Principal Investigator: Mr Adam Kurkiewicz
Institution: University of GlasgowTags: 44545, blood pressure, Machine Learning, phenotype, predictors
Accurate prediction of disease risk from genetic data can have enormous public health benefits, by allowing individuals at risk to make informed choices about their health, and especially in such widespread diseases as cardiovascular diseases. Existing models, which predict such risk, like for example the ACC model of cardiovascular risk, do not take into account genetic information, and as a result have limited accuracy. The research work to construct better predictors of heritable features (height, eye colour, blood pressure, etc.) from genetic data from an individual is ongoing and currently focuses on complex (multi-gene) features which are very often not linked to diseases, e.g. height. This is why for this research we chose to focus on more relevant phenotype, specifically blood pressure. The aim of our research is to construct a blood pressure predictor from genotype data, which eventually will translate to a better understanding of this very important cardiovascular disease risk factor and better preventive healthcare of patients at risk of cardiovascular disease. We plan to make our findings publicly available by October 2020.