Conservation Genetics

, 7:167

A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci*


DOI: 10.1007/s10592-005-9100-y

Cite this article as:
Waples, R. Conserv Genet (2006) 7: 167. doi:10.1007/s10592-005-9100-y


Analysis of linkage disequilibrium (\(\hat{r}^2\)=mean squared correlation of allele frequencies at different gene loci) provides a means of estimating effective population size (Ne) from a single sample, but this method has seen much less use than the temporal method (which requires at least two samples). It is shown that for realistic numbers of loci and alleles, the linkage disequilibrium method can provide precision comparable to that of the temporal method. However, computer simulations show that estimates of Ne based on \(\hat{r}^2\) for unlinked, diallelic gene loci are sharply biased downwards (\(\hat{N}_{\rm e}/N <0.1\) in some cases) if sample size (S) is less than true Ne. The bias is shown to arise from inaccuracies in published formula for \(E(\hat{r}^2)\) when S and/or Ne are small. Empirically derived modifications to \(E(\hat{r}^2)\) for two mating systems (random mating and lifetime monogamy) effectively eliminate the bias (residual bias in \(\hat{N}_{\rm e}<5\)% in most cases). The modified method also performs well in estimating Ne in non-ideal populations with skewed sex ratio or non-random variance in reproductive success. Recent population declines are not likely to seriously affect \(\hat{N}_{\rm e}\), but if N has recently increased from a bottleneck \(\hat{N}_{\rm e}\) can be biased downwards for a few generations. These results should facilitate application of the disequilibrium method for estimating contemporary Ne in natural populations. However, a comprehensive assessment of performance of \(\hat{r}^2\) with highly polymorphic markers such as microsatellites is needed.


computer simulationsmating systemsnon-ideal populationsprecisionsample sizetemporal method

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Laboratoire d’Ecologie Alpine (LECA), Génomique des Populations et BiodiversitéUniversité Joseph FourierGrenobleFrance‰