Angela
Elite member
- Messages
- 21,842
- Reaction score
- 12,308
- Points
- 113
- Ethnic group
- Italian
More great work from Graham Coop:
See:
[FONT="]Gideon S. Bradburd[/FONT][FONT="], [/FONT][FONT="][FONT=hwicons !important][/FONT] View ORCID ProfileGraham M. Coop[/FONT][FONT="] and [/FONT][FONT="][FONT=hwicons !important][/FONT] View ORCID ProfilePeter L. Ralph
"[/FONT][FONT="]Inferring Continuous and Discrete Population Genetic Structure Across Space"
[/FONT]http://www.genetics.org/content/210/1/33
"ABSTRACT A classic problem in population genetics is the characterization of discrete population structure in the presence ofcontinuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignmentmethods may incorrectly ascribe differentiation due to continuous processes (e.g., geographic isolation by distance) to discreteprocesses, such as geographic, ecological, or reproductive barriers between populations. This reflects a shortcoming of currentmethods for inferring and visualizing population structure when applied to genetic data deriving from geographically distributedpopulations. Here, we present a statistical framework for the simultaneous inference of continuous and discrete patterns ofpopulation structure. The method estimates ancestry proportions for each sample from a set of two-dimensional populationlayers, and, within each layer, estimates a rate at which relatedness decays with distance. This thereby explicitly addressesthe “clines versus clusters" problem in modeling population genetic variation, and remedies some of the overfitting to whichnonspatial models are prone. The method produces useful descriptions of structure in genetic relatedness in situations whereseparated, geographically distributed populations interact, as after a range expansion or secondary contact. We demonstratethe utility of this approach using simulations and by applying it to empirical datasets of poplars and black bears in North America."
See:
[FONT="]Gideon S. Bradburd[/FONT][FONT="], [/FONT][FONT="][FONT=hwicons !important][/FONT] View ORCID ProfileGraham M. Coop[/FONT][FONT="] and [/FONT][FONT="][FONT=hwicons !important][/FONT] View ORCID ProfilePeter L. Ralph
"[/FONT][FONT="]Inferring Continuous and Discrete Population Genetic Structure Across Space"
[/FONT]http://www.genetics.org/content/210/1/33
"ABSTRACT A classic problem in population genetics is the characterization of discrete population structure in the presence ofcontinuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignmentmethods may incorrectly ascribe differentiation due to continuous processes (e.g., geographic isolation by distance) to discreteprocesses, such as geographic, ecological, or reproductive barriers between populations. This reflects a shortcoming of currentmethods for inferring and visualizing population structure when applied to genetic data deriving from geographically distributedpopulations. Here, we present a statistical framework for the simultaneous inference of continuous and discrete patterns ofpopulation structure. The method estimates ancestry proportions for each sample from a set of two-dimensional populationlayers, and, within each layer, estimates a rate at which relatedness decays with distance. This thereby explicitly addressesthe “clines versus clusters" problem in modeling population genetic variation, and remedies some of the overfitting to whichnonspatial models are prone. The method produces useful descriptions of structure in genetic relatedness in situations whereseparated, geographically distributed populations interact, as after a range expansion or secondary contact. We demonstratethe utility of this approach using simulations and by applying it to empirical datasets of poplars and black bears in North America."
