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Signatures of Introgression across the Allele Frequency Spectrum
The detection of introgression from genomic data is transforming our view of species and the origins of adaptive variation. Among the most widely used approaches to detect introgression is the so-called ABBA–BABA test or D-statistic, which identifies excess allele sharing between nonsister taxa. Par...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7826190/ https://www.ncbi.nlm.nih.gov/pubmed/32941617 http://dx.doi.org/10.1093/molbev/msaa239 |
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author | Martin, Simon H Amos, William |
author_facet | Martin, Simon H Amos, William |
author_sort | Martin, Simon H |
collection | PubMed |
description | The detection of introgression from genomic data is transforming our view of species and the origins of adaptive variation. Among the most widely used approaches to detect introgression is the so-called ABBA–BABA test or D-statistic, which identifies excess allele sharing between nonsister taxa. Part of the appeal of D is its simplicity, but this also limits its informativeness, particularly about the timing and direction of introgression. Here we present a simple extension, D frequency spectrum or D(FS), in which D is partitioned according to the frequencies of derived alleles. We use simulations over a large parameter space to show how D(FS) carries information about various factors. In particular, recent introgression reliably leads to a peak in D(FS) among low-frequency derived alleles, whereas violation of model assumptions can lead to a lack of signal at low frequencies. We also reanalyze published empirical data from six different animal and plant taxa, and interpret the results in the light of our simulations, showing how D(FS) provides novel insights. We currently see D(FS) as a descriptive tool that will augment both simple and sophisticated tests for introgression, but in the future it may be usefully incorporated into probabilistic inference frameworks. |
format | Online Article Text |
id | pubmed-7826190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-78261902021-01-27 Signatures of Introgression across the Allele Frequency Spectrum Martin, Simon H Amos, William Mol Biol Evol Methods The detection of introgression from genomic data is transforming our view of species and the origins of adaptive variation. Among the most widely used approaches to detect introgression is the so-called ABBA–BABA test or D-statistic, which identifies excess allele sharing between nonsister taxa. Part of the appeal of D is its simplicity, but this also limits its informativeness, particularly about the timing and direction of introgression. Here we present a simple extension, D frequency spectrum or D(FS), in which D is partitioned according to the frequencies of derived alleles. We use simulations over a large parameter space to show how D(FS) carries information about various factors. In particular, recent introgression reliably leads to a peak in D(FS) among low-frequency derived alleles, whereas violation of model assumptions can lead to a lack of signal at low frequencies. We also reanalyze published empirical data from six different animal and plant taxa, and interpret the results in the light of our simulations, showing how D(FS) provides novel insights. We currently see D(FS) as a descriptive tool that will augment both simple and sophisticated tests for introgression, but in the future it may be usefully incorporated into probabilistic inference frameworks. Oxford University Press 2020-09-17 /pmc/articles/PMC7826190/ /pubmed/32941617 http://dx.doi.org/10.1093/molbev/msaa239 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methods Martin, Simon H Amos, William Signatures of Introgression across the Allele Frequency Spectrum |
title | Signatures of Introgression across the Allele Frequency Spectrum |
title_full | Signatures of Introgression across the Allele Frequency Spectrum |
title_fullStr | Signatures of Introgression across the Allele Frequency Spectrum |
title_full_unstemmed | Signatures of Introgression across the Allele Frequency Spectrum |
title_short | Signatures of Introgression across the Allele Frequency Spectrum |
title_sort | signatures of introgression across the allele frequency spectrum |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7826190/ https://www.ncbi.nlm.nih.gov/pubmed/32941617 http://dx.doi.org/10.1093/molbev/msaa239 |
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