Statistical genomics and systems genetics

Our interest lies in computational approaches to unravel the genotype– phenotype map on a genome-wide scale. How do genetic background and environment jointly shape phenotypic traits or causes diseases? How are genetic and external factors integrated at different molecular layers, and how variable are these molecular readouts between individual cells?

We use statistics as our main tool to answer these questions. To make accurate inferences from high-dimensional ‘omics datasets, it is essential to account for biological and technical noise and to propagate evidence strength between different steps in the analysis. To address these needs, we develop statistical analysis methods in the areas of gene regulation, genome wide association studies (GWAS) and causal reasoning in molecular systems.

Our methodological work ties in with experimental collaborations and we are actively developing methods to fully exploit large-scale datasets that are obtained using the most recent technologies. In doing so, we derive computational methods to dissect phenotypic variability at the level of the transcriptome and the proteome and we derive new tools for single-cell biology.  

Overview of the single-cell latent variable model, an approach to decompose heterogeneity in single-cell transcriptome data.

Illustration of statistical methodology to dissect transcriptional heterogenetiy in single-cell RNA-Seq datasets (adapted from Buettner et al. 2015). Left: underlying source of variation in single-cell transcriptome data. Right: Illustration of our scLVM approach to identify and account for such factors.

Future goals and projects

We will continue to develop innovative statistical approaches to analyze data from high-throughput genetic and molecular profiling studies. We are particularly interested in following up our recent efforts to model single-cell variation data. A major challenge in this area will be the integration of multiple modalities in single-cell genomics, for example linking single-cell epigenome variation with single-cell RNA-Seq. We are particularly interested in applying these methods to data from the Human Induced Pluripotent Stem Cell Initiative (HipSci), in which we are a partner. 

Job opportunities

Postdoctoral positions: In addition to advertised postiison, we have curently postdoc opportunitites in the area of single-cell genomics and statistical genetics. If you are interested in our work and would like to discuss opportunities, please contact contact Oliver Stegle

PhD studentships: Please check the list of projects to see whether we are currently offering predoctoral positions in the group. Further details can be found in the guidelines for applicants

Internships: We host interns for specific projects of 4 months or longer. Please contact Oliver Stegle with a concrete proposal to enquire about current opportunities. 

Postdocs: CTTV

Opporunitities in collaboration with the Centre for Theurapeutic Target Validation:

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