Markov chain Monte Carlo sampling of gene genealogies conditional on unphased SNP genotype data

Abstract

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 and has potential application in gene-mapping. Markov chain Monte Carlo approaches that sample genealogies conditional on observed genetic data typically assume that haplotype data are observed even though commonly-used genotyping technologies provide only unphased genotype data. We have extended our haplotype-based genealogy sampler, sampletrees, to handle unphased genotype data. We use the sampled haplotype configurations as a diagnostic for adequate sampling of the tree space based on the reasoning that if haplotype sampling is restricted, sampling from the tree space will also be restricted. We compare the distributions of sampled haplotypes across multiple runs of sampletrees, and to those estimated by the phase inference program, PHASE. Performance was excellent for the majority of individuals as shown by the consistency of results across multiple runs. However, for some individuals in some datasets, sampletrees had problems sampling haplotype configurations; longer run lengths would be required for these datasets. For many datasets though, we expect that sampletrees will be useful for sampling from the posterior distribution of gene genealogies given unphased genotype data.