A Markov chain Monte Carlo sampler for gene genealogies conditional on haplotype data

The gene genealogy is a tree describing the ancestral relationships among genes sampled from unrelated individuals. Knowledge of the tree is useful for inference of population-genetic parameters such as migration or recombination rates. It also has potential application in gene-mapping, as individuals with similar trait values will tend to be more closely related genetically at the location of a trait-influencing mutation. One way to incorporate genealogical trees in genetic applications is to sample them conditional on observed genetic data. We have implemented a Markov chain Monte Carlo based genealogy sampler that conditions on observed haplotype data. Our implementation is based on an algorithm sketched by Zöllner and Pritchard but with several differences described herein. We also provide insights from our interpretation of their description that were necessary for efficient implementation. Our sampler can be used to summarize the distribution of tree-based association statistics, such as case-clustering measures.