Computational Genomics and System Genetics
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.