# Multi-ethnic risk prediction for complex human diseases integrating multi-source genetic and non-genetic information

> **NIH NIH K99** · JOHNS HOPKINS UNIVERSITY · 2022 · $94,976

## Abstract

Project Summary/Abstract
In genome-wide association studies (GWAS), the lack of data sources for non-European populations results in
polygenic risk predictions that could exacerbate health inequity. This racial/ethnic disparity problem exists in
many epidemiologic studies and impacts public health much more broadly. Furthermore, the rapid identification
of novel risk factors for complex diseases brings increasing opportunities to develop comprehensive risk
prediction models to combine information on genetic and other types of risk factors. The scientific goal of this
proposal is to provide enhanced disease risk prediction tools for ethnically diverse populations integrating genetic
and other data sources across disparate studies. The specific aims include: (Aim 1) develop enhanced multi-
ethnic genetic risk prediction models combining ancestry-specific GWAS summary statistics with external
genomic information, and extend the method to jointly analyze multiple related diseases; (Aim 2) develop a
flexible statistical framework that can integrate ancestry-specific, summary-level risk parameter estimates for
genetic markers and a variety of other risk factors to further improve multi-ethnic disease risk prediction; and
(Aim 3) develop and validate the risk prediction models for leading causes of mortality and other complex
traits/diseases, distribute user-friendly software and tools, and investigate their clinical utilization through
applications in precision medicine.
Dr. Jin’s long-term goal is to establish an interdisciplinary research program that combines statistical genetics,
functional genomics and epidemiology, and develop novel statistical and computational methodologies for
integrating multi-source health-related data to improve healthcare and reduce health inequities. This award will
facilitate the necessary training required for Jin’s successful transition to independence, including support from
the mentoring and advisory committee, advanced coursework, and active participation in collaborations,
workshops, and scientific conferences. Jin will gain expertise that complements her current skill set through
working closely with a highly multidisciplinary mentoring team with a combined expertise in statistical genetics,
genomics, epidemiology, and precision medicine. Johns Hopkins University provides young researchers with an
active and engaging intellectual environment, with tremendous opportunities for interdisciplinary collaborations
and career development services such as teaching institute, grant writing workshops and interview skills practice.
The research supported by this grant will generate enhanced, user-friendly disease risk prediction tools for the
underrepresented minority populations, as well as general data integration methodologies that can be widely
implemented by the community to accelerate future research in disease risk prediction and prevention. Upon
completing this award, Jin will gain a critical set of skills in researc...

## Key facts

- **NIH application ID:** 10349828
- **Project number:** 1K99HG012223-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Jin Jin
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $94,976
- **Award type:** 1
- **Project period:** 2022-02-15 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10349828

## Citation

> US National Institutes of Health, RePORTER application 10349828, Multi-ethnic risk prediction for complex human diseases integrating multi-source genetic and non-genetic information (1K99HG012223-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10349828. Licensed CC0.

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