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The objective of this study was to investigate genetic variation and genotype by environment (G × E) interactions for fertility (including age at first calving and calving interval), somatic cell score (SCS), and milk production traits for Iranian Holsteins. Different environments were defined based on the climatic zones (cold, semi-cold, and moderate) and considering the herd location. Data were collected between 2003 and 2018 by the National Animal Breeding Center of Iran (Karaj). Variance and covariance components and genetic correlations were estimated using 2 different models, which were analyzed using Bayesian methods. For both models, performance of traits in each climate were considered as different traits. Fertility traits were analyzed using a trivariate model. Furthermore, SCS and production traits were analyzed using trivariate random regression models (records in different climate zones considered as different traits). For the fertility traits, the largest estimates of heritability were observed in cold climate. Fertility performance was always better in cold environment. Genetic correlations between climatic zones ranged from 0.85 to 0.94. For daily measurements of SCS and production traits, heritability ranged from 0.031 to 0.037 and 0.069 to 0.209, respectively. Genetic variances were the highest in the semi-cold and moderate climates for the SCS and production traits, respectively. Furthermore, across the studied climates, 305-d genetic correlation ranged from 0.756 to 0.884 for SCS and from 0.925 to 0.957 for the production traits. The structure of genetic correlation within each climate indicated a negative correlation between early and late lactation for SCS, especially in the cold climate and for milk production in the moderate climate. For fat percentage, in all climatic zones, the lowest genetic correlations were observed between early and mid-lactation. In addition, for protein production in the cold climate, a negative correlation was observed between early and late lactation. Results indicated heterogeneous variance components for all the studied traits across various climatic zones. Estimated genetic correlations for SCS revealed that the genetic expression of animals may vary by climatic zone. Results indicated the existence of G × E interaction due to the climatic condition, only for SCS. Therefore, in Iranian Holsteins, the effect of G × E interactions should not be neglected, especially for SCS, as different sires might be optimal for use in different climatic zones.

The aim of this study was to increase knowledge on the genetic background of noise reactivity in dogs, with a study on heritability based on owner-collected data on standard poodles’ degree of fear of sudden loud noises and fireworks. Previous studies have shown significant breed differences, and we expected to find significant heritability for noise reactivity. 1148 standard poodles are included in the study. The estimated heritabilities are between 0.09 and 0.16. Reaction to sudden loud noises and fireworks are genetically highly correlated (rg > 0.95).

Breeding programs using genomic information have become common in broilers. In broilers, the main traits for selection are body weight and feed efficiency. These traits are measured in both sexes and before sexual maturity. Thus, increases in genetic gain in broiler breeding programs due to the use of genomic information are attributable primarily to the increased accuracy of predicted breeding values of potential parents. As not all birds can be genotyped due to economic constraints, an optimal genotyping strategy needs to be chosen. The objective of this study was to investigate the rates of genetic gain and inbreeding obtained by varying three factors: the percentage of birds genotyped (PG), the percentage of birds genotyped randomly (PRG) or selectively, and the percentage of males among genotyped birds (PMG). Stochastic computer simulation with a full factorial experimental design was used. Significant interaction among the factors (PG, PRG, and PMG) was observed for the rate of genetic gain in body weight and aggregate breeding value, but not for the gain in residual feed intake or the rate of inbreeding per generation. Our findings indicate that the PG, PRG, and PMG need to be considered when selecting a genotyping strategy for a broiler breeding program. If available resources allow only to genotype a small percentage (e.g., 2.5% or 5%) of all birds, the genotyping of 50–100% of male birds and selection of birds to be genotyped using phenotypic information is best. If resources allow to genotype more (e.g., ≥20%) candidates, genotyping of equal numbers of each sex, and low PRG level (i.e., preselection of the majority of candidate based on performance) is best. Provided that a proper genotyping strategy is chosen, we conclude that the incorporation of genomic information in broiler breeding programs can substantially increase the rate of genetic gain.

