Determination of correlations between polygenic risk scores of 40 traits and actual outcomes using 500.000 individuals
Principal Investigator: Professor Bruce Wolffenbuttel
Approved Research ID: 55495
Approval date: December 9th 2019
Scientifically validated methods to calculate the health risks of an individual, such as polygenic risk scores, readily exist, but the public is not benefiting from these as they is not applied beyond research. We will focus on making personalized genomics directly accessible to the public by delivering actionable results, which are aimed at improving their health and lifestyle and ultimately their well-being. We will employ scientific methods to assess the health risks of individuals, based genetic profiling, blood measurements, physical assessment, a questionnaire and activity monitoring using the Fitbit Inspire watch. Based on the individuals health risks, dietary and life style recommendations will be made. We believe the use of large datasets is the best way to conduct proper research, for which reason the UK Biobank data is uniquely suited for our project. This project specifically entails the risk prediction of 40 traits for 500.000 individuals, such as high cholesterol levels or diabetes, based on genetic profiles and then correlating them to the actual measured values. The strength of that correlation will indicate how well we are able to predict actual outcomes, based on genetic profiles. The outcome will allow us to determine which traits we can and should be using when determining our health recommendations to an individual. Furthermore, we aim to identify lifestyle and dietary changes that could offset the increased disease risk in individuals prone to a disease. These can then be used to advice individuals prone to disease to allow them to live healthier lives. For example, if turns out that there is a strong correlation between individuals that are diabetic and their genetic risk to become diabetic, we can use this to make health recommendations that reduce the chances of developing diabetes. For example, if an individual is genetically prone to become diabetic and has a high sugar diet, a low sugar diet will be recommended, with the advice to monitor insulin levels on a 3 monthly interval basis. The aim is to prevent disease, rather than curing it, for which purpose the genetic data is uniquely suited.