2015
DOI: 10.1101/013474
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A Spatial Framework for Understanding Population Structure and Admixture.

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… Show more

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Cited by 57 publications
(83 citation statements)
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References 84 publications
(71 reference statements)
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“…Whole-genome relationships, as summarized using principal components analysis (PCA; Fig. S1, Supporting information), show the pattern expected based on previous research (Alcaide et al 2014; see also Bradburd et al 2016), of two highly distinct Siberian forms (viridanus and plumbeitarsus) and a progression of genomic signatures through the ring of populations to the south. Note that Alcaide et al (2014) summarized variation in the same GBS reads, but used an entirely distinct bioinformatics pipeline and based their PCA on only 2334 SNPs due to very restrictive filtering; the fact that the current study recovers similar patterns using much less restrictive filtering and approximately 250 times the number of SNPs gives strong confidence in the inferred relationships.…”
Section: Resultssupporting
confidence: 70%
“…Whole-genome relationships, as summarized using principal components analysis (PCA; Fig. S1, Supporting information), show the pattern expected based on previous research (Alcaide et al 2014; see also Bradburd et al 2016), of two highly distinct Siberian forms (viridanus and plumbeitarsus) and a progression of genomic signatures through the ring of populations to the south. Note that Alcaide et al (2014) summarized variation in the same GBS reads, but used an entirely distinct bioinformatics pipeline and based their PCA on only 2334 SNPs due to very restrictive filtering; the fact that the current study recovers similar patterns using much less restrictive filtering and approximately 250 times the number of SNPs gives strong confidence in the inferred relationships.…”
Section: Resultssupporting
confidence: 70%
“…As our sampling design did not include the purported cytotype contact zone (shaded area in Fig. 1C; Smith 1969), and thus created a discontinuity in sampling, clustering may be a product of the interaction between incomplete sampling and isolation-bydistance rather than representing a discrete barrier to gene flow (see Balkenhol et al 2015;Bradburd et al 2016;Perez et al 2018). To evaluate this possibility, we examined the estimated effective migration surface (EEMS; Petkova et al 2016), which is a Bayesian estimate of effective migration rates based on SNP frequencies and a prior hypothesis of isolation-by-distance.…”
Section: Demographic Analysesmentioning
confidence: 99%
“…We applied an alternate method using the program SPACEMIX (Bradburd et al 2015) which estimates allele frequency covariances associated with geography to explore spatial patterns of genetic variation and explore the extent of admixture between lineages. SPACEMIX provides a better summary of the data than PCoA because it accounts for geographic proximity in genetic covariance estimates (Bradburd et al 2015). High covariances over large distances are indicative of nonlocal gene flow (admixture).…”
Section: Computational Analysesmentioning
confidence: 99%