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PhD Topics 2023

  • Adaptation from reduced genetic variation. Summary
  • Development of haplotype-based inference methods. Summary
  • Evolution from de novo mutations - influence of elevated mutation rates. Summary
  • Evolution of sex-specific neuronal signaling. Summary
  • Inference of selection signatures from time-series data. Summary
  • Long-term dynamics of local Drosophila populations. Summary
  • Speciation from standing genetic variation. Summary
  • Studying the evolution of gene expression with single cell RNA-Seq. Summary
  • Understanding how selection acts on multiple tightly linked variants. Summary


Adaptation from reduced genetic variation

It is well-understood that most adaptive traits are highly polygenic. This implies that adaptation is typically the result of small allele frequency changes at many loci. As a consequence, the identification and characterization of contributing loci is challenging, if not impossible. This project uses an innovative approach to reduce this problem: by reducing the genetic variation in the founder population highly parallel selection responses can be obtained and the localization of selection targets is greatly facilitated. The PhD student will build on this experimental system to obtain unprecedented insights into the genetic architecture of polygenic adaptation.


Development of haplotype-based inference methods

Populations evolve through the changing frequencies of blocks of genome. Yet, using current methodologies, what we observe and analyse are the frequencies of millions of individual SNP. For example, if a favourable allele sweeps through a population. It will be more or less associated with large numbers of linked SNP, which will show a “signature of selection” that depends on their arbitrary initial association with the causal allele. Thus, the trajectories of underlying haplotypes are reflected only vaguely by the SNP that we see; the sheer volume of sequence data can mislead. In this project, the student will develop methods for inference that go beyond the currently predominant site-by-site methods, by exploiting haplotype structure.


Evolution from de novo mutations - influence of elevated mutation rates

Most new mutations are deleterious and will be purged from evolving populations. This PhD project will evaluate how a genetically identical population of fruit flies can adapt to a new polygenic phenotype by novel mutations. By contrasting populations with different mutation rates and population sizes this project seeks to characterize the adaptive response of a polygenic trait. Since only a few mutations are expected to be beneficial, this project provides the opportunity to identify and characterize individual alleles contributing to a highly polygenic trait.

The project is particularly well-suited for students with a keen interest to combine experimental fly work with state of the art bioinformatic analyses characterizing the trajectories of selected alleles. Candidates are provided the opportunity to test beneficial alleles in transgenic assays.  Experience with CRISPR/Cas9 is beneficial for the functional characterization, but not required for the project.


Evolution of sex-specific neuronal signaling

Previous work showed that neuronal signaling evolved in Drosophila simulans from Florida in response to a new temperature regime. Interestingly, this evolutionary response occurred in a sex-specific manner: males and females modified different signaling pathways.

This PhD project will use a combination of single cell RNA-Seq and behavioral analyses to identify the selective forces driving this sex specific signaling. A comparison to flies from other populations and species that evolved under the same selection regime will inform us to what extent this evolutionary response is population specific or provides a general temperature adaptation.

Future PhD students should have a vivid interest in exploring new data analyses and develop new innovative phenotyping methods.

References:

  • Hsu SK, Jaksic AM, Nolte V, Lirakis M, Kofler R, Barghi N, Versace E, Schlötterer C. 2020. Rapid sex-specific adaptation to high temperature in Drosophila. Elife 9.
  • Jaksic AM, Karner J, Nolte V, Hsu SK, Barghi N, Mallard F, Otte KA, Svecnjak L, Senti KA, Schlötterer C. 2020. Neuronal function and dopamine signaling evolve at high temperature in Drosophila. Molecular biology and evolution doi:10.1093/molbev/msaa116


Inference of selection signatures from time-series data

Molecular population genetics has a long-standing tradition to infer selection signatures from genomic data. Most of the developed methods rely either on a single population or the contrast of multiple populations with different selection pressure in the past. With the increasing availability of time series data from ancient DNA and experimental evolution, it has become possible to study time-series data. Hence, the temporal pattern of allele frequency changes provides extremely rich information to distinguish selection from neutral patterns. This PhD project builds on an exceptionally powerful experimental evolution study, with 15 replicate Drosophila populations adapting to a novel environment for more than 100 generations. Genomic data are available in 10 generation intervals to study the allele frequency trajectories at high temporal resolution.

