Scientific rationale: Genetics of disease are usually studied through statistical methods, which evaluate the information of single genetic variations in relation to a disease trait. A combination of multiple genetic variations have been found useful when investigating the genetic effects in complex diseases. A polygenic risk score is a measure of the underlying genetic risk of developing a given trait. Traditionally, these are calculated using simple models and combined information of carefully selected genetic variations. However, these models have limitations, such as the selection of genetic variations to include in the scores and inability to account for complex genetic structures.
Aims: Over 3 years, we aim to develop a new strategy to select the optimal set genetic variants and combine these in polygenic risk scores. These scores will grant the probability of developing a disease. These strategies will be able to account for the complex genetic structures and include more information on human biology, as well as move beyond the use of single genetic effects. We will apply this across more diseases including diabetes and leukemia, as well as evaluate the potential applications of the polygenic risk scores in clinical settings.
Public health: The output of this study is a computational framework for determining individual risk of complex diseases using disease-specific selected genetics. This will add to the information available to clinicians, thereby help guide decisions, risk assessments and potentially improve the personalized treatment strategies.