# Multi-omic Risk Prediction of Chronic Obstructive Pulmonary Disease in European- and African-Ancestry Populations

> **NIH NIH K08** · BRIGHAM AND WOMEN'S HOSPITAL · 2024 · $168,480

## Abstract

PROJECT SUMMARY/ABSTRACT
Chronic obstructive pulmonary disease (COPD) is a leading cause of respiratory mortality worldwide15.
Identifying highly susceptible individuals early in their disease course and understanding pathogenic
mechanisms, before irreversible loss of lung function, is of utmost importance16,17. Genetics account for about
40% of COPD susceptibility18–20. Genome-wide association studies (GWASs) have identified multiple variants
associated with COPD21–23. Individual variants are poor for risk prediction, but in aggregate genetic variants can
account for a substantial portion of risk. Pooling millions of GWAS variants, I created a polygenic risk score (PRS)
for COPD that can identify individuals at high risk for COPD, though performance was less optimal in non-
Europeans24. Multi-ancestry PRSs are needed as genetic ancestry is not readily determined in clinical practice.
Further, gene expression, reflecting genetic and environmental influences, provides pathobiologic information
for COPD susceptibility and heterogeneity. A transcriptional risk score (TRS) for COPD that adds predictive value
above clinical risk factors25 has yet to be developed. The appeal of using -Omics data for risk stratification is that
these data can lend insight into why certain COPD subgroups are at elevated risk of progression. Gene
regulatory networks26 have been used to uncover mechanisms of COPD heterogeneity that were not found by
traditional gene-based approaches. Therefore, we hypothesize that polygenic and transcriptional risk scores will
substantially improve upon clinical factors in identifying those at higher risk for COPD and related phenotypes,
and can be used to identify pathways for therapeutic intervention. We will train multi-ancestry PRSs using 4,225
African ancestry individuals from UK Biobank and existing analyses of 8,429 African-Americans from CHARGE,
and test in the Genetic Epidemiology of COPD (COPDGene: n=10,198) study and Lung Tissue Research
Consortium (LTRC: n=1,078). We will create a multi-ancestry transcriptional risk score (TRS) using whole blood
RNA-sequencing (RNA-seq) data in training (n=3,394) and evaluate predictive performance in testing samples
(n=1,131) of COPDGene. We will use Connectivity Map (CMap)8,27 to identify drug repurposing candidates based
on TRS transcripts. We will leverage lung RNA-seq data from LTRC to create a lung TRS, and test in COPDGene
blood samples. We will classify COPDGene participants along the axes of the existing PRS and lung TRS (e.g.
“High” PRS, “Low” TRS), which we expect will identify those at high risk for COPD-related phenotypes and
progression. To understand why certain individuals are at high risk for COPD phenotypes, we will utilize gene
regulatory networks to identify pathways differing between PRS/TRS classifications, and use the Gene
RegulAtory Network Database (GRAND)9 to prioritize drug repurposing candidates. These aims will generate
data for future studies, which will focus on v...

## Key facts

- **NIH application ID:** 10812517
- **Project number:** 5K08HL159318-03
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Matthew R Moll
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $168,480
- **Award type:** 5
- **Project period:** 2022-04-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10812517

## Citation

> US National Institutes of Health, RePORTER application 10812517, Multi-omic Risk Prediction of Chronic Obstructive Pulmonary Disease in European- and African-Ancestry Populations (5K08HL159318-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10812517. Licensed CC0.

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