Prediction of genetic merit for live weight and body condition score in dairy cows using routinely available linear type and carcass data.

Accurate estimates of genetic merit for both live weight and body condition score (BCS) could be useful additions to both national- and herd-breeding programs. Although recording live weight and BCS is not technologically arduous, data available for use in routine genetic evaluations are generally lacking. The objective of the present study was to explore the usefulness of routinely recorded data, namely linear type traits (which also included BCS but only assessed visually) and carcass traits in the pursuit of genetic evaluations for both live weight and BCS in dairy cows. The data consisted of on-farm records of live weight and BCS (assessed using both visual and tactile cues) from 33,242 dairy cows in 201 commercial Irish herds. These data were complemented with information on 6 body-related linear type traits (i.e., stature, angularity, chest width, body depth, BCS, and rump width) and 3 cull cow carcass measures (i.e., carcass weight, conformation, and fat cover) on a selection of these animals plus close relatives. (Co)variance components were estimated using animal linear mixed models. The genetic correlation between the type traits stature, angularity, body depth, chest width, rump width, and visually-assessed BCS with live weight was 0.68, -0.28, 0.43, 0.64, 0.61, and 0.44, respectively. The genetic correlation between angularity and BCS measured on farm (based on both visual and tactile appraisal) was -0.79; the genetic and phenotypic correlation between BCS assessed visually as part of the linear assessment with BCS assessed by producers using both tactile and visual cues was 0.90 and 0.27, respectively. The genetic (phenotypic) correlation between cull cow carcass weight and live weight was 0.81 (0.21), and the genetic (phenotypic) correlation between cull cow carcass fat cover and BCS assessed on live cows was 0.44 (0.12). Estimated breeding values (EBV) for live weight and BCS in a validation population of cows were generated using a multitrait evaluation with observations for just the type traits, just the carcass traits, and both the type traits and carcass traits; the EBV were compared with the respective live weight and BCS phenotypic observations. The regression of phenotypic live weight on its EBV from the multitrait evaluations was 1.00 (i.e., the expectation) when the EBV was generated using just linear type trait data, but less than 1 (0.83) when using just carcass data. However, the regression changed across parities and stages of lactation. The partial correlation (after adjusting for contemporary group, parity by stage of lactation, heterosis, and recombination loss) between phenotypic live weight and EBV for live weight estimated using the 3 different scenarios (i.e., type only, carcass only, type plus carcass) ranged from 0.38 to 0.43. Although the prediction of phenotypic BCS from its respective EBV was relatively good when using just the linear type trait data (regression coefficient of 0.83 with a partial correlation of 0.22), the predictive ability of BCS EBV based on just carcass data was poor and should not be used. Overall, linear type trait data are a useful source of information to predict live weight and BCS with minimal additional predictive value from also including carcass data. Nonetheless, in the absence of linear type trait data, information on carcass traits can be useful in predicting genetic merit for mature cow live weight. Prediction of cow BCS from cow carcass data is not recommended.

How herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic merit.

Sustainable dairy cow performance relies on coevolution in the development of breeding and management strategies. Tailoring breeding programs to herd performance metrics facilitates improved responses to breeding decisions. Although herd-level raw metrics on performance are useful, implicitly included within such statistics is the mean herd genetic merit. The objective of the present study was to quantify the expected response from selection decisions on additive and nonadditive merit by herd performance metrics independent of herd mean genetic merit. Performance traits considered in the present study were age at first calving, milk yield, calving to first service, number of services, calving interval, and survival. Herd-level best linear unbiased estimates (BLUE) for each performance trait were available on a maximum of 1,059 herds, stratified as best, average, and worst for each performance trait separately. The analyses performed included (1) the estimation of (co)variance for each trait in the 3 BLUE environments and (2) the regression of cow-level phenotypic performance on either the respective estimated breeding value (EBV) or the heterosis coefficient of the cow. A fundamental assumption of genetic evaluations is that 1 unit change in EBV equates to a 1 unit change in the respective phenotype; results from the present study, however, suggest that the realization of the change in phenotypic performance is largely dependent on the herd BLUE for that trait. Herds achieving more yield, on average, than expected from their mean genetic merit, had a 20% greater response to changes in EBV as well as 43% greater genetic standard deviation relative to herds within the worst BLUE for milk yield. Conversely, phenotypic performance in fertility traits (with the exception of calving to first service) tended to have a greater response to selection as well as a greater additive genetic standard deviation within the respective worst herd BLUE environments; this is suggested to be due to animals performing under more challenging environments leading to larger achievable gains. The attempts to exploit nonadditive genetic effects such as heterosis are often the basis of promoting cross-breeding, yet the results from the present study suggest that improvements in phenotypic performance is largely dependent on the environment. The largest gains due to heterotic effects tended to be within the most stressful (i.e., worst) BLUE environment for all traits, thus suggesting the heterosis effects can be beneficial in mitigating against poorer environments.

