correlations between linear type traits and survival from one parity to the next and, in doing so, evaluate if those genetic correlations change with advancing parity. After edits, 152,894 lactation survival records (first to ninth parity) were available from 52,447 Holstein-Friesian cows, along with linear type trait records from 52,121 Holstein-Friesian cows. A series of bivariate random regression models were used to estimate the genetic covariances between survival in different parities and each linear type trait. Heritability estimates for survival per parity ranged from 0.02 (SE = 0.004; first parity) to 0.05 (SE = 0.01; ninth parity). Pairwise genetic correlations between survival among different parities varied from 0.42 (first and ninth parity) to 1.00 (eighth to ninth parity), with the strength of these genetic correlations being inversely related to the interval between the compared parities. The genetic correlations between survival and the individual linear type traits varied across parities for 9 of the 20 linear type traits examined, but the correlations with only 3 of these linear type traits strengthened as the cows aged; these 3 traits were rear udder height, teat length, and udder depth. Given that linear type traits are frequently scored in first parity and are genetically correlated with survival in older parities, they may be suitable early predictors of survival, especially for later parity cows. Additionally, the direction of the genetic correlations between survival and rear udder height, teat length, and udder depth did not change between parities; hence, selection for survival in older parities using these linear type traits should not hinder genetic improvement for survival in younger parities.

ABSTRACT: The inclusion of reproductive performance in dairy cow breeding schemes has resulted in a cumulative improvement in genetic merit for reproductive performance; this improvement should manifest in longer productive lives through a reduced requirement for involuntary culling. Nonetheless, the average length of dairy cow productive life has not changed in most populations, suggesting that risk factors for culling, especially in older cows, are possibly more associated with lower yield or high somatic cell score (SCS) than compromised reproductive performance. The objective of the present study was to understand the dynamics of lactation yields and SCS in dairy cows across parities and, in doing so, quantify the potential to alter this trajectory through breeding. After edits, 3,470,520 305-d milk, fat, and protein yields, as well as milk fat and protein percentage and somatic cell count records from 1,162,473 dairy cows were available for analysis. Random regression animal models were used to identify the parity in which individual cows reached their maximum lactation yields, and highest average milk composition and SCS; also estimated from these models were the (co)variance components for yield, composition, and SCS per parity across parities. Estimated breeding values for all traits per parity were calculated for cows reaching ≥fifth parity. Of the cows included in the analyses, 91.0%, 92.2%, and 83.4% reached maximum milk, fat, and protein yield in fifth parity, respectively. Conversely, 95.9% of cows reached their highest average fat percentage in first parity and 62.9% of cows reached their highest average protein percentage in third parity. In contrast to both milk yield and composition traits, 98.4% of cows reached their highest average SCS in eighth parity. Individual parity estimates of heritability for milk yield traits, milk composition, and SCS ranged from 0.28 to 0.44, 0.47 to 0.69, and 0.13 to 0.23, respectively. The strength of the genetic correlations per trait among parities was inversely related to the interval between the parities compared; the weakest genetic correlation was 0.67 (standard error = 0.02) between milk yield in parities 1 and 8. Eigenvalues and eigenfunctions of the additive genetic covariance matrices for all investigated traits revealed potential to alter the trajectory of parity profiles for milk yield, milk composition, and SCS. This was further demonstrated when evaluating the trajectories of animal estimated breeding values per parity.

A genetic improvement programme is a sustainable, cumulative and permanent approach to achieving year-on-year performance gains. Its success is predicated not only on an efficient and effective breeding programme but also on a vision of the traits of importance in the future. A single, industry-owned, centralised database for cattle and sheep has been the foundation for genetic improvement programmes in Ireland. While DNA information has been heralded as a breakthrough for accelerating genetic gain, the basic principles of a successful animal breeding programme still remain the same: (1) a pertinent breeding goal, (2) the appropriate breeding objective to deliver on the breeding goal, (3) an accurate genetic evaluation system, (4) an efficient and effective breeding scheme, and (5) a system to disseminate the elite germplasm to the end user; also of importance is a system for validating the underlying procedures and principles. The constituent traits and their relative emphasis within breeding objectives will continue to be contentious. Traits that will need to be considered more in future ruminant breeding objectives include environmental impact, product quality and animal well-being, including health; while not always explicitly included in Irish breeding objectives for cattle and sheep, indirect improvements for many are expected via the genetic improvement in traits like reproductive performance and survival as well as macro measures of quality such as milk fat and protein concentration and carcass merit. Crucial for the future sustainability of ruminant production systems is the co-evolution of management systems and breeding programmes so that the animal of the future is suited to the most sustainably efficient production system.

ABSTRACT: Measuring dry matter intake (DMI) in grazing dairy cows using currently available techniques is invasive, time consuming, and expensive. An alternative to directly measuring DMI for use in genetic evaluations is to identify a set of readily available animal features that can be used in a multitrait genetic evaluation for DMI. The objectives of the present study were thus to estimate the genetic correlations between readily available body-related linear type traits and DMI in grazing lactating Holstein-Friesian cows, but importantly also estimate the partial genetic correlations between these linear traits and DMI, after adjusting for differences in genetic merit for body weight. Also of interest was whether the predictive ability derived from the estimated genetic correlations materialized upon validation. After edits, a total of 8,055 test-day records of DMI, body weight, and milk yield from 1,331 Holstein-Friesian cows were available, as were chest width, body depth, and stature from 47,141 first lactation Holstein-Friesian cows. In addition to considering the routinely recorded linear type traits individually, novel composite traits were defined as the product of the linear type traits as an approximation of rumen volume. All linear type traits were moderately heritable, with heritability estimates ranging from 0.27 (standard error = 0.14) to 0.49 (standard error = 0.15); furthermore, all linear type traits were genetically correlated (0.29 to 0.63, standard error 0.14 to 0.12) with DMI. The genetic correlations between the individual linear type traits and DMI, when adjusted for genetic differences in body weight, varied from −0.51 (stature) to 0.48 (chest width). These genetic correlations between DMI and linear type traits suggest linear type traits may be useful predictors of DMI, even when body weight information is available. Nonetheless, estimated genetic merit of DMI derived from a multitrait genetic evaluation of linear type traits did not correlate strongly with actual DMI in a set of validation animals; the benefit was even less if body weight data were also available.

