OptM: estimating the optimal number of migration edges on population trees using Treemix
Abstract The software Treemix has become extensively used to estimate the number of migration events, or edges (m), on population trees from genome-wide allele frequency data. However, the appropriate number of edges to include remains unclear. Here, I show that an optimal value of m can be inferred...
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| Published in | Biology methods and protocols Vol. 6; no. 1; p. bpab017 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Oxford University Press
2021
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Abstract
The software Treemix has become extensively used to estimate the number of migration events, or edges (m), on population trees from genome-wide allele frequency data. However, the appropriate number of edges to include remains unclear. Here, I show that an optimal value of m can be inferred from the second-order rate of change in likelihood (Δm) across incremental values of m. Repurposed from its original use to estimate the number of population clusters in the software Structure (ΔK), I show using simulated populations that Δm performs equally as well as current recommendations for Treemix. A demonstration of an empirical dataset from domestic dogs indicates that this method may be preferable in large, complex population histories and can prioritize migration events for subsequent investigation. The method has been implemented in a freely available R package called “OptM” and as a web application (https://rfitak.shinyapps.io/OptM/) to interface directly with the output files of Treemix. |
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| AbstractList | The software Treemix has become extensively used to estimate the number of migration events, or edges (m), on population trees from genome-wide allele frequency data. However, the appropriate number of edges to include remains unclear. Here, I show that an optimal value of m can be inferred from the second-order rate of change in likelihood (Δm) across incremental values of m. Repurposed from its original use to estimate the number of population clusters in the software Structure (ΔK), I show using simulated populations that Δm performs equally as well as current recommendations for Treemix. A demonstration of an empirical dataset from domestic dogs indicates that this method may be preferable in large, complex population histories and can prioritize migration events for subsequent investigation. The method has been implemented in a freely available R package called “OptM” and as a web application (https://rfitak.shinyapps.io/OptM/) to interface directly with the output files of Treemix. Abstract The software Treemix has become extensively used to estimate the number of migration events, or edges (m), on population trees from genome-wide allele frequency data. However, the appropriate number of edges to include remains unclear. Here, I show that an optimal value of m can be inferred from the second-order rate of change in likelihood (Δm) across incremental values of m. Repurposed from its original use to estimate the number of population clusters in the software Structure (ΔK), I show using simulated populations that Δm performs equally as well as current recommendations for Treemix. A demonstration of an empirical dataset from domestic dogs indicates that this method may be preferable in large, complex population histories and can prioritize migration events for subsequent investigation. The method has been implemented in a freely available R package called “OptM” and as a web application (https://rfitak.shinyapps.io/OptM/) to interface directly with the output files of Treemix. The software Treemix has become extensively used to estimate the number of migration events, or edges (m), on population trees from genome-wide allele frequency data. However, the appropriate number of edges to include remains unclear. Here, I show that an optimal value of m can be inferred from the second-order rate of change in likelihood (Δm) across incremental values of m. Repurposed from its original use to estimate the number of population clusters in the software Structure (ΔK), I show using simulated populations that Δm performs equally as well as current recommendations for Treemix. A demonstration of an empirical dataset from domestic dogs indicates that this method may be preferable in large, complex population histories and can prioritize migration events for subsequent investigation. The method has been implemented in a freely available R package called "OptM" and as a web application (https://rfitak.shinyapps.io/OptM/) to interface directly with the output files of Treemix.The software Treemix has become extensively used to estimate the number of migration events, or edges (m), on population trees from genome-wide allele frequency data. However, the appropriate number of edges to include remains unclear. Here, I show that an optimal value of m can be inferred from the second-order rate of change in likelihood (Δm) across incremental values of m. Repurposed from its original use to estimate the number of population clusters in the software Structure (ΔK), I show using simulated populations that Δm performs equally as well as current recommendations for Treemix. A demonstration of an empirical dataset from domestic dogs indicates that this method may be preferable in large, complex population histories and can prioritize migration events for subsequent investigation. The method has been implemented in a freely available R package called "OptM" and as a web application (https://rfitak.shinyapps.io/OptM/) to interface directly with the output files of Treemix. The software Treemix has become extensively used to estimate the number of migration events, or edges ( m ), on population trees from genome-wide allele frequency data. However, the appropriate number of edges to include remains unclear. Here, I show that an optimal value of m can be inferred from the second-order rate of change in likelihood (Δ m ) across incremental values of m . Repurposed from its original use to estimate the number of population clusters in the software Structure (Δ K ), I show using simulated populations that Δ m performs equally as well as current recommendations for Treemix . A demonstration of an empirical dataset from domestic dogs indicates that this method may be preferable in large, complex population histories and can prioritize migration events for subsequent investigation. The method has been implemented in a freely available R package called “OptM” and as a web application ( https://rfitak.shinyapps.io/OptM/ ) to interface directly with the output files of Treemix . |
| Author | Fitak, Robert R |
| AuthorAffiliation | Department of Biology, Genomics and Bioinformatics Cluster, University of Central Florida , Orlando, FL 32816, USA |
| AuthorAffiliation_xml | – name: Department of Biology, Genomics and Bioinformatics Cluster, University of Central Florida , Orlando, FL 32816, USA |
| Author_xml | – sequence: 1 givenname: Robert R orcidid: 0000-0002-7398-6259 surname: Fitak fullname: Fitak, Robert R email: Robert.fitak@ucf.edu organization: Department of Biology, Genomics and Bioinformatics Cluster, University of Central Florida, Orlando, FL 32816, USA |
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| Keywords | population genomics SNPs likelihood structure |
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The Terriers year: 1903 ident: 2021092805433257600_bpab017-B27 |
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The software Treemix has become extensively used to estimate the number of migration events, or edges (m), on population trees from genome-wide allele... The software Treemix has become extensively used to estimate the number of migration events, or edges (m), on population trees from genome-wide allele... The software Treemix has become extensively used to estimate the number of migration events, or edges ( m ), on population trees from genome-wide allele... |
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| SubjectTerms | Bioinformatics Biology Datasets Domestic animals Gene frequency Genomes Genomics Innovations Migration Population Population genetics Simulation Software packages Trees |
| Title | OptM: estimating the optimal number of migration edges on population trees using Treemix |
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