# BridgePRS: bridging the gap in polygenic risk scores between ancestries.

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $641,630

## Abstract

PROJECT SUMMARY
The key appeal of polygenic risk scores (PRS) is the provision of individual-level estimates of genetic liability to
complex disease. These proxies of genetic liability enable a raft of applications across basic research and clinical
settings. However, while PRS are set to play a pivotal role in the future of biomedical research, their present
formulation is suboptimal for application across diverse and admixed populations.
To address this we propose to develop high-resolution modeling to optimize the computation of PRSs across
diverse populations, which will: (i) use Bayesian hierarchical modeling to account for the population genetic
and statistical causes of low PRS portability between populations, (ii) deconstruct genetic risk into shared,
ancestry-specific and gene*environment sub-components, (iii) produce pathway-based PRSs that can help
expose the functional sources of the portability problem and explain ancestry disease heterogeneity. The key
deliverable will be the production of a suite of powerful PRS tools tailored to diverse and admixed populations.
The rationale is that failure to model important structural features that are inherent to diverse clinical populations
constitutes a vital loss of information. By modeling this high-resolution data in statistically principled and
rigorous ways, researchers will be equipped to perform powerful PRS prediction across all human populations
and in all individuals. This will offer unprecedented predictive power and insights into disease mechanisms.
In Aim 1, we develop a Bayesian hierarchical PRS method, BridgePRS3, that models differences in LD, effect
sizes and allele frequencies between ancestries and their constituent sub-ancestries. In Aim 2, we build a novel
method, admixPRS, for application to admixed individuals that deconstructs the genome into local ancestry
tracts corresponding to sub-ancestries, accounting for known admixture history, and decomposing genetic risk
into 3 sub-components. In Aim 3, we develop a pathway-based PRS method for diverse populations, PRSet+.
Finally, we will build a unifying PRS method, globalPRS, that calculates PRS in any individual, of any ancestry.
Our proposal is significant because the burgeoning application of PRS means that reducing disparity in PRS
predictive power will have immediate, high impact in diverse populations. By performing high-resolution modeling
to boost PRS predictive power by mirroring the structure of human populations, and exposing gene*environment
and pathway-level contributions to the PRS portability problem, our suite of PRS tools have the potential to
increase the clinical utility of PRS and our understanding of how genetic risk varies in global populations.
Our proposal is innovative because we develop the first Bayesian hierarchical PRS tools to model the high-
resolution structure of diverse and admixed populations, in relation to: (i) ancestry (modeling sub-ancestries),
genetic risk (3-component admixPRS model...

## Key facts

- **NIH application ID:** 10930175
- **Project number:** 5R01HG012773-02
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Paul Francis O'Reilly
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $641,630
- **Award type:** 5
- **Project period:** 2023-09-15 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10930175

## Citation

> US National Institutes of Health, RePORTER application 10930175, BridgePRS: bridging the gap in polygenic risk scores between ancestries. (5R01HG012773-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10930175. Licensed CC0.

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