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Cited by 302Forensic and Genetic ResearchGenetic diversity and population structureRace, Genetics, and Society

PCA-Correlated SNPs for Structure Identification in Worldwide Human Populations

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Paschou, Peristera, Ziv, Elad, Burchard, Esteban G, Choudhry, Shweta, Rodriguez-Cintron, William, Mahoney, Michael W, Drineas, Petros·2007·PLoS Genetics
DOI 10.1371/journal.pgen.0030160PMC PMC1988848
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Abstract

Existing methods to ascertain small sets of markers for the identification of human population structure require prior knowledge of individual ancestry. Based on Principal Components Analysis (PCA), and recent results in theoretical computer science, we present a novel algorithm that, applied on genomewide data, selects small subsets of SNPs (PCA-correlated SNPs) to reproduce the structure found by PCA on the complete dataset, without use of ancestry information. Evaluating our method on a previously described dataset (10,805 SNPs, 11 populations), we demonstrate that a very small set of PCA-correlated SNPs can be effectively employed to assign individuals to particular continents or populations, using a simple clustering algorithm. We validate our methods on the HapMap populations and achieve perfect intercontinental differentiation with 14 PCA-correlated SNPs. The Chinese and Japanese populations can be easily differentiated using less than 100 PCA-correlated SNPs ascertained after evaluating 1.7 million SNPs from HapMap. We show that, in general, structure informative SNPs are not portable across geographic regions. However, we manage to identify a general set of 50 PCA-correlated SNPs that effectively assigns individuals to one of nine different populations. Compared to analysis with the measure of informativeness, our methods, although unsupervised, achieved similar results. We proceed to demonstrate that our algorithm can be effectively used for the analysis of admixed populations without having to trace the origin of individuals. Analyzing a Puerto Rican dataset (192 individuals, 7,257 SNPs), we show that PCA-correlated SNPs can be used to successfully predict structure and ancestry proportions. We subsequently validate these SNPs for structure identification in an independent Puerto Rican dataset. The algorithm that we introduce runs in seconds and can be easily applied on large genome-wide datasets, facilitating the identification of population substructure, stratification assessment in multi-stage whole-genome association studies, and the study of demographic history in human populations.

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  • The bounds of Theorem 2 tend to be loose on real-world datasets.

    “Such a phenomenon is not a new observation, and much prior work has shown that the bounds tend to be loose on real-world datasets (Broadbent et al., 2010; Paschou et al., 2007; Jolliffe, 1972).”

    From Compactor: Calibrated Query-Agnostic KV Cache Compression with Approximate Leverage Scores (2025) · cites this paper as Paschou et al., 2007

    The Methods section on estimating significant principal components discusses eigenvalue bounds and tests on the paper’s datasets, but does not assess whether those bounds are loose. Searches for Theorem 2 and for loose or non-tight bounds on real-world data found no passage addressing the claim. No page numbers were available in our copy. (LLM, conf=0.76)

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Authors
Paschou, P., Ziv, E., Burchard, E. G., Choudhry, S., Rodriguez-Cintron, W., Mahoney, M. W., & Drineas, P.
Affiliations
  • Democritus University of Thrace (GR)
  • University of California, San Francisco (US)
  • Yahoo (United States) (US)
Corresponding author
Paschou, Peristera
ORCID
Venue
PLoS Genetics
Publisher
Public Library of Science (PLoS)
Published
2007-09-21
Volume
3
Issue
9
Pages
e160
ISSN
  • 1553-7390 (ISSN-L)
  • 1553-7404
DOI
10.1371/journal.pgen.0030160
PMC
PMC1988848
OpenAlex
W2024027525
Cited by
302
References
6
Citations by year
2026: 6 · 2025: 9 · 2024: 7 · 2023: 7 · 2022: 11 · 2021: 29 · 2020: 10 · 2019: 18 · 2018: 20 · 2017: 14 · 2016: 19 · 2015: 21 · 2014: 14 · 2013: 16 · 2012: 27
FWCI
8.60
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Genetics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, other-oa)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
Funders
  • National Science Foundation (CAREER)
  • U.S. Department of Defense
  • American Thoracic Society
  • National Institutes of Health
  • National Institute on Aging (U19AG23122, U19AG023122)
  • National Heart, Lung, and Blood Institute (HL078885, R01HL078885)
  • National Cancer Institute (K22CA109351)
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 › Race, Genetics, and Society
Keywords
population structure, principal component analysis, SNP selection, ancestry inference, admixed populations, Puerto Rican population, HapMap populations, population stratification, whole-genome association studies, human population genetics
MeSH terms
Algorithms, Genetics, Population, Humans, Polymorphism, Single Nucleotide, Principal Component Analysis

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