This research project aims to improve the way we predict the risk, progression, and outcomes of various diseases, such as heart disease, cancer, and Alzheimer’s, by using both genetic information and other health data like lifestyle and environmental factors. We will use data from the UK Biobank, which includes genetic, clinical, and lifestyle information from hundreds of thousands of participants. By combining these different types of data, we hope to build better tools to identify people who are at higher risk of developing these conditions.
The scientific rationale behind this study is that diseases are influenced by a combination of genetic and non-genetic factors. While we already know that certain genetic variants can increase the likelihood of developing a disease, those variants alone don’t tell the full story. Lifestyle choices, environmental exposure, and other health conditions also play a role. Our project will analyze the combined impact of these factors to create more accurate prediction models.
We will also apply cutting-edge machine learning techniques to help us analyze these large and complex datasets. The goal is to develop models that can predict not just the likelihood of getting a disease, but how quickly it may progress and what outcomes to expect. This will allow healthcare professionals to intervene earlier and tailor treatments to the specific needs of individuals.
The project is composed of many phases and is expected to take around ten years to complete. The potential public health impact is significant. By identifying people who are at high risk early, we can help prevent diseases or slow their progression, which could lead to better health outcomes and reduced healthcare costs. Additionally, this study may help discover new drug targets, opening the door to developing better treatments for these diseases in the future.
