Our interest lies in computational methods 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 molecular states between individual cells?
We use statistics and machine learning as our main tool to address 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. We develop methods that enable connecting genetic factors to phenotypes and to integrate multiomics data in health and disease.

Our methodological work ties in with biomedical collaborations, and we are developing methods to fully exploit high-throughput datasets from the most recent profiling 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.