Such methods can deal with many populations or individuals as the unit of analysis. Other approaches are designed only to visualize patterns of genetic relatedness and population structure, without using a particular population genetic model. There has been considerable recent progress in this area, using a variety of summaries such as the allele frequency spectrum, or approximations to the coalescent applied to sequence data. Model-based approaches focus on developing a detailed view of the migrational history of a small number of populations, often assuming one or a small number of large, randomly mating populations (i.e. There are many different methods to learn how population structure and demographic processes have left their mark on patterns of genetic variation within and between populations. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: This work was supported by the following grants: National Science Foundation (nsf.gov) Grant Number 1262645 to PLR and GMC National Science Foundation (nsf.gov) Grant Numbers 11488725 to GB National Institute of Health ( ) Grant numbers RO1GM83098 and RO1GM107374 to GC. Received: FebruAccepted: NovemPublished: January 15, 2016Ĭopyright: © 2016 Bradburd et al. University of California, Berkeley, UNITED STATES The inferred geogenetic map is an intuitive and information-rich visual summary of patterns of population structure.Ĭitation: Bradburd GS, Ralph PL, Coop GM (2016) A Spatial Framework for Understanding Population Structure and Admixture. We depict the effect of admixture using arrows, from a source of admixture to its target, on the inferred map. Added to this, “admixture” can be thought of as the outcome of unusually long-distance gene flow it results in relatedness between populations that is anomalously high given the distance that separates them. ![]() The result is a “geogenetic” map in which the distances between populations are effective distances, indicative of the way that populations perceive the distances between themselves: the “organism’s-eye view” of the world. Two populations that are sampled at distant locations but that are genetically similar (perhaps one was recently founded by a colonization event from the other) may have inferred locations that are nearby, while two populations that are sampled close together, but that are genetically dissimilar (e.g., are separated by a barrier), may have inferred locations that are farther apart. In this paper, we introduce a statistical method for inferring, for a set of sequenced samples, a map in which the distances between population locations reflect genetic, rather than geographic, proximity. This new tool for understanding and visualizing patterns of population structure is implemented in a Bayesian framework in the program SpaceMix. ![]() ![]() 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. 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. This admixture is explicitly co-estimated and depicted as arrows, from the source of admixture to the recipient, on the geogenetic map. 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. 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. 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. Geographic patterns of genetic variation within modern populations, produced by complex histories of migration, can be difficult to infer and visually summarize.
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