Evaluating genetic drift in time-series evolutionary analysis. (21st January 2018)

• Record Type: Journal Article
• Authors: R. Nené, Nuno; Mustonen, Ville; J. R. Illingworth, Christopher
• Is Part Of: Journal of theoretical biology. Volume 437(2018)
• Page Start: 51
• Page End: 57
• Publication Date: 2018-01-21
• Subjects: Genetic drift -- Time-resolved genome sequence data -- Wright–Fisher model -- Experimental evolution; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2017.09.021
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: We assess the inferrability of a Wright–Fisher drift model from time-resolved genome sequence data. We identify thresholds at which a Wright–Fisher model can be distinguished from Gaussian diffusion. Considering a recent experimental dataset, a Wright–Fisher model is favoured. We infer chromosome dependent effective population sizes for this dataset. 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.