Collaboration with Prof. Mihai Netea (Radboud University Medical Center) and Prof. Cisca Wijmenga (University Medical Center Groningen) 

Recent studies have highlighted that metabolites have a role in immune diseases, but it remains unknown how much of this is driven by genetic and non-genetic host factors. We systematically investigated circulating metabolites in a cohort of 500 healthy subjects (500FG) in whom immune function and activity have been deeply measured and genetics profiled. This study provides a comprehensive map of integration between the blood metabolome and immune phenotypes, reveals novel genetics factors that regulate blood metabolite concentrations, and proposes an integrative approach for identifying new disease treatment targets.

Collaboration with Prof. Lude Franke (University Medical Center Groningen)

Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, the current methods are labor-intensive and expensive. Here we introduce a new method, Decon2, a statistical framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) and consecutive deconvolution of cell type eQTLs (Decon-eQTL). 

Our new method provides a way to assign cell type effects to eQTLs from bulk blood, which is useful in pinpointing the most relevant cell type for a certain complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2), and as a web tool (www.molgenis.org/deconvolution).

approach for identifying new disease treatment targets.

Collaboration with Prof. Serena Sanna and Prof Cisca Wijmenga (University Medical Center Groningen) 

Robust inference of causal relationships between gene expression and complex traits using Mendelian Randomization (MR) approaches is confounded by pleiotropy and linkage disequilibrium (LD) between gene expression quantitative loci (eQTLs). Here we propose a new MR method, MR-link, that accounts for unobserved pleiotropy and LD by leveraging information from individual-level data. Our results showcase the strength of MR-link for transcriptome-wide causal inferences.

Collaboration with Prof. Cisca Wijmenga (University Medical Center Groningen) and Prof. Frits Koning (Leiden University Medical Center) 

Cealic disease (CeD) is associated with genetics. HLA locus contributes around 20-40% of genetic variation in CeD. Specially, HLA-DQ2 and/or -DQ8 variants are needed for developing CeD, but only 3% of carriers develop it. To date, it is largely unknown the role of different cell types in CeD onset and how the genetic background affects these cell types. Thus, we aim to study the single-cell heterogeneity that may exist between individuals at increased risk of developing CeD in a disease onset context.