GENEALOGICAL INFERENCE FROM MICROSATELLITE DATA

Ease and accuracy of typing, together with high levels of polymorphism and widespread distribution in the genome, make microsatellite (or sh ort tandem repeat) loci an attractive potential source of information about both population histories and evolutionary processes. However, m icrosatellite data are difficult to interpret, in particular because o f the frequency of back-mutations. Stochastic models for the underlyin g genetic processes can be specified, but in the past they have been t oo complicated for direct analysis. Recent developments in stochastic simulation methodology now allow direct inference about both historica l events, such as genealogical coalescence times, and evolutionary par ameters, such as mutation rates. A feature of the Markov chain Monte C arlo (MCMC) algorithm that we propose here is that the likelihood comp utations are simplified by treating the (unknown) ancestral allelic st ates as auxiliary parameters. We illustrate the algorithm by analyzing microsatellite samples simulated under the model. Our results suggest that a single microsatellite usually does not provide enough informat ion for useful inferences, but that several completely linked microsat ellites can be informative about some aspects of genealogical history and evolutionary processes. We also reanalyze data from a previously p ublished human Y chromosome microsatellite study, finding evidence for an effective population size for human Y chromosomes in the low thous ands and a recent time since their most recent common ancestor: the 95 % interval runs from similar to 15,000 to 130,000 years, with most lik ely values around 30,000 years.