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Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data
Several methods based on the Sequential Markovian coalescence (SMC) have been developed that make use of genome sequence data to uncover population demographic history, which is of interest in its own right and is a key requirement to generate a null model for selection tests. While these methods ca...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7173940/ https://www.ncbi.nlm.nih.gov/pubmed/32251472 http://dx.doi.org/10.1371/journal.pgen.1008698 |
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author | Sellinger, Thibaut Paul Patrick Abu Awad, Diala Moest, Markus Tellier, Aurélien |
author_facet | Sellinger, Thibaut Paul Patrick Abu Awad, Diala Moest, Markus Tellier, Aurélien |
author_sort | Sellinger, Thibaut Paul Patrick |
collection | PubMed |
description | Several methods based on the Sequential Markovian coalescence (SMC) have been developed that make use of genome sequence data to uncover population demographic history, which is of interest in its own right and is a key requirement to generate a null model for selection tests. While these methods can be applied to all possible kind of species, the underlying assumptions are sexual reproduction in each generation and non-overlapping generations. However, in many plants, invertebrates, fungi and other taxa, those assumptions are often violated due to different ecological and life history traits, such as self-fertilization or long term dormant structures (seed or egg-banking). We develop a novel SMC-based method to infer 1) the rates/parameters of dormancy and of self-fertilization, and 2) the populations’ past demographic history. Using simulated data sets, we demonstrate the accuracy of our method for a wide range of demographic scenarios and for sequence lengths from one to 30 Mb using four sampled genomes. Finally, we apply our method to a Swedish and a German population of Arabidopsis thaliana demonstrating a selfing rate of ca. 0.87 and the absence of any detectable seed-bank. In contrast, we show that the water flea Daphnia pulex exhibits a long lived egg-bank of three to 18 generations. In conclusion, we here present a novel method to infer accurate demographies and life-history traits for species with selfing and/or seed/egg-banks. Finally, we provide recommendations for the use of SMC-based methods for non-model organisms, highlighting the importance of the per site and the effective ratios of recombination over mutation. |
format | Online Article Text |
id | pubmed-7173940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-71739402020-05-01 Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data Sellinger, Thibaut Paul Patrick Abu Awad, Diala Moest, Markus Tellier, Aurélien PLoS Genet Research Article Several methods based on the Sequential Markovian coalescence (SMC) have been developed that make use of genome sequence data to uncover population demographic history, which is of interest in its own right and is a key requirement to generate a null model for selection tests. While these methods can be applied to all possible kind of species, the underlying assumptions are sexual reproduction in each generation and non-overlapping generations. However, in many plants, invertebrates, fungi and other taxa, those assumptions are often violated due to different ecological and life history traits, such as self-fertilization or long term dormant structures (seed or egg-banking). We develop a novel SMC-based method to infer 1) the rates/parameters of dormancy and of self-fertilization, and 2) the populations’ past demographic history. Using simulated data sets, we demonstrate the accuracy of our method for a wide range of demographic scenarios and for sequence lengths from one to 30 Mb using four sampled genomes. Finally, we apply our method to a Swedish and a German population of Arabidopsis thaliana demonstrating a selfing rate of ca. 0.87 and the absence of any detectable seed-bank. In contrast, we show that the water flea Daphnia pulex exhibits a long lived egg-bank of three to 18 generations. In conclusion, we here present a novel method to infer accurate demographies and life-history traits for species with selfing and/or seed/egg-banks. Finally, we provide recommendations for the use of SMC-based methods for non-model organisms, highlighting the importance of the per site and the effective ratios of recombination over mutation. Public Library of Science 2020-04-06 /pmc/articles/PMC7173940/ /pubmed/32251472 http://dx.doi.org/10.1371/journal.pgen.1008698 Text en © 2020 Sellinger et al 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 use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Sellinger, Thibaut Paul Patrick Abu Awad, Diala Moest, Markus Tellier, Aurélien Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data |
title | Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data |
title_full | Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data |
title_fullStr | Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data |
title_full_unstemmed | Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data |
title_short | Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data |
title_sort | inference of past demography, dormancy and self-fertilization rates from whole genome sequence data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7173940/ https://www.ncbi.nlm.nih.gov/pubmed/32251472 http://dx.doi.org/10.1371/journal.pgen.1008698 |
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