Bayesian prediction of breeding values for multivariate binary and continuous traits in simulated horse populations using threshold–linear models with Gibbs sampling

Abstract

Simulated data were used to determine the properties of multivariate prediction of breeding values for categorical and continuous traits using phenotypic, molecular genetic and pedigree information by mixed linear-threshold animal models via Gibbs sampling. Simulation parameters were chosen such that the data resembled situations encountered in Warmblood horse populations. Genetic evaluation was performed in the context of the radiographic findings in the equine limbs. The simulated pedigree comprised seven generations and 40 000 animals per generation. The simulated data included additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits. For one of the binary traits, quantitative trait locus (QTL) effects and genetic markers were simulated, with three different scenarios with respect to recombination rate (r) between genetic markers and QTL and polymorphism information content (PIC) of genetic markers being studied: r 5 0.00 and PIC 5 0.90 (r0p9), r 5 0.01 and PIC 5 0.90 (r1p9), and r 5 0.00 and PIC 5 0.70 (r0p7). For each scenario, 10 replicates were sampled from the simulated horse population, and six different data sets were generated per replicate. Data sets differed in number and distribution of animals with trait records and the availability of genetic marker information. Breeding values were predicted via Gibbs sampling using a Bayesian mixed linear-threshold animal model with residual covariances fixed to zero and a proper prior for the genetic covariance matrix. Relative breeding values were used to investigate expected response to multiand single-trait selection. In the sires with 10 or more offspring with trait information, correlations between true and predicted breeding values ranged between 0.89 and 0.94 for the continuous traits and between 0.39 and 0.77 for the binary traits. Proportions of successful identification of sires of average, favourable and unfavourable genetic value were 81% to 86% for the continuous trait and 57% to 74% for the binary traits in these sires. Expected decrease of prevalence of the QTL trait was 3% to 12% after multi-trait selection for all binary traits and 9% to 17% after single-trait selection for the QTL trait. The combined use of phenotype and genotype data was superior to the use of phenotype data alone. It was concluded that information on phenotypes and highly informative genetic markers should be used for prediction of breeding values in mixed linear-threshold animal models via Gibbs sampling to achieve maximum reduction in prevalences of binary traits.

Simulation study on the effects of excluding offspring information for genetic evaluation versus using genomic markers for selection in dog breeding

“…Correlations between true breeding values and BV predicted on the basis of own phenotypes and random samples of offspring phenotypes were similar to those found in previous simulation studies (Stock et al. 2007, 2008).…”

“…However, comparison of selection responses revealed a clear decrease of selection efficiency with increasing amount of exclusion of disease information in the data available for genetic evaluation. Selection responses in the order of 11–14% as measured by the relative decrease of the trait prevalence have been previously reported for a binary traits with a heritability of 0.25 (Stock et al. 2008).…”