Inference of Population Structure using Dense Haplotype Data
Open accessAbstract
The advent of genome-wide dense variation data provides an opportunity to investigate ancestry in unprecedented detail, but presents new statistical challenges. We propose a novel inference framework that aims to efficiently capture information on population structure provided by patterns of haplotype similarity. Each individual in a sample is considered in turn as a recipient, whose chromosomes are reconstructed using chunks of DNA donated by the other individuals. Results of this "chromosome painting" can be summarized as a "coancestry matrix," which directly reveals key information about ancestral relationships among individuals. If markers are viewed as independent, we show that this matrix almost completely captures the information used by both standard Principal Components Analysis (PCA) and model-based approaches such as STRUCTURE in a unified manner. Furthermore, when markers are in linkage disequilibrium, the matrix combines information across successive markers to increase the ability to discern fine-scale population structure using PCA. In parallel, we have developed an efficient model-based approach to identify discrete populations using this matrix, which offers advantages over PCA in terms of interpretability and over existing clustering algorithms in terms of speed, number of separable populations, and sensitivity to subtle population structure. We analyse Human Genome Diversity Panel data for 938 individuals and 641,000 markers, and we identify 226 populations reflecting differences on continental, regional, local, and family scales. We present multiple lines of evidence that, while many methods capture similar information among strongly differentiated groups, more subtle population structure in human populations is consistently present at a much finer level than currently available geographic labels and is only captured by the haplotype-based approach. The software used for this article, ChromoPainter and fineSTRUCTURE, is available from http://www.paintmychromosomes.com/.
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- Authors
- Lawson, D. J., Hellenthal, G., Myers, S., & Falush, D.
- Affiliations
- University of Bristol (GB)
- Centre for Human Genetics (GB)
- University of Oxford (GB)
- University College Cork (IE)
- Corresponding author
- Falush, Daniel
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-01-26
- Volume
- 8
- Issue
- 1
- Pages
- e1002453
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC3266881
- OpenAlex
- W2080873675
- Cited by
- 1,188
- References
- 50
- Citations by year
- 2026: 72 · 2025: 102 · 2024: 102 · 2023: 86 · 2022: 106 · 2021: 155 · 2020: 104 · 2019: 115 · 2018: 91 · 2017: 62 · 2016: 65 · 2015: 50 · 2014: 33 · 2013: 26 · 2012: 18
- FWCI
- 24.48
- 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)
- MPG.PuRe (Max Planck Society) (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Wellcome Trust (075491, WT082930MA, 075491/Z/04,, 090532, 018827, 05/FE1/B882)
- University of Bristol
- Max-Planck-Gesellschaft
- 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 structure, chromosome painting, ancestry inference, linkage disequilibrium, fine structure, principal component analysis, structure, fine-scale population structure
- MeSH terms
- Algorithms, Computer Simulation, Haplotypes, Humans, Models, Theoretical, Population, Software, Linkage Disequilibrium, Genome, Human, Human Genome Project, Polymorphism, Single Nucleotide, Principal Component Analysis, Racial Groups
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