LIKELIHOOD-BASED INFERENCE IN ISOLATION-BY-DISTANCE MODELS USING THE SPATIAL DISTRIBUTION OF LOW-FREQUENCY ALLELES |
| |
Authors: | John Novembre Montgomery Slatkin |
| |
Institution: | Department of Ecology and Evolutionary Biology, Interdepartmental Program in Bioinformatics, University of California Los Angeles, Los Angeles, California 90095;Department of Integrative Biology, University of California Berkeley, Berkeley, California 94720;E-mail: |
| |
Abstract: | 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 ρσ2, the product of population density (ρ) and the variance of the dispersal displacement distribution (σ2). 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 ρσ2. 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 . |
| |
Keywords: | Dispersal importance sampling intraallelic genealogy isolation-by-distance likelihood low-frequency alleles |
|
|