Attention is increasing on both cow size and body weight (BW) as energy sinks and thus as contributors to differences in production efficiency among cows. What is not currently clear, however, is how cow BW affects the increase in yield per cow per unit increase in genetic merit for milk production. This void in knowledge was filled in the present study using BW data from 20,470 lactations on 16,980 Holstein-Friesian dairy cows stratified into 4 groups on BW adjusted for differences in parity, days in milk, and body condition score. Using linear mixed models that adjusted for nuisance factors, cow phenotypic milk production variables were regressed on estimates of parental average genetic merit for the respective trait within each stratum of BW defined within contemporary group; estimates of genetic merit were from the national genetic evaluations. Both the intercept and linear regression coefficients on genetic merit were compared across BW strata. The intercepts representing the mean phenotypic yield at a genetic merit of zero differed among BW strata; irrespective of yield trait, the least squares means yield per BW stratum increased numerically as cows got heavier, although not every stepwise increase in BW stratum was associated with significantly greater yield compared with the previous (lighter) stratum. Nonetheless, the yield of the cows in the lightest of the 4 strata was always less than that of the heaviest 2 strata; relative to the lightest stratum, cows in the heaviest BW stratum produced only 3 to 4% more yield. Furthermore, the association between phenotypic yield and its respective measures of genetic merit differed by BW stratum; the response to selection for each of the yield traits was 15 to 23% greater for the heaviest stratum of cows compared with their contemporaries in the lightest stratum. Although BW stratum was associated with mean fat and protein concentration after adjusting for differences in genetic merit for fat and protein concentration, the association did not differ by BW stratum for either fat or protein concentration. The effect of BW on efficiency should consider the association between BW and not only mean phenotypic yield at a given genetic merit, but also how the differences in yield diverge as genetic merit increases.

Selection bias is introduced when selection among individuals exists but the information used to inform that selection decision is not considered in downstream genetic evaluations. Genetic evaluations are undertaken in several countries for carcass-related metrics in prime cattle; no consideration is generally taken for animals that are harvested at a younger age for veal production and thus do not express the prime carcass phenotype. Although no veal industry exists in Ireland, dairy calves are routinely exported to continental Europe for veal production. The objective of the present study, based on a cross-sectional analysis of calf export data, was to determine quantitatively if genetic variability exists in whether purchased dairy-bred bull calves are immediately exported or retained within the country for domestic production. Also of interest was whether such a genetic difference was associated with differences in carcass weight, conformation score, and fat score in prime cattle relatives. Editing criteria were imposed to consider only Holstein-Friesian bull calves. Post-editing, the fate of 43,890 Holstein-Friesian bull calves (

The growing awareness and scrutiny of the management of young dairy calves, especially male calves, necessitates a support tool to aid in the planning of resource allocation on dairy farms. There is a desire among some vendors for a minimum calf weight when purchasing young dairy bull calves. Hence, the objective of the present study was to investigate whether live weight of young calves (approximately 10–50 d old) can be predicted using readily accessible animal-level features, especially features that may be available in advance of birth. A multiple linear regression mixed model was developed with the live weight of 602 dairy bull calves aged between 10 and 42 d as the dependent variable; the age at which an animal is predicted to reach a predefined live weight was then estimated based on the model regression coefficients. Fixed effects included in the multiple regression model were dam parity, gestation length, and parental average genetic merit for relevant traits available in Ireland; namely, birth weight, birth size, and carcass weight. Herd of origin was included as a random effect, with all calves having been sold directly from the farm of birth. Live weight data were recorded at the point of sale when calves were, on average, 26 d old with a mean live weight of 56.6 kg. Animals were randomly assigned to 10 separate (i.e., folds) cross-validation data sets without replacement (i.e., each fold consisted of a different 10% of the data to test the model, with the remaining 90% of data being used to train the model) to quantify the accuracy of prediction. Across all data, the correlation between actual and predicted live weight was 0.76; the regression coefficient of actual live weight on predicted live weight across all data was 0.99. The root mean squared error of prediction varied from 4.40 to 6.66 kg per fold. Across all data, the root mean squared error was 5.61 kg, implying that 68% of live weight predictions were within 5.61 kg of the actual live weight. Given the potential availability of all model features in advance of birth (gestation length can be predicted from ultrasound examination of the pregnant uterus, although substituting parental average genetic merit for gestation length had minimal effect on model performance), predictions can be integrated into a dairy farm decision support tool to aid in the management of labor and infrastructure resources to achieve minimum live weight specifications before sale.

Genetic susceptibility to scrapie, a fatal disease of sheep and goats, is modulated by polymorphisms in the prion protein (PrP). Neither the frequency of the PrP genotypes nor their association with animal performance has been investigated in a large multibreed Irish sheep population. Scrapie genotypes were available on 16 416 animals; the breeds represented included purebred Belclare (733), Charollais (333), Suffolk (739), Texel (1 857), Vendeen (191), and crossbreds (12 563). Performance data on lambing, lamb and ewe performance as well as health traits were available. The association between alternative approaches of describing the PrP genotype (i.e. 15 individually called PrP genotypes, five genotype classes representing susceptibility to scrapie, or number of ARR haplotypes) and animal performance were quantified using animal linear mixed models. All 15 of the possible scrapie genotypes were detected, although the frequency differed by breed. The frequency of the five PrP haplotypes in the entire population were 0.70 (ARR), 0.15 (ARQ), 0.11 (ARH), 0.02 (AHQ) and 0.01 (VRQ); the most susceptible haplotype (VRQ) was only detected in purebred Texels and crossbreds. No association was detected between the PrP genotype of either the animal or dam and any of the lambing traits (i.e. lambing difficulty score, perinatal mortality and birth weight). With the exception of ultrasound muscle depth, no association between the PrP genotype and any of the lamb performance traits (i.e. lamb BW and carcass) was observed. Lambs carrying the category four PrP genotype (i.e. ARR/VRQ) had 1.20 (SE = 0.45) mm, 1.38 (SE = 0.12) mm, 1.47 (S = 0.25) mm shallower ultrasound muscle depth relative to lambs of the less susceptible scrapie categories of 1, 2, 3, respectively (P 0.05). For ewe mature BW, ARH/VRQ ewes differed from most other ewe PrP genotypes and were, on average, 3.79 (SE = 1.66) kg heavier than ARR/ARR genotype ewes. Lamb dag score differed by dam PrP genotype (P

Monitoring the rate of change in inbreeding and genetic diversity within a population is important to guide breeding programmes. Such interest stems from the impact of loss in genetic diversity on sustainable genetic gain but also the impact on performance (i.e. inbreeding depression). The objective of the present study was to evaluate trends in inbreeding and genetic diversity in 43 066 Belclare, 120 753 Charollais, 22 652 Galway, 78 925 Suffolk, 187 395 Texel, and 19 821 Vendeen purebred sheep. The effective population size for each of the six breeds was between 116.0 (Belclare population) and 314.8 (Charollais population). The Charollais population was the most genetically diverse with the greatest number of effective founders, effective ancestors, and effective founder genomes; conversely, the Belclare was the least genetically diverse population with the fewest number of effective founders, effective ancestors, and effective founder genomes for each of the six breeds investigated. Overall, the effective population sizes and the total genetic diversity within each of the six breeds were above the minimum thresholds generally considered to be required for the long-term viability of a population.