The future PhD student will have the opportunity to analyze the best time series data set available for a sexual organism. Hence, experience with handling large data sets is clearly a benefit and candidates with a keen interest to advance currently available statistical methods to analyze time-series data are particularly welcome to apply.

References:

  • Vlachos, C. Burny, C., Pelizzola, M., Borges, R., Futschik, A., Kofler, R. & Schlötterer, C. Benchmarking software tools for detecting and quantifying selection in evolve and resequencing studies. Genome Biology 20, 169, doi:10.1186/s13059-019-1770-8 (2019).
  • Taus, T., Futschik, A. & Schlötterer, C. Quantifying Selection with Pool-Seq Time Series Data. Molecular Biology and Evolution 34, 3023-3034, doi:10.1093/molbev/msx225 (2017).
  • Barghi, N., Tobler, R., Nolte, V., Jakšić, A. M., Mallard, F., Otte, K. A., Dolezal, M., Taus, T., Kofler, R. & Schlötterer, C. Genetic redundancy fuels polygenic adaptation in Drosophila. PLoS Biology 17, e3000128, doi:10.1371/journal.pbio.3000128 (2019).


Long-term dynamics of local Drosophila populations

Most inference of adaptation in natural populations is either based on genomic polymorphisms patterns of a single population or the contrast between populations adapted to different environments. Since time series data provide a very powerful approach to distinguish selection from other evolutionary forces changing allele frequencies, longitudinal sampling of a single population provides a hitherto underexplored approach to study the evolutionary dynamics of natural populations. The future PhD student will be granted access to an unique collection of samples covering more than 10 years with multiple samples throughout the entire season. This outstanding data set will provide new insights in the dynamics of local populations and their evolutionary response to a changing climate.

This project is particularly well suited for PhD students with a background in population genetics, who are interested to develop new cutting edge data analyses that incorporate time series data to distinguish selection from other forces. Whole genome polymorphism Pool-Seq data will be available as well as sequence data from individual flies to facilitate haplotype-based analyses.


Speciation from standing genetic variation

Most speciation models rely on novel mutations occurring in distinct lineages, but it is not yet well-understood if speciation can also occur from standing genetic variation. We recently showed that the two major speciation processes associated with adaptation, mutation order speciation and ecological speciation, can also be fueled by standing genetic variation. The proposed project aims to test the generality of this observation by means of experimental evolution. The successful candidate will have access to a large collection of independently evolved populations, to determine the likelihood of reproductive isolation during the adaptation of these populations.


Studying the evolution of gene expression with single cell RNA-Seq

Previous work from our laboratory demonstrated that adaptation to a novel temperature regime is accompanied by changes in gene expression-frequently in a sex-specific manner. Until now, we focused on the analysis of whole body adults, but the extent to which these expression differences are determined by altered gene expression patterns of individual cells is not yet understood. This PhD project aims to address this question using large-scale single cell RNA-seq on a population level. This will provide unprecedented insights into the evolution of gene expression in populations. The project provides an excellent opportunity for PhD students with strong interest to combine latest bioinformatics methods with population genetic theory.

References:

  • Hsu SK, Jaksic AM, Nolte V, Lirakis M, Kofler R, Barghi N, Versace E, Schlötterer C. 2020. Rapid sex-specific adaptation to high temperature in Drosophila. Elife 9.
  • Mallard F, Nolte V, Schlötterer C. 2020. The evolution of phenotypic plasticity in response to temperature selection. Genome Biol. Evol. 12: 2429-2440.
  • Zappia L, Theis FJ. 2021. Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape. Genome Biology 22:301


Understanding how selection acts on multiple tightly linked variants

How can natural selection disentangle linked variants, so as to bring together favourable combinations of alleles? Even in a large population, negative associations develop - that is, favourable alleles are found on different genetic backgrounds more often than expected. This "Hill-Robertson”  effect gives an advantage to recombination that may well explain its ubiquity in eukaryotes: the main role of sex and recombination may be to allow selection to efficiently assemble well-adapted sets of alleles. In this project, the student will investigate how selection can disentangle linked variants, specifically studying how favourable alleles can escape the drag from linked deleterious mutations, and how linkage impedes adaptation to a new optimum under stabilising selection. The focus will be on tight linkage, asking whether complex alleles, in which multiple mutations combine within a gene, are likely to evolve.

Fond zur Förderung der wissenschaftlichen Forschung
vetmed uni vienna
Gregor Mendel Institute of Molecular Plant Biology
Universität Wien