Estimating variance components and predicting breeding values for eventing disciplines and grades in sport horses.

Eventing competitions in Great Britain (GB) comprise three disciplines, each split into four grades, yielding 12 discipline-grade traits. As there is a demand for tools to estimate (co)variance matrices with a large number of traits, the aim of this work was to investigate different methods to produce large (co)variance matrices using GB eventing data. Data from 1999 to 2008 were used and penalty points were converted to normal scores. A sire model was utilised to estimate fixed effects of gender, age and class, and random effects of sire, horse and rider. Three methods were used to estimate (co)variance matrices. Method 1 used a method based on Gibbs sampling and data augmentation and imputation. Methods 2a and 2b combined sub-matrices from bivariate analyses; one took samples from a multivariate Normal distribution defined by the covariance matrix from each bivariate analysis, then analysed these data in a 12-trait multivariate analysis; the other replaced negative eigenvalues in the matrix with positive values to obtain a positive definite (co)variance matrix. A formal comparison of models could not be conducted; however, estimates from all methods, particularly Methods 2a/2b, were in reasonable agreement. The computational requirements of Method 1 were much less compared with Methods 2a or 2b. Method 2a heritability estimates were as follows: for dressage 7.2% to 9.0%, for show jumping 8.9% to 16.2% and for cross-country 1.3% to 1.4%. Method 1 heritability estimates were higher for the advanced grades, particularly for dressage (17.1%) and show jumping (22.6%). Irrespective of the model, genetic correlations between grades, for dressage and show jumping, were positive, high and significant, ranging from 0.59 to 0.99 for Method 2a and 0.78 to 0.95 for Method 1. For cross-country, using Method 2a, genetic correlations were only significant between novice and pre-novice (0.75); however, using Method 1 estimates were all significant and low to moderate (0.36 to 0.70). Between-discipline correlations were all low and of mixed sign. All methods produced positive definite 12 × 12 (co)variance matrices, suitable for the prediction of breeding values. Method 1 benefits from much reduced computational requirements, and by performing a true multivariate analysis.

Genetic parameters of egg defects and egg quality in layer chickens.

Genetic parameters were estimated for egg defects, egg production, and egg quality traits. Eggs from 11,738 purebred brown-egg laying hens were classified as salable or as having one of the following defects: bloody, broken, calcium deposit, dirty, double yolk, misshapen, pee-wee, shell-less, and soft shelled. Egg quality included albumen height, egg weight, yolk weight, and puncture score. Body weight, age at sexual maturity, and egg production were also recorded. Heritability estimates of liability to defects using a threshold animal model were less than 0.1 for bloody and dirty; between 0.1 and 0.2 for pee-wee, broken, misshapen, soft shelled, and shell-less; and above 0.2 for calcium deposit and double yolk. Quality and production traits were more heritable, with estimates ranging from 0.29 (puncture score) to 0.74 (egg weight). High-producing hens had a lower frequency of egg defects. High egg weight and BW were associated with an increased frequency of double yolks, and to a lesser extent, with more shell quality defects. Estimates of genetic correlations among defect traits that were related to shell quality were positive and moderate to strong (0.24-0.73), suggesting that these could be grouped into one category or selection could be based on the trait with the highest heritability or that is easiest to measure. Selection against defective eggs would be more efficient by including egg defect traits in the selection criterion, along with egg production rate of salable eggs and egg quality traits.

Genetic heterogeneity of variance in production traits of laying hens.

Genetic parameters for mean and for environmental variation in egg weight, body weight, age at first egg, and egg production were estimated in eight layer lines. The data were recorded from years 1999-2007, with on average of 6500 birds per line. An iterative mean-variance REML method was applied with a sire-plus-dam model for mean, a sire model for variance, and a fixed effect of hatch within generation for both mean and variance. The estimated heritability of environmental variation averaged about 5% for body weight, 3% for egg weight, and 11% for age at first egg, but was inconsistent among the lines for egg production (0-15%). Correlations between means and variances were slightly positive for body weight, egg weight, and age at first egg; and strongly negative for egg production. Age at first egg had the highest heritability of environmental variation; and favourable correlations with egg production and with variation in other production traits indicate that reducing variation in this trait may have potential benefits for the breeding industry.

Inheritance of hatchability in broiler chickens and its relationship to egg quality traits.

The first objective of this study on broiler breeders was to investigate the genetic basis of variability in hatchability over age using a longitudinal model. Weekly percentage hatch of fertile and hatch of set eggs were available for 23,250 dams mated to 3,106 sires of the same age between the 28th and 54th week of life. Hatch of set was very highly correlated with fertility and showed a similar pattern through lay. There was a genetic contribution of the dam but not the sire to hatch of fertile; its heritability was about 6% from peak lay onward but lower earlier. The second objective was to investigate the relationship between hatchability and internal and external egg quality traits measured at 48 wk of age. These traits, specific gravity, weight loss, egg weight, and Haugh units, had moderate to high heritabilities, 0.53, 0.38, 0.65, and 0.38, respectively. Parameters of the genetic trend in weekly hatchability (mean and persistency) were significantly correlated with these egg quality traits, suggesting that in a bulk mating situation in which individual recording of hatchability is not possible, these quality traits could provide some indication on the trend in flock hatchability.

Contributions of genetic and environmental components to changes in phenotypic variation between generations.

