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.