# Methods for disease mapping in multi-ethnic populations

> **NIH NIH R01** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2020 · $475,311

## Abstract

Project Summary/Abstract
Genome-wide association studies (GWAS) have been successful in identifying genetic variants affecting the
risk of common diseases. GWAS have identified thousands of associated variants, and in some instances the
underlying causal variants have been fine-mapped, providing key biological insights. Most studies have been
conducted in populations of European ancestry, but many studies now include multi-ethnic samples. Analysis
of multi-ethnic data presents many advantages, including increased power to detect associated variants that
are rare or absent in Europeans and increased resolution for fine-mapping, but also many challenges. The
extent to which genetic architectures are shared across ethnicities is not well-understood, the implications for
meta-analyzing studies across ethnicities are uncertain, and the optimal strategy for performing fine-mapping
in multi-ethnic data remains an open question, particularly when allowing for multiple causal variants at a locus.
These challenges can inhibit multi-ethnic study designs, limiting opportunities to detect new associations and
address health disparities in minority populations. Here, we propose to develop a complete set of methods
and software for disease mapping in multi-ethnic populations, building on the extensive progress of our
research program over the past four years. Our goal is to make fully powered association and fine-mapping
studies as practical in multi-ethnic populations as in studies of a single continental population. Our methods
research will be driven by empirical data from >900,000 samples (>700,000 with raw genotypes/phenotypes
and >200,000 with summary statistics), including African American, Latino, East Asian and South Asian
samples spanning a wide range of quantitative and disease phenotypes. We will develop methods for both
raw genotype/phenotype data and summary association statistic data, and the methods will be applicable to
both common and rare variation, including gene-based tests.

## Key facts

- **NIH application ID:** 9920764
- **Project number:** 5R01HG006399-09
- **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:** 2020
- **Award amount:** $475,311
- **Award type:** 5
- **Project period:** 2011-06-15 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9920764

## Citation

> US National Institutes of Health, RePORTER application 9920764, Methods for disease mapping in multi-ethnic populations (5R01HG006399-09). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9920764. Licensed CC0.

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