Natural selection in the laboratory
Experimental evolution provides the advantage that populations can be maintained in the laboratory under controlled conditions. Furthermore, replicate populations can be maintained in the same environment, which greatly facilitates the distinction between selective forces and neutral change. We are maintaining D. melanogaster and D. simulans populations in different temperature regimes in order to study how populations adapt to new environments. In addition to phenotypic analyses, we also use next generation sequencing to monitor allele frequency changes during the adaptation to the novel environment. This project is funded by the ERC Advanced Grant ARCHADAPT.
Evolution of gene expression
We are studying various aspects of gene expression, including cis-, and trans-regulation, sex-biased gene expression, and alternative splicing. Of particular interest is the interplay between variation in natural populations and environmental effects on gene expression.
Association studies for traits of different complexity
We study pigmentation and temperature stress resistance in natural Drosophila populations. These traits have been chosen for their potential ecological relevance and their contrasting genetic architecture. While pigmentation is a relatively simple trait, with a well-characterized genetic basis, temperature stress resistance is complex. Using Pool-GWAS, we are exploring how the underlying architecture of these traits is affected by population genetic differences and environmental variation. We are also investigating species differences in the genetic architecture of these traits.
The laboratory has a long-standing interest in the evolution of repetitive DNA, ranging from rDNA and microsatellites to transposable elements. Of particular interest to us is how these repeats contribute to adaptive variation in natural populations. In addition to repetitive DNA, we also study the evolution of introns and novel genes (orphans).
Inference of selection from population data
We use whole genome polymorphism data to identify selected genomic regions from ecologically differentiated populations. Since past demographic events can lead to erroneous identification selection signatures, we combine demographic inference with the identification of selected loci.