# Causal network inference with application to breast cancer

> **NIH NIH P20** · UNIVERSITY OF IDAHO · 2020 · $153,372

## Abstract

Complex diseases often involve changes in DNA sequence, transcription, and epigenetic processes such as
methylation. These changes lead to a wide range of symptoms or multiple subtypes of the same disease. In
order to develop more effective treatments for different disease subtypes, we need to better understand the
genes and processes (i.e., transcription and methylation) that drive these differences. Unfortunately,
identification of genes and processes that underlie a disease is often compromised by inference based on
correlation, not causation. Our long-term goal is to develop computational methods to infer gene regulatory
networks that are causal for multiple clinical phenotypes using genomic and clinical data of complex diseases.
In this project, we will develop and test new statistical approaches to identify regulatory networks involving both
transcription and methylation that are potentially causal for disease subtypes. Our strategy is to use the
principle of Mendelian randomization. This assumes that the alleles of a genetic variant are randomly assigned
to individuals in a population, analogous to a natural perturbation experiment. Whereas most existing methods
for studying interactions among genes look at correlation (or association), this principle allows us to separate
correlation due to causation from correlation not due to causation. We will develop our approaches via three
specific aims and will use breast cancer as the disease model: (1) Develop a causal network model using
genotypes, expression and methylation of single genes. (2) Develop a causal network model to identify
individual genes whose transcription or methylation is causal for multiple clinical phenotypes. (3) Develop a
causal network model to identify combinations of genes whose transcription or methylation are causal for
multiple clinical phenotypes. The models and algorithms developed in this proposal will allow us to make
causal statements about the two processes at multiple genes and account for confounding variables, neither of
which has been examined before in similar studies. These models will identify key genes for specific breast
cancer subtypes and the roles for transcription and methylation when many genes are involved, leading to
better diagnoses and development of novel drug targets.

## Key facts

- **NIH application ID:** 10026004
- **Project number:** 2P20GM104420-06A1
- **Recipient organization:** UNIVERSITY OF IDAHO
- **Principal Investigator:** Audrey Qiuyan Fu
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $153,372
- **Award type:** 2
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10026004

## Citation

> US National Institutes of Health, RePORTER application 10026004, Causal network inference with application to breast cancer (2P20GM104420-06A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10026004. Licensed CC0.

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