Bait-ER: a Bayesian method to detect targets of selection in Evolve-and-Resequence experiments

Abstract

For over a decade, experimental evolution has been combined with high-throughput sequencing techniques in so-called Evolve-and-Resequence (E&R) experiments. This allows testing for selection in populations kept in the laboratory under given experimental conditions. 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. However, some care must be taken when analysing specific allele trajectories, particularly those 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 very difficult trajectories. 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://github.com/mrborges23/Bait-ER.

Selection on the fly: short term adaptation to an altered sexual selection regime inDrosophila pseudoobscura

“…We performed a genome scan of the time-series data across all time points using Bait-ER (Barata et al, 2020). The signal of selection is substantially higher in E versus M lines ( fig.…”

“…(a) and (c) are manhattan plots of Bait-ER (Barata et al, 2020) logBF for each allele frequency trajectory. Statistically significant SNPs are coloured in green (M, top) or orange (E, bottom).…”

“…Finally, we investigated potential targets of selection using a Bayesian genome scan on the time-series - Bait-ER (Barata et al, 2020). It was designed for E&R experiments as it accounts for added binomial, or beta-binomial, sampling noise from pool-seq.…”

“…The approach models drift variance to obtain a temporal estimator for N e (also referred to as variance-N e ). Estimates were computed for 2k SNP windows with a 10% overlap assuming that populations were sampled according to Finally, we investigated potential targets of selection using a Bayesian genome scan on the time-series -Bait-ER (Barata et al, 2020). It was designed for E&R experiments as it accounts for added binomial, or beta-binomial, sampling noise from pool-seq.…”

“…We estimated the effective population size – N e – which will be influenced by census size, mating system, and strength of selection for the four main D. pseudoobscura chromosomes: 2, 3, 4 and X. We used a Bayesian modelling approach – Bait-ER (Barata et al, 2020) – to infer selection on individual SNPs that allows for finding potential targets of selection. We combined individual SNP tests for selection with window-based estimates of the effective population size, N e , which gave us a clearer view of the process of adaptation.…”