It would be desirable in everyday clinical practice to use genetic associations to predict disease risk, the further development of a disease, or the course of therapy better.

Good quality risk models can be used to assess the individual risk of patients in order to make decisions on preventive measures or therapeutic options. In general, the accuracy of the prediction is controlled using new data that are independent of the data used to fit the model. However, in the context of genetic data, such independent data sets for validation are only rarely available.

Therefore, methods to validate models without further independent data are in development. Of particular interest here are genetic risk models of patient survival. We continue to work on polygenic risk scores and kernel methods including the development of methods to study the longitudinal course of a trait.

Last updated March 2023