Bayesian Inference of Joint Coalescence Times for Sampled Sequences
The site frequency spectrum (SFS) is a commonly used statistic to summarize genetic variation in a sample of genomic sequences from a population. Such a genomic sample is associated with an imputed genealogical history with attributes such as branch lengths, coalescence times and the time to the most recent common ancestor (TMRCA) as well as topological and combinatorial properties. We present a Bayesian model for sampling from the joint posterior distribution of coalescence times conditional on the SFS associated with a sample of sequences in the absence of selection. In this model, the combinatorial properties of a genealogy, which is represented as a coalescent tree, are expressed as matrices. This facilitates the calculation of likelihoods and the effective sampling of the entire space of tree structures according to the Equal Rates Markov (or Yule-type) measure. Unlike previous methods, assumptions as to the type of stochastic process that generated the genealogical tree are not required. Novel approaches to defining both uninformative and informative prior distributions are employed. The uncertainty in inference due to the stochastic nature of mutation and the unknown tree structure is expressed by the shape of the posterior distributions. The method is implemented using the general purpose Markov Chain Monte Carlo software PyMC3. From the sampled posterior distribution of coalescence times, one can also infer related quantities such as the number of ancestors of a sample at a given time in the past (ancestral distribution) and the probability of specific relationships between branch lengths (for example, that the most recent branch is longer than all the others). The performance of the method is evaluated against simulated data and is also applied to historic mitochondrial data from the Nuu-Chah-Nulth people of North America. The method can be used to obtain estimates of the TMRCA of the sample. The relationship of these estimates to those given by "Thomsons estimator" is explored.