# Statistical Methods for Genetic Risk Predictions across Diverse Populations

> **NIH NIH R01** · YALE UNIVERSITY · 2023 · $83,926

## Abstract

Project Summary
The ultimate goal of the parent grant is to develop rigorous, efficient, and robust integrative modeling
approaches for risk prediction across populations by capitalizing on the vast amount of publicly available
GWAS summary data, abundant functional annotations, and a growing number of studies with participants
from underrepresented populations. There are five specific aims in the parent grant, with the first three aims
developing three complementary approaches for cross-population risk predictions, including: (Aim 1) a
Bayesian approach (ME-Pred), along the line of our published work to incorporate either functional annotation
information or multiple trait information, that explicitly models joint effect sizes from multiple populations and
functional annotations; (Aim 2) an empirical Bayes approach (GWEB) that considers a more general and
flexible effect size distribution and statistical inference that does not need a validation cohort for tuning some
model parameters; and (Aim 3) a fast and robust Bayesian nonparametric method (SDPR) that is highly
adaptive to different genetic architectures and is computationally efficient. Aim 4 and Aim 5 of the parent grant
focus on implementation and applications of the developed tools to a number of studies, including the
Generations Project jointly initiated by the Yale School of Medicine and the Yale New Haven Health System.
We have assembled a team of investigators with expertise in statistical genetics, medical genetics, and high-
performance computing to develop, implement, evaluate, and disseminate the proposed methods. For this
diversity supplement project, we will consider risk prediction in admixed individuals, a topic related to but
covered by the parent grant. In addition, we will consider the applications of our methods to evaluate disease
risk for participants in the All of Us Research Program with a substantial number of underrepresented
individuals where methods tailored for admixed subjects may provide significantly improved predictions. This
supplement will not only provide an excellent training opportunity, but will also develop new tools for disease
risk predictions.

## Key facts

- **NIH application ID:** 10731582
- **Project number:** 3R01HG012735-02S1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** HONGYU ZHAO
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $83,926
- **Award type:** 3
- **Project period:** 2022-07-08 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10731582

## Citation

> US National Institutes of Health, RePORTER application 10731582, Statistical Methods for Genetic Risk Predictions across Diverse Populations (3R01HG012735-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10731582. Licensed CC0.

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