We evaluate the extent to which changes in phenotypic variation among generations of populations kept in the same environment are due to changes in genetic (V(A)) or in environmental (V(E)) variance. Data were available on body weight of adult poultry on a total of 89186 birds (mainly females) from six generations of each of seven lines of layers. There was substantial heterogeneity of variation between generations, shown to be in both V(A) and V(E) components. Based on the Akaike information criterion (AIC), the best fit was with both components changing, and a better fit was obtained if V(A)/V(E) (i.e. heritability) or V(E), rather than V(A), was assumed constant. In analyses of quantitative genetic data spanning environmental groups, attention should be paid to whether and how the variance components change among groups before undertaking detailed variance partition that may be sensitive to such changes.

Evidence of genetic resistance of cattle to infection with Mycobacterium bovis.

Anecdotal evidence points to genetic variation in resistance of cattle to infection with Mycobacterium bovis, the causative agent of bovine tuberculosis (BTB), and published experimental evidence in deer and cattle suggests significant genetic variation in resistance and reactivity to diagnostic tests. However, such genetic variation has not been properly quantified in the United Kingdom dairy cattle population; it is possible that it exists and may be a factor influencing the occurrence of BTB. Using models based on the outcome of the process of diagnosis (ultimate fate models) and on the outcome of a single stage of diagnosis (continuation ratio models, herd test-date models), this study shows that there is heritable variation in individual cow susceptibility to BTB, and that selection for milk yield is unlikely to have contributed to the current epidemic. Results demonstrate that genetics could play an important role in controlling BTB by reducing both the incidence and the severity of herd breakdowns.

Genetic variability in residual variation of body weight and conformation scores in broiler chickens.

The aim of the study was to estimate genetic variance in residual variation of BW and body conformation scores of 34-d-old male and female broiler chickens. The data set included performance records of 52,349 female and 49,001 male birds. Two models were used: an iterative mean-variance restricted maximum likelihood sire plus dam model and a Bayesian sire plus dam model, in each case with heterogeneity in residual variances across sire families. They gave similar results. Estimates of heritability for BW in females and males were, respectively, 0.25 and 0.21, and for conformation score were 0.24 and 0.15, and genetic correlations for the same trait between sexes were high. Estimates of genetic correlations between means and variances were positive for both sexes and both traits. Estimates of the heritability of residual variation were low, between 0.023 and 0.038 for both traits, but genetic CV were substantial (between 0.25 and 0.40), indicating the possibility of improving uniformity of BW and conformation by means of selection.

Characterization of the dairy farm environment in Great Britain and the effect of the farm environment on cow life span.

Dairy farms vary a great deal in the feeding and management systems that are used. These differences affect the performance of the cows, and some genotypes may be affected more than others. If effects of such genotype-by-environment interactions (GxE) are large, then farmers must be made aware of them to make informed breeding decisions. To investigate GxE, a classification system for farm environments was developed based on national- and fine-level data from dairy herds across the United Kingdom. The national data included herd and yield characteristics and local weather information. The fine-level data included information on feeding and management systems on farms, and was obtained from survey results from 778 farms. A principal components analysis of the surveys identified 2 major dimensions characterizing the data. The first dimension explained 14.6% of the variation and was related to the level of production intensity. The second dimension explained 11.5% of the variation and was related to climate. Information on milk yield, herd characteristics, and climate was then extracted from national databases for the survey farms. A canonical correlation analysis was used to relate the survey data to the variables extracted from the national data set to determine the most relevant variables. The canonical correlation between the chosen sets of national data and survey variables was 0.62. This environmental classification was then used to determine how the farm environment affects the life span of dairy cows. The life span of the daughters of 1,000 sires was related to the type of farm environment. The daughters of a majority of sires showed a "plastic" response, with increased life span in less intensive farms. The daughters of a smaller number of sires showed a more generalized response, with life span being less affected by the environment. This GxE suggested that sires vary in the sensitivity of their daughters to different farm environments. This variation in response could allow breeding companies and farmers to match sires to particular farm environments.

The relationship between fertility, rump angle, and selected type information in Holstein-Friesian cows.

Some dairy cattle breeders believe that dairy cows with high pin bones will have poorer fertility. The aim of this study was to examine this claim by investigating the relationships, both genetic and phenotypic, between fertility, rump angle, and other selected type traits in first-lactation Holstein-Friesians. Results showed an unfavorable genetic correlation (-0.16) between calving interval and rump angle, suggesting that animals with high pin bones would have a longer calving interval. However, no significant genetic or phenotypic correlation between days to first service and nonreturn rate and rump angle was observed. No evidence of a relationship, linear or quadratic, between any fertility trait and rump angle was found. Udder support and mammary system were unfavorably correlated to calving interval (0.25 and 0.14, respectively), suggesting that cows with more functional udders would have a longer calving interval. Legs and feet score was favorably correlated to nonreturn rate, suggesting that animals with good legs and feet would be less likely to return to service.

Contrasting models for lactation curve analysis.

Several statistical models have been proposed for the genetic evaluation of production traits in dairy cattle based on test-day records. Three main approaches have been put forward in the literature: random regression, orthogonal polynomials, and, more recently, character process models. The aim of this paper is to show how these different approaches are related, to compare their performance for the genetic analysis of lactation curves, and to assess equivalence between sire and animal models for repeated measures analyses. It was found that, with an animal model, a character process model with 11 parameters performed better, regarding the likelihood criterion, than a quartic random regression model (with 31 parameters). However, although the likelihood was higher, the genetic variance was very different with the character process model from the unstructured model, which raises important issues concerning model selection criteria. There are advantages in combining methodologies. A quadratic random regression model for the environmental part, combined with a character process model for the residual, performed better than the quartic random regression model and had fewer parameters. A character process structure allowing for a correlation pattern modeled the residual better than a simple quadratic variance, and had only one extra parameter.