# Functional Genomics and Bioinformatics Data management Core

> **NIH NIH U54** · UNIVERSITY OF CALIFORNIA-IRVINE · 2023 · $2,293,280

## Abstract

FGBDMC ABSTRACT:
 The University of California, Irvine (UCI) MODEL-AD Functional Genomics and Bioinformatics Data
Management Core (FGBDMC) will centralize all functional genomics as well as bioinformatics tasks to support
the efforts of the UCI MODEL-AD Center. The core will characterize deeply UCI Late-Onset AD (LOAD) mouse
models using bulk as well as single-cell techniques, and advise the UCI MODEL-AD Disease Model
Development and Phenotyping Project (DMP) as to the best combination of variants and genetic backgrounds
for subsequent models. Specifically, the FGBDMC will: 1) characterize novel mouse AD models using bulk RNA
sequencing to identify model-specific changes in gene expression, 2) characterize selected mouse AD models
using single-cell and single-nucleus RNA-seq and ATAC-seq to identify cell-type specific changes in gene
expression and chromatin accessibility associated with AD GWAS risk-variant function, 3) analyze integratively
all UCI MODEL-AD ‘omics data with AMP-AD and other publicly-available data in order to characterize the
human AD relevance of the novel UCI mouse AD models as well as to predict the best variant combination to
support DMP efforts, and 4) make UCI MODEL-AD results accessible on the Sage Bionetworks Synapse Portal.

## Key facts

- **NIH application ID:** 10708162
- **Project number:** 5U54AG054349-07
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** Seyed Ali Mortazavi
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $2,293,280
- **Award type:** 5
- **Project period:** 2017-09-15 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10708162

## Citation

> US National Institutes of Health, RePORTER application 10708162, Functional Genomics and Bioinformatics Data management Core (5U54AG054349-07). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10708162. Licensed CC0.

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