# Precision IBD via genetics and genomics: integrating International and multi-omic datasets, expanding studies in diverse populations, and defining mechanisms of unmet clinical needs in IBD

> **NIH NIH U24** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2022 · $1,210,619

## Abstract

PROJECT ABSTRACT
The NIDDK IBD Genetics Consortium’s (IBDGC) mission is to characterize the genetic architecture of IBD
phenotypes, particularly in populations currently underrepresented in IBD genomic studies, and to elucidate the
biological mechanisms by which genetic variants influence IBD pathophysiology, phenotypes and clinical course.
This proposal is to serve as the data coordinating center (DCC) for the IBDGC in a cooperative agreement with
genetics research centers (GRCs) to benefit the field broadly. The DCC has played an essential role in the
International IBD Genetics Consortium (IIBDGC), resulting in very large, well-powered cohorts that can
statistically refine association signals. However, less than 10% of present IIBDGC patients are from African-
American and Hispanic/LatinX IBD patients. For both scientific and social justice reasons, the NIDDK IBDGC is
strongly prioritizing recruitment of African-American and Hispanic/LatinX IBD patients for genetic and genomic
studies. In Aim 1, the DCC will optimize power and enhance pathophysiologic insight across diverse populations
affected by IBD by coordinating, standardizing and tracking increased recruitment of African-American and
Hispanic/LatinX IBD patients by the GRCs to more than double present cohort sizes. By so doing, substantially
increased power to refine allelic effects may be achieved. Integration with multi-omic ATAC + transcriptome
single cell data may provide further refinement of association signals between Crohn’s disease and ulcerative
colitis and across populations. Complete mapping of sequence data for the NIDDK IBDGC lymphoblastoid
lymphocyte repository from over 9000 patients will enable for broad use and dissemination. In Aim 2, the DCC
will provide high-quality operational services optimized to the scientific objectives of the Consortium, including
data collection, management and distribution; guidance in study design and data analysis; researching and
facilitating use of new platforms and technologies; and supporting Consortium governance, communication and
administration. We shall also substantially enhance our Data Commons and website to promote and facilitate
discovery and usage. In Aim 3, in selected clinical scenarios of unmet medical needs, the DCC will enable
longitudinal analyses prioritized by the NIDDK IBDGC Steering Committee, and to scale and standardize multi-
omic cellular data. The NIDDK IBDGC has made major investments in serial biosampling in ileal resection
Crohn’s disease cohorts, which powerfully tracks the earliest steps in disease recurrence. Mechanistic insight
may well be accrued via serial sampling (systemic and tissue-based) in other key scenarios, such as acute,
hospitalized flares in ulcerative colitis and perianal fistulae in Crohn’s disease. The power of GWAS locus
identification lies in the definitive mapping from trait to outcome (disease development). Given the polygenicity
of IBD, more complete explication of multi-omics in dive...

## Key facts

- **NIH application ID:** 10543377
- **Project number:** 2U24DK062429-23
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** JUDY H. CHO
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,210,619
- **Award type:** 2
- **Project period:** 2002-09-30 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10543377

## Citation

> US National Institutes of Health, RePORTER application 10543377, Precision IBD via genetics and genomics: integrating International and multi-omic datasets, expanding studies in diverse populations, and defining mechanisms of unmet clinical needs in IBD (2U24DK062429-23). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10543377. Licensed CC0.

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