Bait‐ER: A Bayesian method to detect targets of selection in Evolve‐and‐Resequence experiments. (21st December 2022) - Barata, Carolina; Borges, Rui; Kosiol, Carolin
Abstract: For over a decade, experimental evolution has been combined with high‐throughput sequencing techniques. In so‐called Evolve‐and‐Resequence (E&R) experiments, populations are kept in the laboratory under controlled experimental conditions where their genomes are sampled and allele frequencies monitored. However, identifying signatures of adaptation in E&R datasets is far from trivial, and it is still necessary to develop more efficient and statistically sound methods for detecting selection in genome‐wide data. Here, we present Bait‐ER – a fully Bayesian approach based on the Moran model of allele evolution to estimate selection coefficients from E&R experiments. The model has overlapping generations, a feature that describes several experimental designs found in the literature. We tested our method under several different demographic and experimental conditions to assess its accuracy and precision, and it performs well in most scenarios. Nevertheless, some care must be taken when analysing trajectories where drift largely dominates and starting frequencies are low. We compare our method with other available software and report that ours has generally high accuracy even for trajectories whose complexity goes beyond a classical sweep model. Furthermore, our approach avoids the computational burden of simulating an empirical null distribution, outperforming available software in terms of computational time and facilitating its use on genome‐wide data. We implemented and released our method in a new open‐source software package that can be accessed at https://doi.org/10.5281/zenodo.7351736 . Abstract : We present Bait‐ER ‐ a method for estimating selection in allele frequency trajectory data from E&R experiments. Bait‐ER models allele frequencies through time in a population with overlapping generations. Our method performs well in both simulated and real time series data and it tests for selection without the need for simulating an empirical null distribution.