Knowledge of population structure and breed composition of a population can be advantageous for a number of reasons; these include designing optimal (cross)breeding strategies in order to maximise non-additive genetic effects, maintaining flockbook integrity by authenticating animals being registered and as a quality control measure in the genotyping process. The objectives of the present study were to 1) describe the population structure of 24 sheep breeds, 2) quantify the breed composition of both flockbook-recorded and crossbred animals using single nucleotide polymorphism BLUP (SNP-BLUP), and 3) quantify the accuracy of breed composition prediction from low-density genotype panels containing between 2000 and 6000 SNPs. In total, 9334 autosomal SNPs on 11 144 flockbook-recorded animals and 1172 crossbred animals were used. The population structure of all breeds was characterised by principal component analysis (PCA) as well as the pairwise breed fixation index (Fst). The total number of animals, all of which were purebred, included in the calibration population for SNP-BLUP was 2579 with the number of animals per breed ranging from 9 to 500. The remaining 9559 flockbook-recorded animals, composite breeds and crossbred animals represented the test population; three breeds were excluded from breed composition prediction. The breed composition predicted using SNP-BLUP with 9334 SNPs was considered the gold standard prediction. The pairwise breed Fst ranged from 0.040 (between the Irish Blackface and Scottish Blackface) to 0.282 (between the Border Leicester and Suffolk). Principal component analysis revealed that the Suffolk from Ireland and the Suffolk from New Zealand formed distinct, non-overlapping clusters. In contrast, the Texel from Ireland and that from New Zealand formed integrated, overlapping clusters. Composite animals such as the Belclare clustered close to its founder breeds (i.e., Finn, Galway, Lleyn and Texel). When all 9334 SNPs were used to predict breed composition, an animal that had a majority breed proportion predicted to be ≥0.90 was defined as purebred for the present study. As the panel density decreased, the predicted breed proportion threshold, used to identify animals as purebred, also decreased (≥0.85 with 6000 SNPs to ≥0.60 with 2000 SNPs). In all, results from the study suggest that breed composition for purebred and crossbred animals can be determined with SNP-BLUP using ≥5000 SNPs.

The net benefit from investing in any technology is a function of the cost of implementation and the expected return in revenue. The objective of the present study was to quantify, using deterministic equations, the net monetary benefit from investing in genotyping of commercial females. Three case studies were presented reflecting dairy cows, beef cows and ewes based on Irish population parameters; sensitivity analyses were also performed. Parameters considered in the sensitivity analyses included the accuracy of genomic evaluations, replacement rate, proportion of female selection candidates retained as replacements, the cost of genotyping, the sire parentage error rate and the age of the female when it first gave birth. Results were presented as an annualised monetary net benefit over the lifetime of an individual, after discounting for the timing of expressions. In the base scenarios, the net benefit was greatest for dairy, followed by beef and then sheep. The net benefit improved as the reliability of the genomic evaluations improved and, in fact, a negative net benefit of genotyping was less frequent when the reliability of the genomic evaluations was high. The impact of a 10% point increase in genomic reliability was, however, greatest in sheep, followed by beef and then dairy. The net benefit of genotyping female selection candidates reduced as replacement rate increased. As genotyping costs increased, the net benefit reduced irrespective of the percentage of selection candidates kept, the replacement rate or even the population considered. Nonetheless, the association between the genotyping cost and the net benefit of genotyping differed by the percentage of selection candidates kept. Across all replacement rates evaluated, retaining 25% of the selection candidates resulted in the greatest net benefit when genotyping cost was low but the lowest net benefit when genotyping cost was high. Genotyping breakeven cost was non-linearly associated with the percentage of selection candidates retained, reaching a maximum when 50% of selection candidates were retained, irrespective of replacement rate, genomic reliability or the population. The genotyping breakeven cost was also non-linearly associated with replacement rate. The approaches outlined within provide the back-end framework for a decision support tool to quantify the net benefit of genotyping, once parameterised by the relevant population metrics.

Milk mineral concentration is important from both the perspective of processing milk into dairy products and its nutritive value for human consumption. Precise estimates of genetic parameters for milk mineral concentration are lacking because of the considerable resources required to collect vast phenotypes quantities. The milk concentration of calcium (Ca), potassium (K), magnesium (Mg), sodium (Na) and phosphorus (P) in the present study was quantified from mid-IR spectroscopy on 12 223 test-day records from 1717 Holstein-Friesian cows. (Co)variance components were estimated using random regressions to model both the additive genetic and within-lactation permanent environmental variances of each trait. The coefficient of genetic variation averaged across days-in-milk (DIM) was 6.93%, 3.46%, 6.55%, 5.20% and 6.68% for Ca, K, Mg, Na and P concentration, respectively; heritability estimates varied across lactation from 0.31±0.05 (5 DIM) to 0.67±0.04 (181 DIM) for Ca, from 0.18±0.03 (60 DIM) to 0.24±0.05 (305 DIM) for K, from 0.08±0.03 (15 DIM) to 0.37±0.03 (223 DIM) for Mg, from 0.16±0.03 (30 DIM) to 0.37±0.04 (305 DIM) for Na and from 0.21±0.04 (12 DIM) to 0.57±0.04 (211 DIM) for P. Genetic correlations within the same trait across different DIM were almost unity between adjacent DIM but weakened as the time interval between pairwise compared DIM lengthened; genetic correlations were weaker than 0.80 only when comparing both peripheries of the lactation. The analysis of the geometry of the additive genetic covariance matrix revealed that almost 90% of the additive genetic variation was accounted by the intercept term of the covariance functions for each trait. Milk protein concentration and mineral concentration were, in general, positively genetically correlated with each other across DIM, whereas milk fat concentration was positively genetically correlated throughout the entire lactation with Ca, K and Mg; the genetic correlation with fat concentration changed from negative to positive with Na and P at 243 DIM and 50 DIM, respectively. Genetic correlations between somatic cell score and Na ranged from 0.38±0.21 (5 DIM) to 0.79±0.18 (305 DIM). Exploitable genetic variation existed for all milk minerals, although many national breeding objectives are probably contributing to an indirect positive response to selection in milk mineral concentration.