BLUP (best linear unbiased prediction) is the standard for predicting breeding values, where different assumptions can be made on variance–covariance structure, which may influence predictive ability. Herein, we compare accuracy of prediction of four derived‐BLUP models: (a) a pedigree relationship matrix (PBLUP), (b) a genomic relationship matrix (GBLUP), (c) a weighted genomic relationship matrix (WGBLUP) and (d) a relationship matrix based on genomic features that consisted of only a subset of SNP selected on a priori information (GFBLUP). We phenotyped a commercial population of broilers for body weight (BW) in five successive weeks and genotyped them using a 50k SNP array. We compared predictive ability of univariate models using conservative cross‐validation method, where each full‐sib group was divided into two folds. Results from cross‐validation showed, with WGBLUP model, a gain in accuracy from 2% to 7% compared with GBLUP model. Splitting the additive genetic matrix into two matrices, based on significance level of SNP (Gf: estimated with only set of SNP selected on significance level, Gr: estimated with the remaining SNP), led to a gain in accuracy from 1% to 70%, depending on the proportion of SNP used to define Gf. Thus, information from GWAS in models improves predictive ability of breeding values for BW in broilers. Increasing the power of detection of SNP effects, by acquiring more data or improving methods for GWAS, will help improve predictive ability.

The aim of this study was to examine (i) the genetic variation in macro-environmental sensitivity (macro-ES) for ADG in Danish Duroc pigs, (ii) the genetic heterogeneity among sexes, and (iii) residual variance heterogeneity among herds. Record of ADG for 32,297 boars (19 herds) and 42,724 gilts (16 herds) was used for analysis. The data were provided by the National Danish Pig Research Centre. The analysis was performed by fitting univariate reaction norm models with the herd-year-month on test (HYM) effect as environmental covariates and herd-specific residual variance for boars and gilts separately under a Bayesian setting. The environmental covariate was inferred simultaneously with other parameters of the model. Gibbs sampling was used to sample model dispersion and location parameters. The posterior means and highest posterior density intervals of the additive genetic variance, genetic correlations for ADG, and heritability were calculated over the continuous environmental range of -3σh to +3σh (SD of the HYM effect). The coheritability of ADG at the average environmental level and ADG in the environments along the -3σh to +3σh environmental gradient were also calculated. The analysis showed significant variation in macro-ES, revealing genotype by environment interactions (G × E) for ADG. The presence of G × E resulted in changes in additive genetic variance and heritability across the -3σh to +3σh range. The genetic correlations were high and positive between ADG in environments differing by 1σh units or less and decreased to moderately positive between ADG in the extreme environments in both sexes. The coheritability of ADG in the environment at the average level and the -3σh environment for boars were greater than the heritability in the environment at the average level, while it was less for gilts. The coheritability of ADG in the environment at the average level and the +3σh environment for boars was less than heritability in the environment at the average level, while it was either the same or greater for gilts, depending on the residual variance. Boars had larger additive genetic and residual variances than gilts. Heterogeneity of residual variances across herds was shown for both sexes. In conclusion, this study shows the presence of macro-ES, genetic variance heterogeneity among sexes for ADG in pigs, and residual variance heterogeneity across herds.

Automated milking systems (AMS) have become popular on dairy farms. Due to a different test-day recording scheme the variation of test-day observations differ from AMS differ from those of conventional milking system (CMS). An approach is presented for upgrading the genetic evaluation model for production traits milk, protein and fat yield by including residual covariance matrices for AMS and CMS test-day observations. Residual variances were found to be 16–37% smaller for milk and protein yields and 42–47% larger for fat yield when recorded under AMS herds compared to CMS herds. Daily heritability was higher for milk and protein yield and lower for fat yield when traits were recorded under AMS compared to recording under CMS. No difference was found between AMS and CMS in 305-day heritability for milk and protein yield except for second lactation milk yield. 305-day heritability for fat was lower for all lactations under AMS.

This work focuses on the effects of variable amount of genomic information in the Bayesian estimation of unknown variance components associated with single-step genomic prediction. We propose a quantitative criterion for the amount of genomic information included in the model and use it to study the relative effect of genomic data on efficiency of sampling from the posterior distribution of parameters of the single-step model when conducting a Bayesian analysis with estimating unknown variances. The rate of change of estimated variances was dependent on the amount of genomic information involved in the analysis, but did not depend on the Gibbs updating schemes applied for sampling realizations of the posterior distribution. Simulation revealed a gradual deterioration of convergence rates for the locations parameters when new genomic data were gradually added into the analysis. In contrast, the convergence of variance components showed continuous improvement under the same conditions. The sampling efficiency increased proportionally to the amount of genomic information. In addition, an optimal amount of genomic information in variance-covariance matrix that guaranty the most (computationally) efficient analysis was found to correspond a proportion of animals genotyped ***0.8. The proposed criterion yield a characterization of expected performance of the Gibbs sampler if the analysis is subject to adjustment of the amount of genomic data and can be used to guide researchers on how large a proportion of animals should be genotyped in order to attain an efficient analysis.

