In a genetic analysis of German trotters, the performance trait racing time per km was analysed by using a random regression model on six different age classes (2-, 3-, 4-, 5- and 6-year-old and older trotters; the age class of 3-year-old trotters was additionally divided by birth months of horses into two seasons). The best-fitting random regression model for the trait racing time per km on six age classes included as fixed effects sex, race track, condition of race track (fitted as second-order polynomial on age), distance of race and each driver (fitted as first-order polynomial on age) as well as the year-season (fitted independent of age). The random additive genetic and permanent environmental effects were fitted as second-order polynomials on age. Data consisted of 138,620 performance observations from 2,373 trotters and the pedigree data contained 9,952 horses from a four-generation pedigree. Heritabilities for racing time per km increased from 0.01 to 0.18 at age classes from 2- to 4-year-old trotters, then slightly decreased for 5 year and substantially decreased for 6-year-old horses. Genetic correlations of racing time per km among the six age classes were very high (rg = 0.82-0.99). Heritability was h2 = 0.13 when using a repeatability animal model for racing time per km considering the six age classes as fixed effect. Breeding values using repeatability analysis over all and within age classes resulted in slightly different ranking of trotters than those using random regression analysis. When using random regression analysis almost no reranking of trotters over time took place. Generally, the analyses showed that using a random regression model improved the accuracy of selection of trotters over age classes.

The genetic associations between racing performance and preselection of horses considered as the binary trait racing status (trotters without or with at least one racing performance in life were classified as 0 and 1, respectively) as well as disqualified races (disqualified and non-disqualified trotters were classified as 1 and 0, respectively) were analysed in German trotters. Variance components for racing performance traits square root of rank at finish, racing time per km, and log of earnings with racing status were estimated based on an animal model using REML. Heritabilities of racing status, racing time and rank at finish were 0.30, 0.21, and 0.06, respectively. The genetic correlations between racing status and racing time or rank at finish were -0.74 and -0.32, indicating that horses started at least once showed a higher genetic potential in racing time or finishing ability than never started horses. This showed the high preselection of German trotters especially based on racing time. To account for this preselection, it was recommended for additional use of racing status in the German evaluation system. Breeding values of the three racing performance traits were estimated by two distinct models, in- or excluding racing status and compared by using three criteria. Racing time per km showed the highest correlation (r = 0.98) between breeding values evaluated by these two distinct models. Therefore, incorrect selection rate of horses using breeding values from the model without racing status, was lowest for racing time per km (9.7%). Selection response increased about 1% for this trait after including racing status in the model. For the estimation of rank at finish, inclusion of racing status in the multiple trait model was much more important as indicated by a low correlation between breeding values (r = 0.29) and high percentage of incorrectly selected stallions (97.5%). The trait disqualified races was first analysed using an univariate threshold model. Heritability of this trait was low (h(2) = 0.12) and repeatability (r = 0.43) showed a moderate magnitude. Using a linear multiple trait animal model, disqualified races showed a low heritability (h(2) = 0.05) and a moderate favourable genetic correlation (r(g) = 0.43) with racing time per km. Consequently, selection on racing time per km is expected to improve indirectly the reliability of racing performance. Combined selection of reduction in disqualified races and racing time may even further improve the reliability of racing trotters.

The objectives of this study were the analysis of the effect of driver on racing performances of trotters and development of a genetic model in order to estimate genetic parameters for German trotters. Data on 6,611 trotters with 163,322 records during 1997 and 1999 were analysed with a repeatability animal model using each individual start of trotters and pedigree information of up to I I generations (13,202 horses). Besides the driver effect, the genetic model included year-season, age and sex of trotter, racing track, distance and condition of race track as fixed effects as well as additive genetic and permanent environmental effects as random effects. Traits analysed were square root of rank at finish, racing time per kin and the logarithms of earnings per start. Ignoring the effect of driver resulted in an overestimation of heritability of 60, 24 and 44% for rank at finish, racing time and earnings, respectively, which shows the necessity to include the driver effect in the model. Drivers regarded as fixed or random effects resulted in a marginal change in parameters. Heritabilities based on the model with fixed driver effect were 0.05, 0.29 and 0.09 for ranks at finish, racing time and earnings, respectively. Genetic correlation between rank and racing time was 0.81. Both traits were highly correlated with earnings of -0.98 and -0.89 for ranking and racing time, respectively. Most important trait for selection of racing performance was the racing time due to its substantial higher heritability and its high genetic correlation to earnings. Additionally, rank at finish has to be included in the breeding goal because it reflects more the potential of trotters to win at finish and accounts for records without earnings.

