Monte Carlo methods for linkage analysis of two-locus disease models.

Parametric linkage analysis of simultaneous mapping of the two disease loci of a qualitative trait governed by a two-locus model has been shown to provide greater power in detecting linkage than standard lod-score analysis that maps a single disease locus. Despite its great potential for power gains, two-locus parametric analysis has not been used routinely in disease gene mapping. due to the computational intensity of currently available methods and programs. In this paper, we propose a Markov chain Monte Carlo (MCMC) method for performing lod-score analysis of qualitative traits governed by two-locus models. This method obtains lod-score estimates that can be arbitrarily close to their corresponding exact values. The algorithm implementing this MCMC method is linear in the number of markers. This feature enables us to perform two-locus analysis mapping each trait to a set of markers, instead of just to a single marker. We analyzed an alcohol dependence dataset composed of 105 pedigrees with various sizes and various degrees of missingness in the observed marker and disease data. The estimates from our MCMC procedure match up well with the lod scores from exact analysis, but it took much less time for the MCMC procedure to obtain the results. We also performed a simulation study to investigate power gains with additional markers. Our results indicate that an additional marker on each map can provide a great deal more information for linkage measured in terms of the magnitude of lod scores.