eQTLs: We are interested in studying the role of genetic variation in the regulation of gene expression and its impact on disease risk. To this end, we use eQTL analysis to identify genetic variants that are associated with changes in gene expression levels. We do this in large-scale projects in blood, brain and gut, enabling us to detect both cis- and trans-eQTLs.

Since many omics datasets have been generated by many researchers across the world, we develop federated strategies to leverage these datasets through meta-analysis. We believe that new insights can be gained by combining published datasets. However, these datasets are often either privacy-sensitive or too large to share. For this reason we develop federated pipelines that allow analysis on these large datasets without actually sharing any privacy-sensitive data. We are leading several large international consortia to develop the pipelines to conduct eQTL mapping at the bulk (eQTLGen consortium: https://eqtlgen.org/) and single-cell level (sc-eQTLGen consortium: https://eqtlgen.org/sc/) in a federated manner.