Jane Remfert has successfully completed the necessary steps to proceed to Doctoral Candidate by completing her written and oral defense and submitting her research proposal. Thank you to Drs. Eckert, Gough, Johnson, and Keyghobadi for their insightful comments and expertise in helping to shape a dynamic and exciting research project.
This is the main package that provides data types and routines for spatial analysis of genetic marker data. The previous version is currently available on CRAN and you can install it rom within your R environtment by invoking the command
If you want to keep up with the latest developments of this package, you can use the version found on GitHub. Install it from within R as:
Landscape genetics is a burgeoning field of interest that focuses on how site-specific factors influence the distribution of genetic variation and the genetic connectivity of individuals and populations. In this manuscript, we focus on two methodological extensions for landscape genetic analyses: the use of conditional genetic distance (cGD) derived from population networks and the utility of extracting potentially confounding effects caused by correlations between phylogeographic history and contemporary ecological factors. Individual-based simulations show that when describing the spatial distribution of genetic variation, cGD consistently outperforms the traditional genetic distance measure of linearized FST under both 1- and 2-dimensional stepping stone models and Cavalli-Sforza and Edward’s chord distance Dc in 1-dimensional landscapes. To show how to identify and extract the effects of phylogeographic history prior to embarking on landscape genetic analyses, we use nuclear genotypic data from the Sonoran desert succulent Euphorbia lomelii (Euphrobiaceae), for which a detailed phylogeographic history has previously been determined. For E. lomelii, removing the effect of phylogeographic history significantly influences our ability to infer both the identity and the relative importance of spatial and bio-climatic variables in subsequent landscape genetic analyses. We close by discussing the utility of cGD in landscape genetic analyses.
The analysis of genetic marker data is increasingly being conducted in the context of the spatial arrangement of strata (e.g. populations) necessitating a more flexible set of analysis tools. GeneticStudio consists of four interacting programs: (i) Geno a spreadsheet-like interface for the analysis of spatially explicit marker-based genetic variation; (ii) Graph software for the analysis of Population Graph and network topologies, (iii) Manteller, a general purpose for matrix analysis program; and (iv) SNPFinder, a program for identifying single nucleotide polymorphisms. The GeneticStudio suite is available as source code as well as binaries for OSX and Windows and is distributed under the GNU General Public License.
This manuscript explores the simultaneous evolution of population genetic parameters and topological features within a population graph through a series of Monte Carlo simulations. I show that node centrality and graph breadth are significantly correlated to population genetic parameters FST and M (ρ = -0.95; ρ -0.98, respectively), which are commonly used in quantifying among population genetic structure and isolation by distance. Next, the topological consequences of migration patterns are examined by contrasting N-island and stepping stone models of gene movement. Finally, I show how variation in migration rate influences the rate of formation of specific topological features with particular emphasis to the phase transition that occurs when populations begin to become fixed due to restricted movement of genes among populations. I close by discussing the utility of this method for the analysis of intra-specific genetic variation.