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.
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.