# Investigating a molecular basis for Alzheimer's disease subtypes using multiomic data integration and machine-learning

> **NIH NIH F31** · UNIVERSITY OF PENNSYLVANIA · 2022 · $46,752

## Abstract

PROJECT ABSTRACT
Investigating a molecular basis for AD subtypes using multiomic data integration
 and machine-learning
Despite intense investigation into preclinical Alzheimer’s Disease (AD) disease models, all potential disease-
modifying drugs have failed in clinical trials. Numerous genetic studies have proposed a number of biological
mechanisms, however there has been no consensus on the genetic etiology of AD. This is likely because the
prevailing view of AD as a singular disease is oversimplified and does not consider heterogeneous pathogenic
variation in AD genetic architecture. High-throughput studies indicate that AD is a result of complex, nonlinear
interactions within and between the genome, transcriptome, epigenome, and proteome. While genome-wide
association studies have successfully revealed genes associated with AD, these genes explain disease in a
small proportion of the patient population, and the question of “missing heritability” remains. Thus, in Aim 1, I
propose using linear and nonlinear methods in an integrated multiomics framework with machine learning to
identify pathways significant in AD. While almost all AD patients present the hallmark b-amyloid and
neurofibrillary tangle pathology, they also present significant variability in cognitive symptoms, behaviors, and
neurophysiology. Given this, I hypothesize that inter-individual variation in AD-associated and immune pathways
drives different disease etiologies across the patient population culminating in a common pathophysiology. One
source of heterogeneity may be in immune pathways differentially regulating neuroinflammatory response during
AD. In Aim 2, I propose using an unsupervised classification approach to determine subtypes of AD based on
patient similarity in pathway variation across omic levels, imaging data, and phenotypic data. Specifically, I
hypothesize that pathogenic variation within innate immunity pathways plays a critical role in driving different
disease etiologies between patients. In aim 3, I propose characterizing each omic subtype by generating protein
interaction networks for drug target prioritization. Knowledge from these aims will inform a shift in the current AD
drug development paradigm by informing a precision medicine approach to target specific omic subtypes of AD
instead of a “one size fits all” approach that has failed to date. Investigating genomic heterogeneity in AD through
these aims has the potential to impact detection of pre-symptomatic AD individuals as well as reveal more
insights into the complex genetic architecture of AD.

## Key facts

- **NIH application ID:** 10368920
- **Project number:** 5F31AG069441-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Pankhuri Singhal
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $46,752
- **Award type:** 5
- **Project period:** 2020-12-01 → 2023-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10368920

## Citation

> US National Institutes of Health, RePORTER application 10368920, Investigating a molecular basis for Alzheimer's disease subtypes using multiomic data integration and machine-learning (5F31AG069441-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10368920. Licensed CC0.

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