Results of a simulation study with two methods of analysis of data simulated under the mixed model on a 232-member pedigree are presented. The programs Pedigree Analysis Package (PAP), which approximate the likelihoods needed in a complex segregation analysis, and MIXD, which uses Monte Carlo Markov chain (MCMC), to estimate likelihoods were used. PAP obtained unbiased estimates of the major locus genotype means and the gene frequency, but biased estimates of the environmental variance component, and thus the heritability. A substantial fraction of the runs did not converge to an internal set of parameter estimates when analyzed with PAP. MIXD, which uses the Gibbs sampler to perform the MCMC sampling, produced unbiased estimates of all parameters with considerably more accuracy than obtained with PAP, and did not suffer from convergence of estimates to the boundary of the parameter space. The difference in behavior and accuracy of parameter estimates between PAP and MIXD was most apparent for models with either high or low residual additive genetic variance. Thus in situations where accuracy of the model is important, use of MCMC methods may be useful. In situations where less accuracy is needed, approximation methods may be adequate. Practical issues in using MCMC as implemented in MIXD to fit the mixed model are also discussed. Results of the simulations indicate that, unlike PAP, the starting configurations of most parameter estimates do not substantially influence the final parameter estimates in analysis with MIXD.