TreeMix

Many aspects of the historical relationships between populations in a species are
reflected in genetic data. Inferring these relationships from genetic data,
however, remains a challenging task. In this paper, we present a statistical
model for inferring the patterns of population splits and mixtures in multiple
populations. In our model, the sampled populations in a species are related to
their common ancestor through a graph of ancestral populations. Using genome-wide
allele frequency data and a Gaussian approximation to genetic drift, we infer the
structure of this graph. We applied this method to a set of 55 human populations
and a set of 82 dog breeds and wild canids. In both species, we show that a
simple bifurcating tree does not fully describe the data; in contrast, we infer
many migration events. While some of the migration events that we find have been
detected previously, many have not. For example, in the human data, we infer that
Cambodians trace approximately 16% of their ancestry to a population ancestral to
other extant East Asian populations. In the dog data, we infer that both the
boxer and basenji trace a considerable fraction of their ancestry (9% and 25%,
respectively) to wolves subsequent to domestication and that East Asian toy
breeds (the Shih Tzu and the Pekingese) result from admixture between modern toy
breeds and "ancient" Asian breeds. Software implementing the model described
here, called TreeMix, is available at http://treemix.googlecode.com.

Inference of population splits and mixtures from genome-wide allele frequency data.

TreeMix is a tool that finds patterns in population splits and infers mixtures in the history of populations. The TreeMix algorithm uses allele frequencies with Gaussian approximation to genetic drift to infer the structure.

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