Characterization of best linear unbiased estimates generated from national genetic evaluations of reproductive performance, survival, and milk yield in dairy cows.

Genetic evaluations decompose an observed phenotype into its genetic and nongenetic components; the former are termed BLUP with the solutions for the systematic environmental effects in the statistical model termed best linear unbiased estimates (BLUE). Geneticists predominantly focus on the BLUP and rarely consider the BLUE. The objective of this study, however, was to define and quantify the association between 8 herd-level characteristics and BLUE for 6 traits in dairy herds, namely (1) age at first calving, (2) calving to first service interval (CFS), (3) number of services, (4) calving interval (CIV), (5) survival, and (6) milk yield. Phenotypic data along with the fixed and random effects solutions were generated from the Irish national multi-breed dairy cow fertility genetic evaluations on 3,445,557 cows; BLUE for individual contemporary groups were collapsed into mean herd-year estimates. Data from 5,707 spring-calving herds between the years 2007 and 2016 inclusive were retained; association analyses were undertaken using linear mixed multiple regression models. Pearson coefficient correlations were used to quantify the relationships among individual trait herd-year BLUE, and transition matrices were used to understand the dynamics of mean herd BLUE estimates over years. Based on the mean annual trends in raw, BLUP, and BLUE, it was estimated that BLUE were associated with at least two-thirds of the improvement in CIV and milk production over the past 10 yr. Milk recording herds calved heifers for the first time on average 15 d younger, had an almost 2 d longer CFS but 2.3 d shorter CIV than non-milk-recording herds. Larger herd sizes were associated with worse BLUE for both CFS and CIV. Expanding herds and herds that had the highest proportion of cows born on the farm itself, on average, calved heifers younger and had shorter CIV. By separating the raw performance of a selection of herds into their respective BLUE and BLUP, it was possible to identify herds with inferior management practices that were being compensated by superior genetics; similarly, herds were identified with superior BLUE, but because of their inferior genetic merit, were not reaching their full potential. This suggests that BLUE could have a pivotal role in a tailored decision support tool that would enable producers to focus on the most limiting factor hindering them from achieving their maximum performance.

Genetic selection for hoof health traits and cow mobility scores can accelerate the rate of genetic gain in producer-scored lameness in dairy cows.

Cattle breeding programs that strive to reduce the animal-level incidence of lameness are often hindered by the availability of informative phenotypes. As a result, indicator traits of lameness (i.e., hoof health and morphological conformation scores) can be used to improve the accuracy of selection and subsequent genetic gain. Therefore, the objectives of the present study were to estimate the variance components for hoof health traits using various phenotypes collected from a representative sample of Irish dairy cows. Also of interest to the present study was the genetic relationship between both hoof health traits and conformation traits with producer-scored lameness. Producer-recorded lameness events and linear conformation scores from 307,657 and 117,859 Irish dairy cows, respectively, were used. Data on hoof health (i.e., overgrown sole, white line disease, and sole hemorrhage), mobility scores, and body condition scores were also available from a research study on up to 11,282 Irish commercial dairy cows. Linear mixed models were used to quantify variance components for each trait and to estimate genetic correlations among traits. The estimated genetic parameters for hoof health traits in the present study were greater (i.e., heritability range: 0.005 to 0.27) than previously reported in dairy cows. With the exception of analyses that considered hoof health traits in repeatability models, little difference in estimated variance components existed among the various hoof-health phenotypes. Results also suggest that producer-recorded lameness is correlated with both hoof health (i.e., genetic correlation up to 0.48) and cow mobility (i.e., genetic correlation = 0.64). Moreover, cows that genetically tend to have rear feet that appear more parallel when viewed from the rear are also genetically more predisposed to lameness (genetic correlation = 0.39); genetic correlations between lameness and other feet and leg type traits, as well as between lameness and frame type traits, were not different from zero. Results suggest that if the population breeding goal was to reduce lameness incidence, improve hoof health, or improve cow mobility, genetic selection for either of these traits should indirectly benefit the other traits. Results were used to quantify the genetic gains achievable for lameness when alternative phenotypes are available.

