A Bayesian Markov chain Monte Carlo approach to map disease genes in simulated GAW11 data.

Abstract

A Bayesian method for multipoint mapping of disease genes based on Markov chain Monte Carlo algorithms was applied to the simulated GAW11 data (Study 2). The method is based on repeated Gibbs and more general Metropolis-Hastings steps. For simplicity we assumed a single disease locus model with two alleles. A normal distribution for the underlying latent variable of the qualitative phenotype was assumed. Based on a single replicate of the data no clear evidence of any of the genes underlying the simulated disease was found. However, when three replicates were combined the method was able to locate the locus C correctly on chromosome 3.