Article

Conservation Genetics

, Volume 7, Issue 2, pp 303-308

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

  • Phillip R. EnglandAffiliated withCSIRO Division of Marine & Atmospheric Research Email author 
  • , Jean-Marie CornuetAffiliated withCentre de Biologie et de Gestion des Populations
  • , Pierre BerthierAffiliated withPopulations genetik, Zoologisches Institut, Universität Bern
  • , David A. TallmonAffiliated withBiology Program, University of Alaska Southeast
  • , Gordon LuikartAffiliated withDivision of Biological Sciences, University of MontanaCentro de Investigação em Biodiversidade e␣Recursos Geneticos (CIBIO-UP), Universidade do Porto

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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.

Keywords

effective population size linkage disequilibrium sampling bias