# Improving Methods and Practices for Trans-Ethnic Genetic Studies

> **NIH NIH R56** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $474,694

## Abstract

ABSTRACT
Trans-ethnic genetic analysis can facilitate the discovery of trait- or disease-associated loci, characterize
shared and differential genetic architectures across populations, improve the delineation of causal variants,
and is critical for equal delivery of genomic knowledge and precision healthcare globally. However, current
trans-ethnic genetic research is impeded by (i) limited genomic resources for non-European populations; and
(ii) limited statistical methods that can appropriately model and integrate data from diverse populations. This
project will address these challenges by (1) aggregating and harmonizing genetic data, physical measures,
laboratory tests and disease information from global biobanks and multiple health care systems in the United
States, with >680K samples of non-European ancestry and a total sample size >1.4M by 2022; and (2)
developing statistical methods and best practices to integrate multi-ethnic data for improved cross-population
characterization of genetic architectures, meta-analysis, statistical fine-mapping and polygenic prediction.
Specifically, in Aim 1, we will systematically characterize the comparative genetic architectures of physical
measures, biomarkers and disease phenotypes at variant, locus and genome-wide levels within and across
continental populations, and discover novel genetic loci through trans-ethnic meta-analysis. In Aim 2, we will
develop novel statistical methods and establish best practices for trans-ethnic fine-mapping, delineate putative
causal genetic variants for a range of complex traits and diseases, and explore the biological mechanisms of
fine-mapped variants. In Aim 3, we will develop novel haplotype-based methods for trans-ethnic polygenic
prediction, comprehensively assess the factors that might affect the transferability of polygenic risk scores
(PRS) and benchmark the clinical utility of biomarker PRS in disease risk prediction across diverse
populations. We are committed to resource sharing and will publicly release genome-wide association
summary statistics, reference panels, fine-mapping results, and polygenic prediction pipelines produced in this
project. All statistical methods and bioinformatic tools developed in this project will be disseminated as publicly
available software packages.

## Key facts

- **NIH application ID:** 10661266
- **Project number:** 1R56HG012354-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Tian Ge
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $474,694
- **Award type:** 1
- **Project period:** 2022-09-12 → 2023-05-17

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10661266

## Citation

> US National Institutes of Health, RePORTER application 10661266, Improving Methods and Practices for Trans-Ethnic Genetic Studies (1R56HG012354-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10661266. Licensed CC0.

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