# Statistical Models for Genetic Studies, Using Network and Integrative Analysis

> **NIH NIH R01** · OHIO STATE UNIVERSITY · 2020 · $332,796

## Abstract

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated
with hundreds of phenotypes and diseases, which in some cases have provided clinical and medical benefits
to patients with novel biomarkers and therapeutic targets. However, investigation of complex traits often
suffers from limited statistical power due to polygenicity, high dimensionality, and moderate sample size. While
it is practically challenging and costly to recruit patients to attain sufficient sample size to identify all associated
genetic variants, we recently showed that statistical power to identify risk associated genetic variants can be
significantly increased by 1) considering genetic basis shared among multiple phenotypes, namely pleiotropy,
and 2) incorporating genomic and genetic annotation data. However, effective integration of these datasets
becomes statistically more challenging as the number of genetic studies and annotation data increases.
 The objective of this proposal is to develop statistical methods and software to improve identification and
interpretation of risk variants and to promote understanding of genetic relationship among phenotypes. This
objective will be attained by pursuing four specific aims. In Aim 1, we will develop a Bayesian graphical model
to identify risk variants and construct a phenotype network, by integrating multiple GWAS datasets with various
annotation data. In Aim 2, we will develop a Bayesian graphical model to build a phenotype network from
biomedical literature. In Aim 3, we will develop a statistical method to construct meta-annotations that can
effectively summarize high dimensional annotation data without losing interpretability. In Aim 4, we will apply
these methods to genetic studies of vascular complications and autoimmune diseases in African American
populations, with PubMed literature and various annotation datasets. The proposed research is innovative
because it proposes a novel statistical framework that integrates multiple GWAS, biomedical literature, and
annotation datasets to improve identification and interpretation of risk variants. The proposed research is
significant because it is expected to help improve diagnosis and treatment of diseases with more effective
identification of risk variants and enhanced understanding of common etiology among diseases.

## Key facts

- **NIH application ID:** 9920162
- **Project number:** 5R01GM122078-06
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Dongjun Chung
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $332,796
- **Award type:** 5
- **Project period:** 2016-07-21 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9920162

## Citation

> US National Institutes of Health, RePORTER application 9920162, Statistical Models for Genetic Studies, Using Network and Integrative Analysis (5R01GM122078-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9920162. Licensed CC0.

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