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)

Abstract

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].
• Status: Published
• Refereed: Yes
• 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
• Publisher: Elsevier
• ISSN: 0022-5193
• ISSN (Online): 1095-8541
• Published Online: 25 September 2017