# Characterization of Alternative Polyadenylation in Alzheimer's Disease

> **NIH NIH R03** · INDIANA UNIVERSITY INDIANAPOLIS · 2022 · $73,618

## Abstract

Abstract
Alzheimer's disease (AD) is a slowly progressive brain disorder characterized by cognitive decline, irreversible
memory loss, disorientation, and language impairment. Recent advances in genomic technologies and the
explosive genomic information related to disease have accelerated the convergence of discovery science with
clinical medicine. We aim to utilize cutting-edge techniques in computational biology, RNA biology, and
systems biology to identify novel prognostic and diagnostic biomarkers and to develop innovative therapeutic
strategies for AD. We will establish a comprehensive archive of human polyadenylation sites by combining
various APA databases. We will train a reliable deep neural network (DNN) model by considering both cis ad
trans factors, and then apply this DNN prediction model to characterize APA events in AD samples across
several
AD
consortia (Aim 1.1). We will develop highly efficient and accurate approaches based on deep
learning to identify apaQTLs in order to maximize the utility of genotyping data to understand the functional
effects of genetic variants in AD. We will perform integrative analysis with multi-omics data generated by other
projects to understand the regulatory network, aiming to provide additional evidence for functional
interpretation of apaQTLs in AD (Aim 1.2). We will perform integrative analysis with our established rigorous
computational approaches to identify APA events associated with AD traits, in order to identify novel prognostic
and diagnostic biomarkers for AD (Aim 2.1). To facilitate the utilization of large-scale data by the broad
biomedical community, we will develop a comprehensive data resource to provide a computational framework
that enables user-friendly interactive exploration and visualization of the biomedical significance of APA events
(Aim 2.2). We expect to build a critical foundation to demonstrate that APA events represent novel types of
biomarkers and serve as promising therapeutic targets to improve patient outcomes. Our proposed research
could pave the innovative way for aiding precision medicine because we will develop highly innovative
computational framework based on deep learning to identify APA events and perform apaQTL analysis to
identify a novel class of APA-based biomarkers and therapeutic targets. The proposed research is of high
significance because it will fundamentally advance our knowledge about the molecular basis of AD and
contribute to a broader understanding of the overall complexity of AD.

## Key facts

- **NIH application ID:** 10917789
- **Project number:** 7R03AG070417-04
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Leng Han
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $73,618
- **Award type:** 7
- **Project period:** 2021-01-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10917789

## Citation

> US National Institutes of Health, RePORTER application 10917789, Characterization of Alternative Polyadenylation in Alzheimer's Disease (7R03AG070417-04). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10917789. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*