AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learning

Project Summary In response to PAR-19-269 “Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data”, in this proposal we assemble an interdisciplinary team to develop novel and robust analytical approaches to effectively address the current challenges in capitalizing on genetics, omics and neuroimaging data in Alzheimer’s disease (AD). Our team expertise covers complex disease genetics, functional genomics and regulation, machine learning/deep learning, systems-oriented research, neuroimaging, drug informatics, computational neuroscience, and clinical and translational science. Artificial intelligence (AI) has been shown powerful in uncovering hidden features that are critical to disease diagnosis or etiology. However, merely making the AI models “explainable” does nothing for explainability of AD, including major effects detailed in molecular biology, pathology, and neuroimaging. Our overall goal is to develop and implement a robust AI framework, namely AIM-AI, for transforming the genetic catalog of AD in a way that is Actionable, Integrated and Multiscale, so that genetic factors have clear utility for subsequent etiological studies. To make our findings Actionable, we explore multiple-omics systems that functionally intercept the effects of genetic factors at the cell-type-specific and single-cell resolution. We will develop Integrated and brain-data-driven collective systems, covering genetic, phenotypic, multi-omics, cell context, neuroimaging and knowledgebase information. Finally, a Multiscale systems biology approach will be implemented to identify genetic, neuroimaging, and phenotypic changes, which in combination can better explain the genetic architecture of AD and its cognitive decline. We will mine the AD characteristics at functional, cellular, tissue- and cell type-specific, and neuroimaging levels, enabling more rigorous assessment and validation that genetics effects indeed play out in cognitive decline and AD phenotypes. Our proposal has three specific aims. Aim 1: Develop a deep learning framework, “DeepBrain-AD”, to characterize the genetic risk of AD using both bulk brain tissue and single-cell regulatory genomics. Aim 2. Identify variants that account for cognitive decline due to AD progression by developing deep learning models that connect multiple modalities (imaging, clinical, genomics) in a joint analysis framework. Aim 3. Assess and validate the genetic variants from Aims 1 and 2 using multiple omics data to illustrate molecular systems which mediate their effects. In summary, we will uniquely investigate and validate genetic variants and other markers in AD at multi-omics level, at the cell-type context and single-cell resolution; and link the genetic association signals with functional regulation, protein expression, and neuroimaging context; and finally explain their roles in cognitive decline due to AD progression. The successful completion of this project will generate a robust AIM-AI framework, including machine learning methods/tools, resources, and scientific discoveries through integrative omics, deep learning, and other systems-based approaches, which will be immediately shared with AD and other disease research communities.