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, 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 o

SUBMITTER: Barata C 

PROVIDER: S-EPMC10108205 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature