Data from: Population transcriptomics in Daphnia: the role of thermal selection
Cite this dataset
Herrmann, Maike; Ravindran, Suda Parimala; Schwenk, Klaus; Cordellier, Mathilde (2017). Data from: Population transcriptomics in Daphnia: the role of thermal selection [Dataset]. Dryad. https://doi.org/10.5061/dryad.p85m5
The complex interplay of forces influencing genetic divergence among populations complicates the discovery of the genetic basis underlying local adaptation. Here, we utilized for the first time a combined reverse ecology and population transcriptomic approach to assess the contribution of thermal selection to population differentiation, thereby considering transcriptome-wide variation in both gene expression profiles and DNA sequences. We compared transcriptomes among four Daphnia galeata populations and identified transcripts potentially responding to local thermal selection based on an extensive literature search for candidate genes possibly under thermal selection in arthropods. Overrepresentation of temperature-relevant candidate genes among transcripts strongly contributing to sequence divergence among two populations indicates that local thermal selection acted on the coding sequence level. We identified a large number of transcripts which may contribute to local thermal adaptation based on outlier tests and distinctive expression profiles. However, among these, temperature-relevant candidate genes were not overrepresented compared to the global gene set; suggesting that thermal selection played a minor role in divergence among Daphnia populations. Interestingly, although the majority of genes contributing strongly to sequence divergence did not contribute strongly to divergence at the expression level and vice versa, the affected gene functions were largely consistent between the two data sets. This suggests that genetic and regulatory variation constitute alternative routes for responses to natural selection. Our combined utilization of a population transcriptomics approach and literature-based identification of ecologically informative candidate genes represents a useful and powerful methodology with a wide range of applications in evolutionary biology.