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Genealogies of rapidly adapting populations

Neher, Richard A. and Hallatschek, Oskar. (2013) Genealogies of rapidly adapting populations. Proceedings of the National Academy of Sciences, 110 (2). pp. 437-442.

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Official URL: http://edoc.unibas.ch/53992/

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Abstract

The genetic diversity of a species is shaped by its recent evolutionary history and can be used to infer demographic events or selective sweeps. Most inference methods are based on the null hypothesis that natural selection is a weak or infrequent evolutionary force. However, many species, particularly pathogens, are under continuous pressure to adapt in response to changing environments. A statistical framework for inference from diversity data of such populations is currently lacking. Towards this goal, we explore the properties of genealogies in a model of continual adaptation in asexual populations. We show that lineages trace back to a small pool of highly fit ancestors, in which almost simultaneous coalescence of more than two lineages frequently occurs. Whereas such multiple mergers are unlikely under the neutral coalescent, they create a unique genetic footprint in adapting populations. The site frequency spectrum of derived neutral alleles, for example, is nonmonotonic and has a peak at high frequencies, whereas Tajima's D becomes more and more negative with increasing sample size. Because multiple merger coalescents emerge in many models of rapid adaptation, we argue that they should be considered as a null model for adapting populations.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Computational Modeling of Biological Processes (Neher)
UniBasel Contributors:Neher, Richard
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:National Academy of Sciences
ISSN:0027-8424
e-ISSN:1091-6490
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Identification Number:
Last Modified:18 Sep 2019 06:06
Deposited On:30 Nov 2017 13:02

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