PCA-based population structure inference with generic clustering algorithms
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BACKGROUND: Handling genotype data typed at hundreds of thousands of loci is very time-consuming and it is no exception for population structure inference. Therefore, we propose to apply PCA to the genotype data of a population, select the significant principal components using the Tracy-Widom distribution, and assign the individuals to one or more subpopulations using generic clustering algorithms. RESULTS: We investigated K-means, soft K-means and spectral clustering and made comparison to STRUCTURE, a model-based algorithm specifically designed for population structure inference. Moreover, we investigated methods for predicting the number of subpopulations in a population. The results on four simulated datasets and two real datasets indicate that our approach performs comparably well to STRUCTURE. For the simulated datasets, STRUCTURE and soft K-means with BIC produced identical predictions on the number of subpopulations. We also showed that, for real dataset, BIC is a better index than likelihood in predicting the number of subpopulations. CONCLUSION: Our approach has the advantage of being fast and scalable, while STRUCTURE is very time-consuming because of the nature of MCMC in parameter estimation. Therefore, we suggest choosing the proper algorithm based on the application of population structure inference.
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No evidence was found for unusual ancestral clustering in parents of probands with 16p11.2 or 7q11.23 CNVs.
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From Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism (2011) · cites this paper as Lee et al., 2009
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Determining population structure from k-mer frequencies.Preprint
Hrytsenko, Y., Daniels, N. M., & Schwartz, R. S. (2022) · Research Square · doi:10.21203/rs.3.rs-1689838/v1
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Genetics and Population Analysis
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ProtoConNet: Prototypical augmentation and alignment for open-set few-shot image classification
Shi, K., Qi, Z., Zhu, J., Meng, L., Zhang, Y., Huang, H., & Meng, X. (2026) · Displays · doi:10.1016/j.displa.2026.103364 · Cited by 1 · see citing works
Anatone, K., Bither, L. E., Neri, N. R., Loomis, S. J., Ejaz, F., Kraczkowski, M. L., Earley, T. S., & Chernoff, B. (2026) · Open Journal of Ecology · doi:10.4236/oje.2026.166017
- Authors
- Lee, C., Abdool, A., & Huang, C.-H.
- Affiliations
- University of Connecticut (US)
- Corresponding author
- Lee, Chih
- Huang, Chun-Hsi
- ORCID
- Huang, Chun-Hsi
- Venue
- BMC Bioinformatics
- Published
- 2009-01
- Volume
- 10
- Issue
- S1
- Pages
- S73-S73
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC2648762
- OpenAlex
- W1975612392
- Cited by
- 97
- References
- 18
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 5 · 2023: 5 · 2022: 6 · 2021: 2 · 2020: 7 · 2019: 7 · 2018: 7 · 2017: 3 · 2016: 6 · 2015: 3 · 2014: 8 · 2013: 6 · 2012: 9
- FWCI
- 4.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Science Foundation (0755373)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- population structure inference, principal component analysis, genotype data, Tracy-Widom distribution, clustering algorithms, K-means clustering, spectral clustering, structural comparison, Bayesian information criterion, likelihood comparison, scalability
- MeSH terms
- Algorithms, Genetics, Population, Genotype, Humans, Genome, Human, Cluster Analysis, Population Groups
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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