Approved research

Genomic data as a personalized medicine approach to evaluate the risk of chronic diseases and their public health implications

Lay summary

Common chronic diseases such as cardiovascular diseases and diabetes have long dominated the global disease burden estimates. Chronic diseases develop due to behavioral (eg dietary, tobacco, low physical activity) and metabolic (eg high blood pressure, fasting glucose, and cholesterol levels, and body mass index) risk factors. According to global studies, preventing these risk factors can improve both public health and the national economy. Traditionally, the assessment of the risk of chronic diseases in healthcare has been based on the patient's lifestyle and family history of the disease . In the case of rare hereditary diseases, risk assessment has been based on unique single clinical variants (SCVs), which significantly increase the risk of disease. However, over the past ten years, the new methods in genetic research - mainly Genome-Wide Association Study (GWAS) - have made it possible to combine information from thousands of genomic regions into polygenic risk scores (PRSs) for hundreds of common diseases and features. These PRSs describe a person's inherited disease risk: people with a high PRS have a significantly higher risk of disease. Our research project aims to build optimized genetic risk prediction models for chronic diseases which would ultimately improve the prevention, diagnosis and treatment of these diseases. With the UK Biobank data we are able to perform large scale analysis to fulfil our aims.