Topic
Isolation by distance
About: Isolation by distance is a research topic. Over the lifetime, 2965 publications have been published within this topic receiving 126676 citations.
Papers published on a yearly basis
Papers
More filters
5,446 citations
TL;DR: Spag e d i as discussed by the authors is a software primarily designed to characterize the spatial genetic structure of mapped individuals or populations using genotype data of codominant markers, which is useful for detecting isolation by distance within or among populations and estimating gene dispersal parameters; assessing genetic relatedness between individuals and its actual variance, a parameter of interest for marker-based inferences of quantitative inheritance.
Abstract: spag e d i version 1.0 is a software primarily designed to characterize the spatial genetic structure of mapped individuals or populations using genotype data of codominant markers. It computes various statistics describing genetic relatedness or differentiation between individuals or populations by pairwise comparisons and tests their significance by appropriate numerical resampling. spag e d i is useful for: (i) detecting isolation by distance within or among populations and estimating gene dispersal parameters; (ii) assessing genetic relatedness between individuals and its actual variance, a parameter of interest for marker based inferences of quantitative inheritance; (iii) assessing genetic differentiation among populations, including the case of haploids or autopolyploids.
3,509 citations
TL;DR: In this paper, the use of isolation by distance models as a basis for the estimation of demographic parameters from measures of population subdivision was re-examined, and the results for values of F-statistics in one-dimensional models and coalescence times in 2D models were provided.
Abstract: I reexamine the use of isolation by distance models as a basis for the estimation of demographic parameters from measures of population subdivision. To that aim, I first provide results for values of F-statistics in one-dimensional models and coalescence times in two-dimensional models, and make more precise earlier results for F-statistics in two-dimensional models and coalescence times in one-dimensional models. Based on these results, I propose a method of data analysis involving the regression of F(ST)/(1 - F(ST)) estimates for pairs of subpopulations on geographic distance for populations along linear habitats or logarithm of distance for populations in two-dimensional habitats. This regression provides in principle an estimate of the product of population density and second moment of parental axial distance. In two cases where comparison to direct estimates is possible, the method proposed here is more satisfactory than previous indirect methods.
3,331 citations
TL;DR: Analytical theory shows that there is a simple relationship between M̂ and geographic distance in both equilibrium and non‐equilibrium populations and that this relationship is approximately independent of mutation rate when the mutation rate is small.
Abstract: It is shown that for allele frequency data a useful measure of the extent of gene flow between a pair of populations is M∘=(1/FST-1)/4, which is the estimated level of gene flow in an island model at equilibrium. For DNA sequence data, the same formula can be used if FST is replaced by NST . In a population with restricted dispersal, analytic theory shows that there is a simple relationship between M and geographic distance in both equilibrium and non-equilibrium populations and that this relationship is approximately independent of mutation rate when the mutation rate is small. Simulation results show that with reasonable sample sizes, isolation by distance can indeed be detected and that, at least in some cases, non-equilibrium patterns can be distinguished. This approach to analyzing isolation by distance is used for two allozyme data sets, one from gulls and one from pocket gophers.
2,499 citations
TL;DR: The samova algorithm was applied to a set of European roe deer populations examined for their mitochondrial DNA (mtDNA) HVRI diversity and the inferred genetic structure seemed to confirm the hypothesis that some Italian populations were recently reintroduced from a Balkanic stock.
Abstract: We present a new approach for defining groups of populations that are geographically homogeneous and maximally differentiated from each other. As a by-product, it also leads to the identification of genetic barriers between these groups. The method is based on a simulated annealing procedure that aims to maximize the proportion of total genetic variance due to differences between groups of populations (spatial analysis of molecular variance; samova). Monte Carlo simulations were used to study the performance of our approach and, for comparison, the behaviour of the Monmonier algorithm, a procedure commonly used to identify zones of sharp genetic changes in a geographical area. Simulations showed that the samova algorithm indeed finds maximally differentiated groups, which do not always correspond to the simulated group structure in the presence of isolation by distance, especially when data from a single locus are available. In this case, the Monmonier algorithm seems slightly better at finding predefined genetic barriers, but can often lead to the definition of groups of populations not differentiated genetically. The samova algorithm was then applied to a set of European roe deer populations examined for their mitochondrial DNA (mtDNA) HVRI diversity. The inferred genetic structure seemed to confirm the hypothesis that some Italian populations were recently reintroduced from a Balkanic stock, as well as the differentiation of groups of populations possibly due to the postglacial recolonization of Europe or the action of a specific barrier to gene flow.
1,831 citations