Understanding how critical sow live-weight and back-fat depth during gestation are in ensuring optimum sow productivity is important. The objective of this study was to quantify the association between sow parity, live-weight and back-fat depth during gestation with subsequent sow reproductive performance. Records of 1058 sows and 13 827 piglets from 10 trials on two research farms between the years 2005 and 2015 were analysed. Sows ranged from parity 1 to 6 with the number of sows per parity distributed as follows: 232, 277, 180, 131, 132 and 106, respectively. Variables that were analysed included total born (TB), born alive (BA), piglet birth weight (BtWT), pre-weaning mortality (PWM), piglet wean weight (WnWT), number of piglets weaned (Wn), wean to service interval (WSI), piglets born alive in subsequent farrowing and sow lactation feed intake. Calculated variables included the within-litter CV in birth weight (LtV), pre-weaning growth rate per litter (PWG), total litter gain (TLG), lactation efficiency and litter size reared after cross-fostering. Data were analysed using linear mixed models accounting for covariance among records. Third and fourth parity sows had more (P0.05). Heavier sow live-weight throughout gestation was associated with an increase in PWM (P0.05). In conclusion, this study showed that sow parity, live-weight and back-fat depth can be used as indicators of reproductive performance. In addition, this study also provides validation for future development of a benchmarking tool to monitor and improve the productivity of modern sow herd.

Body condition score (BCS) is a subjective assessment of the proportion of body fat an animal possesses and is independent of frame size. There is a growing awareness of the importance of mature animal live-weight given its contribution to the overall costs of production of a sector. Because of the known relationship between BCS and live-weight, strategies to reduce live-weight could contribute to the favouring of animals with lesser body condition. The objective of the present study was to estimate the average difference in live-weight per incremental change in BCS, measured subjectively on a scale of 1 to 5. The data used consisted of 19 033 BCS and live-weight observations recorded on the same day from 7556 ewes on commercial and research flocks; the breeds represented included purebred Belclare (540 ewes), Charollais (1484 ewes), Suffolk (885 ewes), Texel (1695 ewes), Vendeen (140 ewes), as well as, crossbreds (2812 ewes). All associations were quantified using linear mixed models with the dependent variable of live-weight; ewe parity was included as a random effect. The independent variables were BCS, breed (n=6), stage of the inter-lambing interval (n=6; pregnancy, lambing, pre-weaning, at weaning, post-weaning and mating) and parity (1, 2, 3, 4 and 5+). In addition, two-way interactions were used to investigate whether the association between BCS and live-weight differed by parity, a period of the inter-lambing interval or breed. The association between BCS and live-weight differed by parity, by a period of the inter-lambing interval and by breed. Across all data, a one-unit difference in BCS was associated with 4.82 (SE=0.08) kg live-weight, but this differed by parity from 4.23 kg in parity 1 ewes to 5.82 kg in parity 5+ ewes. The correlation between BCS and live-weight across all data was 0.48 (0.47 when adjusted for nuisance factors in the statistical model), but this varied from 0.48 to 0.53 by parity, from 0.36 to 0.63 by stage of the inter-lambing interval and from 0.41 to 0.62 by breed. Results demonstrate that consideration should be taken of differences in BCS when comparing ewes on live-weight as differences in BCS contribute quite substantially to differences in live-weight; moreover, adjustments for differences in BCS should consider the population stratum, especially breed.

Body condition score (BCS) is a known risk factor for cow health and well-being. Many different BCS scales and systems for assessment exist;while the scales used for assessing BCS vary, differences in how BCS is assessed (i.e., visual versus visual plus tactile) and the extent of training and experience of the assessor (i.e., professional linear classifiers versus producers) also contributes to the underlying variability. Registered dairy cows globally are routinely assessed for linear type traits which describe biological extremes in the morphological attributes; BCS and a correlated trait angularity are within this suite of traits assessed. These linear-type data are used to generate estimates of genetic merit (predicted transmitting ability), but how these estimates manifest themselves as phenotypic differences when assessed by producers on commercial multiparous cows has never been quantified. To evaluate this, 58440 phenotypic BCS records from 48823 lactations in 38608 cows were used. Associations were undertaken using linear mixed models relating phenotypic BCS to genetic merit after accounting for nuisance factors. Differences in genetic merit for either BCS or angularity (assessed visually by professionals on a 1 to 9 scale just once during lactation in primiparous registered cows) translated to phenotypic difference in BCS (assessed by producers using both tactile and visual assessment on a 1 to 5 scale across lactation in commercial dairy cows). The partial correlation between test phenotypic BCS and genetic merit for either BCS or angularity was 0.13 and 0.10, respectively. Based on the model coefficients estimated in the present study, the mean expected difference in phenotypic BCS on a 1 to 5 scale between the top and bottom 10% on genetic merit for BCS or angularity was 0.28 and 0.31 units, respectively. Results from the present study clearly provide confidence that genetic merit for BCS or angularity based on a single visual assessment in primiparous cows is useful to breed for cows of better body condition, irrespective of stage of lactation or parity.

While breeding indexes exist globally to identify candidate parents of the next generation, fewer tools exist that provide guidance on the expected monetary value of young animals. The objective of the present study was therefore to develop the framework for a cattle decision-support tool which incorporates both the genetic and non-genetic information of an animal and, in doing so, better predict the potential market value of an animal, whatever the age. Two novel monetary indexes were constructed and their predictive ability of carcass value was compared to that of the Irish national Terminal breeding index, typical of other terminal indexes used globally. A constructed Harvest index was composed of three carcass-related traits [i.e., 1) carcass weight, 2) carcass conformation and 3) carcass fat, each weighted by their respective economic value] and aimed at purchasers of animals close to harvest; the second index, termed the Calf index, also included docility and feed intake (weighted by their respective economic value), thus targeting purchasers of younger calves for growing (and eventually harvesting). Genetic and non-genetic fixed and random effect model solutions from the Irish national genetic evaluations underpinned all indexes. The two novel indexes were formulated using three alternative estimates of an animal's total merit for comparative purposes: 1) an index based solely on the animal's breed solutions, 2) an index which also included within-breed animal differences, and 3) an index which, as well as considering additive and non-additive genetic effects, also included non-genetic effects (referred to as production values [PVs]). As more information (i.e., within breed effects and subsequently non-genetic effects) was included in the total merit estimate, the correlations strengthened between the two proposed indexes and the animal's calculated carcass market value; the correlation coefficients almost doubled in strength when total merit was based on PV-based estimates as compared to the breed solutions alone. Including phenotypic live-weight data, collected during the animal's life, strengthened the predictive ability of the indexes further. Based on the results presented, the proposed indexes may fill the void in decision support when purchasing or selling cattle. In addition, given the dynamic nature of indexes, they have the potential to be updated in real-time as information becomes available.

