Statistical genomics and systems genetics

Stegle group figureStatistical inference pinpoints likely causal genetic variants with an effect on gene expression levels in A. thaliana (left; adapted from Gan, Stegle et al. 2012.). Genome reconstruction and gene re-annotation allow for mapping these associations to variant categories in the vicinity of coding genes (right).

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 such as transcription and translation?

To answer these pertinent questions, we build on statistics as our main tool. 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. With this in mind, we develop statistical analysis methods in the area of gene regulation, genome wide association studies and causal discovery in molecular systems. Our methodological work is tight in with experimental collaborations, where we study the variability of molecular traits in different systems, including yeast models, plants and human genetics.

Future goals and projects

We will continue to devise statistical methods to model and analyse data from high-throughput genetic and molecular profiling experiments. Technically, the development of new approaches for tying together quantitative readouts across multiple molecular layers will increase in importance. To this end, we develop causal inference methods to deduce functional relationships from the wealth of correlative omics datasets being generated. In humans, we will apply these techniques to a diverse panel of rich molecular phenotypes in an iPSC panel generated as part of the HipSci consortium. 

Job Opportunities

Postdoctoral positions: We are currently looking for enthusiastic team members with a strong background in bioinformatics, machine learning, statistics, physics or a related discipline. Current research directions include the development of statistical methodology for genome-wide association studies, methods to dissect the genetic architecture of molecular traits and devising systems genetics approaches to integrate different omics data at genome-wide scale. For informal enquiries, please 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 3 months or longer. Please contact Oliver Stegle with a concrete proposal to enquire about current opportunities. 

Postdocs

EBPOD postdoctoral programme

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