Evaluating genetic drift in time-series evolutionary analysis

R. Nené, N., Mustonen, V. and Illingworth, C. J.R. (2018) Evaluating genetic drift in time-series evolutionary analysis. Journal of Theoretical Biology, 437, pp. 51-57. (doi: 10.1016/j.jtbi.2017.09.021) (PMID:28958783) (PMCID:PMC5703635)

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The Wright–Fisher model is the most popular population model for describing the behaviour of evolutionary systems with a finite population size. Approximations have commonly been used but the model itself has rarely been tested against time-resolved genomic data. Here, we evaluate the extent to which it can be inferred as the correct model under a likelihood framework. Given genome-wide data from an evolutionary experiment, we validate the Wright–Fisher drift model as the better option for describing evolutionary trajectories in a finite population. This was found by evaluating its performance against a Gaussian model of allele frequency propagation. However, we note a range of circumstances under which standard Wright–Fisher drift cannot be correctly identified.

Item Type:Articles
Additional Information:This work was supported by a Sir Henry Dale Fellowship, jointly funded by the Wellcome Trust and the Royal Society [Grant Number 101239/Z/13/Z].
Glasgow Author(s) Enlighten ID:Illingworth, Dr Chris
Authors: R. Nené, N., Mustonen, V., and Illingworth, C. J.R.
College/School:College of Medical Veterinary and Life Sciences > School of Infection & Immunity
College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Virus Research
Journal Name:Journal of Theoretical Biology
ISSN (Online):1095-8541
Published Online:25 September 2017

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