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Data from: Inferring Continuous and Discrete Population Genetic Structure Across Space
This repo contains a snapshot of the data files, analyses, and code for the method conStruct at the time of publication.
conStruct.zip

Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: 1148897; 1402725 ; 1262645

", "type": { "id": "notes", "title": { "de": "Anmerkungen", "en": "Notes" } } } ], "creators": [ { "affiliations": [ { "name": "Michigan State University" } ], "person_or_org": { "family_name": "Bradburd", "given_name": "Gideon S.", "name": "Bradburd, Gideon S.", "type": "personal" } }, { "affiliations": [ { "name": "University of California, Davis" } ], "person_or_org": { "family_name": "Coop", "given_name": "Graham M.", "name": "Coop, Graham M.", "type": "personal" } }, { "affiliations": [ { "name": "University of Oregon" } ], "person_or_org": { "family_name": "Ralph", "given_name": "Peter L.", "name": "Ralph, Peter L.", "type": "personal" } } ], "description": "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.", "locations": { "features": [ { "place": "North America" } ] }, "publication_date": "2018-08-02", "publisher": "Zenodo", "related_identifiers": [ { "identifier": "10.1534/genetics.118.301333", "relation_type": { "id": "iscitedby", "title": { "de": "Wird zitiert von", "en": "Is cited by" } }, "scheme": "doi" } ], "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "rights": [ { "description": { "en": "CC0 waives copyright interest in a work you've created and dedicates it to the world-wide public domain. Use CC0 to opt out of copyright entirely and ensure your work has the widest reach." }, "icon": "cc-cc0-icon", "id": "cc0-1.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/publicdomain/zero/1.0/legalcode" }, "title": { "en": "Creative Commons Zero v1.0 Universal" } } ], "subjects": [ { "subject": "isolation by distance" }, { "subject": "Populus trichocarpa" }, { "subject": "Model-based Clustering" }, { "subject": "Holocene" }, { "subject": "Populus balsamifera" } ], "title": "Data from: Inferring continuous and discrete population genetic structure across space" }, "parent": { "access": { "owned_by": { "user": "90070" } }, "communities": { "default": "734b6452-1f40-4db0-b218-08ab1d568b7f", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_submission_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2020-02-04T14:03:17.992421+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "734b6452-1f40-4db0-b218-08ab1d568b7f", "links": {}, "metadata": { "curation_policy": "

The Dryad collection in Zenodo contains datasets mirrored from the Dryad Digital Repository. If you want your dataset to appear here, submit it through https://datadryad.org. No other datasets will be accepted.

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Dryad is a community-governed open data publishing and curation repository. Through Dryad and Zenodo's partnership, Zenodo hosts a preservation copy of Dryad datasets, available through this Zenodo Community. For more information or to access the original published datasets, visit https://datadryad.org.

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