A multivariate model was developed and used to estimate genetic parameters of body weight (BW) at 1–6 weeks of age of broilers raised in a commercial environment. The development of model was based on the predictive ability of breeding values evaluated from a cross‐validation procedure that relied on half‐sib correlation. The multivariate model accounted for heterogeneous variances between sexes through standardization applied to male and female BWs differently. It was found that the direct additive genetic, permanent environmental maternal and residual variances for BW increased drastically as broilers aged. The drastic increase in variances over weeks of age was mainly due to scaling effects. The ratio of the permanent environmental maternal variance to phenotypic variance decreased gradually with increasing age. Heritability of BW traits ranged from 0.28 to 0.33 at different weeks of age. The direct genetic effects on consecutive weekly BWs had high genetic correlations (0.85–0.99), but the genetic correlations between early and late BWs were low (0.32–0.57). The difference in variance components between sexes increased with increasing age. In conclusion, the permanent environmental maternal effect on broiler chicken BW decreased with increasing age from weeks 1 to 6. Potential bias of the model that considered identical variances for sexes could be reduced when heterogeneous variances between sexes are accounted for in the model.

Records on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The study was based on a simulated pig population, including three scenarios of group structure and size. The results showed that both the genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs) for traits with group records. The accuracies of EBV obtained from group records with a size 24 were much lower than those with a size 12. Random assignment of animals to pens led to lower accuracy due to the weaker relationship between individuals within each group. It suggests that group records are valuable for genetic evaluation of a trait that is difficult to record on individuals, and the accuracy of genetic evaluation can be considerably increased using genomic information. Moreover, the genetic evaluation for a trait with group records can be greatly improved using a bivariate model, including correlated traits that are recorded individually. For efficient use of group records in genetic evaluation, relatively small group size and close relationships between individuals within one group are recommended.

Background The objectives of this study were to (1) simultaneously estimate genetic parameters for BW, feed intake (FI), and body weight gain (Gain) during a FI test in broiler chickens using multi-trait Bayesian analysis; (2) derive phenotypic and genetic residual feed intake (RFI) and estimate genetic parameters of the resulting traits; and (3) compute a Bayesian measure of direct and correlated superiority of a group selected on phenotypic or genetic residual feed intake. A total of 56,649 male and female broiler chickens were measured at one of two ages ($${\text{t}}$$ t or $${\text{t}} - 6$$ t - 6 days). BW, FI, and Gain of males and females at the two ages were considered as separate traits, resulting in a 12-trait model. Phenotypic RFI ($${\text{RFI}}_{\text{P}}$$ RFI P ) and genetic RFI ($${\text{RFI}}_{\text{G}}$$ RFI G ) were estimated from a conditional distribution of FI given BW and Gain using partial phenotypic and partial genetic regression coefficients, respectively. Results Posterior means of heritability for BW, FI and Gain were moderately high and estimates were significantly different between males and females at the same age for all traits. In addition, the genetic correlations between male and female traits at the same age were significantly different from 1, which suggests a sex-by-genotype interaction. Genetic correlations between $${\text{RFI}}_{\text{P}}$$ RFI P and $${\text{RFI}}_{\text{G }}$$ RFI G were significantly different from 1 at an older age but not at a younger age. Conclusions The results of the multivariate Bayesian analyses in this study showed that genetic evaluation for production and feed efficiency traits should take sex and age differences into account to increase accuracy of selection and genetic gain. Moreover, for communicating with stakeholders, it is easier to explain results from selection on $${\text{RFI}}_{\text{G}}$$ RFI G than selection on $${\text{RFI}}_{\text{P}}$$ RFI P , since $${\text{RFI}}_{\text{G}}$$ RFI G is genetically independent of production traits and it explains the efficiency of birds in nutrient utilization independently of energy requirements for production and maintenance.

