# Statistical methods for population-level cell-type-specific analyses of tissue omics data for Alzheimer's disease

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $378,325

## Abstract

PROJECT SUMMARY/ABSTRACT
Alzheimer's disease (AD) accounts for 60-80% of dementia cases and causes progressive neurodegeneration
that ultimately leads to death. While the number of US people with late-onset AD is expected to reach 13.8 million
by 2050, its prevention and treatment remain only modestly effective. Many efforts have been made to study AD
pathophysiology by collecting and curating rich omics data from AD-affected or unaffected human brains, e.g.,
the National Institute on Aging's Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) project.
Most of those omics data, such as gene expression, DNA methylation, and proteomics, are collected at the tissue
level, and thus the cell-type-specific (CTS) signals are masked. Recently, with the emerging single-cell
techniques, single-cell RNA-seq and DNA methylation data have been generated. However, given the difficulty
of quantifying a small number of molecules and associated high costs, single-cell data suffer from high technical
variation and are constrained to a small number of samples that lack representativity. To address these issues
in AD research and accelerate our understanding of cellular multi-omics mechanisms underlying AD, we aim to:
1) Improve estimation of cellular fractions in brain tissue samples by the ensemble over existing methods and
considering cell-type hierarchy. 2) Identify CTS differentially methylated regions (DMR) associated with AD. We
will consider the spatial correlation of CpG sites and cell-type specificity. 3) We will further build statistical models
to systematically integrate those CTS omics estimates via omics-wide association studies and causal mediation
analyses. Through extensive analyses of several large cohorts in AMP-AD datasets, we will produce statistically
significant and biologically meaningful omics results at an unprecedented population-scale and cell-type
resolution, which will improve our understanding of complex AD biology. We will validate our findings using
additional data available within and outside the AMP-AD project, including single-cell multi-omics data. The
resulting methods will be implemented as efficient computational algorithms via public software readily available
to the research community. Successful completion of this project will provide state-of-the-art methods for cell-
type deconvolution and integrative multi-omics analyses and advance our knowledge of genes/proteins
contributing to AD in selectively vulnerable brain regions and cell types.

## Key facts

- **NIH application ID:** 10771271
- **Project number:** 5R01AG080590-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Christopher McKennan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $378,325
- **Award type:** 5
- **Project period:** 2023-02-01 → 2027-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10771271

## Citation

> US National Institutes of Health, RePORTER application 10771271, Statistical methods for population-level cell-type-specific analyses of tissue omics data for Alzheimer's disease (5R01AG080590-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10771271. Licensed CC0.

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