# Genomic and Metabolomic Data Integration in a Longitudinal Cohort at Risk for Alzheimer's Disease

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2020 · $727,928

## Abstract

PROJECT SUMMARY
A longitudinal multi-omics examination of β-amyloid (Aβ) deposition, cognitive decline, and resilience in the
years prior to Alzheimer's disease (AD) diagnosis is critical to better understand, prevent, diagnose, and treat
the disease. Common genetic variants in APOE and 19 additional genes have been associated with AD. Low
frequency and rare functional variants with moderate effects on risk for AD have recently been identified
through sequencing studies, with additional variants remaining to be discovered. Metabolic profiles, which
reflect the combined effect of genes, intrinsic metabolism, environmental exposure, and interactions between
these, have also been associated with AD. However, the cause of AD is not well understood and there are
currently no effective preventions or treatments for this common disease. Integration of genomic and
metabolomic data will enable thorough and comprehensive modeling and identification of the complex interplay
of genes and metabolites involved in AD pathology. Continuing to focus on single-data-type study designs,
which do not accurately reflect the complexity of AD, will hinder progress in our understanding of this disease
and of effective prevention, diagnosis, and treatment. Our long-term goal is to elucidate complex interactions
involved in AD pathology using a comprehensive set of omics data in order to inform precision medicine for
AD. The objective of this proposal is to define the role of genomics and metabolomics in Aβ deposition,
cognitive decline, and resilience in initially asymptomatic participants with a parental history of AD diagnosed
by age 75, with replication in an existing independent cohort. The specific aims are to use genomic sequencing
and metabolomics to identify 1) common and rare genetic variants, 2) metabolic profiles, and 3) complex
interactions between genomics and metabolomics that are involved in AD, Aβ deposition, cognitive decline,
and cognitive resilience. A sample of 344 eligible participants, aged 40-65 at baseline, have been identified
from the longitudinal Wisconsin Registry for Alzheimer's Prevention (WRAP) and ~506 extended family
members of these participants will also be enrolled. Participants from the longitudinal Wisconsin-Alzheimer's
Disease Research Center (W-ADRC) will be used for replication.

## Key facts

- **NIH application ID:** 9949573
- **Project number:** 5R01AG054047-05
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Corinne D. Engelman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $727,928
- **Award type:** 5
- **Project period:** 2016-08-15 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9949573

## Citation

> US National Institutes of Health, RePORTER application 9949573, Genomic and Metabolomic Data Integration in a Longitudinal Cohort at Risk for Alzheimer's Disease (5R01AG054047-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9949573. Licensed CC0.

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