There are 94 genetic variants associated with stroke, but little is known about how they actually affect a person's risk of having a stroke.
With the new funding from Genome Canada, Genome Alberta and others, the team will use high-tech "multiomics" methods to examine the DNA (genomics), RNA (transcriptomics), proteins (proteomics) and metabolism (metabolomics) in biobanked blood samples from 3,200 Canadian stroke patients. They will then cross-reference those results with clinical and other records to search for patterns using machine learning.
The goal is to identify biomarkers - or molecular indicators - that could lead to better stroke prevention and risk assessment tools, and ultimately the discovery of precision treatments that target the newfound risk factors.
