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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Pathway and network analysis with high-density allelic association data.

Ali Torkamani1, Nicholas J Schork

  • 1University of California, San Diego, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|July 15, 2009
PubMed
Summary
This summary is machine-generated.

Network and pathway analysis can now interpret large-scale genetic association data for complex diseases. This approach aids in identifying potential drug targets and disease biomarkers.

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Network and pathway analysis tools are crucial for understanding biological processes in gene expression data.
  • These analyses are underutilized for interpreting large-scale genetic association studies, especially for complex diseases.
  • Complex diseases involve numerous genes and environmental factors, unlike simple Mendelian disorders.

Purpose of the Study:

  • To describe methods for applying network and pathway analysis to large-scale genetic association data.
  • To demonstrate the utility of these methods for complex disease research.
  • To highlight the potential for identifying therapeutic targets and biomarkers.

Main Methods:

  • Data organization and preparation for genetic association studies.
  • Development and application of SNP (single nucleotide polymorphism) weighting schemes.
  • Implementation of pathway analysis methods for genetic data.

Main Results:

  • Demonstrated application of network analysis to cancer tumor resequencing data.
  • Successfully applied the approach to a genome-wide association study (GWAS).
  • Showcased how network analysis can elucidate genetic networks affected by variations.

Conclusions:

  • Network and pathway analysis offers a powerful approach to interpreting complex genetic association data.
  • This methodology can reveal insights into the genetic architecture of complex diseases.
  • The approach facilitates the identification of novel therapeutic strategies and diagnostic markers.