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  • An Addendum to this article was published on 13 November 2008

Abstract

Understanding the genetic structure of human populations is of fundamental interest to medical, forensic and anthropological sciences. Advances in high-throughput genotyping technology have markedly improved our understanding of global patterns of human genetic variation and suggest the potential to use large samples to uncover variation among closely spaced populations1,2,3,4,5. Here we characterize genetic variation in a sample of 3,000 European individuals genotyped at over half a million variable DNA sites in the human genome. Despite low average levels of genetic differentiation among Europeans, we find a close correspondence between genetic and geographic distances; indeed, a geographical map of Europe arises naturally as an efficient two-dimensional summary of genetic variation in Europeans. The results emphasize that when mapping the genetic basis of a disease phenotype, spurious associations can arise if genetic structure is not properly accounted for. In addition, the results are relevant to the prospects of genetic ancestry testing6; an individual’s DNA can be used to infer their geographic origin with surprising accuracy—often to within a few hundred kilometres.

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Acknowledgements

We thank J. Kooner and J. Chambers of the LOLIPOP study and G. Waeber, P. Vollenweider, D. Waterworth, J. S. Beckmann, M. Bochud and V. Mooser of the CoLaus study for providing access to their collections. Financial support was provided by the Giorgi-Cavaglieri Foundation (S.B.), the Swiss National Science Foundation (S.B.), US National Science Foundation Postdoctoral Fellowship in Bioinformatics (J.N.), US National Institutes of Health (M.S., C.D.B.) and GlaxoSmithKline (M.R.N.).

Author Contributions M.R.N. coordinated sample collection and genotyping. K.S.K., A.I., J.N. and A.R.B. performed quality control and prepared genotypic and demographic data for further analyses. C.B., M.S., M.R.N., S.B., J.N., T.J., K.B., Z.K., A.R.B. and A.A. all contributed to the design of analyses. J.N., S.B., T.J., K.B. and Z.K. performed PCA analyses. M.S. and J.N. designed and performed assignment-based analyses. T.J. and J.N. performed genome-wide association simulations. J.N., C.B., M.S., M.R.N. and A.A. wrote the paper. All authors discussed the results and commented on the manuscript.

Author information

Affiliations

  1. Department of Ecology and Evolutionary Biology, Interdepartmental Program in Bioinformatics, University of California–Los Angeles, Los Angeles, California 90095, USA

    • John Novembre
  2. Department of Human Genetics,

    • John Novembre
    •  & Matthew Stephens
  3. Department of Statistics, University of Chicago, Chicago, Illinois 60637, USA

    • Matthew Stephens
  4. Department of Medical Genetics,

    • Toby Johnson
    • , Zoltán Kutalik
    •  & Sven Bergmann
  5. University Institute for Social and Preventative Medecine, Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne, Rue de Bugnon 27 - DGM 328, CH-1005 Lausanne, Switzerland

    • Toby Johnson
  6. Swiss Institute of Bioinformatics, Central Administration, Quartier Sorge - Batiment Genopode, 1015 Lausanne, Switzerland

    • Toby Johnson
    • , Zoltán Kutalik
    •  & Sven Bergmann
  7. Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14853, USA

    • Katarzyna Bryc
    • , Adam R. Boyko
    • , Adam Auton
    • , Amit Indap
    •  & Carlos D. Bustamante
  8. GlaxoSmithKline, Research Triangle Park, North Carolina 27709, USA

    • Karen S. King
    •  & Matthew R. Nelson

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Corresponding author

Correspondence to John Novembre.

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    Supplementary Information

    This file contains Supplementary Notes, Supplementary Figures 1-6 with legends, and Supplementary Tables 1-5.

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DOI

https://doi.org/10.1038/nature07331

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