Bait-ER : a Bayesian method to detect targets of selection in Evolve-and-Resequence experiments
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Date
09/01/2023Funder
Grant ID
BB/W000768/1
MA16-061
RG170315
RIG007474
105621/Z/14/Z
N/A
Keywords
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Show full item recordAbstract
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.
Citation
Barata , C D C B R , Borges , R & Kosiol , C 2023 , ' Bait-ER : a Bayesian method to detect targets of selection in Evolve-and-Resequence experiments ' , Journal of Evolutionary Biology , vol. 36 , no. 1 , pp. 29-44 . https://doi.org/10.1111/jeb.14134
Publication
Journal of Evolutionary Biology
Status
Peer reviewed
ISSN
1010-061XType
Journal article
Rights
Copyright © 2022 The Authors. Journal of Evolutionary Biology published by John Wiley & Sons Ltd on behalf of European Society for Evolutionary Biology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Description
funding: This research was funded in part, by the Vienna Science and Technology Fund (WWTF) [MA16-061], the Biotechnology and Biological Sciences Research Council (BBSRC) [BB/W000768/1], and the Austrian Science Fund (FWF) [P34524-B]. CK received funding from the Royal Society (RG170315) and Carnegie Trust (RIG007474). The computational results presented have been partly achieved using the St Andrews Bioinformatics Unit (StABU), which is funded by a Wellcome Trust ISSF award (grant 105621/Z/14/Z).Collections
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