Evaluating genetic drift in time-series evolutionary analysis

TL;DR

This paper assesses the validity of the Wright-Fisher model for describing genetic drift in finite populations using genome-wide data, highlighting its strengths and limitations in evolutionary analysis.

Contribution

It provides empirical validation of the Wright-Fisher model against genomic data and discusses potential for faster approximations.

Findings

Wright-Fisher model better explains variance in finite populations than Gaussian models.

The model cannot always be correctly identified in certain circumstances.

Potential for developing more rapid approximations to the Wright-Fisher model.

Abstract

The Wright-Fisher model is the most popular population model for describing the behaviour of evolutionary systems with a finite 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 finite 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 identified. We discuss the potential for more rapid approximations to the Wright-Fisher model.