# Leveraging functional data to predict disease risk in multi-ethnic populations

> **NIH NIH R01** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2022 · $450,000

## Abstract

Project Summary
Genome-wide association studies (GWAS) have been broadly successful in identifying genetic variants
associated to common disease risk, leading to successes in predicting disease risk in European populations
using polygenic risk scores. Unfortunately, there is a large gap in the accuracy of polygenic risk scores
between European and non-European populations, such that clinical efforts to improve biomedical outcomes
via precision medicine may exacerbate health disparities. The increasing availability of multi-ethnic data in
larger sample sizes provides opportunities to improve the accuracy of polygenic risk scores, by improving
localization of causal variants and aiding identification of variants with population-specific effects. Notably,
functional genomics data has great potential to improve all of these efforts, but has yet to be adequately
integrated into multi-ethnic approaches. Here, we propose to reap the advantages of integrative analyses of
multi-ethnic and functional data, building on the extensive progress of our research program on disease
mapping in multi-ethnic populations over the past 8 years; the focus of our current application is on adapting
existing statistical methods to a new setting, integrating multi-ethnic and functional data, which currently suffers
a large gap in available methods. Our research will be driven by empirical data from >2,500,000 multi-ethnic
samples (>1,500,000 with genotype/phenotype data and >1,000,000 with summary association statistics),
including African American, Latino, East Asian and South Asian samples spanning a wide range of diseases
and quantitative phenotypes. We will analyze both individual-level data and summary-level data and
incorporate functional data sets, including genome-wide functional annotations and gene expression data.

## Key facts

- **NIH application ID:** 10479045
- **Project number:** 5R01HG006399-11
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** ALKES L PRICE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $450,000
- **Award type:** 5
- **Project period:** 2011-06-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10479045

## Citation

> US National Institutes of Health, RePORTER application 10479045, Leveraging functional data to predict disease risk in multi-ethnic populations (5R01HG006399-11). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10479045. Licensed CC0.

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