Accurate genomic analyses are predicated on access to a large quantity of accurately genotyped and phenotyped animals. Because the cost of genotyping is often less than the cost of phenotyping, interest is increasing in generating genotypes for phenotyped animals. In some instances this may imply the requirement to genotype older animals with greater phenotypic information content. Biological material for these older informative animals may, however, no longer exist. The objective of the present study was to quantify the ability to impute 11 129 single nucleotide polymorphism (SNP) genotypes of non-genotyped animals (in this instance sires) from the genotypes of their progeny with or without including the genotypes of the progenys’ dams (i.e. mates of the sire to be imputed). The impact on the accuracy of genotype imputation by including more progeny (and their dams’) genotypes in the imputation reference population was also quantified. When genotypes of the dams were not available, genotypes of 41 sires with at least 15 genotyped progeny were used for the imputation; when genotypes of the dams were available, genotypes of 21 sires with at least 10 genotyped progeny were used for the imputation. Imputation was undertaken exploiting family and population level information. The mean and variability in the proportion of genotypes per individual that could not be imputed reduced as the number of progeny genotypes used per individual increased. Little improvement in the proportion of genotypes that could not be imputed was achieved once genotypes of seven progeny and their dams were used or genotypes of 11 progeny without their respective dam’s genotypes were used. Mean imputation accuracy per individual (depicted by both concordance rates and correlation between true and imputed) increased with increasing progeny group size. Moreover, the range in mean imputation accuracy per individual reduced as more progeny genotypes were used in the imputation. If the genotype of the mate of the sire was also used, high accuracy of imputation (mean genotype concordance rate per individual of 0.988), with little additional benefit thereafter, was achieved with seven genotyped progeny. In the absence of genotypes on the dam, similar imputation accuracy could not be achieved even using genotypes on up to 15 progeny. Results therefore suggest, at least for the SNP density used in the present study, that it is possible to accurately impute the genotypes of a non-genotyped parent from the genotypes of its progeny and there is a benefit of also including the genotype of the sire’s mate (i.e. dam of the progeny).

The ability to properly assess and accurately phenotype true differences in feed efficiency among dairy cows is key to the development of breeding programs for improving feed efficiency. The variability among individuals in feed efficiency is commonly characterised by the residual intake approach. Residual feed intake is represented by the residuals of a linear regression of intake on the corresponding quantities of the biological functions that consume (or release) energy. However, the residuals include both, model fitting and measurement errors as well as any variability in cow efficiency. The objective of this study was to isolate the individual animal variability in feed efficiency from the residual component. Two separate models were fitted, in one the standard residual energy intake (REI) was calculated as the residual of a multiple linear regression of lactation average net energy intake (NEI) on lactation average milk energy output, average metabolic BW, as well as lactation loss and gain of body condition score. In the other, a linear mixed model was used to simultaneously fit fixed linear regressions and random cow levels on the biological traits and intercept using fortnight repeated measures for the variables. This method split the predicted NEI in two parts: one quantifying the population mean intercept and coefficients, and one quantifying cow-specific deviations in the intercept and coefficients. The cow-specific part of predicted NEI was assumed to isolate true differences in feed efficiency among cows. NEI and associated energy expenditure phenotypes were available for the first 17 fortnights of lactation from 119 Holstein cows; all fed a constant energy-rich diet. Mixed models fitting cow-specific intercept and coefficients to different combinations of the aforementioned energy expenditure traits, calculated on a fortnightly basis, were compared. The variance of REI estimated with the lactation average model represented only 8% of the variance of measured NEI. Among all compared mixed models, the variance of the cow-specific part of predicted NEI represented between 53% and 59% of the variance of REI estimated from the lactation average model or between 4% and 5% of the variance of measured NEI. The remaining 41% to 47% of the variance of REI estimated with the lactation average model may therefore reflect model fitting errors or measurement errors. In conclusion, the use of a mixed model framework with cow-specific random regressions seems to be a promising method to isolate the cow-specific component of REI in dairy cows.

Early detection of karyotype abnormalities, including aneuploidy, could aid producers in identifying animals which, for example, would not be suitable candidate parents. Genome-wide genetic marker data in the form of single nucleotide polymorphisms (SNPs) are now being routinely generated on animals. The objective of the present study was to describe the statistics that could be generated from the allele intensity values from such SNP data to diagnose karyotype abnormalities; of particular interest was whether detection of aneuploidy was possible with both commonly used genotyping platforms in agricultural species, namely the Applied BiosystemsTM AxiomTM and the Illumina platform. The hypothesis was tested using a case study of a set of dizygotic X-chromosome monosomy 53,X sheep twins. Genome-wide SNP data were available from the Illumina platform (11 082 autosomal and 191 X-chromosome SNPs) on 1848 male and 8954 female sheep and available from the AxiomTM platform (11 128 autosomal and 68 X-chromosome SNPs) on 383 female sheep. Genotype allele intensity values, either as their original raw values or transformed to logarithm intensity ratio (LRR), were used to accurately diagnose two dizygotic (i.e. fraternal) twin 53,X sheep, both of which received their single X chromosome from their sire. This is the first reported case of 53,X dizygotic twins in any species. Relative to the X-chromosome SNP genotype mean allele intensity values of normal females, the mean allele intensity value of SNP genotypes on the X chromosome of the two females monosomic for the X chromosome was 7.45 to 12.4 standard deviations less, and were easily detectable using either the AxiomTM or Illumina genotype platform; the next lowest mean allele intensity value of a female was 4.71 or 3.3 standard deviations less than the population mean depending on the platform used. Both 53,X females could also be detected based on the genotype LRR although this was more easily detectable when comparing the mean LRR of the X chromosome of each female to the mean LRR of their respective autosomes. On autopsy, the ovaries of the two sheep were small for their age and evidence of prior ovulation was not appreciated. In both sheep, the density of primordial follicles in the ovarian cortex was lower than normally found in ovine ovaries and primary follicle development was not observed. Mammary gland development was very limited. Results substantiate previous studies in other species that aneuploidy can be readily detected using SNP genotype allele intensity values generally already available, and the approach proposed in the present study was agnostic to genotype platform.

