PhD Topics 2024

Adaptation from reduced genetic variation. Summary
Adaptation to complex environments. Summary
Inference of selection signatures from time-series data. Summary
Long-term dynamics of local Drosophila populations. Summary
Making sense of whole-genome polymorphism data. Summary
Speciation from standing genetic variation. Summary
The role of deleterious mutations for adaptation and maintenance of variation. Summary
Unraveling the impact of gene flow during species divergence. Summary

More topics and positions may be available within our partner program SFB Polygenic adaptation

Adaptation from reduced genetic variation

Advisor: C. Schlötterer

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.

Adaptation to complex environments

Advisor: C. Schlötterer

Natural environments are complex with multiple biotic and abiotic factors imposing stress on evolving populations. While the phenotypic consequences of multiple stressors have been studied, very little is known about the genomic changes contributing to the adaptation to multiple stressors. Experimental evolution provides the unique opportunity to test the interaction of multiple stressors and the genetic basis of the adaptive response. This project will use experimental evolution to measure the selection response in evolving populations that are either exposed to single stressors or a combination of different stressors. Whole genome sequencing and RNA-Seq in combination with the analysis of life history traits will provide a new understanding of adaptation to complex environments from standing genetic variation in a sexual species (Drosophila).

Reference:

Burny C, Nolte V, Dolezal M, Schlötterer C: Genome-wide selection signatures reveal widespread synergistic effects of two different stressors in Drosophila melanogaster. Proc Biol Sci 2022, 289:20221857.

Inference of selection signatures from time-series data

Advisor: C. Schlötterer

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).

Making sense of whole-genome polymorphism data

Advisor: M. Nordborg

Our view of genetic polymorphism has been distorted by methods that provided a limited and reference-biased picture. Long-read sequencing technologies, which are starting to provide nearly complete genome sequences for population samples, should solve the problem—except that characterizing and making sense of non-SNP variation is difficult even with perfect sequence data. Using available genomes from Arabidopsis thaliana, we are actively developing a framework for analyzing whole-genome polymorphism data, addressing fundamental questions ranging from mutational mechanisms to transposon dynamics, and there is room for students with a serious interest in population genetics, molecular evolution, and genome biology.

Long-term dynamics of local Drosophila populations

Advisor: C. Schlötterer

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

Advisor: C. Schlötterer

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.

The role of deleterious mutations for adaptation and maintenance of variation

Advisor: C. Schlötterer

The impact of beneficial mutations on the patterns of variation is a popular research theme in evolutionary biology. Since deleterious mutations do not only affect the dynamics of linked beneficial alleles [1] but have also profound effects on the maintenance of variation. A well-known, but insufficiently studied phenomenon is associative overdominance, where recessive deleterious alleles on different chromosomes reduce the loss of variation in small populations [2]. We have recently shown that even more than 15 generations of brother-sister inbreeding variation is maintained across large genomic blocks in 50 D. simulans lines originating from three different continents. This project will take advantage of experimental evolution in combination with whole genome sequencing to provide an unprecedented characterization of deleterious mutations and their impact on selection signatures in evolving populations.

References:

(1) Assaf ZJ, Petrov DA, Blundell JR: Obstruction of adaptation in diploids by recessive, strongly deleterious alleles. Proc Natl Acad Sci U S A 2015, 112:E2658-2666.
(2) Schou MF, Loeschcke V, Bechsgaard J, Schlötterer C, Kristensen TN: Unexpected high genetic diversity in small populations suggests maintenance by associative overdominance. Mol Ecol 2017, 26:6510-6523.

Unraveling the impact of gene flow during species divergence

Advisor: R. Borges

Understanding how species diverge through the exchange of genetic material is a fascinating area of study. While evidence of gene flow exists across diverse biological groups, there is a critical need to assess its prevalence and impact at phylogenetic time scales. Current models fall short, particularly when evaluating gene flow along multiple speciation events, especially at deep time horizons.

We are seeking a highly motivated PhD student with a strong background in computational biology and/or biostatistics to embark on a cutting-edge research project. The goal is to develop a powerful and realistic framework that expands the applications of existing methods. This framework will enable the analysis of extensive genomic datasets, now commonplace in evolutionary biology, and address the understudied realm of gene flow at phylogenetic evolutionary scales.

Join us in unraveling the mysteries of species divergence and contribute to advancing the field of evolutionary biology!

Overview: The project will utilize complex phylogenetic models and draw insights from diverse organisms, including grasshoppers, fruit flies, persimmon trees, and fireflies. We aim to answer key questions:

What is the evolutionary significance of gene flow during species divergence?
Are most regions of the genome affected by gene flow, or do some regions show an elevated impact of gene flow?
What is the interplay between natural selection and gene flow during species evolution?

Requirements: We are looking for a candidate with a strong foundation in computational biology and/or biostatistics. Proficiency in a coding language, such as Python, C++, or C, is a prerequisite.