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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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Related Experiment Video

Updated: Jun 3, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Bayesian semiparametric meta-analysis for genetic association studies.

Maria De Iorio1, Paul J Newcombe, Ioanna Tachmazidou

  • 1Department of Epidemiology and Biostatistics, Imperial College, London, United Kingdom. m.deiorio@imperial.ac.uk

Genetic Epidemiology
|March 15, 2011
PubMed
Summary

This study introduces a Bayesian model for analyzing genetic marker data in case-control studies. It improves meta-analysis by accounting for linkage disequilibrium (LD) between markers, enhancing the accuracy of genetic association findings.

Related Experiment Videos

Last Updated: Jun 3, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Area of Science:

  • Genetics
  • Biostatistics
  • Bioinformatics

Background:

  • Candidate gene studies often analyze individual genetic markers, ignoring correlations due to linkage disequilibrium (LD).
  • Marker-wise meta-analyses can reduce statistical power by excluding studies that did not genotype specific markers.
  • Existing methods fail to assess the relative importance of different variants within a genetic region.

Purpose of the Study:

  • To develop a Bayesian semiparametric model for meta-analysis of candidate gene studies with binary outcomes.
  • To incorporate pairwise LD measurements between genetic markers into the meta-analysis.
  • To provide posterior inference on adjusted genetic effects, borrowing strength across studies and markers.

Main Methods:

  • A Bayesian semiparametric model using a mixture of Dirichlet processes.
  • Modeling observed genotype group frequencies conditional to case/control status.
  • Utilizing pairwise LD measurements as prior information for posterior inference.
  • Employing Markov chain Monte Carlo (MCMC) algorithms for full posterior inference.

Main Results:

  • The developed model allows for the meta-analysis of candidate gene studies by accounting for LD.
  • It enables borrowing of statistical strength across both studies and genetic markers.
  • The approach provides adjusted effect estimates for genetic variants, correcting for correlated markers.

Conclusions:

  • The proposed Bayesian model offers a more robust and powerful approach for meta-analysis in candidate gene studies.
  • Incorporating LD information improves the accuracy of estimating genetic variant effects.
  • This method enhances the comprehensive analysis of genetic association studies.