Bayesian analysis of haplotypes for linkage disequilibrium mapping

Haplotype analysis of disease chromosomes can help identify probable histor ical recombination events and localize disease mutations. Most available an alyses use only marginal and pairwise allele frequency information. We have developed a Bayesian framework that utilizes full haplotype information to overcome various complications such as multiple founders, unphased chromos omes, data contamination, and incomplete marker data. A stochastic model is used to describe the dependence structure among several variables characte rizing the observed haplotypes, for example, the ancestral haplotypes and t heir ages, mutation rate, recombination events, and the location of the dis ease mutation. An efficient Markov chain Monte Carlo algorithm was develope d for computing the estimates of the quantities of interest. The method is shown to perform well in both real data sets (cystic fibrosis data and Frie dreich ataxia data) and simulated data sets. The program that implements th e proposed method, BLADE, as well as the two real datasets, can be obtained from http://www.fas.harvard.edu/similar to junliu/TechRept/Olfolder/diseq_ prog.tar.gz.