Principal Investigator: Professor Paul Franks
Department: Lund UniversityTags: 57232, cluster analysis, functional annotation, genetic burden, lifestyle, obesity, Type 2 diabetes
Genetics plays an important role in determining health outcomes in each one of us. There is however little information on how total genetic risk affects different health outcomes and the effect of lifestyle changes in reducing or changing this risk. In this study we aim to describe this total genetic risk and how different combinations of genetic variants affect health. In addition, we will estimate the effect of lifestyle change in modifying this genetic risk. This project will enhance our understanding of the genetic risk of different health outcomes, the impact of lifestyle adaptations and also help us identify sub-populations of individuals that differ in their risk profile and response to lifestyle interventions. This will, in turn, help us in devising targeted risk reduction interventions for these subgroups.
Obesity is a very heterogeneous condition for which the current therapies are rarely individualized. Using genetic data, we will try determine which groups of conditions are different between obesity with and without diabetes and cardiovascular disease. We will use genetic epidemiology tools to elucidate the causal chain between these conditions and the possibilities for new targets. Then we will use the genetic variants associated with these group of conditions to classify obesity into subclasses and build risk scores in order to evaluate their predictive potential for complications related to obesity. Finally, we will use functional annotation tools to discover the underlying mechanisms of these relationships.