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Inference of distribution of fitness effects and proportion of adaptive substitutions from polymorphism data

Paula Tataru, Maéva Mollion, Sylvain Glemin, Thomas Bataillon
doi: https://doi.org/10.1101/062216
Paula Tataru
*Bioinformatics Research Centre, Aarhus University, C.F. Møllers Allé 8, Aarhus C, 8000, Denmark
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Maéva Mollion
*Bioinformatics Research Centre, Aarhus University, C.F. Møllers Allé 8, Aarhus C, 8000, Denmark
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Sylvain Glemin
†Evolutionary Biology Centre, Uppsala University, Norbyvägen 14-18, Uppsala, 752 36, Sweden
‡Institut des Sciences de l’Evolution de Montpellier, UMR 5554 CNRS, Place Eugéne Bataillon, 34095 Montpellier cedex 5, France
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Thomas Bataillon
*Bioinformatics Research Centre, Aarhus University, C.F. Møllers Allé 8, Aarhus C, 8000, Denmark
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ABSTRACT

The distribution of fitness effects (DFE) encompasses deleterious, neutral and beneficial mutations. It conditions the evolutionary trajectory of populations, as well as the rate of adaptive molecular evolution (α). Inference of DFE and α from patterns of polymorphism (SFS) and divergence data has been a longstanding goal of evolutionary genetics. A widespread assumption shared by numerous methods developed so far to infer DFE and α from such data is that beneficial mutations contribute only negligibly to the polymorphism data. Hence, a DFE comprising only deleterious mutations tends to be estimated from SFS data, and α is only predicted by contrasting the SFS with divergence data from an outgroup. Here, we develop a hierarchical probabilistic framework that extends on previous methods and also can infer DFE and α from polymorphism data alone. We use extensive simulations to examine the performance of our method. We show that both a full DFE, comprising both deleterious and beneficial mutations, and α can be inferred without resorting to divergence data. We demonstrate that inference of DFE from polymorphism data alone can in fact provide more reliable estimates, as it does not rely on strong assumptions about a shared DFE between the outgroup and ingroup species used to obtain the SFS and divergence data. We also show that not accounting for the contribution of beneficial mutations to polymorphism data leads to substantially biased estimates of the DFE and α. We illustrate these points using our newly developed framework, while also comparing to one of the most widely used inference methods available.

Footnotes

  • ↵1 Bioinformatics Research Centre, Aarhus University, C.F. Møllers Alle 8, Aarhus C 8000, Denmark. paula{at}cs.au.dk

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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 July 05, 2016.
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Inference of distribution of fitness effects and proportion of adaptive substitutions from polymorphism data
Paula Tataru, Maéva Mollion, Sylvain Glemin, Thomas Bataillon
bioRxiv 062216; doi: https://doi.org/10.1101/062216
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Inference of distribution of fitness effects and proportion of adaptive substitutions from polymorphism data
Paula Tataru, Maéva Mollion, Sylvain Glemin, Thomas Bataillon
bioRxiv 062216; doi: https://doi.org/10.1101/062216

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