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Population Graphs: the graph theoretic shape of genetic structure
Abstract
Patterns of intraspecific genetic variation result from interactions among both historical and contemporary evolutionary processes. Traditionally, population geneticists have used methods such as F ‐statistics, pairwise isolation by distance models, spatial autocorrelation and coalescent models to analyse this variation and to gain insight about causal evolutionary processes. Here we introduce a novel approach (Population Graphs) that focuses on the analysis of marker‐based population genetic data within a graph theoretic framework. This method can be used to estimate traditional population genetic summary statistics, but its primary focus is on characterizing the complex topology resulting from historical and contemporary genetic interactions among populations. We introduce the application of Population Graphs by examining the range‐wide population genetic structure of a Sonoran Desert cactus ( Lophocereus schottii ). With this data set, we evaluate hypotheses regarding historical vicariance, isolation by distance, population‐level assignment and the importance of specific populations to species‐wide genetic connectivity. We close by discussing the applicability of Population Graphs for addressing a wide range of population genetic and phylogeographical problems.
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Network-based connectivity statistics are often dependent on an assumed graph-theoretic model and are often computer resource-limited as larger areas are considered.
“Network-based representations can be analyzed to derive meaningful connectivity statistics [22]–[25], though these are often dependent on an assumed graph-theoretic model (e.g., complete graph, minimum planar graph) [19] and are often computer resource-limited as larger areas are considered [26].”
From Applying circuit theory for corridor expansion and management at regional scales: tiling, pinch points, and omnidirectional connectivity (2014) · cites this paper as [25]
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Bollobás, B. (2001) · doi:10.1017/cbo9780511814068 · Cited by 2,639 · cited 3×
“A graph ( G ) is a mathematical mapping of two disjoint sets, a set of nodes ( V ) and a set of edges ( E ), and is denoted as G = { V, E } (Bollobás 2001).”
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Research works printed but not linked: 47
- 10.1103/RevModPhys.74.47 · DOI · Paper page
- Avise JC, 2000, PhyloGeographyraphy: the History and Formation of Species, 10.2307/j.ctv1nzfgj7 · DOI
- 10.1006/tpbi.1999.1447 · DOI
- 10.1073/pnas.081068098 · DOI
- Box GEP, 1987, Empirical Model‐Building and Response Surfaces.
- Box GEP, 1978, Statistics for Experimenters: an Introduction to Design, Data Analysis, and Model Building
- Case TJ, 2002, A New Island Biogeography of the Sea of Cortez, 10.1093/oso/9780195133462.001.0001 · DOI
- 10.1093/genetics/153.4.1989 · DOI
- Cox JM, 1996, Multivariate Dependencies: Models, Analysis, and Interpretation
- 10.2307/2992369 · DOI
- 10.1016/S0169-5347(00)01998-4 · DOI
- 10.2307/2528966 · DOI
- Draper NR, 1981, Applied Regression Analysis
- 10.1046/j.1365-294X.2002.01650.x · DOI
- 10.1007/978-1-4684-0481-4 · DOI
- 10.2307/2656651 · DOI
- 10.1111/j.1365-2656.2006.01186.x · DOI
- 10.1016/S0169-5347(97)01098-7 · DOI
- 10.1093/biomet/53.3-4.325 · DOI
- 10.1046/j.1365-294x.2001.01202.x · DOI
- Johnson RA, 1992, Applied Multivariate Statistical Analysis
- Kempthorne O, 1969, An Introduction to Genetic Statistics
- 10.1046/j.1365-294X.2002.01410.x · DOI
- 10.2307/2992184 · DOI
- 10.1093/genetics/117.2.273 · DOI
- 10.1111/j.0014-3820.2001.tb00823.x · DOI
- Manly BFJ, 1997, Randomization, Bootstrap and Monte Carlo Methods in Biology
- 10.1111/j.0014-3820.2002.tb00146.x · DOI
- 10.1086/282771 · DOI
- 10.1093/genetics/89.3.583 · DOI
- Peñalba MC, 1998, Cambios de vegetacíon y clima en Baja California, México, durante los últimos 20 000 años, Geología Del Noroeste, 2, 21
- Rhodes OEJ, 1996, Population Dynamics in Ecological Space and Time
- 10.1016/0169-5347(96)10032-X · DOI
- 10.1073/pnas.250413397 · DOI
- 10.1111/j.1365-294X.2005.02568.x · DOI
- 10.1146/annurev.es.16.110185.002141 · DOI
- 10.1111/j.1558-5646.1993.tb01215.x · DOI
- 10.1086/283925 · DOI
- Sokal RR, 1995, Biometry
- Sork VL, 2001, Landscape approaches to historical and contemporary gene flow in plants, Trends in Ecology and Evolution, 14, 219, 10.1016/S0169-5347(98)01585-7 · DOI
- 10.1046/j.1365-294x.1998.00289.x · DOI
- 10.1006/qres.1994.1011 · DOI
- 10.1111/j.1558-5646.1984.tb05657.x · DOI
- 10.1007/BF00120649 · DOI
- Whittaker J, 1990, Graphical Methods in Applied Multivariate Statistics.
- 10.1111/j.1469-1809.1949.tb02451.x · DOI
- 10.1086/375120 · DOI · Paper page
Cited by
Sorted by citations
Inference of population splits and mixtures from genome-wide allele frequency data
Pickrell, J. K., & Pritchard, J. K. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002967 · Cited by 2,731
Pelletier, D., Clark, M., Anderson, M. G., Rayfield, B., Wulder, M. A., & Cardille, J. A. (2014) · PLOS One · doi:10.1371/journal.pone.0084135 · Cited by 158
Metadata
- Authors
- Dyer, R. J., & Nason, J. D.
- Venue
- Molecular Ecology
- Publisher
- Wiley
- Published
- 2004-04-22
- Volume
- 13
- Issue
- 7
- Pages
- 1713-1727
- ISSN
- 0962-1083
- 1365-294X
- Cited by
- 285
- References
- 1
- Language
- en
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