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.
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.
Casale, P., Rakitsch, B. et al. Efficient set tests for the genetic analysis of correalted traits. Nat Meth, advance online
Buttner, F. et al. (2015) Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotech 33, 155-160
Fusi, N. et al. (2014) Warped linear mixed models for the genetic analysis of transformed phenotypes. Nat Comm 5, 5890
Smallwood, S. et al. (2014) Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Meth 11, 817-820
Gagneur, J., et al. (2013) Genotype-environment interactions reveal pathways that mediate genetic effects on phenotype. PLoS Genet 7, 500-7
Stegle, O., et al. (2012) Using Probabilistic Estimation of Expression Residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat Protoc 7, 500-507
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.
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.