Evaluating genetic drift in time-series evolutionary analysis.
R Nené, Nuno
J R Illingworth, Christopher
Journal of theoretical biology
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R Nené, N., Mustonen, V., & J R Illingworth, C. (2018). Evaluating genetic drift in time-series evolutionary analysis.. Journal of theoretical biology, 437 51-57. https://doi.org/10.1016/j.jtbi.2017.09.021
The Wright-Fisher model is the most popular population model for describing the behaviour of evolutionary systems with a nite population size. Approximations to the model have commonly been used for the analysis of time-resolved genome sequence data, but the model itself has rarely been tested against genomic data. Here, we evaluate the extent to which it can be inferred as the correct model given experimental data. Given genome wide data from an evolutionary experiment we validate the Wright-Fisher model as the better model for variance in a nite population in contrast to a Gaussian model of allele frequency propagation. However, we note a range of circumstances under which the Wright-Fisher model cannot be correctly identi ed. We discuss the potential for more rapid approximations to the Wright-Fisher model.
Animals, Humans, Genetics, Population, Population Density, Evolution, Molecular, Gene Frequency, Genetic Drift, Genome, Algorithms, Models, Genetic, Time Factors, Computer Simulation
Wellcome Trust (101239/Z/13/Z)
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External DOI: https://doi.org/10.1016/j.jtbi.2017.09.021
This record's URL: https://www.repository.cam.ac.uk/handle/1810/274958
Attribution 4.0 International
Licence URL: http://creativecommons.org/licenses/by/4.0/
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