# Integrating multidimensional genomic data to discover clinically-relevant predictive models-Alzheimer's Supplement

> **NIH NIH R00** · UNIVERSITY OF ALABAMA AT BIRMINGHAM · 2021 · $213,964

## Abstract

Genomic instability (GIN) is a primary hallmark of aging, which is the greatest known risk factor for both
Alzheimer’s disease (AD) and cancer. Because there is no consensus on which measures of GIN are most
biologically and clinically relevant, in our parent project we are testing GIN metrics and developing tools for
assessing GINs reproducibly across cancer. Our approaches are designed to be technology-, platform-, and
disease-agnostic and therefore should also apply to AD. Our focus, thus far, has been on chromosomal
instability (CIN, altered chromosome number and structure; e.g., total number of breakpoints, percent of bases
with copy number variation, total functional aneuploidy, etc.) and DNA methylation instabilities (DNAm, e.g.,
CpG island methylator phenotype; CIMP, widespread altered promoter methylation, density of methylated to
non-methylated CpGs, etc.). In cancer we and others have shown GIN is linked to disease etiology and
progression, response to therapeutics, and is a potential disease biomarker. While AD animal models confirm
DNA integrity impacts neuronal development, function, and maintenance and human aging studies further
implicate a role for GIN in brain deterioration, GIN’s role in AD is not clear. There is a critical need to evaluate
AD-specific GIN, particularly as potential precision therapy targets and early biomarkers defining therapeutic
windows. Our interdisciplinary research team has AD, aging, genomic instability, cancer, genomics, and data
science expertise and is well positioned to undertake these studies. Our long term research goal is to
understand the role of GIN in the context of aging for multiple conditions and how GIN further contributes to
disease etiology, progression, and treatment. Here, we propose the first steps towards demonstrating utility of
our methodology in additional diseases by applying them to publicly available AD human and mouse data and
comparing the resulting GIN profiles to cancer data analyses in our parent award. We hypothesize this will
determine the extent and type of CIN (Aim 1) and DNAm instability (Aim 2) in AD. Critically, we will
demonstrate how generalizable our methods and gained knowledge are, add AD examples and vignettes to
the tools we are developing, and compare GINs across diseases (AD and cancers), species (human and
mouse), and with respect to sex and age. Additionally, we will generate genotype and DNAm data from
3xTG-AD mouse hippocampus (AD-relevant brain tissue), tibialis anterior muscle (as a sentinel organ), and
plasma (as a circulating factor) to investigate GIN as an AD biomarker. Critically, with this supplement we will
demonstrate generalizability of the parent award methods and knowledge by expanding our existing non-AD
NHGRI award to have an AD focus. This work will also stimulate additional activity and collaborations in AD
and related dementias by providing preliminary data for several future grant proposals targeting the role of GIN
in aging as a gene...

## Key facts

- **NIH application ID:** 10286414
- **Project number:** 3R00HG009678-04S1
- **Recipient organization:** UNIVERSITY OF ALABAMA AT BIRMINGHAM
- **Principal Investigator:** Brittany Nicole Lasseigne
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $213,964
- **Award type:** 3
- **Project period:** 2021-04-12 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10286414

## Citation

> US National Institutes of Health, RePORTER application 10286414, Integrating multidimensional genomic data to discover clinically-relevant predictive models-Alzheimer's Supplement (3R00HG009678-04S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10286414. Licensed CC0.

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