The K = 2 conundrum
Assessments of population genetic structure have become an increasing focus as they can provide valuable insight into patterns of migration and gene flow. structure, the most highly cited of several clustering‐based methods, was developed to provide robust estimates without the need for populations...
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| Veröffentlicht in: | Molecular ecology Jg. 26; H. 14; S. 3594 - 3602 |
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| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
England
Blackwell Publishing Ltd
01.07.2017
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| Schlagworte: | |
| ISSN: | 0962-1083, 1365-294X, 1365-294X |
| Online-Zugang: | Volltext |
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| Abstract | Assessments of population genetic structure have become an increasing focus as they can provide valuable insight into patterns of migration and gene flow. structure, the most highly cited of several clustering‐based methods, was developed to provide robust estimates without the need for populations to be determined a priori. structure introduces the problem of selecting the optimal number of clusters, and as a result, the ΔK method was proposed to assist in the identification of the “true” number of clusters. In our review of 1,264 studies using structure to explore population subdivision, studies that used ΔK were more likely to identify K = 2 (54%, 443/822) than studies that did not use ΔK (21%, 82/386). A troubling finding was that very few studies performed the hierarchical analysis recommended by the authors of both ΔK and structure to fully explore population subdivision. Furthermore, extensions of earlier simulations indicate that, with a representative number of markers, ΔK frequently identifies K = 2 as the top level of hierarchical structure, even when more subpopulations are present. This review suggests that many studies may have been over‐ or underestimating population genetic structure; both scenarios have serious consequences, particularly with respect to conservation and management. We recommend publication standards for population structure results so that readers can assess the implications of the results given their own understanding of the species biology. |
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| AbstractList | Assessments of population genetic structure have become an increasing focus as they can provide valuable insight into patterns of migration and gene flow. structure, the most highly cited of several clustering-based methods, was developed to provide robust estimates without the need for populations to be determined a priori. structure introduces the problem of selecting the optimal number of clusters, and as a result, the [Delta]K method was proposed to assist in the identification of the "true" number of clusters. In our review of 1,264 studies using structure to explore population subdivision, studies that used [Delta]K were more likely to identify K = 2 (54%, 443/822) than studies that did not use [Delta]K (21%, 82/386). A troubling finding was that very few studies performed the hierarchical analysis recommended by the authors of both [Delta]K and structure to fully explore population subdivision. Furthermore, extensions of earlier simulations indicate that, with a representative number of markers, [Delta]K frequently identifies K = 2 as the top level of hierarchical structure, even when more subpopulations are present. This review suggests that many studies may have been over- or underestimating population genetic structure; both scenarios have serious consequences, particularly with respect to conservation and management. We recommend publication standards for population structure results so that readers can assess the implications of the results given their own understanding of the species biology. Assessments of population genetic structure have become an increasing focus as they can provide valuable insight into patterns of migration and gene flow. structure, the most highly cited of several clustering‐based methods, was developed to provide robust estimates without the need for populations to be determined a priori. structure introduces the problem of selecting the optimal number of clusters, and as a result, the ΔK method was proposed to assist in the identification of the “true” number of clusters. In our review of 1,264 studies using structure to explore population subdivision, studies that used ΔK were more likely to identify K = 2 (54%, 443/822) than studies that did not use ΔK (21%, 82/386). A troubling finding was that very few studies performed the hierarchical analysis recommended by the authors of both ΔK and structure to fully explore population subdivision. Furthermore, extensions of earlier simulations indicate that, with a representative number of markers, ΔK frequently identifies K = 2 as the top level of hierarchical structure, even when more subpopulations are present. This review suggests that many studies may have been over‐ or underestimating population genetic structure; both scenarios have serious consequences, particularly with respect to conservation and management. We recommend publication standards for population structure results so that readers can assess the implications of the results given their own understanding of the species biology. Assessments of population genetic structure have become an increasing focus as they can provide valuable insight into patterns of migration and gene flow. structure, the most highly cited of several clustering-based methods, was developed to provide robust estimates without the need for populations to be determined a priori. structure introduces the problem of selecting the optimal number of clusters, and as a result, the ΔK method was proposed to assist in the identification of the "true" number of clusters. In our review of 1,264 studies using structure to explore population subdivision, studies that used ΔK were more likely to identify K = 2 (54%, 443/822) than studies that did not use ΔK (21%, 82/386). A troubling finding was that very few studies performed the hierarchical analysis recommended by the authors of both ΔK and structure to fully explore population subdivision. Furthermore, extensions of earlier simulations indicate that, with a representative number of markers, ΔK frequently identifies K = 2 as the top level of hierarchical structure, even when more subpopulations are present. This review suggests that many studies