Self-contained Beta-with-Spikes Approximation for Inference Under a Wright-Fisher Model
This provides a reliable inference tool for evolutionary biology and cultural evolution, addressing specific bottlenecks in parameter estimation, but it is incremental as it builds on existing approximations.
The researchers tackled the problem of estimating evolutionary parameters from time-series data under the Wright-Fisher model, introducing a self-contained Beta-with-Spikes approximation method that shows robustness in challenging regimes like strong-selection and near-extinction, and applied it to detect selection in yeast and parameter changes in Spanish language evolution.
We construct a reliable estimation of evolutionary parameters within the Wright-Fisher model, which describes changes in allele frequencies due to selection and genetic drift, from time-series data. Such data exists for biological populations, for example via artificial evolution experiments, and for the cultural evolution of behavior, such as linguistic corpora that document historical usage of different words with similar meanings. Our method of analysis builds on a Beta-with-Spikes approximation to the distribution of allele frequencies predicted by the Wright-Fisher model. We introduce a self-contained scheme for estimating the parameters in the approximation, and demonstrate its robustness with synthetic data, especially in the strong-selection and near-extinction regimes where previous approaches fail. We further apply to allele frequency data for baker's yeast (Saccharomyces cerevisiae), finding a significant signal of selection in cases where independent evidence supports such a conclusion. We further demonstrate the possibility of detecting time-points at which evolutionary parameters change in the context of a historical spelling reform in the Spanish language.