Principal Investigator: Dr Janet Cady
Parabon NanoLabs, Inc., Reston, Virginia, USATags: 46198, Alzheimer's Disease, endophenotype, Epistasis, genetics/genotyping, machinelearning, risk prediction
Late-onset Alzheimer’s Disease affects millions of people, yet there are no treatments that can meaningfully affect disease progression once symptoms have appeared. A widely-available genetic test that can accurately predict a person’s Alzheimer’s Disease risk in middle age or earlier would allow at-risk individuals to prepare financially for the future, change their lifestyle, undergo regular screenings, and/or enroll in clinical trials. A sufficiently accurate test could also improve the outcomes of clinical trials by increasing the probability of detecting treatment effects. Despite recent advancements, genetic tests for Alzheimer’s Disease lack sufficient accuracy to support these applications. In this project, we will address this need by creating a highly accurate genetic test able to predict a person’s risk of developing Alzheimer’s Disease at any age.