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.
%0 Journal Article
%1 novembre2009likelihoodbased
%A Novembre, J
%A Slatkin, M
%D 2009
%J Evolution
%K branching_brownian_motion continuous_populations demographic_inference dispersal_estimation isolation_by_distance population_genetics rare_alleles spatial_branching spatial_structure
%N 11
%P 2914-2925
%R 10.1111/j.1558-5646.2009.00775.x
%T Likelihood-based inference in isolation-by-distance models using the spatial distribution of low-frequency alleles
%U http://www.ncbi.nlm.nih.gov/pubmed/19624728
%V 63
%X 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.
@article{novembre2009likelihoodbased,
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 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.},
added-at = {2014-04-19T06:10:14.000+0200},
author = {Novembre, J and Slatkin, M},
biburl = {https://www.bibsonomy.org/bibtex/2f0100bc66c5997d0606aaf9caf0cdea2/peter.ralph},
doi = {10.1111/j.1558-5646.2009.00775.x},
interhash = {94de7a70edd279598d10b24b5a11ea17},
intrahash = {f0100bc66c5997d0606aaf9caf0cdea2},
journal = {Evolution},
keywords = {branching_brownian_motion continuous_populations demographic_inference dispersal_estimation isolation_by_distance population_genetics rare_alleles spatial_branching spatial_structure},
month = nov,
number = 11,
pages = {2914-2925},
pmid = {19624728},
timestamp = {2016-01-29T03:01:11.000+0100},
title = {Likelihood-based inference in isolation-by-distance models using the spatial distribution of low-frequency alleles},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19624728},
volume = 63,
year = 2009
}