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A Spatial Framework for Understanding Population Structure and Admixture

View ORCID ProfileGideon S. Bradburd, View ORCID ProfilePeter L. Ralph, View ORCID ProfileGraham M. Coop
doi: https://doi.org/10.1101/013474
Gideon S. Bradburd
1 Center for Population Biology, Department of Evolution and Ecology, University of California, Davis, CA 95616
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  • For correspondence: gbradburd@ucdavis.edu
Peter L. Ralph
2 Department of Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089
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  • For correspondence: pralph@usc.edu
Graham M. Coop
1 Center for Population Biology, Department of Evolution and Ecology, University of California, Davis, CA 95616
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  • For correspondence: gmcoop@ucdavis.edu
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Abstract

Geographic patterns of genetic variation within modern populations, produced by complex histories of migration, can be difficult to infer and visually summarize. A general consequence of geographically limited dispersal is that samples from nearby locations tend to be more closely related than samples from distant locations, and so genetic covariance often recapitulates geographic proximity. We use genome-wide polymorphism data to build “geogenetic maps”, which, when applied to stationary populations, produces a map of the geographic positions of the populations, but with distances distorted to reflect historical rates of gene flow. In the underlying model, allele frequency covariance is a decreasing function of geogenetic distance, and nonlocal gene flow such as admixture can be identified as anomalously strong covariance over long distances. This admixture is explicitly co-estimated and depicted as arrows, from the source of admixture to the recipient, on the geogenetic map. We demonstrate the utility of this method on a circum-Tibetan sampling of the greenish warbler (Phylloscopus trochiloides), in which we find evidence for gene flow between the adjacent, terminal populations of the ring species. We also analyze a global sampling of human populations, for which we largely recover the geography of the sampling, with support for significant histories of admixture in many samples. This new tool for understanding and visualizing patterns of population structure is implemented in a Bayesian framework in the program SpaceMix.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 07, 2015.
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A Spatial Framework for Understanding Population Structure and Admixture
Gideon S. Bradburd, Peter L. Ralph, Graham M. Coop
bioRxiv 013474; doi: https://doi.org/10.1101/013474
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A Spatial Framework for Understanding Population Structure and Admixture
Gideon S. Bradburd, Peter L. Ralph, Graham M. Coop
bioRxiv 013474; doi: https://doi.org/10.1101/013474

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