Inferring Continuous and Discrete Population Genetic Structure Across Space
| Autor: | Bradburd, Gideon S., Coop, Graham M., Ralph, Peter L. |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
0301 basic medicine Data Interpretation Computer science isolation by distance Inference Population genetics Overfitting computer.software_genre 01 natural sciences Models Cluster Analysis 10. No inequality Genetics 0303 health sciences education.field_of_study Ecology Sampling (statistics) Statistical Populus Data Interpretation Statistical Genetic structure Data mining Ursidae Gene Flow Population Sample (statistics) Investigations Biology 010603 evolutionary biology 03 medical and health sciences Genetic Population Groups Genetic variation Animals Humans education Cluster analysis 030304 developmental biology Isolation by distance Models Genetic Genetic Variation population genetics model-based clustering population structure Genetics Population 030104 developmental biology Evolutionary biology North America Statistical Genetics and Genomics computer Developmental Biology |
| Zdroj: | Genetics, vol 210, iss 1 Bradburd, GS; Coop, GM; & Ralph, PL. (2018). Inferring continuous and discrete population genetic structure across space. Genetics, 210(1), 33-52. doi: 10.1534/genetics.118.301333. UC Davis: Retrieved from: http://www.escholarship.org/uc/item/21808058 Genetics |
| DOI: | 10.1534/genetics.118.301333. |
| Popis: | An important step in the analysis of genetic data is to describe and categorize natural variation. Individuals that live close together are, on average, more genetically similar than individuals sampled farther apart... A classic problem in population genetics is the characterization of discrete population structure in the presence of continuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignment methods may incorrectly ascribe differentiation due to continuous processes (e.g., geographic isolation by distance) to discrete processes, such as geographic, ecological, or reproductive barriers between populations. This reflects a shortcoming of current methods for inferring and visualizing population structure when applied to genetic data deriving from geographically distributed populations. Here, we present a statistical framework for the simultaneous inference of continuous and discrete patterns of population structure. The method estimates ancestry proportions for each sample from a set of two-dimensional population layers, and, within each layer, estimates a rate at which relatedness decays with distance. This thereby explicitly addresses the “clines versus clusters” problem in modeling population genetic variation, and remedies some of the overfitting to which nonspatial models are prone. The method produces useful descriptions of structure in genetic relatedness in situations where separated, geographically distributed populations interact, as after a range expansion or secondary contact. We demonstrate the utility of this approach using simulations and by applying it to empirical datasets of poplars and black bears in North America. |
| Databáze: | OpenAIRE |
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