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Updated: Dec 27, 2025
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
Published on: July 1, 2020
Identification of supervised and sparse functional genomic pathways
Fan Zhang1, Jeffrey C Miecznikowski2, David L Tritchler2,3
1Department of Biostatistics, SUNY University at Buffalo, Buffalo NY14214,USA.
Abstract:
Functional pathways involve a series of biological alterations that may result in the occurrence of many diseases including cancer. With the availability of various "omics" technologies it becomes feasible to integrate information from a hierarchy of biological layers to provide a more comprehensive understanding to the disease. In many diseases, it is believed that only a small number of networks, each relatively small in size, drive the disease. Our goal in this study is to develop methods to discover these functional networks across biological layers correlated with the phenotype. We derive a novel Network Summary Matrix (NSM) that highlights potential pathways conforming to least squares regression relationships. An algorithm called Decomposition of Network Summary Matrix via Instability (DNSMI) involving decomposition of NSM using instability regularization is proposed. Simulations and real data analysis from The Cancer Genome Atlas (TCGA) program will be shown to demonstrate the performance of the algorithm.
Insights
This study introduces a new method to find small, key biological networks driving diseases like cancer. The approach integrates multi-omics data to identify disease-associated functional pathways.
Area of Science:
- Systems biology
- Computational biology
- Genomics
Background:
- Functional pathways underpin disease development, including cancer.
- Multi-omics technologies enable integrated analysis of biological layers for disease understanding.
- Small, key networks are hypothesized to drive many diseases.
Purpose of the Study:
- Develop methods to discover functional networks across biological layers correlated with phenotype.
- Identify key biological networks driving disease.
- Integrate multi-omics data for comprehensive disease insights.
Main Methods:
- Derivation of a novel Network Summary Matrix (NSM).
- Proposal of the Decomposition of Network Summary Matrix via Instability (DNSMI) algorithm.
- Utilizing instability regularization for NSM decomposition.
Main Results:
- The NSM highlights potential pathways conforming to least squares regression.
- The DNSMI algorithm effectively decomposes the NSM.
- Demonstration of algorithm performance via simulations and TCGA data analysis.
Conclusions:
- The developed methods can identify disease-driving functional networks.
- Integration of multi-omics data provides a more comprehensive understanding of disease.
- The NSM and DNSMI offer a novel approach for network-based disease research.