It is of practical importance to ensure the data quality from a milk-recording system before use for genetic evaluation. A procedure was developed for detection of multivariate outliers based on an approximation for Mahalanobis distance and was implemented in the Nordic Holstein and Red population. The general target of this procedure is based on the Nordic Cattle Genetic Evaluation yield model, which is a 9-trait model for milk, protein, and fat in the first 3 lactations. The procedure is based on the phenotypic correlation structure as a function of days in milk (DIM) and on computation of trait means and standard deviations within a production year, lactation, and DIM. For each record in the data, a Mahalanobis distance value was computed based on the trait mean and the covariance matrix for the actual production year, lactation, and DIM. A set of cutoff values, ranging from 10 to 100 with steps of 10, for discarding multivariate outliers was investigated. Prediction accuracy was calculated as the Pearson correlations between estimated breeding values predicted by full data set and estimated breeding values predicted by reduced data set for cows without records in the reduced data set and with 1 or more records deleted due to the editing rules on Mahalanobis distance. The results showed that, averaged over all scenarios, gains of 0.005 to 0.048 on prediction accuracy have been obtained by deleting the multivariate outliers. The improvements were more profound for progeny of young bulls compared with progeny of proven bulls. It is easy to implement this multivariate outlier-detection procedure in the routine genetic evaluation for different dairy cattle breeds; however, an optimal cutoff value for Mahalanobis distance needs to be defined to achieve an acceptable compromise between genetic evaluation accuracy and data deletion.

Data from 54 selection rounds (SRs) for growth rate in a commercial broiler chicken line were used for analysis to estimate genetic parameters for body weight (BW) in males and females at three different ages (t-7, t-4 and t days). First BW was recorded at t days of age for 39 SRs, however, as selection continued the birds start to grow faster and attain desired body weight earlier, therefore, the weighing age was changed and BW was recorded at t − 4 and t − 7 days of age for 7 and 8 SRs, respectively. Multivariate animal model was used to estimate genetic parameters for BW in males and females with REML analysis using DMU software package. In males, heritability estimates of BW were found to be 0.37, 0.33 and 0.29 at t − 7, t − 4 and t days of age, respectively, while, in females, heritability estimates of BW at t− 7, t − 4 and t days of age were found to be 0.40, 0.38 and 0.38, respectively. The genetic correlations of BW between males and females at t − 7, t − 4 and t days of age were found to be 0.94, 0.89 and 0.89, respectively, which is significantly different from unity. Also the genetic correlations of BW between ages were significantly different from unity. The heritability and genetic correlation estimates in this study suggest that BW in males and females should be considered as two different traits. Moreover, BW in the three ages should be considered as three different traits.

Single-step genomic prediction models utilizing both genotyped and nongenotyped animals are likely to become the prevailing tool in genetic evaluations of livestock. Various single-step prediction models have been proposed, based either on estimation of individual marker effects or on direct prediction via a genomic relationship matrix. In this study, a classical pedigree-based animal model, a regular single-step genomic BLUP (ssGBLUP) model, algorithm for proven and young (APY) with 2 strategies for choosing core animals, and a single-step Bayesian regression (ssBR) model were compared for 305-d production traits (milk, fat, protein) in the Finnish red dairy cattle population. A residual polygenic effect with 10% of total genetic variance was included in the single-step models to reduce inflation of genomic predictions. Validation reliability was calculated as the squared Pearson correlation coefficient between genomically enhanced breeding value (GEBV) and yield deviation for masked records for 2,056 validation cows from the last year in the data set investigated. The results showed that gains of 0.02 to 0.04 on validation reliability were achieved by using single-step methods compared with the classical animal model. The regular ssGBLUP model and ssBR model with an extra polygenic effect yielded the same results. The APY methods yielded similar reliabilities as the regular ssGBLUP and ssBR. Exact prediction error variance of GEBV could be obtained by ssBR to avoid any approximation methods used for ssGBLUP when inversion left-hand side of mixed model equations is computationally infeasible for large data sets.

Most Warmblood horse studbooks aim to improve the performance in dressage and show jumping. The Dutch Royal Warmblood Studbook (KWPN) includes the highest score achieved in competition by a horse to evaluate its genetic ability of performance. However, the records collected during competition are associated with some aspects that might affect the quality of the genetic evaluation based on these records. These aspects include the influence of rider, censoring and preselection of the data. The aim of this study was to quantify the impact of rider effect, censoring and preselection on the genetic analysis of competition data of dressage and show jumping of KWPN. Different models including rider effect were evaluated. To assess the impact of censoring, genetic parameters were estimated in data sets that differed in the degree of censoring. The effect of preselection on variance components was analysed by defining a binary trait (sport-status) depending on whether the horse has a competition record or not. This trait was included in a bivariate model with the competition trait and used all horses registered by KWPN since 1984. Results showed that performance in competition for dressage and show jumping is a heritable trait (h(2) ~ 0.11-0.13) and that it is important to account for the effect of rider in the genetic analysis. Censoring had a small effect on the genetic parameter for highest performance achieved by the horse. A moderate heritability obtained for sport-status indicates that preselection has a genetic basis, but the effect on genetic parameters was relatively small.

