Linkage disequilibrium (LD) is a non-random association of alleles at loci that are in close proximity on a chromosome. LD mapping is based on the fact that a disease mutation arises on a particular chromosome and is initially exclusively associated with marker alleles present on that chromosome.Over time, recombination breaks down this association, narrowing it to markers that are very close to the disease mutation on the chromosome. To map mutations influencing susceptibility to a particular disease using LD, a population sample of haplotypes, or genotypes, from unrelated individuals that are either affected, or unaffected, by a disease is collected. Often, a set of markers are chosen that span a larger candidate region identified first by conventional family-based linkage analysis, but LD mapping can also be used in other circumstances. Most pedigrees available for conventional linkage analysis include a few hundred meiotic events at most, limiting the resolution to about 1 cM (roughly 1 MB), or more. LD mapping potentially provides a much higher resolution localization of a disease mutation because it relies on recombination events over an extended genealogy that relates individuals in a population; this genealogy may extend back thousands of generations, including thousands of meiotic events that are opportunities for recombination. LD mapping can thus provide a resolution orders of magnitude greater than that available from linkage analysis (often less than 100 KB). The DMLE+ program allows multipoint LD mapping using an arbitrary number of SNPs or microsatellite markers. DMLE+ implements Markov chain Monte Carlo (MCMC) methods to allow Bayesian estimation of the posterior probability density of the position of a disease mutation relative to a set of markers. The program can use either haplotypes or genotypes as input. Other parameters of interest can be estimated, such as the age of a disease mutation. An annotated human genome sequence and database tabulating the frequencies of disease mutations in introns, exons, etc can be used to generate a prior distribution for the position of the disease mutation, further narrowing the candidate region and making more efficient use of all available information.