How does the genomic footprint of climate adaptation looks like? Which genomic patterns underly the phenotypic adaptation to climatic factors, such as temperature or precipitation?
- Using the aquatic midge Chironomus riparius, I investigated the genomic underpinnings of temperature adaptation. We experimentally demonstrated fitness-relevant thermal adaptation on the phenotypic level of five natural populations sampled along a climatic gradient across Europe. With the aim to uncover the genomic basis of this phenotypic adaptation pattern, we invested much effort in separating signatures of clinal temperature adaptation from signatures of other evolutionary forces, such as demographic processes, genetic drift and adaptation to nonclinal conditions of the immediate local environment. By combining a Fst outlier approach with a genome-wide environmental association analysis it was possible to reveal that allele frequency differences in 1.2 % of all protein coding genes in our reference assembly are significantly associated to the variation of climate factors. Interesting GO terms, such as 'response to heat' and 'apoptotic process' where found to be significantly enriched among candidate genes.
- In collaboration with Ruth Müller from the Institute of Tropical Medicine in Antwerp (project AECO), we are currently investigating the genomic footprint of climate adaptation in natural populations of the two mosquito species Aedes aegypti and A. albopictus, sampled along an elevational gradient in Nepal.
- In our latest article we review the short history of genotype-environment association (GEA) studies, summarizing available studies, organisms, data type and data availability for these studies. GEA is a powerful tool in climate change research. While our initial aim was to compare results of existing studies to identify common patterns or differences in climate adaptation, we quickly realized that such a meta-analysis approach is currently unfeasible. Based on our literature review we discuss the current shortcomings and lack of data accessibility which impede meta-analyses. Such meta-analyses would allow to draw conclusions on traits and functions crucial to adapt to different environmental, e.g. climate conditions, across species.