Monte Carlo Bayesian methods for quantitative traits
Many common diseases are believed to have a genetic component, and the mixed model is often used to describe the underlying mechanism of these traits. Since exact likelihood computation of the mixed model for data from large pedigrees is infeasible, this paper presents Monte Carlo Bayesian Analysis (MCBA) methods for complex segregation analysis and combined segregation and linkage analysis of the mixed model. Results from a simulation study of complex segregation analyses with data from a 232-member pedigree showed that MCBA provided good estimates for the model parameters. Compared to results from two other methods, PAP and MIXD, MCBA provided comparable estimates and was more efficient computationally. MCBA was also used to perform a set of combined segregation and linkage analyses, and good estimates resulted. Some sensitivity analyses indicated that the choice of prior distributions and starting configurations might result in elevated variance estimates, but did not substantially influence the final estimates of the other parameters.
monte carlo mixed model bayesian analysis complex segregation analysis linkage analysis markov chain