Two-locus modeling of asthma in a Hutterite pedigree via Markov chain Monte Carlo

Bayesian Markov chain Monte Carlo (MCMC) segregation analysis for asthma was performed on the whole 1,544-member Hutterite pedigree. Heterogeneous and epistatic two-locus models and complex one-locus models were investigated, with trait loci postulated to be linked to markers in regions previously found to be possibly linked to asthma or atopy. The epistatic two-locus dominant-dominant model provided the best estimates, among the models investigated, in terms of prediction of population prevalence and relative risk for sibs of the affected.