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

Phillip R. England, Jean Marie Cornuet, Pierre Berthier, David A. Tallmon, Gordon Luikart

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135 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)303-308
Number of pages6
JournalConservation Genetics
Volume7
Issue number2
DOIs
StatePublished - Apr 2006

Funding

PRE was supported by an Australian Postdoctoral Fellowship, a Région Bourgogne Fellowship and the European Union Econogene project. Andrea Taylor kindly provided northern hairy-nosed wombat microsatellite genotypes. PRE, JMC and GL were supported by the Bureau des Ressources Génétiques. GL also received support from the Division of Biological Sciences at University of Montana.

    Keywords

    • Effective population size
    • Linkage disequilibrium
    • Sampling bias

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