Information on the genetic diversity and population structure of cattle breeds is useful when deciding the most optimal, for example, crossbreeding strategies to improve phenotypic performance by exploiting heterosis. The present study investigated the genetic diversity and population structure of the most prominent dairy and beef breeds used in Ireland. Illumina high-density genotypes (777 962 single nucleotide polymorphisms; SNPs) were available on 4623 purebred bulls from nine breeds; Angus (n=430), Belgian Blue (n=298), Charolais (n=893), Hereford (n=327), Holstein-Friesian (n=1261), Jersey (n=75), Limousin (n=943), Montbéliarde (n=33) and Simmental (n=363). Principal component analysis revealed that Angus, Hereford, and Jersey formed non-overlapping clusters, representing distinct populations. In contrast, overlapping clusters suggested geographical proximity of origin and genetic similarity between Limousin, Simmental and Montbéliarde and to a lesser extent between Holstein, Friesian and Belgian Blue. The observed SNP heterozygosity averaged across all loci was 0.379. The Belgian Blue had the greatest mean observed heterozygosity (HO=0.389) among individuals within breed while the Holstein-Friesian and Jersey populations had the lowest mean heterozygosity (HO=0.370 and 0.376, respectively). The correlation between the genomic-based and pedigree-based inbreeding coefficients was weak (r=0.171; P

Angus and Hereford beef is marketed internationally for apparent superior meat quality attributes; DNA-based breed authenticity could be a useful instrument to ensure consumer confidence on premium meat products. The objective of this study was to develop an ultra-low-density genotype panel to accurately quantify the Angus and Hereford breed proportion in biological samples. Medium-density genotypes (13 306 single nucleotide polymorphisms (SNPs)) were available on 54 703 commercial and 4042 purebred animals. The breed proportion of the commercial animals was generated from the medium-density genotypes and this estimate was regarded as the gold-standard breed composition. Ten genotype panels (100 to 1000 SNPs) were developed from the medium-density genotypes; five methods were used to identify the most informative SNPs and these included the Delta statistic, the fixation (Fst) statistic and an index of both. Breed assignment analyses were undertaken for each breed, panel density and SNP selection method separately with a programme to infer population structure using the entire 13 306 SNP panel (representing the gold-standard measure). Breed assignment was undertaken for all commercial animals (n=54 703), animals deemed to contain some proportion of Angus based on pedigree (n=5740) and animals deemed to contain some proportion of Hereford based on pedigree (n=5187). The predicted breed proportion of all animals from the lower density panels was then compared with the gold-standard breed prediction. Panel density, SNP selection method and breed all had a significant effect on the correlation of predicted and actual breed proportion. Regardless of breed, the Index method of SNP selection numerically (but not significantly) outperformed all other selection methods in accuracy (i.e. correlation and root mean square of prediction) when panel density was ⩾300 SNPs. The correlation between actual and predicted breed proportion increased as panel density increased. Using 300 SNPs (selected using the global index method), the correlation between predicted and actual breed proportion was 0.993 and 0.995 in the Angus and Hereford validation populations, respectively. When SNP panels optimised for breed prediction in one population were used to predict the breed proportion of a separate population, the correlation between predicted and actual breed proportion was 0.034 and 0.044 weaker in the Hereford and Angus populations, respectively (using the 300 SNP panel). It is necessary to include at least 300 to 400 SNPs (per breed) on genotype panels to accurately predict breed proportion from biological samples.

The objective of the present study was to quantify the extent of genetic variation in three health-related traits namely dagginess, lameness and mastitis, in an Irish sheep population. Each of the health traits investigated pose substantial welfare implications as well as considerable economic costs to producers. Data were also available on four body-related traits, namely body condition score (BCS), live weight, muscle depth and fat depth. Animals were categorised as lambs (

As the environments in which livestock are reared become more variable, animal robustness becomes an increasingly valuable attribute. Consequently, there is increasing focus on managing and breeding for it. However, robustness is a difficult phenotype to properly characterise because it is a complex trait composed of multiple components, including dynamic elements such as the rates of response to, and recovery from, environmental perturbations. In this review, the following definition of robustness is used: the ability, in the face of environmental constraints, to carry on doing the various things that the animal needs to do to favour its future ability to reproduce. The different elements of this definition are discussed to provide a clearer understanding of the components of robustness. The implications for quantifying robustness are that there is no single measure of robustness but rather that it is the combination of multiple and interacting component mechanisms whose relative value is context dependent. This context encompasses both the prevailing environment and the prevailing selection pressure. One key issue for measuring robustness is to be clear on the use to which the robustness measurements will employed. If the purpose is to identify biomarkers that may be useful for molecular phenotyping or genotyping, the measurements should focus on the physiological mechanisms underlying robustness. However, if the purpose of measuring robustness is to quantify the extent to which animals can adapt to limiting conditions then the measurements should focus on the life functions, the trade-offs between them and the animal’s capacity to increase resource acquisition. The time-related aspect of robustness also has important implications. Single time-point measurements are of limited value because they do not permit measurement of responses to (and recovery from) environmental perturbations. The exception being single measurements of the accumulated consequence of a good (or bad) adaptive capacity, such as productive longevity and lifetime efficiency. In contrast, repeated measurements over time have a high potential for quantification of the animal’s ability to cope with environmental challenges. Thus, we should be able to quantify differences in adaptive capacity from the data that are increasingly becoming available with the deployment of automated monitoring technology on farm. The challenge for future management and breeding will be how to combine various proxy measures to obtain reliable estimates of robustness components in large populations. A key aspect for achieving this is to define phenotypes from consideration of their biological properties and not just from available measures.

The objective of this study was to establish the risk factors associated with both lambing difficulty and lamb mortality in the Irish sheep multibreed population. A total of 135 470 lambing events from 42 675 ewes in 839 Irish crossbred and purebred flocks were available. Risk factors associated with producer-scored ewe lambing difficulty score (scale of one (no difficulty) to four (severe difficulty)) were determined using linear mixed models. Risk factors associated with the logit of the probability of lamb mortality at birth (i.e. binary trait) were determined using generalised estimating equations. For each dependent variable, a series of simple regression models were developed as well as a multiple regression model. In the simple regression models, greater lambing difficulty was associated with quadruplet bearing, younger ewes, of terminal breed origin, lambing in February; for example, first parity ewes experienced greater (P7.0 kg) birth weights, quadruplet born lambs and lambs that experienced a more difficult lambing (predicted probability of death for lambs that required severe and veterinary assistance of 0.15 and 0.32, respectively); lambs from dual-purpose breeds and born to younger ewes were also at greater risk of mortality. In the multiple regression model, the association between ewe parity, age at first lambing, year of lambing and lamb mortality no longer persisted. The trend in solutions of the levels of each fixed effect that remained associated with lamb mortality in the multiple regression model, did not differ from the trends observed in the simple regression models although the differential in relative risk between the different lambing difficulty scores was greater in the multiple regression model. Results from this study show that many common flock- and animal-level factors are associated with both lambing difficulty and lamb mortality and management of different risk category groups (e.g. scanned litter sizes, ewe age groups) can be used to appropriately manage the flock at lambing to reduce their incidence.

