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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...
Epistasis Analysis01:09

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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Related Experiment Video

Updated: May 25, 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

A pathway analysis method for genome-wide association studies.

Babak Shahbaba1, Catherine M Shachaf, Zhaoxia Yu

  • 1Department of Statistics, University of California, Irvine, CA, USA.

Statistics in Medicine
|February 4, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian method to identify significant biological pathways in genome-wide association studies (GWAS). The approach improves pathway analysis by incorporating gene-level data and predicting pathways for genes with unknown associations.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genetics and Genomics
  • Statistical Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with diseases.
  • Traditional gene-centric analyses in GWAS can lack statistical power and robustness.
  • Pathway-based analysis enhances the power of GWAS but often excludes genes with unknown pathway information.

Purpose of the Study:

  • To develop a novel statistical method for identifying significant biological pathways in GWAS.
  • To address the challenge of handling genes with unknown pathway associations within pathway analysis.
  • To improve the statistical power and reliability of pathway identification in genetic studies.

Main Methods:

  • Aggregation of single-nucleotide polymorphism (SNP) data to gene-level summary measures.
  • Application of a hierarchical Bayesian model using gene-level summary measures for pathway relevance assessment.
  • Utilizing a Bayesian multinomial logit model to predict pathways for genes with unknown associations, using known pathways as training data.

Main Results:

  • The proposed method successfully identifies significant biological pathways relevant to disease status.
  • The hierarchical Bayesian model accounts for uncertainty in predicted pathway assignments.
  • Application to two independent type 2 diabetes studies revealed statistically significant overlap in results, demonstrating robustness.

Conclusions:

  • The novel Bayesian approach effectively identifies significant pathways in GWAS, even with incomplete gene pathway information.
  • The method enhances statistical power and provides robust results for pathway-based genetic association studies.
  • The approach is validated through application to real-world genetic data and simulated datasets.