Research areas:

Our group is actively involved in several research areas, including:

• 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.

• Federated analysis: Many omics datasets have been generated by many researchers across the world. 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.

• Gene expression data: We are leveraging large-scale gene expression datasets to identify patterns of gene expression that are associated with different diseases and to understand the underlying molecular mechanisms that drive these diseases. We also use gene expression data to predict functions of genes, enabling us to assist our clinical genetics colleagues in the discovery of mutations that cause rare diseases.

• Single-cell techniques: We apply various (spatial) single-cell technologies (e.g. single-cell RNA sequencing, single-cell multiomics) to study gene expression and its regulation at the single-cell level, which allows us to better understand the heterogeneity within cell populations and the underlying molecular mechanisms that drive cellular diversity. We generate this data in large population-based cohorts, enabling us to identify cell-type- and context-specific-eQTLs and co-expression QTLs. To pinpoint the regulatory or causal genetic variants, we combine these association studies with high-throughput experimental techniques in which we assess the regulatory capacity of genetic variants in massively parallel reporter assays and assess the downstream expression effects ofspecific mutations using multiplexed CRISPR/base editing coupled with single-cell read-outs.

• Gene regulatory network reconstruction: Using single-cell and bulk genomics and multi-omics resources we generate cell-type-specific gene regulatory networks. We use different strategies to make these networks as accurate as possible. For instance, we are currently working on making these networks directed, and aim to do this in a robust way.

• Drug target identification and target (in)validation: we employ the identified eQTLs and gene regulatory networks to identify drug targets, and also use them to (in)validate drug targets.

Key accomplishments:

Our group has made several notable contributions to the field, including:

• The identification of considerable numbers of cis- and trans-eQTLs in blood (eQTLGen) and brain (MetaBrain), and identification of the upstream genes that regulate these eQTLs (through single-cell co-expression QTL mapping). 

• These resources enable the identification of novel targets for several blood and brain diseases.

• The development of algorithms for the interpretation of genome-wide association studies (DEPICT and Downstreamer)

• The development of new computational algorithms that improved the accuracy and efficiency to detect eQTLs (MixupMapper, PICALO, Decon-QTL).

• The discovery of several novel genes that are involved in the development of various rare diseases, including cardiovascular diseases (GADO).