| dc.description.abstract | Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by neurodegeneration, memory loss and cognitive impairment. Sporadic late-onset AD is the most common form of the disease; it makes up approximately 95% of all cases. Approximately 6 million individuals are afflicted with AD, and that number is expected to more than double to over 13 million individuals by 2050 suggesting that it will become a major public health crisis in the future.
To date, there is currently no cure for Alzheimer's disease and only one disease-modifying therapy has been FDA-approved; other therapeutics only help to manage symptoms. Despite years of efforts, AD drug discovery has been plagued by challenges including numerous clinical trial failures. Historically, proposed therapeutics have focused on amyloidosis, with many trials being halted due to toxicity or a lack of clinical efficacy on cognitive endpoints despite successfully lowering brain amyloid levels. In addition, AD is notably heterogeneous in presentation, making therapeutic development complex.
The availability of "-omic" data (e.g., genomic, transcriptomic, proteomic, etc.) has not only helped to diversify therapeutic targets for AD, but also reveal novel insights into the biological mechanisms behind AD. The present dissertation focuses on the concept of resilience to AD or protection from AD, which refers to older individuals who have less brain atrophy or cognitive impairment than expected, given a particular level of AD neuropathology. Therefore, we believe that there are genetic and molecular factors that are protecting these individuals from the downstream consequences of pathology. Leveraging multi-omic data, we utilize interaction models to identify novel factors that are protective against AD even in the presence of known AD risk factors such as cerebrospinal fluid (CSF) beta-amyloid and tau levels as well as APOE4, the most common genetic risk factor of AD. Furthermore, we examine AD these factors on three biological levels: variant-level, gene-level, and network-level and offer proof-of-concepts for how these analyses can be used for therapeutic and biomarker discovery. | |
