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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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Updated: May 28, 2026

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

Strategies for pathway analysis from GWAS data.

Brian L Yaspan1, Olivia J Veatch

  • 1Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Current Protocols in Human Genetics
|October 7, 2011
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) identify significant single nucleotide polymorphisms (SNPs). A pathway-based approach can reveal complex genetic influences on phenotypes missed by traditional GWAS methods.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) are standard for linking human genome variation to phenotypes.
  • Traditional GWAS primarily identifies statistically significant single nucleotide polymorphisms (SNPs), often overlooking complex genetic architectures.
  • Locus heterogeneity, epistasis, and small gene effects contribute to genetic complexity, making many biologically relevant associations difficult to detect.

Purpose of the Study:

  • To move beyond single-SNP analysis in GWAS.
  • To develop and advocate for a pathway-based approach to analyze GWAS data.
  • To uncover biologically meaningful genetic associations with lower effect sizes that are missed by conventional methods.

Main Methods:

  • Organizing individual SNPs into biologically relevant groups.
  • Analyzing the collective effects of SNPs within genes and pathways.
  • Implementing a pathway-based analytical framework for GWAS data.

Main Results:

  • The pathway-based approach offers deeper insights into the functional genetic underpinnings of phenotypes.
  • This method can identify significant associations that are masked by stringent statistical thresholds in single-SNP analyses.
  • It allows for the exploration of complex genetic scenarios involving multiple genes and interactions.

Conclusions:

  • A pathway-based approach enhances the utility of GWAS data by revealing complex genetic architectures.
  • This strategy provides a more comprehensive understanding of the genetic basis of phenotypes.
  • It enables researchers to test more sophisticated genetic models and discover novel genotype-phenotype relationships.