Senior researcher Peter Sørensen from Center for Quantitative Genetics and Genomics (QGG) is the leader of the project Bayesian Analysis of Diabetes for Enhanced biomarkerR and drug target (BALDER). Over the next three years, the project will develop a new and more accurate mapping of genetic variants that can lead to Type 2 diabetes, thereby identifying medical treatment targeted the individual genetic variants. The project is carried out in collaboration with Mads Fuglsang Kjølby from the Steno Diabetes Center at Aarhus University, and researchers from the Novo Nordisk Foundation.

Research in individual medicine tailored to the individual patient has exploded in recent years. By mapping many genetic variants of a given disease, it becomes possible to target medical treatment to precisely these areas, instead of applying a scattergun technique using the same standard medicine for all patients with a given diagnosis, as medical science has practiced in many years.

The BALDER project's research object, Type 2 diabetes mellitus (T2DM), is a widespread diagnosis in Denmark with around 250,000 patients. Furthermore, the disorder has a high heritability. If you have a parent with T2DM, you have a 40% risk of getting the disease yourself. Type 2 diabetes is also associated with a wide range of sequelae such as obesity, cardiovascular diseases, high blood pressure, high cholesterol and chronic kidney disease.

At present, research into the genetic variants of Type 2 Diabetes can only explain 3% of the risk of getting the disease. In order to be able to go further and map more genetic variants, the research can go two ways: either by expanding the scope of the test material considerably or by using existing data better and more accurately.

-’The project will work with a better utilization of existing data. We will increase the detection of genetic variants for Type 2 diabetes by developing more accurate, statistical models that use both information from traits correlated with Type 2 diabetes and use information about functional marker groups to capture the weak, genetic signals that are not captured with current models', Peter Sørensen explains, and continues:

-‘In this way, the etiology of the disease (the doctrine of the causes of diseases, ed.) can be elucidated more precisely and help to improve the identification processes for individualized medicine ’.

Because Type 2 diabetes is closely linked to a number of complex traits and sequelae, there are a number of common genetic influences for these diseases. By applying genomic information to correlated traits of sequelae, the statistical power to detect causal variants of Type 2 diabetes can be increased. The result will be a better understanding of disease that can provide information for research in targeted medicine.

Peter Sørensen and Mads Fuglsang Kjølby are both enthusiastic about the grant, which is the result of a long-term strategy, and the opportunity to collaborate with the Novo Nordisk Foundation and their group in Oxford:

-‘We are convinced that our innovative approach to statistical modeling and a genomically supported identification strategy for individualized medicine can be applied to other complex diseases, such as cardiovascular diseases, autoimmune diseases or Alzheimer's,’ says Peter Sørensen.

ODIN – Open Discovery Innovation Network, from which the grant comes, is a research initiative funded by the Novo Nordisk Foundation. The basic idea is that researchers share their results openly so that others can work further with them. Thus, research into targeted medicine can more quickly come to fruition with market-ready medicines that can benefit patients.

Peter Sørensen elaborates:

-‘To ensure that other research groups in both academia and the medicine industry can benefit from our work, we implement our statistical and bioinformatics tools in an open-source software package. In this project, the focus is on improving the research process for targeted medicine, but our model approach can also be used to develop more accurate indicators of genetic risk in terms of other complex diseases. Indication of genetic risk can be a powerful approach to identifying individuals with a higher - or lower - risk for specific diseases or targeting medical treatment. '