Including genotyped females in a reference population (RP) is an obvious way to increase the RP in genomic selection, especially for dairy breeds of limited population size. However, the incorporation of these females must be conducted cautiously because of the potential preferential treatment of the genotyped cows and lower reliabilities of phenotypes compared with the proven pseudo-phenotypes of bulls. Breeding organizations in Denmark, Finland, and Sweden have implemented a female-genotyping project with the possibility of genotyping entire herds using the low-density (LD) chip. In the present study, 5 scenarios for building an RP were investigated in the Nordic Jersey population: (1) bulls only, (2) bulls with females from the LD project, (3) bulls with females from the LD project plus non-LD project females genotyped before their first calving, (4) bulls with females from the LD project plus non-LD project females genotyped after their first calving, and (5) bulls with all genotyped females. The genomically enhanced breeding value (GEBV) was predicted for 8 traits in the Nordic total merit index through a genomic BLUP model using deregressed proof (DRP) as the response variable in all scenarios. In addition, (daughter) yield deviation and raw phenotypic data were studied as response variables for comparison with the DRP, using stature as a model trait. The validation population was formed using a cut-off birth year of 2005 based on the genotyped Nordic Jersey bulls with DRP. The average increment in reliability of the GEBV across the 8 traits investigated was 1.9 to 4.5 percentage points compared with using only bulls in the RP (scenario 1). The addition of all the genotyped females to the RP resulted in the highest gain in reliability (scenario 5), followed by scenario 3, scenario 2, and scenario 4. All scenarios led to inflated GEBV because the regression coefficients are less than 1. However, scenario 2 and scenario 3 led to less bias of genomic predictions than scenario 5, with regression coefficients showing less deviation from scenario 1. For the study on stature, the daughter yield deviation/daughter yield deviation performed slightly better than the DRP as the response variable in the genomic BLUP (GBLUP) model. Therefore, adding unselected females in the RP could significantly improve the reliabilities and tended to reduce the prediction bias compared with adding selectively genotyped females. Although the DRP has performed robustly so far, the use of raw data is recommended with a single-step model as an optimal solution for future genomic evaluations.

Genetic evaluations of sport performance typically consider competition records of ranking points in each competition, accumulated lifetime points or annual earnings. Repeated observations have the advantage of allowing for adjustment of effects associated with each competition such as rider experience, judge and competing horses, but also demands more computer capacity than single-trait records, which could prohibit multiple-trait evaluations. The aim of the study was to compare CPU times, estimated breeding values (EBVs), reliabilities and model prediction abilities when modelling repeated competition ranking points (run A), mean ranking points (runs B and C), mean ranking points precorrected for effects associated with each competition (run D) and accumulated lifetime points (run E) for Danish Warmblood horses. CPU times for run A were 632–776 times (show jumping) and 59–96 times (dressage) as high as for runs B–E. EBVs of run D were perfectly correlated (1.00) with those of run A. Reliabilities were highest in runs E and A. Best model prediction ability and least bias were found in run C (dressage) and run E (show jumping), but the best choice in each discipline was not preferable for the other. Run D was the second best in both disciplines (D), and is expected to increase in performance over time as omission of a relatively large amount of historic data becomes less important.

Selection for sow longevity using information from traits, which are expressed in early life and genetically highly related to longevity, is expected to be more effective than direct selection as it can overcome the disadvantage of late recording of true longevity. Our aim was to investigate the correlation between leg conformation recorded on young pigs, litter size at first parity and longevity of Danish Landrace and Yorkshire sows. Information on conformation from 116,733 Landrace and 89,963 Yorkshire pigs and information on reproduction and longevity from 27,070 Landrace and 11,895 Yorkshire sows were analyzed. All considered traits were low to moderately heritable, ranging from 0.02 to 0.41. In general, both conformation and reproduction traits were favorably genetically correlated with longevity (0.07–0.39 and 0.00–0.58, respectively). These estimates suggest a potential of improving sow longevity by selection on conformation recorded at young age and litter size at first parity.

