Oslo University Hospital

Government Agency (Health and Adult Social Care)

OFHS260026

This project aims to understand how our DNA (genetics) influences different human health conditions. We want to find the specific links between mental health conditions and other physical illnesses, such as heart disease, diabetes, and cancer. We are also looking for genetic features that may protect people from these illnesses. While scientists have already found many genes linked to complex health issues, these only tell a small part of the story. Most of the "why" behind how these diseases are passed down remains a mystery. To help solve this, we will use advanced computer programs and Artificial Intelligence (AI). These new tools use mathematical approaches to investigate massive amounts of data more effectively than older methods. Our goal is to better predict who might be at risk for certain diseases and to group patients more accurately based on their unique characteristics, to improve treatment outcomes and daily life. Complex human disorders contribute to illness and early death. While our focus is on mental disorders, these conditions are often linked to physical health conditions and brain-related traits, which also affect the quality of life and life expectancy. To prevent and treat these diseases more effectively, we need to better understand the underlying biological mechanisms. These disorders are influenced by multiple genes and environmental factors, making it difficult to identify the exact mechanisms involved. Large-scale genetic studies have identified many genes linked to disorders. Yet most genetic influence remains unexplained, often called the “missing heritability.” However, much of this can be captured using better analytical methods that consider the combined effects of many genes, including specific changes to DNA. We have developed an advanced mathematical framework for analysing genetic data. By looking at how genes work and how different traits share genetic influences, our approach helps us find important genes and changes in DNA more accurately. These tools also support genetic risk prediction models, which can estimate timing of disease onset. We will improve these methods by integrating changes to DNA that are rare, multiple data types, and data collected over time. This will improve gene discovery and risk prediction.

The genome plays a key role in health and disease. By combining genetic data and health information, we can link DNA changes to complex disorders and better understand the underlying biological processes. Confirming findings in large international datasets will strengthen our understanding of how genetics affects disease. We will identify important genes that can guide research into how diseases develop and highlight potential targets for new treatment and preventation. Combining genetic, clinical, and environmental information will also help develop tools to improve risk prediction and patient subgrouping. Although many genetic risk factors have been identified, progress in understanding how they affect disease has been limited by smaller datasets. Using larger and more detailed data will provide new insights and support the discovery of drug targets. Mapping different types of genetic variation (common, rare, and structural) will explain more differences in disease risk and improve prediction, patient subgrouping, and clinical trials. Combining genetic and environmental risk factors with clinical data over time will support the development of new treatments. This research will also reveal shared genetic links between related health conditions, helping improve care. Overall, this work will benefit public health by improving prediction, supporting precision medicine, and guiding new therapies.

Public Health Research

09/10/2026

26/06/2026

TRE