MCMC estimation of multi-locus genome sharing and multipoint gene locationscores

Effective linkage detection and gene mapping requires analysis of data join tly on members of extended pedigrees, Jointly at multiple genetic markers. Exact likelihood computation is then often infeasible, but Markov chain Mon te Carlo (MCMC) methods permit estimation of posterior probabilities of gen ome sharing among relatives, conditional upon marker data. In principle, MC MC also permits estimation of linkage analysis location score curves, but i n practice effective MCMC samplers are hard to find. Although the whole-mei osis Gibbs sampler (M-sampler) performs well in some cases, for extended pe digrees and tightly linked markers better samplers are needed. However, usi ng the M-sampler as a proposal distribution in a Metropolis-Hastings algori thm does allow genetic interference to be incorporated into the analysis.