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Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data
Open accessAbstract
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.
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“Gene flow from the HGF to domestic HGF in Sudan was indicated in the TreeMix (Pickrell and Pritchard 2012) when m = 1 (supplementary fig. S6, Supplementary Material online) in 99.9% of the bootstrap replicates.”
From Genomic Analyses Unveil Helmeted Guinea Fowl (Numida meleagris) Domestication in West Africa (2021) · cites this paper as Pickrell and Pritchard 2012
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Metadata
- Authors
- Pickrell, J. K., & Pritchard, J. K.
- Affiliations
- University of Chicago (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Pickrell, Joseph K.
- Pritchard, Jonathan K.
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-11-15
- Volume
- 8
- Issue
- 11
- Pages
- e1002967
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC3499260
- OpenAlex
- W2093044565
- Cited by
- 2,731
- References
- 79
- Citations by year
- 2026: 223 · 2025: 260 · 2024: 362 · 2023: 271 · 2022: 277 · 2021: 346 · 2020: 222 · 2019: 175 · 2018: 180 · 2017: 117 · 2016: 120 · 2015: 80 · 2014: 65 · 2013: 22 · 2012: 7
- FWCI
- 33.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Knowledge@UChicago (University of Chicago) (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Nature Precedings (submittedVersion)
- arXiv (Cornell University)
- University of Chicago (cc-by)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health
- National Institute of Mental Health (R01MH084703, MH084703)
- National Institute of General Medical Sciences (F32GM103098)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- population admixture, population structure inference, migration events, dog breeds, wild canids, genetic drift, TreeMix, dog domestication, Shih Tzu, Pekingese
- MeSH terms
- Algorithms, Animals, Breeding, Dogs, Gene Frequency, Humans, Models, Genetic, Population, Wolves, Polymorphism, Single Nucleotide, Genetic Drift, Genome-Wide Association Study
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