Inference of population splits and mixtures from genome-wide allele frequency data
Joseph K. Pickrell, Jonathan K. Pritchard

TL;DR
This paper introduces TreeMix, a statistical model that infers population splits and admixture events from genome-wide allele frequency data, revealing complex migration patterns in humans and dogs beyond simple bifurcating trees.
Contribution
The paper presents a novel method for modeling population relationships with a graph that includes migration events, improving understanding of species' evolutionary history.
Findings
Identified multiple migration events in human populations, including a significant ancestry component in Cambodians.
Detected wolf admixture in dog breeds post-domestication, such as boxers and basenjis.
Revealed admixture between modern and ancient Asian breeds in toy dog lineages.
Abstract
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 this 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,…
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