Recent studies on data from the Dutch Warmblood Studbook (KWPN) have shown that the ongoing specialization of horses for either dressage (DH) or show jumping (JH) has led to a decreasing genetic relationship between the two subpopulations. The aim of this study was to analyse the effect of the specialization process on the genetic parameters of traits measured in the studbook-entry inspection of KWPN during the last fifteen years. Data from 18 125 DH and 23 800 JH recorded from 1998 until 2013 were used to analyse 13 traits scored in both DH and JH. Analyses were performed in a Bayesian framework. Firstly, variance components were estimated based on the whole data set. Secondly, genetic correlations between traits measured in DH or JH were estimated using bivariate analyses. Thirdly, three time periods were defined and genetic correlations between subpopulations were estimated within each period. Heritability was moderate (0.17-0.39) for both DH and JH. Genetic correlations between traits measured in DH or JH were not different from one considering the posterior standard deviation of the estimation; however, in most of the traits, a clear trend in reduction of the genetic correlation for traits expressed in DH and JH and an increase in their posterior standard deviation for recent years was observed. These results suggest that specialization could lead to differences in traits measured in DH and JH in the recent years.

Data from 54 selection rounds (SRs) for growth rate in a selection line from Cobb breeding company was used for analysis with the objective to investigate the extent of sex by genotype interaction for body weight (BW) in a commercial broiler chicken population, estimate genetic parameters of BW at three different ages, and study the development in genetic variance for BW over several generations of selection. BW was measured at three different ages (t, t-4 and t-7 days) in different birds of both sexes. For the first 39 SRs, BW was recorded at t days, however, as selection continued the birds reached desired BW earlier and the weighing age was changed to t-4 days for 7 SRs and then to t-7 days for the last 8 SRs. A bivariate random regression model regarding BW in each sex as different traits using segmented linear splines and heterogeneous residual variance was used to estimate genetic parameters of BW across the selection trajectory via Gibbs sampling. The genetic variance and the heritability estimates of BW in males and females were found to be different at all three ages. The genetic correlation between BW measured in males and females was less than unity and decreased further as age at weighing was increased. This illustrated that BW in the two sexes should be considered as different but correlated traits and models for BW should account for a decreasing genetic correlation as the distance between the weighing ages increased. An increased genetic variance of BW along the selection trajectory in the first period was observed and genetic correlations between BW measured in different SRs gradually decreased as the distance between SRs increased. Genetic correlations lower than unity between BW at early and late SRs in the first period indicated that the genetic background of BW has gradually changed along the selection trajectory. This change probably contributes significantly to maintaining large levels of genetic variance in highly selected broiler populations.

Crossing of lines or strains within and between breeds has been demonstrated to be beneficial for dairy cattle performance. However, even within breed, differences between strains may also give rise to heterosis. A key question is whether an interaction exists between heterosis and environment (H x E) that is independent of genotype by environment (G x E) interactions. In this study, H x E and G x E interactions were estimated in a population of approximately 300,000 Danish Jersey cows. The cows were a mixture of pure Danish Jerseys and crosses of US and Danish Jerseys. The phenotype studied was protein yield. A reaction norm model where the unknown environmental covariates are inferred simultaneously with the other parameters in the model was used to analyze the data. When H x E, but not G x E, was included in the model, heterosis was estimated to be 3.8% for the intermediate environmental level. However, when both H x E and G x E were included in the model, heterosis was estimated to be 4.1% for the intermediate environmental level. Furthermore, when only H x E was included in the model, the regression on the unknown environmental covariate was estimated to be 0.15, interpreted as meaning that an increase of average herd-year protein yield by 1 kg of protein led to an increase in heterosis of 0.15 kg above the average heterosis for a first-cross cow. When both H x E and G x E were included in the model, the regression on the unknown environmental covariate was not significantly different from zero, meaning that heterosis was similar in all environments investigated. The genetic correlation of protein yields for different environmental levels ranged from 0.72 to 0.93, which was significantly different from unity, indicating that G x E exist for protein yield.

