Dissecting the genetic architecture of neurodegenerative diseases

Neurodegenerative diseases (e.g. Alzheimer?s disease and Parkinson?s disease) are a major healthcare burden and the prevalence is predicted to increase significantly with the aging population. Although the majority of the cases with neurodegeneration are sporadic there is still a large genetic component present in these cases which causes disease or increases risk significantly. Using standard genetic association tools and novel machine learning algorithms we aim to identify novel neurodegeneration genetic associations and improved disease prediction models. Identification of genetic associations for neurodegenerative diseases can improve the understanding of the biology of these diseases and help potentially identify therapeutic targets. Additionally, being able to identify individuals at (increased) risk for certain diseases gives to opportunity to intervene in the potential disease mechanisms and delay or prevent disease onset.
Using the UK biobank data we aim to dissect the genetic component of several neurodegenerative diseases. By comparing genetic information of cases and controls we can identify genetic markers specific or overrepresented in disease. When combining these genetic markers we can create disease models that could be used to predict the genetic risk per individual of a given disease.
We would like to obtain the full cohort (n=~500,000) in order to increase the power to detect genetic associations.