The relative weighting on traits within breeding goals are generally determined by bio-economic models or profit functions. While such methods have generally delivered profitability gains to producers, and are being expanded to consider non-market values, current approaches generally do not consider the numerous and diverse stakeholders that affect, or are affected, by such tools. Based on principles of respondent anonymity, iteration, controlled feedback and statistical aggregation of feedback, a Delphi study was undertaken to gauge stakeholder opinion of the importance of detailed milk quality traits within an overall dairy breeding goal for profit, with the aim of assessing its suitability as a complementary, participatory approach to defining breeding goals. The questionnaires used over two survey rounds asked stakeholders: (a) their opinion on incorporating an explicit sub-index for milk quality into a national breeding goal; (b) the importance they would assign to a pre-determined list of milk quality traits and (c) the (relative) weighting they would give such a milk quality sub-index. Results from the survey highlighted a good degree of consensus among stakeholders on the issues raised. Similarly, revelation of the underlying assumptions and knowledge used by stakeholders to make their judgements illustrated their ability to consider a range of perspectives when evaluating traits, and to reconsider their answers based on the responses and rationales given by others, which demonstrated social learning. Finally, while the relative importance assigned by stakeholders in the Delphi survey (4% to 10%) and the results of calculations based on selection index theory of the relative emphasis that should be placed on milk quality to halt any deterioration (16%) are broadly in line, the difference indicates the benefit of considering more than one approach to determining breeding goals. This study thus illustrates the role of the Delphi technique, as a complementary approach to traditional approaches, to defining breeding goals. This has implications for how breeding goals will be defined and in determining who should be involved in the decision-making process.

The increased demand for animal-derived protein and energy for human consumption will have to be achieved through a combination of improved animal genetic merit and better management strategies. The objective of the present study was to quantify whether differences in genetic merit among animals materialised into phenotypic differences in commercial herds. Carcass phenotypes on 156 864 animals from 7301 finishing herds were used, which included carcass weight (kg), carcass conformation score (scale 1 to 15), carcass fat score (scale 1 to 15) at slaughter as well as carcass price. The price per kilogram and the total carcass value that the producer received for the animal at slaughter was also used. A terminal index, calculated in the national genetic evaluations, was obtained for each animal. The index was based on pedigree index for calving performance, feed intake and carcass traits from the national genetic evaluations. Animals were categorised into four terminal index groups on the basis of genetic merit estimates that were derived before the expression of the phenotypic information by the validation animals. The association between terminal index and phenotypic performance at slaughter was undertaken using mixed models; whether the association differed by gender (i.e. young bulls, steers and heifers) or by early life experiences (animals born in a dairy herd or beef herd) was also investigated. The regression coefficient of phenotypic carcass weight, carcass conformation and carcass fat on their respective estimated breeding values (EBVs) was 0.92 kg, 1.08 units and 0.79 units, respectively, which is close to the expectation of one. Relative to animals in the lowest genetic merit group, animals in the highest genetic merit group had, on average, a 38.7 kg heavier carcass, with 2.21 units greater carcass conformation, and 0.82 units less fat. The superior genetic merit animals were, on average, slaughtered 6 days younger than their inferior genetic merit contemporaries. The superior carcass characteristics of the genetically elite animals materialised in carcasses worth €187 more than those of the lowest genetic merit animals. Although the phenotypic difference in carcass traits of animals divergent in terminal index differed statistically by animal gender and early life experience, the detected interactions were generally biologically small. This study clearly indicates that selection on an appropriate terminal index will produce higher performing animals and this was consistent across all production systems investigated.

Healthy calves are fundamental to any profitable dairy enterprise. Research to-date, has focused on year-round calving systems which experience many different challenges compared to spring-calving systems. The objective of the present study was to determine the on-farm dry cow, calving, and colostrum management practices of spring-calving dairy production systems, and quantify their associations with herd size and herd expansion status (i.e. expanding or not expanding). Information on these management practices was available from a survey of 262 Irish spring-calving dairy farmers, representative of the Irish national population. Herd expansion in the 2 years before, and the year that the survey was conducted was not associated with any of the management practices investigated. Fifty-three percent of respondents had an average calving season length of 10 to14 weeks with 35% of herds having a longer calving season. Previous research in cattle has documented that both colostrum source and feeding management are associated with the transmission of infectious disease from cow to calf. In the present study 60% of respondents fed calves colostrum from their own dam; however, 66% of those respondents allowed the calf to suckle the dam, 23% of survey respondents fed calves pooled colostrum. Larger herds were more likely (P

Although food from grazed animals is increasingly sought by consumers because of perceived animal welfare advantages, grazing systems provide the farmer and the animal with unique challenges. The system is dependent almost daily on the climate for feed supply, with the importation of large amounts of feed from off farm, and associated labour and mechanisation costs, sometimes reducing economic viability. Furthermore, the cow may have to walk long distances and be able to harvest feed efficiently in a highly competitive environment because of the need for high levels of pasture utilisation. She must, also, be: (1) highly fertile, with a requirement for pregnancy within ~80 days post-calving; (2) ‘easy care’, because of the need for the management of large herds with limited labour; (3) able to walk long distances; and (4) robust to changes in feed supply and quality, so that short-term nutritional insults do not unduly influence her production and reproduction cycles. These are very different and are in addition to demands placed on cows in housed systems offered pre-made mixed rations. Furthermore, additional demands in environmental sustainability and animal welfare, in conjunction with the need for greater system-level biological efficiency (i.e. ‘sustainable intensification’), will add to the ‘robustness’ requirements of cows in the future. Increasingly, there is evidence that certain genotypes of cows perform better or worse in grazing systems, indicating a genotype×environment interaction. This has led to the development of tailored breeding objectives within countries for important heritable traits to maximise the profitability and sustainability of their production system. To date, these breeding objectives have focussed on the more easily measured traits and those of highest relative economic importance. In the future, there will be greater emphasis on more difficult to measure traits that are important to the quality of life of the animal in each production system and to reduce the system’s environmental footprint.

The aim of the present study was to estimate genetic parameters for calcium (Ca), phosphorus (P) and titratable acidity (TA) in bovine milk predicted by mid-IR spectroscopy (MIRS). Data consisted of 2458 Italian Holstein−Friesian cows sampled once in 220 farms. Information per sample on protein and fat percentage, pH and somatic cell count, as well as test-day milk yield, was also available. (Co)variance components were estimated using univariate and bivariate animal linear mixed models. Fixed effects considered in the analyses were herd of sampling, parity, lactation stage and a two-way interaction between parity and lactation stage; an additive genetic and residual term were included in the models as random effects. Estimates of heritability for Ca, P and TA were 0.10, 0.12 and 0.26, respectively. Positive moderate to strong phenotypic correlations (0.33 to 0.82) existed between Ca, P and TA, whereas phenotypic weak to moderate correlations (0.00 to 0.45) existed between these traits with both milk quality and yield. Moderate to strong genetic correlations (0.28 to 0.92) existed between Ca, P and TA, and between these predicted traits with both fat and protein percentage (0.35 to 0.91). The existence of heritable genetic variation for Ca, P and TA, coupled with the potential to predict these components for routine cow milk testing, imply that genetic gain in these traits is indeed possible.

