PhD Topics 2024

The role of deleterious mutations for adaptation and maintenance of variation. Summary
The evolution of ageing. Summary
Long-term adaptation of local Drosophila populations. Summary
Inference of selection signatures from time-series data. Summary
Making sense of whole-genome polymorphism data. Summary
Studying the genotype-phenotype map. Summary
Stabilising selection during polygenic adaptation. Summary
Evolution of regulatory networks. Summary

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

The role of deleterious mutations for adaptation and maintenance of variation

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.

The evolution of ageing

The project will take advantage of highly replicated Drosophila populations, which were maintained for more than 250 generations in a novel laboratory environment where they experienced a shift towards early fecundity. We showed that this early fecundity is associated with reduced life span. The high level of replication provides the unique opportunity to study ageing through a combination of genomic, transcriptomic and epigenetic analyses. The project will make an important contribution to the clarification of several hypotheses about the evolution of ageing.

Candidates who are interested to combine bioinformatic analyses with phenotypic assays to understand the process of ageing are particularly encouraged to apply.

Long-term adaptation 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.

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).

Making sense of whole-genome polymorphism data

Advisor: Magnus 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.

Studying the genotype-phenotype map

The mapping of quantitative trait loci (QTL) and genome-wide association studies (GWAS) are two widely used techniques for determining the genotype-phenotype map. Nevertheless, both approaches are confronted with particular challenges. The resolution of QTL mapping studies is typically limited, while the frequency of the contributing SNP in GWAS results in unequal power to detect causative variants. This project will utilize a mapping population that has been specifically designed to have all segregating SNPs with the same frequency. In conjunction with the low degree of linkage disequilibrium observed in this mapping population, the project will have an unparalleled power to detect causative variants, thereby facilitating the generation of an unbiased genotype-phenotype map.

Candidates with an interest in combining fly work with bioinformatic analyses are particularly encouraged to apply.

Stabilizing selection during polygenic adaptation

A fundamental tenet of polygenic adaptation theory is that the focal trait is subject to stabilizing selection. A population encountering a novel environment causes a shift in the trait optimum. Although numerous theoretical results have been established regarding the alterations in allele frequency that occur during this process of polygenic adaptation, the empirical evidence remains limited. This project will take advantage of a founder population with low levels of linkage disequilibrium and the same starting frequency of all segregating single-nucleotide polymorphisms (SNPs). The population will be used in an experimental evolution setting that is consistent with the theoretical framework of polygenic adaptation. By employing a multifaceted approach encompassing genomic, transcriptomic, and phenotypic analyses, this project aims to elucidate the intricacies of polygenic adaptation in depth.

Candidates with an interest in the comparison of empirical data with population genetics models of polygenic adaptation are particularly encouraged to apply.

Evolution of regulatory networks

It has been proposed that polygenic adaptation can be more effectively understood by focusing on gene regulatory networks as a whole, rather than on individual single nucleotide polymorphisms (SNPs) or the expression of a single gene. This project will utilize eQTL data generated for ancestral and evolved populations aiming to understand how regulatory networks change during polygenic adaptation. The hypothesis is that replicate populations adapting to the same new environment will do so by changing the expression of different genes (i.e., genetic redundancy). However, at the network level, these differences result in a consistent, parallel change.

Candidates with a strong interest in combining system biology approaches with population genetic questions are particularly encouraged to apply.

References:

Fagny, M. & Austerlitz, F. Polygenic Adaptation: Integrating Population Genetics and Gene Regulatory Networks. Trends Genet 37, 631–638 (2021).
Boyle, E. A., Li, Y. I. & Pritchard, J. K. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell 169, 1177–1186 (2017).
Barghi, N., Hermisson, J. & Schlötterer, C. Polygenic adaptation: a unifying framework to understand positive selection. Nature Reviews Genetics 11, 665–13 (2020).