# Novel computational strategies to deconvolute co-occurring conditions in Down syndrome

> **NIH NIH R01** · UNIVERSITY OF COLORADO DENVER · 2022 · $2,353,137

## Abstract

PROJECT SUMMARY
The triplication of chromosome 21, which encodes ~225 genes, is the cause of Down syndrome. Individuals
with Down syndrome experience a wide and variable spectrum of co-occurring conditions. Though we know
the cause, which is trisomy 21, we have a poor understanding of how and why this chromosomal abnormality
drives associated co-occurring conditions. A better mechanistic understanding of these connections will
provide the basis for not only improving the care of individuals with Down syndrome but also for the general
population. Research efforts such as the Human Trisome and INCLUDE Project are generating multi-omics
data on large cohorts of individuals with trisomy 21 to gain such mechanistic insights. Given the complexity of
the problem – the dosage increased of ~225 genes connected to a wide spectrum of conditions – existing tools
for -omic data analysis struggle to leverage this information properly and separate generic from
context-specific cellular responses. We must be able to analyze these data in the context of the full disease
spectrum that individuals with DS experience, from genes to proteins to pathways. Further complicating these
analyses, we and others have shown that certain genes and pathways are hypersensitive to perturbation, thus
we often identify generic responses through standard analysis methods, when our goal is to find disease- and
context-specific changes. These hyperresponsive genes and pathways obscure context-specific signals. We
propose to develop the methodology to find the context-specific signal associated with trisomy 21. Our first aim
is to develop methods to identify shared genetic mechanisms between complex diseases and molecular
changes in Down syndrome co-occurring conditions. We will leverage genomic and transcriptomic datasets
from a wide array of previously collected association studies. Our second aim is to develop a method to
separate generic from context-specific signals in -omic datasets. We will employ a novel generative neural
network simulation to generate different disease contexts, for which individual -omic samples can be
compared. Our third aim is to determine the molecular connections between chromosome 21 genes and the
co-occurring condition using search over knowledge graphs. All methods developed will be made public
through the INCLUDE Data Coordination Center platforms.

## Key facts

- **NIH application ID:** 10519118
- **Project number:** 1R01HD109765-01
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** James Christopher Costello
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,353,137
- **Award type:** 1
- **Project period:** 2022-09-09 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10519118

## Citation

> US National Institutes of Health, RePORTER application 10519118, Novel computational strategies to deconvolute co-occurring conditions in Down syndrome (1R01HD109765-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10519118. Licensed CC0.

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