may have been over- or underestimating population genetic structure; both scenarios have serious consequences, particularly with respect to conservation and management. We recommend publication standards for population structure results so that readers can assess the implications of the results given their own understanding of the species biology.Assessments of population genetic structure have become an increasing focus as they can provide valuable insight into patterns of migration and gene flow. structure, the most highly cited of several clustering-based methods, was developed to provide robust estimates without the need for populations to be determined a priori. structure introduces the problem of selecting the optimal number of clusters, and as a result, the ΔK method was proposed to assist in the identification of the "true" number of clusters. In our review of 1,264 studies using structure to explore population subdivision, studies that used ΔK were more likely to identify K = 2 (54%, 443/822) than studies that did not use ΔK (21%, 82/386). A troubling finding was that very few studies performed the hierarchical analysis recommended by the authors of both ΔK and structure to fully explore population subdivision. Furthermore, extensions of earlier simulations indicate that, with a representative number of markers, ΔK frequently identifies K = 2 as the top level of hierarchical structure, even when more subpopulations are present. This review suggests that many studies may have been over- or underestimating population genetic structure; both scenarios have serious consequences, particularly with respect to conservation and management. We recommend publication standards for population structure results so that readers can assess the implications of the results given their own understanding of the species biology. Assessments of population genetic structure have become an increasing focus as they can provide valuable insight into patterns of migration and gene flow. structure , the most highly cited of several clustering‐based methods, was developed to provide robust estimates without the need for populations to be determined a priori. structure introduces the problem of selecting the optimal number of clusters, and as a result, the Δ K method was proposed to assist in the identification of the “true” number of clusters. In our review of 1,264 studies using structure to explore population subdivision, studies that used Δ K were more likely to identify K = 2 (54%, 443/822) than studies that did not use Δ K (21%, 82/386). A troubling finding was that very few studies performed the hierarchical analysis recommended by the authors of both Δ K and structure to fully explore population subdivision. Furthermore, extensions of earlier simulations indicate that, with a representative number of markers, Δ K frequently identifies K = 2 as the top level of hierarchical structure, even when more subpopulations are present. This review suggests that many studies may have been over‐ or underestimating population genetic structure; both scenarios have serious consequences, particularly with respect to conservation and management. We recommend publication standards for population structure results so that readers can assess the implications of the results given their own understanding of the species biology. Assessments of population genetic structure have become an increasing focus as they can provide valuable insight into patterns of migration and gene flow. structure, the most highly cited of several clustering‐based methods, was developed to provide robust estimates without the need for populations to be determined a priori. structure introduces the problem of selecting the optimal number of clusters, and as a result, the ΔK method was proposed to assist in the identification of the “true” number of clusters. In our review of 1,264 studies using structure to explore population subdivision, studies that used ΔK were more likely to identify K = 2 (54%, 443/822) than studies that did not use ΔK (21%, 82/386). A troubling finding was that very few studies performed the hierarchical analysis recommended by the authors of both ΔK and structure to fully explore population subdivision. Furthermore, extensions of earlier simulations indicate that, with a representative number of markers, ΔK frequently identifies K = 2 as the top level of hierarchical structure, even when more subpopulations are present. This review suggests that many studies may have been over‐ or underestimating population genetic structure; both scenarios have serious consequences, particularly with respect to conservation and management. We recommend publication standards for population structure results so that readers can assess the implications of the results given their own understanding of the species biology. |
| Author | Malenfant, René M. Dupuis, Julian R. Miller, Joshua M. Gorrell, Jamieson C. Andrew, Rose L. Cullingham, Catherine I. Janes, Jasmine K. |
| Author_xml | – sequence: 1 givenname: Jasmine K. orcidid: 0000-0002-4511-2087 surname: Janes fullname: Janes, Jasmine K. email: jjanes@une.edu.au organization: Vancouver Island University – sequence: 2 givenname: Joshua M. orcidid: 0000-0002-4019-7675 surname: Miller fullname: Miller, Joshua M. organization: Yale University – sequence: 3 givenname: Julian R. surname: Dupuis fullname: Dupuis, Julian R. organization: University of Hawai'i at Mãnoa – sequence: 4 givenname: René M. orcidid: 0000-0003-1505-3669 surname: Malenfant fullname: Malenfant, René M. organization: University of New Brunswick – sequence: 5 givenname: Jamieson C. surname: Gorrell fullname: Gorrell, Jamieson C. organization: Vancouver Island University – sequence: 6 givenname: Catherine I. surname: Cullingham fullname: Cullingham, Catherine I. organization: University of Alberta – sequence: 7 givenname: Rose L. surname: Andrew fullname: Andrew, Rose L. organization: The University of New England |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28544181$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Biological effects Cluster Analysis Clustering clustering methods Clusters conservation delta K founder effect Gene Flow Genetic structure Genetics, Population - methods management Migration Models, Genetic optimal K Population population genetic structure Population genetics Population structure Population studies Readers Research Design Structural hierarchy Subpopulations |
| Title | The K = 2 conundrum |
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