# Methods to Identify, Validate & Interpret GWAS Loci in Multi-ethnic Meta-analysis

> **NIH NIH R56** · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · 2021 · $575,657

## Abstract

ABSTRACT
 Large scale genetic datasets have revolutionized human genetic research. In the past decade, genome-wide
association studies have identified numerous genetic variants associated with various complex traits. These
discoveries have informed new biology and led to novel therapeutics. Yet, most studies focused on European
samples. As the next step, consortia efforts have begun to aggregate datasets from diverse non-European
populations. Most of these studies seek to aggregate summary association statistics and perform meta-analysis
instead of aggregating individual level data, which are easier to implement, equally powerful and more protective
for participants’ privacy. There are many new analytical challenges for trans-ethnic meta-analysis, which
demands new methodology development. In this application, we propose to develop a series of novel
approaches to understand the genetic architecture of complex traits in trans-ethnic meta-analysis. Specifically,
we will develop methods to assess reproducibility of identified GWAS signals (Aim 1). We will improve models
of genetic effect heterogeneity in trans-ethnic meta-analysis, in order to improve the power for association
analysis (Aim 2). We will also adapt the model to enhance the identification of causal variants (Aim 3) and
improve risk predictions (Aim 4). Finally, we will develop innovative software architectures to implement these
methods and make them scalable for meta-analysis of sequencing age (Aim 5). To accomplish these research
goals, we assembled a synergistic research team with leading expertise in complex trait genetics, statistical
genetics and large scale computation. In the past few years, our research team developed software tools that
are being used in hundreds of genetic studies. The team also got extensively involved in applied data analysis.
We will continue our existing collaborations, and team up with leaders in the GSCAN, GIANT, GLGC, T2D and
ICBP consortia to help advance the trans-ethnic analyses for smoking and drinking addiction, anthropometric
traits, lipids levels, type II diabetes and blood pressures. Together, these datasets consist of >20 million
phenotypic measurements on >5 million individuals. These collaborations will greatly advance our understanding
on the genetic architecture, facilitate clinical translation and also maximize the impact of our developed
methodologies.

## Key facts

- **NIH application ID:** 10291183
- **Project number:** 1R56HG011035-01
- **Recipient organization:** PENNSYLVANIA STATE UNIV HERSHEY MED CTR
- **Principal Investigator:** Dajiang Liu
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $575,657
- **Award type:** 1
- **Project period:** 2021-01-01 → 2022-08-09

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10291183

## Citation

> US National Institutes of Health, RePORTER application 10291183, Methods to Identify, Validate & Interpret GWAS Loci in Multi-ethnic Meta-analysis (1R56HG011035-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10291183. Licensed CC0.

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