In this study frequencies of radiograghical evaluations of 472 three year old mares were analysed. In addition, repeated X-rays were recorded of 220 foals at age of 6 month, one year and two years.The results of this study can be summarised as follows:The classification of X-ray findings into four different categories, depending on the intensity of defects, showed that about 25% of all mares, which had findings on navicular disease, sesamoiditis or arthrosis were represented in code classes III and IV. The frequencies of bone diseases of foals increased with rising age during the three repeated evaluations.Estimated heritabilities. based on a sire model, varied between h(2) = 0.15 (sesamoiditis) and h(2) = 0.65 (bone spavin). Heritabilities estimated on foal data varied from h(2) = 0.16 (bone spavin) to h(2) = 0.58 (osteochondrosis dissecans) Heritabilities estimated for bone diseases in mares using an animal model ranged from h(2) = 0.29 (arthrosis) to h(2) = 0.35 for bone spavin. Heritabilities estimated on foal data ranged from h(2) = 0.17 (sesamoiditis) to h(2) = 0.25 (navicular disease) level. All heritabilities estimated for bone diseases of mares and foals ranged from a medium to high magnitude and showed a genetic determination of orthopaedic defects.Phenotypic correlations between bone diseases were low (r(p) = 0.00 to 0.10). The genetic correlations were estimated between r(g) = 0.01 and r(g) = 0.28, so that there is no possibility of recording different X-ray findings.

In order to analyse the importance of different traits in horse breeding, genetic parameters were estimated. In this analysis, the radiographic findings, 10548 results of stutbook registration and 1240 mare performance test results were used. To examine the possibility of considering these different traits in a breeding program, genetic correlations among X-ray findings, the traits of the stutbook registration and mare performance tests were estimated. The results of this study can be summarised as follows: Estimated heritabilities for the conformation traits ranged from h(2) = 0.12 to h(2) = 0.42. Genetic correlations between these traits were in general positive and of high magnitude for some traits, indicating the possibility of reducing the number of recorded traits. Heritabilities estimated for the traits recorded on mare performance test varied between h(2) = 0.23 and h(2) = 0.85. The rideability showed the highest genetic correlation to basic gaits, whereas the trait free jumping showed lower correlations to the basic gaits and the rideability. Estimates of genetic correlation between bone diseases and conformation traits were low. Estimates up to r(g) = -0.17 showed a negative tendency. A carefull interpretation of these correlations might be that the evaluation of conformation traits may have a positive influence on reducing the frequency of bone disorders. Bone diseases and the traits of the mare performance test showed no genetic correlations. Apparently, the considered X-ray findings had no negative influence on the performance of the tested mares. The results of this study clearly indicate that the health status can be improved by additional recording of health traits and selection. Data recording and selection should be used in an optimized breeding programme.

Variance components were estimated for fattening performance traits measured under seven different testing conditions (five nucleus farms and two test stations). Data on 98969 performance tested Large White and Landrace pigs were used. Variance components were estimated for these two dam lines within each nucleus farm considering common environment as random effect. The estimated heritabilities of daily gain varied among nucleus farms between h(2) = 0.26 to 0.49 for Landrace and h(2) = 0.28 to 0.41 for Large White. For backfat thickness the variation was h(2) = 0.25 to 0.53 (Landrace) and h(2) = 0.32 to 0.51 (Large White). The estimated genetic correlations between live weight daily gain and backfat thickness were low and varied from r(g) = 0.01 to r(g) = 0.36 for Landrace and r(g) = -0.04-0.16 for Large White pigs. In a multivariate approach without considering common environmental effects variance components were estimated for live weight daily gain, daily gain on test, backfat thickness and feed intake separately for each of two test stations. In comparison of the heritabilities estimated for each test stations showed that heritabilities for backfat thickness were gave hardly any differences between test station or dam line. Heritability estimates for the other traits showed smal differences between dam lines but varied extremely between test stations with h(2) = 0.15-0.35 for averaged daily gain on test, h(2) = 0.21-0.34 for live weight daily gain and h(2) = 0.24-0.35 for feed intake. Estimated genetic correlations between live weight daily gain and daily gain on test varied from r(g) = 0.74 to 0.77. Daily gain on test and feed intake showed for both test stations a genetic correlation of r(2) = 0.49. Hardly any genetic correlation was observed between daily gain and backfat thickness (r(g) = -0.09-0.08). Based on the large variation of heritabilities in different environments, it was conclused, that selection across farms is only efficient when there is accounted for these heterogeneous variances in estimation of breeding values.

Data of 402 three years old Warmblood mares were used to analyse the relationship between radiographical evaluation and performance traits as well as conformation traits. The data were combined to three categories of bone diseases, which are important in sport horse populations (bone spavin, navicular disease and Osteochondrose dissecans).Variance analysis showed that the training level of mares at the time of evaluation had a significant influence on Osteochondrose. Navicular disease was significantly influenced by fixed environmental effect of performance group. Results which indicate Osteochondrose showed a significant relationship to the traits of conformation. Whereas navicular disease and bone spavin were not influenced by these traits. Relationship between bone diseases and performance test traits were not found.