Greg B. Ewing

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We present a Bayesian statistical inference approach for simultaneously estimating mutation rate, population sizes, and migration rates in an island-structured population, using temporal and spatial sequence data. Markov chain Monte Carlo is used to collect samples from the posterior probability distribution. We demonstrate that this chain implementation(More)
We expand a coalescent-based method that uses serially sampled genetic data from a subdivided population to incorporate changes to the number of demes and patterns of colonization. Often, when estimating population parameters or other parameters of interest from genetic data, the demographic structure and parameters are not constant over evolutionary time.(More)
Using the structured serial coalescent with Bayesian MCMC and serial samples, we estimate population size when some demes are not sampled or are hidden, ie ghost demes. It is found that even with the presence of a ghost deme, accurate inference was possible if the parameters are estimated with the true model. However with an incorrect model, estimates were(More)
The contents of this work reflect the views of the authors who are responsible for the facts and accuracy of the data presented. Responsibility for the application of the material to specific cases, however, lies with any user of the report and no responsibility in such cases will be attributed to the author or to the University of Canterbury. Abstract This(More)
A commentary on Poor man's 1000 genome project: recent human population expansion confounds the detection of disease alleles in 7098 complete mitochondrial genomes The identification of disease causing rare variants is becoming possible with the advent of next generation sequencing explored this task with large numbers of publicly available mito-chondrial(More)
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