Further defining the genetic architecture of Alzheimer's disease
Principal Investigator: Professor Julie Williams
Approved Research ID: 15175
Approval date: February 27th 2017
Our studies of Alzheimer?s disease (AD) genetics indicate that diseased individuals display a risk gene burden spectrum(polygenic risk. Our risk gene burden models predict AD with 78% accuracy. We request Biobank data to test our algorithms in population cohorts. We will further explore polygenic risk, investigating relationships between AD risk genes and environmental factors. We will analyse brain imaging data to explore early detection of AD-associated brain changes and correlate these with gene burden. Results will provide insights into disease-critical environmental cues, and further elucidate themes associated with AD including the immune response. The improved knowledge and understanding of the dysfunctional processes in Alzheimer?s disease, as well as the interaction of our genes and environment, elucidated through exploration of the above research questions, will pave the way for precision medicine and allow the forecasting of clinical need and relevant policymaking strategies. Ultimately insights from this work will lead to better understanding of the disease process, improved diagnosis and better treatment. Our studies of Alzheimer?s disease (AD) genetics show that individuals display a spectrum of risk gene burden (polygenic risk), which predicts 78% of AD risk. We wish to explore polygenic risk distribution in populations, looking for relationships between risk genes and environmental factors. This will provide insights into the environmental cues most critical to disease. We will analyse brain imaging data to explore whether changes can be detected at early disease stages and correlated with gene burden. This knowledge and understanding will pave the way for new precision medicines and inform clinical strategies. All participants with available genomic data should be included in this data request. In addition, we request sufficient information to link this genomic data with the full cohort stored in our institution under Biobank projects 6553 and 17044.