# Genomic Architecture of Common Disease in Diverse Populations

> **NIH NIH UM1** · BAYLOR COLLEGE OF MEDICINE · 2020 · $12,000,000

## Abstract

In the Center's for Common Disease Genomics (CCDG) program, the Human Genome Sequencing
Center (HGSC) has focused on production of genomic data to enable discovery of alleles associated
with common diseases. We recruited samples from large cohorts and focused on early onset cardiac
death (EOCAD) and intracranial hemorrhagic stroke (ICH). We generated whole genome sequences
(WGS) in order to capture non-coding information and to maximize identification of structural variants
and worked with the CCDG consortia to integrate and harmonize data, developing and sharing large
variant call sets. Technical innovations and platform efficiencies reduced genome sequencing costs
approximately two-fold over the program period so far. In year five we will complete the CCDG program,
generating an additional 13,500 additional WGS, divided between cases of EOCAD (9,000 from an
available pool of approximately 14,000) and cases of ICH (4,500 from an available pool of approximately
7,000), with particular emphasis on ethnicities currently under represented in biomedical research. The
data will be subjected to quality control analysis and in conjunction with other available data and other
CCDG members, ascertained for disease-allele association, and submitted to AnVIL and other
appropriate databases.

## Key facts

- **NIH application ID:** 9923401
- **Project number:** 3UM1HG008898-04S1
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** RICHARD A GIBBS
- **Activity code:** UM1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $12,000,000
- **Award type:** 3
- **Project period:** 2016-01-14 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9923401

## Citation

> US National Institutes of Health, RePORTER application 9923401, Genomic Architecture of Common Disease in Diverse Populations (3UM1HG008898-04S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9923401. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*