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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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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.

Statistical Applications in Genetics and Molecular Biology
|February 29, 2020
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Summary
This summary is machine-generated.

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.

Keywords:
instabilitypathway analysissparsesupervised network

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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.