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Detecting and quantifying changing selection intensities from time-sampled polymorphism data

Hyunjin Shim, Stefan Laurent, Matthieu Foll, Jeffrey D. Jensen
doi: https://doi.org/10.1101/027961
Hyunjin Shim
1School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
2Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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Stefan Laurent
1School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
2Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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Matthieu Foll
1School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
2Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
3International Agency for Research on Cancer, Lyon, France
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Jeffrey D. Jensen
1School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
2Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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Abstract

During his well-known debate with Fisher regarding the phenotypic dataset of Panaxia dominula, Wright (1948) suggested fluctuating selection as a potential explanation for the observed change in frequency. This model has since been invoked in a number of analyses, with the focus of discussion centering mainly on random or oscillatory fluctuations of selection intensities. Here, we present a novel method to consider non-random changes in selection intensities using Wright-Fisher approximate Bayesian (ABC)-based approaches, in order to detect and evaluate a change in selection strength from time-sampled data. This novel method jointly estimates the position of a change point as well as the strength of both corresponding selection coefficients (and dominance for diploid cases) from the allele trajectory. The simulation studies of CP-WFABC reveal the combinations of parameter ranges and input values that optimize performance, thus indicating optimal experimental design strategies. We apply this approach to both the historical dataset of Panaxia dominula in order to shed light on this historical debate, as well as to whole-genome time-serial data from influenza virus in order to identify sites with changing selection intensities in response to drug treatment

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 01, 2015.
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Detecting and quantifying changing selection intensities from time-sampled polymorphism data
Hyunjin Shim, Stefan Laurent, Matthieu Foll, Jeffrey D. Jensen
bioRxiv 027961; doi: https://doi.org/10.1101/027961
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Detecting and quantifying changing selection intensities from time-sampled polymorphism data
Hyunjin Shim, Stefan Laurent, Matthieu Foll, Jeffrey D. Jensen
bioRxiv 027961; doi: https://doi.org/10.1101/027961

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