Monte Carlo Markov chain methods and model selection in genetic analysis.

摘要

Model selection and parameter estimation is an integral part of genetic analyses leading up to gene identification. However, exact computation of likelihoods for complex models on large pedigrees is not possible. Monte Carlo Markov chain (MCMC) methods provide a computationally feasible way of estimating these likelihoods and associated parameters. The practical utility of these methods depends on their performance, compared with both existing approximation methods, and to absolute measures. Simulations suggest that a current implementation of MCMC methods offers modest advantages over a current approximation method in the context of the mixed model, but that distinguishing polygenic from oligogenic effects may require more complex models and MCMCmethods for a practical solution.