Estimation of genetic parameters and response to selection for a continuous trait subject to culling before testing.

The consequences of assuming a zero environmental covariance between a binary trait 'test-status' and a continuous trait on the estimates of genetic parameters by restricted maximum likelihood and Gibbs sampling and on response from genetic selection when the true environmental covariance deviates from zero were studied. Data were simulated for two traits (one that culling was based on and a continuous trait) using the following true parameters, on the underlying scale: h² = 0.4; r(A) = 0.5; r(E) = 0.5, 0.0 or -0.5. The selection on the continuous trait was applied to five subsequent generations where 25 sires and 500 dams produced 1500 offspring per generation. Mass selection was applied in the analysis of the effect on estimation of genetic parameters. Estimated breeding values were used in the study of the effect of genetic selection on response and accuracy. The culling frequency was either 0.5 or 0.8 within each generation. Each of 10 replicates included 7500 records on 'test-status' and 9600 animals in the pedigree file. Results from bivariate analysis showed unbiased estimates of variance components and genetic parameters when true r(E) = 0.0. For r(E) = 0.5, variance components (13-19% bias) and especially (50-80%) were underestimated for the continuous trait, while heritability estimates were unbiased. For r(E) = -0.5, heritability estimates of test-status were unbiased, while genetic variance and heritability of the continuous trait together with were overestimated (25-50%). The bias was larger for the higher culling frequency. Culling always reduced genetic progress from selection, but the genetic progress was found to be robust to the use of wrong parameter values of the true environmental correlation between test-status and the continuous trait. Use of a bivariate linear-linear model reduced bias in genetic evaluations, when data were subject to culling.

Effects of integrated genetic evaluations for Icelandic horses on predictive ability, accuracy and selection bias.

The genetic evaluation of Icelandic horses is currently based on results from breeding field tests of riding ability and conformation. The effect of integrating competition traits and/or test status into the genetic evaluation was studied concerning estimation bias, predictive ability, accuracy, correlations between breeding values and ranking of sires. Breeding field test data included 19 954 records from horses assessed in 11 countries during 1994-2008. Competition data included 44 160 records from 7687 horses competing in Iceland and Sweden in 1998-2008. Test status was defined as attendance of horses born in Iceland at breeding field tests and/or in competition. Overall, there were trivial differences between different genetic evaluation models regarding estimation bias and predictive ability. Very strong correlations were estimated between breeding values for combined indexes of conformation, riding ability and total score from different models. Higher accuracy was achieved for most of the traits when competition traits and/or test status were added to the model. Sires ranked differently when the new traits were added to the genetic evaluation model. It was concluded that competition traits should be integrated into the genetic evaluation. Further analyses on genetic parameters for test status and its relationship with the other traits are needed for future inclusion of test status in the genetic evaluation.

Genetic analysis of 'breeding field test status' in Icelandic horses.

Genetic evaluation of Icelandic horses is currently based on results from breeding field tests where riding ability and conformation of the horses are evaluated over the course of 1-2 days. Only a small part of registered horses attend these field tests, and it can be assumed that these are not a random sample of the population. In this study, the trait test status was introduced, describing whether a horse was assessed in a breeding field test. This trait was analysed to find out whether it has a genetic variation and how it correlates genetically to other traits in the breeding goal. Breeding field test data included 39,443 mares born in Iceland in 1990-2001, of which 7431 were assessed in the period 1994-2007. The trait was defined in relation to age, gender and stud of horses. Variance and covariance components were estimated using the Markov Chain Monte Carlo method by applying the Gibbs sampler procedure in the DMU program. Three multivariate analyses were performed where the test status trait was analysed with breeding field test traits. Animal models and sire models were applied. Based on estimated heritabilities (0.51-0.67) and genetic correlations (0.00-0.87), the test status trait showed significant genetic variation and was strongly correlated to some traits. The test status trait reflects preselection in the breeding field test traits and should be included in the genetic evaluation to enhance the procedure, reduce selection bias and increase accuracy of the estimation.