Alzheimer?s disease (AD) is the leading cause of dementia in the United States but has no disease-modifying treatments. AD is also a substantially genetically based disease. Despite the fact that tens of new loci have been uncovered by GWAS to be AD-associated, the majority of the genetic component of AD remains unexplained. We propose an innovative machine learning computational approach to discover new AD genetic loci that leverages the tremendous investment in AD research including the following projects: Alzheimer?s Disease Sequencing Project (ADSP), the Alzheimer?s Disease Genetics Consortium (ADGC), Accelerating Medicines Partnership-Alzheimer?s Disease (AMP-AD), and the Molecular Mechanisms of the Vascular Etiology of Alzheimer?s Disease (MOVE-AD). We propose to use machine learning classification models trained on existing AD genetic studies and publicly available, multi-omics data to generate an AD-specific ?risk score? for each base in the genome. To test these models, we propose a 2-stage (discovery/replication) study using a unique dataset of human postmortem brains from the Rush Memory and Aging Project (MAP) and Religious Orders Study (ROS). Simultaneously, we propose functional studies in model systems to determine whether the selective identified loci could influence AD pathogenesis.
In Aim 1, we propose to build machine learning models and using the models to obtain genome-wide, AD-specific ?risk scores.? These scores will identify novel loci in the genome that increase AD risk were a mutation to be present there.
In Aim 2, we propose to test our models using a unique dataset of 1695 human postmortem brains from the Rush Memory and Aging Project (MAP) and Religious Orders Study (ROS). These prospective longitudinal studies annually collect data cognitive, mental, and physical health, and has the requisite available tissue and data from genomic, transcriptomic, and proteomic studies of the dorsolateral prefrontal cortex (dPFC). We propose a 2-stage genetic study to validate genetic associations with AD by testing whether rare variants in the locus associate with AD or whether there is differential CpG methylation at the locus associated with AD.
In Aim 3, we will examine the functional consequences of top AD-associated ?risk scores? in relevant model systems, including fly, mammalian cell culture and mouse models of AD. This project can potentially identify important molecular contributors of AD that might not be apparent through other approaches, leading to new insights into mechanisms and treatment targets for AD and thereby have an important and sustained impact on public health.
Alzheimer's disease (AD) affects 5.4 million people in the U.S and is the 6th leading cause of death. AD is a substantially genetically based disease, and our proposal aims to find new genetic causes of AD by employing cutting-edge computational approaches, followed by experimental validation. Discovery of new genetic causes of AD will: 1) shed light on new biological mechanisms, 2) facilitate development of new diagnostic tests, and 3) provide new targets for AD treatment.
