State of the art

Genome-wide association studies (GWAs) in complex diseases have identified many single nucleotide polymorphisms (SNPs) associated with disease susceptibility. However, a large fraction of genetic markers associated with complex disease pathogenesis are part of larger haploblocks with high linkage disequilibrium (LD) and many are located within non-coding regions or even so-called gene deserts, adding further difficulty to the decoding of pathological molecular processes underlying complex diseases. Identifying disease loci and genes causal for disease pathogenesis would improve understanding of the disease on a molecular level as well as opening possibilities for therapy optimization, new drug development or repurposing of existing drugs.
As the cost for genomic studies continues to decrease with high-throughput sequencing and imputation, the amount of data generated also increases, requiring advanced bioinformatic tools for processing. In the current post-GWAs era, greater emphasis is placed on fine mapping the true causal variant within a haploblock, elucidating how it affects gene expression and thus determining what role the variant plays in disease pathogenesis. To this end, expression quantitative trait loci (eQTL) studies are performed to link suspected disease susceptibility variants to gene expression data in select tissues. The recent advent of RNAseq technology has enabled full transcriptome studies, providing immense insight in how complex diseases work on the RNA level, increasing the rate at which eQTL are discovered and also allowing discovery of new, disease-specific gene transcript variants.
Furthermore, genome and transcriptome studies enable identification of DNA and RNA markers of drug response. Identifying drug response biomarkers and translating them into clinical praxis would fulfill the ideal of personalized medicine. By using the patient's characteristics on the molecular level, it would be possible to select the correct drug, administer it at the optimal dosage and maximize the chance of a successful treatment outcome while minimizing the severity and likelihood of adverse side effects. Advanced statistics and the principles of machine learning are employed to create accurate biomarker prediction models.

Research areas

To this end, the centre is collecting an extensive bank of biological samples from clinically well characterized patients. The centre researches foremost on immune-mediated complex diseases such as inflammatory bowel disease, rheumatoid arthritis and asthma, but we also research certain types of cancers, ex. breast cancer. For most studies, peripheral venous blood is collected to obtain serum, peripheral blood mononuclear cells (PBMCs) and erythrocytes. If possible, we also collect relevant tissue biopsies. In some cases, saliva and stool samples are also collected.
Currently, the centre is focused mainly on identification of diagnostic and prognostic biological markers in immune-mediated diseases and cancer. To see a list of completed and ongoing research projects, click here. To see a list publications, click here.