Genetic variability in the humoral immune response to bovine herpesvirus-1 infection in dairy cattle and genetic correlations with performance traits.

Bovine herpesvirus-1 (BoHV-1) is a viral pathogen of global significance that is known to instigate several diseases in cattle, the most notable of which include infectious bovine rhinotracheitis and bovine respiratory disease. The genetic variability in the humoral immune response to BoHV-1 has, to our knowledge, not ever been quantified. Therefore, the objectives of the present study were to estimate the genetic parameters for the humoral immune response to BoHV-1 in Irish female dairy cattle, as well as to investigate the genetic relationship between the humoral immune response to BoHV-1 with milk production performance, fertility performance, and animal mortality. Information on antibody response to BoHV-1 was available to the present study from 2 BoHV-1 sero-prevalence research studies conducted between the years 2010 to 2015, inclusive; after edits, BoHV-1 antibody test results were available on a total of 7,501 female cattle from 58 dairy herds. National records of milk production (i.e., 305-d milk yield, fat yield, protein yield, and somatic cell score; n = 1,211,905 milk-recorded cows), fertility performance (i.e., calving performance, pregnancy diagnosis, and insemination data; n = 2,365,657 cows) together with animal mortality data (i.e., birth, farm movement, death, slaughter, and export events; n = 12,853,257 animals) were also available. Animal linear mixed models were used to quantify variance components for BoHV-1 as well as to estimate genetic correlations among traits. The estimated genetic parameters for the humoral immune response to BoHV-1 in the present study (i.e., heritability range: 0.09 to 0.16) were similar to estimates previously reported for clinical signs of bovine respiratory disease in dairy and beef cattle (i.e., heritability range: 0.05 to 0.11). Results from the present study suggest that breeding for resistance to BoHV-1 infection could reduce the incidence of respiratory disease in cattle while having little or no effect on genetic selection for milk yield or milk constituents (i.e., genetic correlations ranged from -0.13 to 0.17). Moreover, even though standard errors were large, results also suggest that breeding for resistance to BoHV-1 infection may indirectly improve fertility performance while also reducing the incidence of mortality in older animals (i.e., animals >182 d of age). Results can be used to inform breeding programs of potential genetic gains achievable for resistance to BoHV-1 infection in cattle.

Development of an index to rank dairy females on expected lifetime profit.

The objective of this study was to develop an index to rank dairy females on expected profit for the remainder of their lifetime, taking cognizance of both additive and nonadditive genetic merit, permanent environmental effects, and current states of the animal including the most recent calving date and cow parity. The cow own worth (COW) index is intended to be used for culling the expected least profitable females in a herd, as well as inform purchase and pricing decisions for trading of females. The framework of the COW index consisted of the profit accruing from (1) the current lactation, (2) future lactations, and (3) net replacement cost differential. The COW index was generated from estimated performance values (sum of additive genetic merit, nonadditive genetic merit, and permanent environmental effects) of traits, their respective net margin values, and transition probability matrices for month of calving, survival, and somatic cell count; the transition matrices were to account for predicted change in a cow's state in the future. Transition matrices were generated from 3,156,109 lactation records from the Irish national database between the years 2010 and 2013. Phenotypic performance records for 162,981 cows in the year 2012 were used to validate the COW index. Genetic and permanent environmental effects (where applicable) were available for these cows from the 2011 national genetic evaluations and used to calculate the COW index and their national breeding index values (includes only additive genetic effects). Cows were stratified per quartile within herd, based on their COW index value and national breeding index value. The correlation between individual animal COW index value and national breeding index value was 0.65. Month of calving of the cow in her current lactation explained 18% of the variation in the COW index, with the parity of the cow explaining an additional 3 percentage units of the variance in the COW index. Females ranking higher on the COW index yielded more milk and milk solids and calved earlier in the calving season than their lower ranking contemporaries. The difference in phenotypic performance between the best and worst quartiles was larger for cows ranked on COW index than cows ranked on the national breeding index. The COW index is useful to rank females before culling or purchasing decisions on expected profit and is complementary to the national breeding index, which identifies the most suitable females for breeding replacements.

Twenty four-hour helpline access to expert management advice for food-allergy-triggered anaphylaxis in infants, children and young people: a pragmatic, randomized controlled trial.

