# Improving Methods and Practices for Trans-Ethnic Genetic Studies

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $583,585

## Abstract

ABSTRACT
Trans-ancestry 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-ancestry 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 (i) 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 >795K samples of non-European ancestry and a total sample size >1.5M by 2023; and (ii)
developing statistical methods and improving practices to integrate multi-ancestry data for cross-population
characterization of genetic architectures, meta-analysis, statistical fine-mapping and polygenic prediction.
Specifically, in Aim 1, we will systematically characterize the genetic underpinnings of human complex traits
and common diseases at variant, locus, regional and genome-wide levels across diverse populations, and
discover and validate novel genetic loci through trans-ancestry meta-analysis. In Aim 2, we will develop
scalable, robust, accurate and flexible statistical methods for trans-ancestry fine-mapping, delineate putative
causal genetic variants for a range of complex traits and diseases, and explore their functional consequences
and biological mechanisms. In Aim 3, we will develop haplotype-based methods for improved trans-ancestry
polygenic prediction, and benchmark the clinical utility of polygenic scores in disease risk prediction across
diverse populations. Leveraging large-scale biobank resources and novel simulation frameworks, we will
additionally enable fair and rigorous comparisons of existing and emerging methods for the integrative analysis
of multi-ancestry data, and assess various analysis choices and practical considerations in trans-ancestry fine-
mapping and genetic prediction in order to inform future study design and analysis plan, as well as methods
development, evaluation and application in trans-ancestry settings.

## Key facts

- **NIH application ID:** 10843775
- **Project number:** 5R01HG012354-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Tian Ge
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $583,585
- **Award type:** 5
- **Project period:** 2023-05-18 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10843775

## Citation

> US National Institutes of Health, RePORTER application 10843775, Improving Methods and Practices for Trans-Ethnic Genetic Studies (5R01HG012354-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10843775. Licensed CC0.

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