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We examine a Bayesian Markov-chain Monte Carlo framework for simultaneous segregation and linkage analysis in the simulated single-nucleotide polymorphism data provided for Genetic Analysis Workshop 16. We conducted linkage only, linkage and association, and association only tests under this framework. We also compared these results with variance-component linkage analysis and regression analyses. The results indicate that the method shows some promise, but finding genes that have very small (<0.1%) contributions to trait variance may require additional sources of information. All methods examined fared poorly for the smallest in the simulated "polygene" range (h 2 of 0.0015 to 0.0002).

A framework for analyzing both linkage and association: an analysis of Genetic Analysis Workshop 16 simulated data