# New computational tools for understanding and predicting AD via age-associated DNA methylation changes

> **NIH NIH RF1** · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · 2022 · $2,020,719

## Abstract

Alzheimer’s disease (AD) is the most common neurodegenerative disorder, with late-onset AD affecting about 1
in 9 people over 65 years old in the US. The increasing elderly population in the US makes AD a major public
health concern and one of the most financially costly diseases. Currently, a major challenge is the lack of reliable,
minimally invasive, inexpensive biomarkers to aid the diagnosis, prognosis, and ultimately development of new
AD treatment strategies. One potential source of biomarkers for AD is DNA methylation (DNAm). Changes in
DNAm have been implicated in both aging and AD. Moreover, DNAm is relatively stable and can be easily
detected. DNAm is an epigenetic mechanism at the interface of the genome and environment, and it is influenced
by aging and many lifestyle factors such as smoking, diet, and exercise, which in turn might modify the risk of
AD. In the past few years, we have developed several innovative open-source software for DNAm and other
genomics data analyses. In this proposal, building on our previous experiences in developing and applying tools
for integrative genomics analyses of large-scale heterogeneous datasets, we propose to harmonize a large
number of DNAm datasets to clarify the role of DNAm in aging and AD, to develop a web interface that
disseminates the analyses results, and to develop epigenetic clocks tailored for predicting AD phenotypes. We
hypothesize a number of DNAm-based regulatory changes are relevant to both aging and AD, and some age-
associated DNAm changes also contribute to AD onset and progression. In Aim 1, we will aggregate, harmonize,
and meta-analyze a large number of DNAm aging datasets measured in brain and blood samples to identify
DNAm changes associated with aging and AD, and determine age-associated DNAm differences that also
contribute to AD. We will develop two tools: (1) a searchable web interface that clarifies the role of DNA
methylation in aging and AD and (2) an open-source R package for performing meta-analyses of DNAm
methylation regions. In Aim 2, we will develop a new epigenetic clock tailored for predicting AD phenotypes. The
diagnostic and prognostic values of the new epigenetic clock will be evaluated using available CSF biomarkers
and clinical cognitive outcomes and compared with known clinical and genetic factors, as well as currently
available plasma biomarkers. The searchable web interface will significantly enhance our understanding and
enable new biological insights on the role of age-associated epigenetic changes in AD. The new epigenetic clock
tailored to predicting AD phenotypes will facilitate the development of surrogate biomarkers that provide a degree
of objectivity for monitoring disease progression in clinical trials, as well as assessing individualized risk profiles
for AD diagnosis and prognosis. The successful completion of the project will also provide us with computational
pipelines and tools that can be easily adapted and applied to analyze datase...

## Key facts

- **NIH application ID:** 10509428
- **Project number:** 1RF1NS128145-01
- **Recipient organization:** UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
- **Principal Investigator:** Lily Wang
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,020,719
- **Award type:** 1
- **Project period:** 2022-09-15 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10509428

## Citation

> US National Institutes of Health, RePORTER application 10509428, New computational tools for understanding and predicting AD via age-associated DNA methylation changes (1RF1NS128145-01). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10509428. Licensed CC0.

---

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