Genomics of Selection Response under Strong Selection
  • Are you interested in understanding how selection operates on complex traits?
  • What is the prevalent mode of response to selection? Hard sweeps? soft sweeps? polygenic adaptation?
  • Can small populations respond effectively to selection?
  • How does the distribution of effect size correspond to shifts in allele frequencies?
If you're excited about exploring the above questions, you may be interested in our dataset and open position.
A postdoctoral researcher position is available in the Chan Lab to work on genomics analyses of selection experiments, with the aim of understanding the molecular basis of phenotypic variation and response to selection in a population genetics and quantitative genetics framework. Funding is available immediately for the position for an initial period of 1.5 years with the possibility of extension. 

We're looking for a Postdoc skilled in population and/or statistical genomics to take on the analysis of a number of selection experiments with replications, chiefly in mice but possibly also in other systems. Relevant datasets may include, but not limited to the High Runner lines (Opens external link in new windowTheodore GARLAND Lab, UCI; see Opens external link in new windowCareau et al., 2013), the Longshanks mice (Opens external link in new windowCampbell ROLIAN Lab, Univ. of Calgary, Canada; see Opens external link in new windowMarchini et al., 2014) and potentially other pedigreed populations demonstrating rapid response to selection. In all cases, we specialize in large-scale genomic data, where whole selection pedigrees and haplotype segregation are reconstructed to reveal the dynamics of response to selection, with the ultimate goal of re-tracing the entire selection experiment in every individual, at every locus, in the entire genome.

You will work with the complete dataset in close coordination with Opens external link in new windowProf. Nick BARTON (IST Austria) and his team to link theory with empirical genomic data. Candidates must have a strong background in bioinformatics, including experience with genomic data analysis and strong quantitative and programming skills. Further background in population genetics and modelling will be an advantage. The postdoc will work closely with Prof. BARTON's group, therefore she or he must show independence and ability to drive her/his own research project. You will enjoy excellent computational and sequencing support, as well as the opportunity to design and conduct functional tests in mice together with our wet-bench team members. 
Our on-going workin the Longshanks mice (ROLIAN group) has found many loci showing very strong response to selection, with a substantial fraction of parallel response. Further dissection of top loci has identified specific mutations in limb enhancers. Our functional test in mice showed that these mutations modulate enhancer activity in a way consistent with increased tibia length. 
Together with our partner groups, we will study the selection response from multiple angles, ranging from trait mapping, population genomics, theory to developmental genetics. The Longshanks selection experiment combines quantitative, developmental and population genetics and offers a unique opportunity to study how the genome responds to strong selection in a model paradigm. 
The Max Planck Campus in Tübingen, Germany is one of the leading campuses in evolutionary genomics research. The Chan Lab enjoys generous funding support by the Max Planck Society as well as the European Research Council (ERC). Our campus hosts world-class research groups, including a Nobel laureate and multiple ERC-funded teams (other groups active in evolutionary genomics include Felicity Jones, Estienne Swart, Hernan Burbano, Detlef Weigel, Ralf Sommer and Ruth Ley). We operate state-of-the-art sequencing (Illumina, PacBio and others) and other core facilities. All seminars and communications are in English.
To Apply: For informal enquiries and applications (cover letter, CV, and two reference contacts), please e-mail Dr. Frank Chan (Opens window for sending emailfrank.chan[at] Consideration of applications will be on-going until the position is filled. 
Note: This is not a re-posting of an earlier, similar position. We have funding for an additional position and datasets.