Likelihood-based inference in isolation-by-distance models using the spatial distribution of low-frequency alleles.
Estimating dispersal distances from population genetic data provides an important alternative to logistically taxing methods for directly observing dispersal. Although methods for estimating dispersal rates between a modest number of discrete demes are well developed, methods of inference applicable to "isolation-by-distance" models are much less established. Here, we present a method for estimating rhosigma(2), the product of population density (rho) and the variance of the dispersal displacement distribution (sigma(2)). The method is based on the assumption that low-frequency alleles are identical by descent. Hence, the extent of geographic clustering of such alleles, relative to their frequency in the population, provides information about rhosigma(2). We show that a novel likelihood-based method can infer this composite parameter with a modest bias in a lattice model of isolation-by-distance. For calculating the likelihood, we use an importance sampling approach to average over the unobserved intraallelic genealogies, where the intraallelic genealogies are modeled as a pure birth process. The approach also leads to a likelihood-ratio test of isotropy of dispersal, that is, whether dispersal distances on two axes are different. We test the performance of our methods using simulations of new mutations in a lattice model and illustrate its use with a dataset from Arabidopsis thaliana.
Novembre
John
J
Department of Ecology and Evolutionary Biology, Interdepartmental Program in Bioinformatics, University of California Los Angeles, Los Angeles, California 90095, USA. jnovembre@ucla.edu
Slatkin
Montgomery
M
D017360:Arabidopsis / Q000235:genetics; D005787:Gene Frequency; D017343:Genes, Plant; D016013:Likelihood Functions; D008962:Models, Theoretical