# DMS/NIGMS 1: Disease gene discovery by Markovian gene network

> **NIH NIH R01** · RUTGERS, THE STATE UNIV OF N.J. · 2024 · $179,703

## Abstract

PROJECT SUMMARY (See instructions):
In this proposal, we develop new interpretable statistical methodology to advance medical genetic, which
seeks to understand the genetic basis of complex diseases and to identify disease-causing gene
mutations. Since genes typically act through modules and pathways in modulating cellular functions, a
standard approach in medical genetics is to follow candidate gene identification from patient data with an
analysis of the network formed by interactions of these candidate genes. The question of how to best
utililize gene-gene interaction (GGI) network information to uncover disease genes constitutes an urgent
bottleneck in medical genetics research, and our goal is to provide a statistical approach to this problem.
Our proposed research is to model gene-gene interactions as a Markovian network, which takes into
account the latent growth process of a network. Markovian models are especially suitable for GGI
networks because new gene interactions arise through evolution over time. We propose four distinct but
highly inter-related projects: (1) we propose a general Markovian network model for GGI networks that
encompass various existing models as special cases; (2) we propose a flexible method based on the repro
samples framework to construct confidence sets for the central root nodes of a Markovian network, which
can be used to identify disease genes on GGI networks with rigorous frequentist significance guarantees,
a task that was not possible or easily achieved in the past; (3) We propose an innovative development to
incorporate community/module structure into Markovian networks and devise novel methods to estimate
the functional modules and hidden gene nodes of a given GGI network; and (4) we give concrete plans to
validate our methods and any empirical findings we obtain using criteria common in bioinformatics and
laboratory experiments. Our research agenda has the potential to transform both medical genetics and
foundational statistical research. The Pl team comprises statisticians and geneticist with expertise in
network analysis, statistical inference, medical genetics, machine learning and it is ideally suitted to
conduct the proposed research. Successful completion of this research is expected to introduce and
validate a new and powerful strategy for understanding the genetic etiology of complex diseases. The
proposed work will also greatly expand the reach of statistical inference and uncertainty quantification, and
fundamentally impact our approach of making inference for many problems in data science.

## Key facts

- **NIH application ID:** 11043520
- **Project number:** 1R01GM157610-01
- **Recipient organization:** RUTGERS, THE STATE UNIV OF N.J.
- **Principal Investigator:** Sijian Wang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $179,703
- **Award type:** 1
- **Project period:** 2024-09-10 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11043520

## Citation

> US National Institutes of Health, RePORTER application 11043520, DMS/NIGMS 1: Disease gene discovery by Markovian gene network (1R01GM157610-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11043520. Licensed CC0.

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