AI tools to predict risk of common diseases using genetic data
Name: Åsa Johansson
Project title: AI tools to predict risk of common diseases using genetic data
Department: Immunology, Genetics and Pathology
Area of research: Human Genomics and Molecular Epidemiology
In this project we will use Artificial Intelligence tools with genetic data, for prediction modelling of disease risk. During the last 15 years, we have performed genome-wide association studies (GWAS), and identified thousands of genetic variants to be associated with common diseases and disorders, such as obesity, myocardial infarction, allergies and asthma. Most common disease are complex, meaning that many (hundreds or thousands) of genetic variants influence the disease risk, together with other exposures, such as lifestyle and environmental factors. Methods available today are poor in accurately prediction individuals of high disease risk, predominantly due to the extremely large number of genetic variants in the human genome, the large number with an effect on each disease, the low effect by each such variants, and possible interactions between genetic variants. Our hypothesis is that by using AI, we can increase our ability to identify individuals that have a high genetic risk of developing a common disease
What do you look forward to the most during your sabbatical?
Human genomics is the fastest growing field with regards to data production and well suited for applying AI tools. However, there is limited experience in our research environment in using AI. I expect this sabbatical should lead to valuable interactions with AI researchers in other fields to share experiences and ideas, but I also hope for more persistent interdisciplinary collaborations.