The main purpose of this study was to evaluate whether the population structure in Danish Jersey (DJ) known from the history of the breed also is reflected in its genomic structure. This is done by comparing the linkage disequilibrium and persistence of phase for subgroups of Jersey animals with high proportions of Danish (DNK) or United States (USJ) origin. Furthermore, it is investigated whether a model explicitly incorporating breed origin of animals, inferred either through the known pedigree or from SNP marker data, leads to improved genomic predictions compared with a model ignoring breed origin. The study of the population structure incorporated 1,730 genotyped Jersey animals. In total 39,542 SNP markers were included in the analysis. The 1,079 genotyped bulls with de-regressed proof for udder health were used in the analysis for the predictions of the genomic breeding values. A range of random regressions models that included the breed origin were analyzed and compared with a basic genomic model that assumes a homogeneous breed structure. The main finding in this study is that the importation of germplasm from the USJ population is readily reflected in the genomes of modern DJ animals. First, linkage disequilibrium in the group of admixed DJ animals is lower compared with the groups of the original DNK and USJ animals. Second, persistence of linkage disequilibrium phase is not conserved for longer marker distances between animals with mainly Danish or United States origin. Third, the STRUCTURE analysis could retrieve genomic-based breed proportions in alignment to the pedigree-based breed proportions. However, including this population structure in a random regression prediction model did not clearly improve the reliabilities of the genomic predictions compared with a basic genomic model.

Crossbreeding has been shown to improve the longevity of dairy cattle in countries across the world. The aim of this study was to estimate heterosis, breed effects, and genetic parameters for longevity in crossbred dairy cattle among Danish Holstein (DH), Danish Red (DR), and Danish Jersey (DJ) breeds. Data were provided from 119 Danish commercial herds that use systematic crossbreeding (i.e., rotational crossbreeding). Additional data from 11 mixed-breed herds with DH and DJ were included to estimate reliable breed effects for DJ. Survival information on 73,741 cows was analyzed with a linear animal model using the artificial insemination–REML algorithm in the DMU package. Five longevity (L) traits were defined: days from first calving until the end of first lactation or culling (L1), days from first calving until the end of second lactation or culling (L2), days from first calving until the end of third lactation or culling (L3), days from first calving until the end of fourth lactation or culling (L4), and days from first calving until the end of fifth lactation or culling (L5). Heritabilities ranged between 0.022 and 0.090. Additive breed effects in units of days were estimated relative to DH for DR as −0.5 (L1), +10.5 (L2), +18.5 (L3), +11.9 (L4), and +28.6 (L5), and corresponding figures for DJ were +2.0, +0.5, +14.2, +27.7, and +44.0. Heterosis effects in L1 were low (1.2%) but favorable in crosses between DH and DR, whereas negative heterosis effects were estimated for crosses between DH and DJ (−2.5%) and DR and DJ (−1.2%). The largest heterosis effects for L2, L3, L4, and L5 were found in DH × DR and were favorable (+3.3, +5.7, +7.7, and +8.5%, respectively). Corresponding figures for heterosis effects in DH × DJ and DR × DJ were favorable as well: +2.3, +4.1, +5.6, and +6.2% in DH × DJ and +3.1, +7.3, +6.9, and +7.2% in DR × DJ. The favorable heterosis effects show that crossbreeding is an efficient tool for improving longevity in Danish dairy cattle.

Sport performance in dressage and show jumping are two important traits in the breeding goals of many studbooks. To determine the optimum selection scheme for jumping and dressage, knowledge is needed on the genetic correlation between both disciplines and between traits measured early in life and performance in competition in each discipline. This study aimed to estimate genetic parameters to support decision-making on specialization of breeding horses for dressage and show jumping in Dutch warmblood horses. Genetic correlations between performance of horses in dressage and show jumping were estimated as well as the genetic correlation between traits recorded during studbook-entry inspections and performance in dressage and show jumping competitions. The information on competition comprised the performance of 82 694 horses in dressage and 62 072 horses in show jumping, recorded in the period 1993-2012. For 26 056 horses, information was available for both disciplines. The information on traits recorded at studbook-entry inspections comprised 62 628 horses, recorded in the period 1992-2013. Genetic parameters were estimated from the whole dataset and from a subset without horses recorded in both disciplines. Additionally, the genetic parameters were estimated in three different time periods defined by horses' birth year. The genetic correlation between dressage and show jumping in the whole dataset was -0.23, and it was -0.03 when it was estimated from horses recorded in only one discipline. The genetic correlation between dressage and show jumping was more negative in the most recent time period in all the cases. The more negative correlation between disciplines in more recent time periods was not reflected in changes in the correlations between competitions traits and the traits recorded in the studbook-first inspection. These results suggest that a breeding programme under specialization might be most effective defining two separate aggregate breeding goals for each of the disciplines.