Human Genetics

, Volume 118, Issue 3–4, pp 366–371 | Cite as

Geography is a better determinant of human genetic differentiation than ethnicity

  • Andrea Manica
  • Franck Prugnolle
  • François BallouxEmail author
Original Investigation


Individuals differ genetically in their susceptibility to particular diseases and their response to drugs. However, personalized treatments are difficult to develop, because disease susceptibility and drug response generally have poorly characterized genetic architecture. It is thus tempting to use the ethnicity of patients to capture some of the variation in allele frequencies at the genes underlying a clinical trait. The success of such a strategy depends on whether human populations can be accurately classified into discrete genetic ethnic groups. Despite the heated discussions and controversies surrounding this issue, there has been essentially no attempt so far to quantify the relative power of ethnic groups and geography at predicting the proportion of shared alleles between human populations. Here, we present the first such quantification using a dataset of 51 populations typed at 377 autosomal microsatellite markers, and show that pair-wise geographic distances across landmasses constitute a far better predictor than ethnicity. Allele-sharing between human populations worldwide decays smoothly with increasing physical distance. We discuss the relevance of these patterns for the expected distribution of variants of medical interest. The distribution patterns of gene coding for simple traits are expected to be highly heterogeneous, as most such genes experienced strong natural selection. However, variants involved in complex traits are expected to behave essentially neutrally, and we expect them to fit closely our predictions based on microsatellites. We conclude that the use of ethnicity alone will often be inadequate as a basis for medical treatment.


Ethnic Group Geographic Distance Allele Sharing Neutral Allele Mantel Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Lori Lawson-Handley, Elizabeth Tyler and Graham Forster and four anonymous reviewers for very helpful comments and suggestions. FP is supported by a Lavoisier Fellowship from the Ministère Français des Affaires Etrangères. FB acknowledges support from the BBRSC.


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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Andrea Manica
    • 1
  • Franck Prugnolle
    • 2
  • François Balloux
    • 2
    • 3
    Email author
  1. 1.Evolutionary Ecology Group, Department of ZoologyUniversity of CambridgeCambridgeUK
  2. 2.Theoretical and Molecular Population Genetics group, Department of GeneticsUniversity of CambridgeCambridgeEngland
  3. 3.Department of GeneticsUniversity of CambridgeCambridgeUK

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