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Analysis of Population Structure: A Unifying Framework and Novel Methods Based on Sparse Factor Analysis
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PCA clustering is sensitive to the sample size of different ancestries and also to their amount of genetic drift, which exaggerates principal component distances.
“It is notable that PCA clustering is sensitive to the sample size of different ancestries and also to their amount of genetic drift, which exaggerates principal component distances 207,208,211,212 .”
From Ancient DNA analysis (2021) · cites this paper as [211]
The Results section, “Discrete and admixed populations” (page unavailable), reports that removing half the African individuals changed PCA results appreciably, supporting sensitivity to ancestry sample sizes. The passages found do not say that differing amounts of genetic drift exaggerate distances between principal-component clusters. (LLM, conf=0.87)
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Principal components analysis corrects for stratification in genome-wide association studies
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Reich, D., Thangaraj, K., Patterson, N., Price, A. L., & Singh, L. (2009) · Nature · doi:10.1038/nature08365 · Cited by 1,674
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Self-citationUse of Unlinked Genetic Markers to Detect Population Stratification in Association Studies
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Correlation between Genetic and Geographic Structure in Europe
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Cited by
Sorted by citations
Inference of Population Structure using Dense Haplotype Data
Lawson, D. J., Hellenthal, G., Myers, S., & Falush, D. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002453 · Cited by 1,188
Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions
Hoban, S., Kelley, J. L., Lotterhos, K. E., Antolin, M. F., Bradburd, G., Lowry, D. B., Poss, M. L., Reed, L. K., Storfer, A., & Whitlock, M. C. (2016) · The American Naturalist · doi:10.1086/688018 · Cited by 876
“…linear model approach typically captures patterns of neutral population structure by incorporating relatedness, principal components (Price et al. 2006), or other matrix factorizations (Engelhardt and Stephens 2010) of the genotype matrix as random factors, latent factors, or covariates in a model.”
A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots
Lawson, D. J., van Dorp, L., & Falush, D. (2018) · Nature Communications · doi:10.1038/s41467-018-05257-7 · Cited by 560
Orlando, L., Allaby, R., Skoglund, P., Der Sarkissian, C., Stockhammer, P. W., Ávila-Arcos, M. C., Fu, Q., Krause, J., Willerslev, E., Stone, A. C., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00011-0 · Cited by 411
Metadata
- Authors
- Engelhardt, B. E., & Stephens, M.
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-09-16
- Volume
- 6
- Issue
- 9
- Pages
- e1001117
- ISSN
- 1553-7404
- PMC
- PMC2940725
- Cited by
- 128
- References
- 8
- Language
- en
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