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Approved Research

Development of polygenic risk scores for the prediction individual genetic risks of common diseases or traits

Principal Investigator: Dr Daniel Wallerstorfer
Approved Research ID: 100033
Approval date: March 3rd 2023

Lay summary

It is intended to develop statistical tools for the prediction of a person's disease risk within a population. The statistics are based on polygenic risk scores which sums up multiple genetic variants to predict the risk of common diseases. Those risk scores provide the opportunity to identify more high-risk individuals to reduce the impact of common diseases. It is aimed to develop risk prediction models for approximately 30 different diseases, with a planned 3-4-month development period per disease. For the reporting of genetic data, it is crucial to choose a customer friendly design and presentation to avoid misinterpretation of the results. The combination of machine-learning with other factors such as age, lifestyle, or family history, allows to visualize the impact of such factors in an understandable way to improve disease prevention on a personal level such as targeted screenings, diagnosis or therapies. Robust extended polygenic risk models as we intend to develop, allow early prevention or diagnosis of high-risk individuals, ultimately improving people's health and relieving pressure on the healthcare system.