BACKGROUND: Anaphylaxis is a life-threatening emergency. If promptly administered, adrenaline is potentially life-saving. Many food-allergic-children/carers are unsure when to use their adrenaline autoinjectors, contributing to a low quality of life and worse outcomes in the setting of an acute allergic reaction. OBJECTIVES: The aim of this study was to assess the effectiveness of 24-hour telephone access to specialist clinical advice on disease-specific quality of life. METHODS: A pragmatic two-arm, parallel-group randomized control trial was conducted. Children/carers (<16 years) with food allergy, trained in adrenaline auto-injector use, were recruited from a hospital-based paediatric allergy clinic. Baseline disease-specific quality of life was ascertained using the validated Food-Allergy-Related Quality-of-Life Questionnaire (FAQLQ), either Parent Form, Child Form or Teenager Form depending on child's age. Participants were then centrally randomized for a 6-month period to 24-hour telephone specialist support line or to usual care. The primary outcome measure was a change in FAQL scores, at one and 6 months postrandomization, compared with baseline. The minimum clinically important difference (MCID) in score is 0.5. RESULTS: Fifty two children/carers were recruited. FAQL scores remained static in the control group across the three time points. Scores gradually improved in the intervention group, with a significant difference seen at 6 months (T1-T3 Mean difference = -1.5, (CI 0.87-2.25) P < 0.005] Follow-up questionnaires, 6 months after the intervention was removed, T4, showed sustained significant difference between the groups (control M = 3.0; intervention M = 1.1[t = -4.113, P < 0.05]). CONCLUSION: The 24-hour helpline improved food-allergy-specific quality of life in children. Six-month intervention support resulted in sustained benefits for at least a further 6 months.

Short communication: Differences in genetic merit for visually-assessed body condition score materialises as phenotypic differences in tactile-based body condition score in commercial dairy cows.

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.

Predicting male dairy calf live weight for use in calf management decision support.

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.

Formulation of a decision support tool incorporating both genetic and non-genetic effects to rank young growing cattle on expected market value.

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.

Risk factors associated with animal mortality in pasture-based, seasonal-calving dairy and beef herds.

Animal mortality is indicative of animal health and welfare standards, which are of growing concern to the agricultural industry. The objective of the present study was to ascertain risk factors associated with mortality at multiple life stages in pasture-based, seasonal-calving dairy and beef herds. Males and females were stratified into seven life stages based on age (0 to 2 d, 3 to 7 d, 8 to 30 d, 31 to 182 d, 183 to 365 d, 366 to 730 d, and 731 to 1,095 d) whereas females with ≥1 calving event were further stratified into five life stages based on cow parity number (1, 2, 3, 4, and 5). Mortality was defined as whether an animal died during each life stage; only animals that either survived the entire duration or died during a life stage were considered. The data, following edits, consisted of 4,404,122 records from 1,358,712 animals. Multivariable logistic regression was used to estimate the logit of the probability of mortality in each life stage separately. The odds of a young animal (i.e., aged ≤ 1,095 d) dying was generally greater if veterinary assistance was required at their birth relative to no assistance (odds ratio [OR]: 3.10 to 31.85), if the animal was a twin relative to a singleton (OR: 1.46 to 2.31) or if the animal was male relative to female (OR: 1.14 to 6.15). Moreover, the odds of a cow (i.e., females with ≥1 calving event) dying were greater when she required veterinary assistance at calving (OR: 2.69 to 7.55) compared with a cow that did not require any assistance, if she produced twin relative to singleton progeny (OR: 1.59 to 2.03) or male relative to female progeny (OR: 1.09 to 1.20). Additionally, the odds of a first or second parity cow dying when she herself had received veterinary assistance at birth were only 0.63 to 0.66 times that of a cow that was provided no assistance at birth. For both young animals and cows, the odds of dying generally increased with herd size, whereas animals residing in expanding herds had lower odds of dying. Results from the present study indicate that the risk factors associated with mortality in pasture-based, seasonal-calving herds are similar to those reported in literature in confinement, nonseasonal-calving herds. Moreover, the present study identifies that these risk factors are similar in both dairy and beef herds, yet the magnitude of the association often differs and also changes with life stage.

Inference of population structure of purebred dairy and beef cattle using high-density genotype data.

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<0.001). Mean genomic inbreeding estimates were greatest for Jersey (0.173) and least for Hereford (0.051). The pair-wise breed fixation index (F st) ranged from 0.049 (Limousin and Charolais) to 0.165 (Hereford and Jersey). In conclusion, substantial genetic variation exists among breeds commercially used in Ireland. Thus custom-mating strategies would be successful in maximising the exploitation of heterosis in crossbreeding strategies.

Ultra-low-density genotype panels for breed assignment of Angus and Hereford cattle.

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 (F st) 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.