Improving the personalized prediction of complex traits and diseases: application to type 2 diabetes
Common complex diseases are among the top leading causes of death globally. Due to their heavy burden on the healthcare systems and on affected individuals themselves, scientists are searching for solutions to delay their onset or even better, to prevent them. Complex diseases result from many genetic and non-genetic (e.g. lifestyle and environment) factors and their interactions, but the specific risk factors differ between individuals. Therefore, prevention of such diseases requires a personalized approach that uses each person’s genetic and non-genetic information to predict his/her disease risk. In the current thesis, type 2 diabetes (T2D) was used as a model example of a common complex disease. T2D occurs when the blood sugar levels are too high and results in severe health complications when appropriate and timely treatment is not guaranteed. Many non-genetic factors have already been established as risk factors for T2D, however, the contributions of genetic risk factors and their interactions with non-genetic risk factors have been less explored. The current thesis presents methodological advancements to better use (epi)genetic information for risk prediction of T2D. It shows that genetic risk profiles can be improved by accounting for overestimation of genetic risk and by incorporating the ancestry of individuals in order to reduce health disparities. The thesis also discusses the current limitations of genetic risk profiles and summarizes the latest genomic advancements in general. In summary, this thesis brings personalized prediction a step closer to successful application with the goal to prevent disease and maintain good health for longer.