# Alzheimer's MultiOme Data Repurposing: Artificial Intelligence, Network Medicine, and Therapeutics Discovery

> **NIH NIH U01** · CLEVELAND CLINIC LERNER COM-CWRU · 2021 · $796,538

## Abstract

PROJECT SUMMARY
Predisposition to AD involves a complex, polygenic, and pleiotropic genetic architecture; furthermore, there are
no disease modifying treatments that slow the neurodegenerative process for AD. Traditional reductionist
paradigms overlook the inherent complexity of AD and have often led to treatments that are lack of clinical
benefits or fraught with adverse effects. Existing multi-omics data resources, including genetics, genomics,
transcriptomics, interactomics (protein-protein interactions and chromatin interactions), have not yet been fully
utilized and integrated to explore the pathobiology and drug discovery for AD. Understanding AD genetics
and genomics from the point-of-view of how cellular systems and molecular interactome perturbations underlie
the disease (termed disease module) is the essence of network medicine. Systematic identification and
characterization of novel underlying pathogenesis and disease module, will serve as a foundation for identifying
and validating novel risk genes and drug targets in AD. Given our preliminary results, we posit that a genome-
wide, multimodal artificial intelligence (AI) framework to identify new risk genes and networks from human
genome/exome sequencing and multi-omics findings enable a more complete mechanistic understanding of AD
pathogenesis and the rapid development of targeted therapeutic intervention for AD with great success. Aim 1
will determine whether rare coding and non-coding variants by whole-genome/exome sequencing (WGS/WES)
are enriched in protein-functional and gene-regulatory regions using sequence and structure-based deep
learning models. These analyses will assemble WGS/WES and clinical data from Alzheimer's Disease
Sequencing Project (ADSP), publicly available protein structure (i.e., protein-protein interfaces, protein-ligand
binding sites, post-translational modifications) and sequence (expression quantitative trait locus [eQTLs],
histone-QTLs, and transcription factor binding-QTLs) information from the PDB database, GTEx, NIH RoadMap,
FANTOM5, PsychENCODE, and NIH 4D Nucleome. Aim 2 will determine whether GWAS common variants
linked to AD pathobiology and endophenotypes are enriched in gene regulatory networks in a cell-type specific
manner using a Bayesian framework. We will validate risk gene and network findings using WGS/WES and
protein panel expression data from our existing cohorts: The Cleveland Clinic Lou Ruvo Center for Brain Health
Aging and Neurodegenerative Disease Biobank (CBH-Biobank) and the Cleveland Alzheimer's Disease
Research Center (CADRC). Aim 3 will test the hypothesis that risk genes and networks can be modulated via
in silico drug repurposing, population-based validation, and functional test, to identify candidate agents and drug
combinations that will modify AD. The successful completion of this project will offer capable and intelligent
computer-based toolboxes that enable searching, sharing, visualizing, querying, and analyzing geneti...

## Key facts

- **NIH application ID:** 10276964
- **Project number:** 1U01AG073323-01
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** Lynn Bekris
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $796,538
- **Award type:** 1
- **Project period:** 2021-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10276964

## Citation

> US National Institutes of Health, RePORTER application 10276964, Alzheimer's MultiOme Data Repurposing: Artificial Intelligence, Network Medicine, and Therapeutics Discovery (1U01AG073323-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10276964. Licensed CC0.

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