A survey of management practices in 309 Irish dairy herds was used to identify risk factors for the presence of antibodies to Salmonella, Neospora caninum and Leptospira interrogans serovar hardjo in extensively managed unvaccinated dairy herds. A previous study documented a herd-level seroprevalence in bulk milk of 49%, 19% and 86% for Salmonella, Neospora caninum and leptospira interrogans serovar hardjo, respectively in the unvaccinated proportion of these 309 herds in 2009. Association analyses in the present study were carried out using multiple logistic regression models. Herds where cattle were purchased or introduced had a greater likelihood of being positive to leptospira interrogans serovar hardjo (P

Excellent reproductive performance in both males and females is fundamental to profitable dairy and beef production systems. In this review we undertook a meta-analysis of genetic parameters for female reproductive performance across 55 dairy studies or populations and 12 beef studies or populations as well as across 28 different studies or populations for male reproductive performance. A plethora of reproductive phenotypes exist in dairy and beef cattle and a meta-analysis of the literature suggests that most of the female reproductive traits in dairy and beef cattle tend to be lowly heritable (0.02 to 0.04). Reproductive-related phenotypes in male animals (e.g. semen quality) tend to be more heritable than female reproductive phenotypes with mean heritability estimates of between 0.05 and 0.22 for semen-related traits with the exception of scrotal circumference (0.42) and field non-return rate (0.001). The low heritability of reproductive traits, in females in particular, does not however imply that genetic selection cannot alter phenotypic performance as evidenced by the decline until recently in dairy cow reproductive performance attributable in part to aggressive selection for increased milk production. Moreover, the antagonistic genetic correlations among reproductive traits and both milk (dairy cattle) and meat (beef cattle) yield is not unity thereby implying that simultaneous genetic selection for both increased (milk and meat) yield and reproductive performance is indeed possible. The required emphasis on reproductive traits within a breeding goal to halt deterioration will vary based on the underlying assumptions and is discussed using examples for Ireland, the United Kingdom and Australia as well as quantifying the impact on genetic gain for milk production. Advancements in genomic technologies can aid in increasing the accuracy of selection for especially reproductive traits and thus genetic gain. Elucidation of the underlying genomic mechanisms for reproduction could also aid in resolving genetic antagonisms. Past breeding programmes have contributed to the deterioration in reproductive performance of dairy and beef cattle. The tools now exist, however, to reverse the genetic trends in reproductive performance underlying the observed phenotypic trends.

The objective of this study was to quantify the accuracy of imputing the genotype of parents using information on the genotype of their progeny and a family-based and population-based imputation algorithm. Two separate data sets were used, one containing both dairy and beef animals (n=3122) with high-density genotypes (735 151 single nucleotide polymorphisms (SNPs)) and the other containing just dairy animals (n=5489) with medium-density genotypes (51 602 SNPs). Imputation accuracy of three different genotype density panels were evaluated representing low (i.e. 6501 SNPs), medium and high density. The full genotypes of sires with genotyped half-sib progeny were masked and subsequently imputed. Genotyped half-sib progeny group sizes were altered from 4 up to 12 and the impact on imputation accuracy was quantified. Up to 157 and 258 sires were used to test the accuracy of imputation in the dairy plus beef data set and the dairy-only data set, respectively. The efficiency and accuracy of imputation was quantified as the proportion of genotypes that could not be imputed, and as both the genotype concordance rate and allele concordance rate. The median proportion of genotypes per animal that could not be imputed in the imputation process decreased as the number of genotyped half-sib progeny increased; values for the medium-density panel ranged from a median of 0.015 with a half-sib progeny group size of 4 to a median of 0.0014 to 0.0015 with a half-sib progeny group size of 8. The accuracy of imputation across different paternal half-sib progeny group sizes was similar in both data sets. Concordance rates increased considerably as the number of genotyped half-sib progeny increased from four (mean animal allele concordance rate of 0.94 in both data sets for the medium-density genotype panel) to five (mean animal allele concordance rate of 0.96 in both data sets for the medium-density genotype panel) after which it was relatively stable up to a half-sib progeny group size of eight. In the data set with dairy-only animals, sufficient sires with paternal half-sib progeny groups up to 12 were available and the within-animal mean genotype concordance rates continued to increase up to this group size. The accuracy of imputation was worst for the low-density genotypes, especially with smaller half-sib progeny group sizes but the difference in imputation accuracy between density panels diminished as progeny group size increased; the difference between high and medium-density genotype panels was relatively small across all half-sib progeny group sizes. Where biological material or genotypes are not available on individual animals, at least five progeny can be genotyped (on either a medium or high-density genotyping platform) and the parental alleles imputed with, on average, ⩾96% accuracy.

Dystocia and perinatal mortality are quantitative traits that significantly impact animal productivity and welfare. Their economic importance is reflected by their inclusion in the national breeding goals of many cattle populations. The genetic architecture that influences these traits, however, has still yet to be thoroughly defined. Regions of the bovine genome associated with calving difficulty (direct and maternal) and perinatal mortality were detected in this study using a Bayesian approach with 43 204 single nucleotide polymorphisms (SNPs) on up to 1970 Holstein–Friesian bulls. Several SNPs on chromosomes 5, 6, 11, 12, 17,18 and 28 were detected to be strongly associated with these calving performance traits. Novel genomic regions with previously reported associations with growth, stature, birth weight and bone morphology were identified in the present study as being associated with the three calving performance traits. Morphological abnormalities are a known contributor to perinatal mortality and the most significantly associated SNP for perinatal mortality in the present study was located in a region in linkage disequilibrium with the gene SLC26A7. This gene, SLC26A7, has similarities and colocalises with SLC4A2, which has previously been associated with osteoporosis and mortality in cattle populations. The HHIP gene that is known to be associated with stature in humans was strongly associated with direct calving difficulty in the present study; large calves are known to, on average, have a greater likelihood of dystocia. A stemloop microRNA, bta-mir-1256, on chromosome 12, involved in post-transcriptional regulation of gene expression was associated with maternal calving difficulty. Previously reported quantitative trait loci associated with calving performance traits in other populations were again identified in this study; with one genomic region on chromosome 18 supporting very strong evidence of an underlying causative mutation and accounting for 2.1% of the genetic variation in direct calving difficulty. Overlapping genomic regions associated with one or more of the calving traits were also detected substantiating the known genetic covariances existing between these traits. Moreover, some genomic regions were only associated with one of the calving traits implying the selective genomic breeding programs exploiting these regions could help resolve genetic antagonisms.