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The genetic architecture of temperature adaptation is shaped by population ancestry and not by selection regime

Citation

Otte, Kathrin; Nolte, Viola; Mallard, Francois; Schlötterer, Christian (2021), The genetic architecture of temperature adaptation is shaped by population ancestry and not by selection regime, Dryad, Dataset, https://doi.org/10.5061/dryad.np5hqbzsp

Abstract

Understanding the genetic architecture of temperature adaptation is key for characterizing and predicting the effect of climate change on natural populations. One particularly promising approach is Evolve and Resequence (E&R), which combines advantages of experimental evolution such as time series, replicate populations and controlled environmental conditions, with whole genome sequencing.

The recent analysis of replicate populations from two different Drosophila simulans founder populations, which were adapting to the same novel hot environment, uncovered very different architectures - either many selection targets with large heterogeneity among replicates or fewer selection targets with a consistent response among replicates.

Here, we exposed the founder population from Portugal to a cold temperature regime. Although almost no selection targets were shared between the hot and cold selection regime, the adaptive architecture was similar: we identified a moderate number of targets under strong selection (19 selection targets, mean selection coefficient = 0.072) and very parallel responses in the cold evolved replicates. This similarity across different environments indicates that the adaptive architecture depends more on the ancestry of the founder population than the specific selection regime. These observations will have broad implications for the correct interpretation of the genomic responses to a changing climate in natural populations.

 

Funding

Deutsche Forschungsgemeinschaft, Award: OT 532/1-1

Austrian Science Fund, Award: P27630

Austrian Science Fund, Award: P29133

European Research Council, Award: ArchAdapt

H2020 Marie Skłodowska-Curie Actions, Award: H2020-MSCA-IF-661149