# Classification of late-onset AD (LOAD) Subtypes According to Patterns of Genomic and Epigenomic Drift

> **NIH NIH F31** · YALE UNIVERSITY · 2021 · $30,444

## Abstract

Despite concise, biomarker-driven definitions of Alzheimer's Disease (AD), there remains no clear
characterization of potential subtypes, defined by disparate biomarker presentation and varied cognitive
impacts. The long-term goal of this proposal is to establish flexible characterization of AD that will not only
distinguish pathological AD from normal aging, but also allow for interventions that will targeting subtypes and
avoid treatment failure in the general AD population. The objective of this proposal is to develop a classification
algorithm capable of identifying individuals with, or at risk for, AD using widespread genomic information, rather
than specifically proposed AD correlated genes. The central hypothesis is that late onset AD (LOAD) is the
result of widespread genomic and epigenomic drift, leading to dysregulation for which AD pathology is a
response, and that these alterations can be used to develop diagnostic biomarkers.
The rationale guiding this proposal is that it will significantly advance the field of Alzheimer's Disease research
by establishing a simple and effective framework for categorizing patients into disease subgroups. This will
allow us to overcome the obfuscation of treating all neuropathologically affected individuals similarly, and thus
may facilitate the development of successful personalized/targeted treatment paradigms for AD. The central
hypothesis will be tested through investigation of 2 specific aims: (1) Identification of subtypes of Alzheimer's
disease through clustering of epigenome-wide patterns of methylation, and (2) Assessment of the degree of
somatic mutational burden acquired by AD afflicted brain regions, beyond baseline brain mutability. The aims
will be addressed using innovative approaches to the field of AD research by capitalizing on multi-omic and
personalized medicine breakthroughs in the study of epigenetic aging and cancer biology.
This proposal is significant because it addresses recent failures to treat and understand AD etiology and
progression through the explicit and direct inclusion of biological heterogeneity in patient populations, which
will advance a trend towards AD treatment using precision medicine and targeted treatment. Addressing
biological heterogeneity in complex diseases can be challenging, as there are enormous sources of variability
that can be difficult to address using traditional approaches applied to the study of AD. However, this project
leverages methods of analysis and modeling that have already shown immense promise in other diseases and
aspects of health. The overall expected outcome of this project is the demonstration that AD to is a complex-
input disease, in which the patient's categorization of genomic and epigenomic drift trajectory is essential to
their diagnosis. Outcomes of this project will have immediate and outstanding positive impact, by both
suggesting modified characterization of research subjects in future and ongoing research for more targe...

## Key facts

- **NIH application ID:** 10313720
- **Project number:** 1F31AG074627-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Kyra Thrush
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $30,444
- **Award type:** 1
- **Project period:** 2022-01-01 → 2022-06-22

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10313720

## Citation

> US National Institutes of Health, RePORTER application 10313720, Classification of late-onset AD (LOAD) Subtypes According to Patterns of Genomic and Epigenomic Drift (1F31AG074627-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10313720. Licensed CC0.

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