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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.
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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Published on: July 1, 2020

Pathway analysis following association study.

Julius S Ngwa1, Alisa K Manning, Jonna L Grimsby

  • 1Department of Biostatistics, School of Public Health, Boston University, 715 Albany Street, Boston, MA 02118, USA. ngwaj@bu.edu.

BMC Proceedings
|March 1, 2012
PubMed
Summary
This summary is machine-generated.

Pathway analysis methods, including gene set enrichment analysis (GSEA) and Ingenuity pathway analysis (IPA), can identify additional genetic associations missed by focusing solely on single-nucleotide polymorphisms (SNPs). These methods offer high power and low error rates for detecting disease-related pathways.

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Published on: December 15, 2023

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) typically prioritize single-nucleotide polymorphisms (SNPs) with the lowest p-values.
  • This approach may overlook valuable genetic associations within biological pathways.

Purpose of the Study:

  • To evaluate the utility of pathway analysis methods for identifying additional genetic associations.
  • To compare gene set enrichment analysis (GSEA), empirical enrichment p-values, and Ingenuity pathway analysis (IPA) in detecting disease-related pathways.

Main Methods:

  • Association tests were conducted for common SNPs and rare variants using traits Q1 and Q4.
  • Three pathway analysis methods (GSEA, empirical p-values, IPA) were applied using gene set information.
  • Methods were assessed based on type I error, statistical power, and the ranking of the Vascular Endothelial Growth Factor (VEGF) pathway.

Main Results:

  • GSEA and IPA demonstrated high power (91.2% and 93%) in detecting the VEGF pathway for trait Q1.
  • Both GSEA and IPA exhibited conservative type I errors (0.0083 and 0.0072), indicating reduced false positives.
  • IPA ranked the VEGF pathway first or second in 123 of 200 replicates, while GSEA ranked it in the top 5 in 114 of 200 replicates.
  • The empirical enrichment method showed lower power and higher type I error compared to GSEA and IPA.

Conclusions:

  • Pathway analysis methods, particularly GSEA and IPA, are effective in identifying biologically relevant pathways associated with disease.
  • These approaches can uncover additional genetic associations beyond those identified by focusing solely on top-ranked SNPs.
  • Pathway analysis offers a valuable strategy for understanding the genetic architecture of complex diseases.