Conservation Genetics

, 7:303

Estimating effective population size from linkage disequilibrium: severe bias in small samples


    • CSIRO Division of Marine & Atmospheric Research
  • Jean-Marie Cornuet
    • Centre de Biologie et de Gestion des Populations
  • Pierre Berthier
    • Populations genetik, Zoologisches InstitutUniversität Bern
  • David A. Tallmon
    • Biology ProgramUniversity of Alaska Southeast
  • Gordon Luikart
    • Division of Biological SciencesUniversity of Montana
    • Centro de Investigação em Biodiversidade e␣Recursos Geneticos (CIBIO-UP)Universidade do Porto

DOI: 10.1007/s10592-005-9103-8

Cite this article as:
England, P.R., Cornuet, J., Berthier, P. et al. Conserv Genet (2006) 7: 303. doi:10.1007/s10592-005-9103-8


Effective population size (N e) is a central concept in evolutionary biology and conservation genetics. It predicts rates of loss of neutral genetic variation, fixation of deleterious and favourable alleles, and the increase of inbreeding experienced by a population. A method exists for the estimation of N e from the observed linkage disequilibrium between unlinked loci in a population sample. While an increasing number of studies have applied this method in natural and managed populations, its reliability has not yet been evaluated. We developed a computer program to calculate this estimator of N e using the most widely used linkage disequilibrium algorithm and used simulations to show that this estimator is strongly biased when the sample size is small (<‰100) and below the true N e. This is probably due to the linkage disequilibrium generated by the sampling process itself and the inadequate correction for this phenomenon in the method. Results suggest that N e estimates derived using this method should be regarded with caution in many cases. To improve the method’s reliability and usefulness we propose a way to determine whether a given sample size exceeds the population N e and can therefore be used for the computation of an unbiased estimate.


effective population size linkage disequilibrium sampling bias

Copyright information

© Springer Science+Business Media, Inc. 2005