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Genetic changes across language boundaries in Europe.

R R Sokal1, N L Oden, B A Thomson

  • 1Department of Ecology and Evolution, State University of New York, Stony Brook 11794-5245.

American Journal of Physical Anthropology
|July 1, 1988
PubMed
Summary

Genetic changes are more pronounced at European language-family boundaries, with allele frequencies showing significant shifts. These findings, revealed by novel analytical methods, highlight the impact of linguistic history on human genetic variation.

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Area of Science:

  • Population Genetics
  • Anthropology
  • Bioinformatics

Background:

  • Investigating the correlation between linguistic borders and genetic variation is crucial for understanding human population history.
  • Previous studies faced challenges due to spatial autocorrelation in both linguistic and biological data.

Purpose of the Study:

  • To determine if allele frequencies and cranial variables exhibit increased change at European language-family boundaries.
  • To develop and apply novel statistical methods to overcome spatial autocorrelation issues.

Main Methods:

  • Employed three distinct methods: quadrat-variance, rate-of-change, and difference methods.
  • These techniques were specifically designed to analyze genetic and cranial data across 29 European language-family boundaries.

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  • A predictive model was constructed based on the historical context of language-family boundaries.

    • Main Results:

    • All three methods consistently indicated increased genetic change at language-family boundaries.
    • Gene frequencies showed more marked changes than cranial variables across these boundaries.
    • Thirteen language-family boundaries exhibited significant gene frequency changes, with specific alleles indicating shifts at different boundaries.

    Conclusions:

    • Language-family boundaries in Europe are associated with significant genetic differentiation.
    • The developed analytical methods effectively identified genetic changes, confirming the influence of linguistic history on genetic landscapes.
    • The predictive model aligns well with observed genetic changes, underscoring the historical basis for genetic variation.