Evaluating genetic drift in time-series evolutionary analysis

• Published in: Journal of theoretical biology Vol. 437; pp. 51 - 57
• Main Authors: R. Nené, Nuno, Mustonen, Ville, J. R. Illingworth, Christopher
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 21.01.2018; Elsevier
• Subjects: Algorithms; Animals; Computer Simulation; Evolution, Molecular; Experimental evolution; Gene Frequency; Genetic Drift; Genetics, Population; Genome - genetics; Humans; Models, Genetic; Population Density; Time Factors; Time-resolved genome sequence data; Wright–Fisher model; Time-resolved genome sequence data; Genetic drift; Wright–Fisher model; Experimental evolution
• ISSN: 0022-5193; 1095-8541
• DOI: 10.1016/j.jtbi.2017.09.021

•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. 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.