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

Published on: June 21, 2018

A mixed two-stage method for detecting interactions in genomewide association studies.

Yijun Zuo1, Guolian Kang

  • 1Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA.

Journal of Theoretical Biology
|November 10, 2009
PubMed
Summary
This summary is machine-generated.

The mixed two-stage method for genomewide association studies (GWAS) shows promise for detecting gene interactions in complex diseases. This approach may offer improved power compared to one-stage methods under specific sample and marker selection criteria.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

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Last Updated: Jun 18, 2026

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

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Genetics
  • Statistical genomics
  • Computational biology

Background:

  • Genomewide association studies (GWAS) aim to identify genetic factors contributing to complex diseases.
  • Complex epistasis, or gene-gene interactions, is increasingly recognized as a significant factor in disease etiology.
  • Current one-stage methods for detecting epistasis in GWAS can be computationally intensive, lack power with increasing markers, and are not cost-effective.

Purpose of the Study:

  • To introduce and evaluate a novel "mixed two-stage method" for detecting epistatic interactions in GWAS.
  • To compare the power of the mixed two-stage method against the traditional one-stage method.
  • To assess the performance of the mixed two-stage method under various two-locus epistatic models in a case-control study.

Main Methods:

  • A mixed two-stage method was developed, involving marker selection in the first stage using a subset of samples, followed by interaction testing in the second stage with the remaining samples.
  • The power of the mixed two-stage method was investigated and compared to the one-stage method using simulated data for different two-locus epistatic models.
  • Performance was evaluated in a case-control study design.

Main Results:

  • The mixed two-stage method demonstrated potentially greater power than the one-stage method when approximately 30% of samples were used for initial single-locus tests and less than or equal to 1% of markers were selected for interaction tests.
  • Comparison with other two-stage methods indicated a potential loss of power in the proposed mixed two-stage method due to the utilization of only a portion of samples in both stages.

Conclusions:

  • The mixed two-stage method offers a potentially more powerful and efficient alternative to one-stage GWAS for detecting epistatic interactions, particularly under optimized sample and marker selection strategies.
  • Further research is needed to refine the mixed two-stage approach to mitigate power loss associated with using partial sample sets in both stages.